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[0] Schmidt EM, McIntosh JS, Durelli L, Bak MJ, Fine control of operantly conditioned firing patterns of cortical neurons.Exp Neurol 61:2, 349-69 (1978 Sep 1)[1] Serruya MD, Hatsopoulos NG, Paninski L, Fellows MR, Donoghue JP, Instant neural control of a movement signal.Nature 416:6877, 141-2 (2002 Mar 14)[2] Fetz EE, Operant conditioning of cortical unit activity.Science 163:870, 955-8 (1969 Feb 28)[3] Fetz EE, Finocchio DV, Operant conditioning of specific patterns of neural and muscular activity.Science 174:7, 431-5 (1971 Oct 22)[4] Fetz EE, Finocchio DV, Operant conditioning of isolated activity in specific muscles and precentral cells.Brain Res 40:1, 19-23 (1972 May 12)[5] Fetz EE, Baker MA, Operantly conditioned patterns on precentral unit activity and correlated responses in adjacent cells and contralateral muscles.J Neurophysiol 36:2, 179-204 (1973 Mar)

[0] Aflalo TN, Graziano MS, Relationship between unconstrained arm movements and single-neuron firing in the macaque motor cortex.J Neurosci 27:11, 2760-80 (2007 Mar 14)

[0] Moran DW, Schwartz AB, Motor cortical representation of speed and direction during reaching.J Neurophysiol 82:5, 2676-92 (1999 Nov)

[0] Shuler MG, Bear MF, Reward timing in the primary visual cortex.Science 311:5767, 1606-9 (2006 Mar 17)

[0] Matsuzaka Y, Picard N, Strick PL, Skill representation in the primary motor cortex after long-term practice.J Neurophysiol 97:2, 1819-32 (2007 Feb)

[0] Diedrichsen J, Hashambhoy Y, Rane T, Shadmehr R, Neural correlates of reach errors.J Neurosci 25:43, 9919-31 (2005 Oct 26)

[0] Mehta MR, Cortico-hippocampal interaction during up-down states and memory consolidation.Nat Neurosci 10:1, 13-5 (2007 Jan)[1] Ji D, Wilson MA, Coordinated memory replay in the visual cortex and hippocampus during sleep.Nat Neurosci 10:1, 100-7 (2007 Jan)

[0] Ji D, Wilson MA, Coordinated memory replay in the visual cortex and hippocampus during sleep.Nat Neurosci 10:1, 100-7 (2007 Jan)

[0] Schicknick H, Schott BH, Budinger E, Smalla KH, Riedel A, Seidenbecher CI, Scheich H, Gundelfinger ED, Tischmeyer W, Dopaminergic modulation of auditory cortex-dependent memory consolidation through mTOR.Cereb Cortex 18:11, 2646-58 (2008 Nov)

[0] Froemke RC, Merzenich MM, Schreiner CE, A synaptic memory trace for cortical receptive field plasticity.Nature 450:7168, 425-9 (2007 Nov 15)

[0] Pawlak V, Kerr JN, Dopamine receptor activation is required for corticostriatal spike-timing-dependent plasticity.J Neurosci 28:10, 2435-46 (2008 Mar 5)

[0] Schultz W, Tremblay L, Hollerman JR, Reward processing in primate orbitofrontal cortex and basal ganglia.Cereb Cortex 10:3, 272-84 (2000 Mar)

[0] Recanzone GH, Schreiner CE, Merzenich MM, Plasticity in the frequency representation of primary auditory cortex following discrimination training in adult owl monkeys.J Neurosci 13:1, 87-103 (1993 Jan)

[0] Nakahara H, Doya K, Hikosaka O, Parallel cortico-basal ganglia mechanisms for acquisition and execution of visuomotor sequences - a computational approach.J Cogn Neurosci 13:5, 626-47 (2001 Jul 1)

[0] Isoda M, Hikosaka O, Switching from automatic to controlled action by monkey medial frontal cortex.Nat Neurosci 10:2, 240-8 (2007 Feb)

[0] Graybiel AM, The basal ganglia: learning new tricks and loving it.Curr Opin Neurobiol 15:6, 638-44 (2005 Dec)

[0] Scott SH, Optimal feedback control and the neural basis of volitional motor control.Nat Rev Neurosci 5:7, 532-46 (2004 Jul)

[0] Boline J, Ashe J, On the relations between single cell activity in the motor cortex and the direction and magnitude of three-dimensional dynamic isometric force.Exp Brain Res 167:2, 148-59 (2005 Nov)

[0] Ojakangas CL, Shaikhouni A, Friehs GM, Caplan AH, Serruya MD, Saleh M, Morris DS, Donoghue JP, Decoding movement intent from human premotor cortex neurons for neural prosthetic applications.J Clin Neurophysiol 23:6, 577-84 (2006 Dec)

[0] DeLong MR, Strick PL, Relation of basal ganglia, cerebellum, and motor cortex units to ramp and ballistic limb movements.Brain Res 71:2-3, 327-35 (1974 May 17)

[0] Ashe J, Force and the motor cortex.Behav Brain Res 87:2, 255-69 (1997 Sep)

[0] Bair W, Koch C, Temporal precision of spike trains in extrastriate cortex of the behaving macaque monkey.Neural Comput 8:6, 1185-202 (1996 Aug 15)[1] Shmiel T, Drori R, Shmiel O, Ben-Shaul Y, Nadasdy Z, Shemesh M, Teicher M, Abeles M, Neurons of the cerebral cortex exhibit precise interspike timing in correspondence to behavior.Proc Natl Acad Sci U S A 102:51, 18655-7 (2005 Dec 20)[2] Mainen ZF, Sejnowski TJ, Reliability of spike timing in neocortical neurons.Science 268:5216, 1503-6 (1995 Jun 9)

[0] Amirikian B, Georgopoulos AP, Directional tuning profiles of motor cortical cells.Neurosci Res 36:1, 73-9 (2000 Jan)

[0] Ostry DJ, Feldman AG, A critical evaluation of the force control hypothesis in motor control.Exp Brain Res 153:3, 275-88 (2003 Dec)

[0] Lavin A, Nogueira L, Lapish CC, Wightman RM, Phillips PE, Seamans JK, Mesocortical dopamine neurons operate in distinct temporal domains using multimodal signaling.J Neurosci 25:20, 5013-23 (2005 May 18)[1] Pirot S, Godbout R, Mantz J, Tassin JP, Glowinski J, Thierry AM, Inhibitory effects of ventral tegmental area stimulation on the activity of prefrontal cortical neurons: evidence for the involvement of both dopaminergic and GABAergic components.Neuroscience 49:4, 857-65 (1992 Aug)

[0] Marzullo TC, Miller CR, Kipke DR, Suitability of the cingulate cortex for neural control.IEEE Trans Neural Syst Rehabil Eng 14:4, 401-9 (2006 Dec)

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ref: -2017 tags: V1 V4 visual cortex granger causality date: 03-20-2019 06:00 gmt revision:0 [head]

PMID-28739915 Interactions between feedback and lateral connections in the primary visual cortex

  • Liang H1, Gong X1, Chen M2,3, Yan Y2,3, Li W4,3, Gilbert CD5.
  • Extracellular ephys on V1 and V4 neurons in macaque monkeys trained on a fixation and saccade task.
  • Contour task: monkeys had to select the patch of lines, chosen to stimulate the recorded receptive fields, which had a continuous contour in it (again chosen to elicit a response in the recorded V1 / V4 neurons).
    • Variable length of the contour: 1, 3, 5, 7 bars. First part of analysis: only 7-bar trials.
  • Granger causality (GC) in V1 horizontal connectivity decreased significantly in the 0-30Hz band after taking into account V4 activity. Hence, V4 explains some of the causal activity in V1.
    • This result holds both with contour-contour (e.g. cells both tuned to the contours in V1), contour-background, and background-background.
    • Yet there was a greater change in the contour-BG and BG-contour cells when V4 was taken into account (Granger causality is directional, like KL divergence).
      • This result passes the shuffle test, where tria identities were shuffled.
      • True also when LFP is measured.
      • That said .. even though GC is sensitive to temporal features, might be nice to control with a distant area.
      • See supplementary figures (of which there are a lot) for the controls.
  • Summarily: Feedback from V4 strengthens V1 lateral connections.
  • Then they looked at trials with a variable number of contour bars.
  • V4 seems to have a greater GC influence on background cells relative to contour cells.
  • Using conditional GC, lateral interactions in V1 contribute more to contour integration than V4.
  • Greater GC in correct trials than incorrect trials.

  • Note: differences in firing rate can affect estimation of GC. Hence, some advise using thinning of the spike trains to yield parity.
  • Note: refs for horizontal connections in V1 [7-10, 37]

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ref: -2012 tags: DiCarlo Visual object recognition inferior temporal cortex dorsal ventral stream V1 date: 03-13-2019 22:24 gmt revision:1 [0] [head]

PMID-22325196 How Does the Brain Solve Visual Object Recognition

  • James DiCarlo, Davide Zoccolan, Nicole C Rust.
  • Infero-temporal cortex is organized into behaviorally relevant categories, not necessarily retinotopically, as demonstrated with TMS studies in humans, and lesion studies in other primates.
    • Synaptic transmission takes 1-2ms; dendritic propagation ?, axonal propagation ~1ms (e.g. pyramidal antidromic activation latency 1.2-1.3ms), so each layer can use several synapses for computation.
  • Results from the ventral stream computation can be well described by a firing rate code binned at ~ 50ms. Such a code can reliably describe and predict behavior
    • Though: this does not rule out codes with finer temporal resolution.
    • Though anyway: it may be inferential issue, as behavior operates at this timescale.
  • IT neurons' responses are sparse, but still contain information about position and size.
    • They are not narrowly tuned detectors, not grandmother cells; they are selective and complex but not narrow.
    • Indeed, IT neurons with the highest shape selectivities are the least tolerate to changes in position, scale, contrast, and visual clutter. (Zoccolan et al 2007)
    • Position information avoids the need to re-bind attributes with perceptual categories -- no need for syncrhony binding.
  • Decoded IT population activity of ~100 neurons exceeds artificial vision systems (Pinto et al 2010).
  • As in {1448}, there is a ~ 30x expansion of the number of neurons (axons?) in V1 vs the optic tract; serves to allow controlled sparsity.
  • Dispute in the field over primarily hierarchical & feed-forward vs. highly structured feedback being essential for performance (and learning?) of the system.
    • One could hypothesize that feedback signals help lower levels perform inference with noisy inputs; or feedback from higher layers, which is prevalent and manifest (and must be important; all that membrane is not wasted..)
    • DiCarlo questions if the re-entrant intra-area and inter-area communication is necessary for building object representations.
      • This could be tested with optogenetic approaches; since the publication, it may have been..
      • Feedback-type active perception may be evinced in binocular rivalry, or in visual illusions;
      • Yet 150ms immediate object recognition probably does not require it.
  • Authors propose thinking about neurons/local circuits as having 'job descriptions', an metaphor that couples neuroscience to human organization: who is providing feedback to the workers? Who is providing feeback as to job function? (Hinton 1995).
  • Propose local subspace untangling; when this is tacked and tiled, this is sufficient for object perception.
    • Indeed, modern deep convolutional networks behave this way; yet they still can't match human performance (perhaps not sparse enough, not enough representational capability)
    • Cite Hinton & Salakhutdinov 2006.
  • The AND-OR or conv-pooling architecture was proposed by Hubbel and Weisel back in 1962! In their paper's formulatin, they call it a Normalized non-linear model, NLN.
  1. Nonlinearities tend to flatten object manifolds; even with random weights, NLN models tend to produce easier to decode object identities, based on strength of normalization. See also {714}.
  2. NLNs are tuned / become tuned to the statistics of real images. But they do not get into discrimination / perception thereof..
  3. NLNs learn temporally: inputs that occur temporally adjacent lead to similar responses.
    1. But: scaades? Humans saccade 100 million times per year!
      1. This could be seen as a continuity prior: the world is unlikely to change between saccades, so one can infer the identity and positions of objects on the retina, which say can be used to tune different retinotopic IT neurons..
    2. See Li & DiCarlo -- manipulation of image statistics changing visual responses.
  • Regarding (3) above, perhaps attention is a modifier / learning gate?

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ref: Schmidt-1978.09 tags: Schmidt BMI original operant conditioning cortex HOT pyramidal information antidromic date: 03-12-2019 23:35 gmt revision:11 [10] [9] [8] [7] [6] [5] [head]

PMID-101388[0] Fine control of operantly conditioned firing patterns of cortical neurons.

  • Hand-arm area of M1, 11 or 12 chronic recording electrodes, 3 monkeys.
    • But, they only used one unit at a time in the conditioning task.
  • Observed conditioning in 77% of single units and 65% of combined units (multiunits?).
  • Trained to move a handle to a position indicated by 8 annular cursor lights.
    • Cursor was updated at 50hz -- this was just a series of lights! talk about simple feedback...
    • Investigated different smoothing: too fast, FR does not stay in target; too slow, cursor acquires target too slowly.
      • My gamma function is very similar to their lowpass filter used for smoothing the firing rates.
    • 4 or 8 target random tracking task
    • Time-out of 8 seconds
    • Run of 40 trials
      • The conditioning reached a significant level of performance after 2.2 runs of 40 trials (in well-trained monkeys); typically, they did 18 runs/day (720 trials)
  • Recordings:
    • Scalar mapping of unit firing rate to cursor position.
    • Filtered 600-6kHz
    • Each accepted spike triggered a generator that produced a pulse of of constant amplitude and width -> this was fed into a lowpass filter (1.5 to 2.5 & 3.5Hz cutoff), and a gain stage, then a ADC, then (presumably) the PDP.
      • can determine if these units were in the pyramidal tract by measuring antidromic delay.
    • recorded one neuron for 108 days!!
      • Neuronal activity is still being recorded from one monkey 24 months after chronic implantation of the microelectrodes.
    • Average period in which conditioning was attempted was 3.12 days.
  • Successful conditioning was always associated with specific repeatable limb movements
    • "However, what appears to be conditioned in these experiments is a movement, and the neuron under study is correlated with that movement." YES.
    • The monkeys clearly learned to make (increasingly refined) movement to modulate the firing activity of the recorded units.
    • The monkey learned to turn off certain units with specific limb positions; the monkey used exaggerated movements for these purposes.
      • e.g. finger and shoulder movements, isometric contraction in one case.
  • Trained some monkeys or > 15 months; animals got better at the task over time.
  • PDP-12 computer.
  • Information measure: 0 bits for missed targets, 2 for a 4 target task, 3 for 8 target task; information rate = total number of bits / time to acquire targets.
    • 3.85 bits/sec peak with 4 targets, 500ms hold time
    • With this, monkeys were able to exert fine control of firing rate.
    • Damn! compare to Paninski! [1]
  • 4.29 bits/sec when the same task was performed with a manipulandum & wrist movement
  • they were able to condition 77% of individual neurons and 65% of combined units.
  • Implanted a pyramidal tract electrode in one monkey; both cells recorded at that time were pyramidal tract neurons, antidromic latencies of 1.2 - 1.3ms.
    • Failures had no relation to over movements of the monkey.
  • Fetz and Baker [2,3,4,5] found that 65% of precentral neurons could be conditioned for increased or decreased firing rates.
    • and it only took 6.5 minutes, on average, for the units to change firing rates!
  • Summarized in [1].

____References____

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ref: -2018 tags: sparse representation auditory cortex excitatation inhibition balance date: 03-11-2019 20:47 gmt revision:1 [0] [head]

PMID-30307493 Sparse Representation in Awake Auditory Cortex: Cell-type Dependence, Synaptic Mechanisms, Developmental Emergence, and Modulation.

  • Sparse representation arises during development in an experience-dependent manner, accompanied by differential changes of excitatory input strength and a transition from unimodal to bimodal distribution of E/I ratios.

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ref: -2015 tags: conjugate light electron tomography mouse visual cortex fluorescent label UNC cryoembedding date: 03-11-2019 19:37 gmt revision:1 [0] [head]

PMID-25855189 Mapping Synapses by Conjugate Light-Electron Array Tomography

  • Use aligned interleaved immunofluorescence imaging follwed by array EM (FESEM). 70nm thick sections.
  • Of IHC, tissue must be dehydrated & embedded in a resin.
  • However, the dehydration disrupts cell membranes and ultrastructural details viewed via EM ...
  • Hence, EM microscopy uses osmium tetroxide to cross-link the lipids.
  • ... Yet that also disrupt / refolds the poteins, making IHC fail.
  • Solution is to dehydrate & embed at cryo temp, -70C, where the lipids do not dissolve. They used Lowicryl HM-20.
  • We show that cryoembedding provides markedly improved ultrastructure while still permitting multiplexed immunohistochemistry.

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ref: -2006 tags: Mark Bear reward visual cortex cholinergic date: 03-06-2019 04:54 gmt revision:1 [0] [head]

PMID-16543459 Reward timing in the primary visual cortex

  • Used 192-IgG-Saporin (saporin immunotoxin)to selectively lesion cholinergic fibers locally in V1 following a visual stimulus -> licking reward delay behavior.
  • Visual stimulus is full-field light, delivered to either the left or right eye.
    • This is scarcely a challenging task; perhaps they or others have followed up?
  • These examples illustrate that both cue 1-dominant and cue 2-dominant neurons recorded from intact animals express NRTs that appropriately reflect the new policy. Conversely, although cue 1- and cue 2-dominant neurons recorded from 192-IgG-saporin-infused animals are capable of displaying all forms of reward timing activity, ‘’’they do not update their NRTs but rather persist in reporting the now outdated policy.’’’
    • NRT = neural reaction time.
  • This needs to be controlled with recordings from other cortical areas.
  • Acquisition of reward based response is simultaneously interesting and boring -- what about the normal, discriminative and perceptual function of the cortex?
  • See also follow-up work PMID-23439124 A cholinergic mechanism for reward timing within primary visual cortex.

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ref: -2018 tags: cortex layer martinotti interneuron somatostatin S1 V1 morphology cell type morphological recovery patch seq date: 03-06-2019 02:51 gmt revision:3 [2] [1] [0] [head]

Neocortical layer 4 in adult mouse differs in major cell types and circuit organization between primary sensory areas

  • Using whole-cell recordings with morphological recovery, we identified one major excitatory and seven inhibitory types of neurons in L4 of adult mouse visual cortex (V1).
  • Nearly all excitatory neurons were pyramidal and almost all Somatostatin-positive (SOM+) neurons were Martinotti cells.
  • In contrast, in somatosensory cortex (S1), excitatory cells were mostly stellate and SOM+ cells were non-Martinotti.
  • These morphologically distinct SOM+ interneurons correspond to different transcriptomic cell types and are differentially integrated into the local circuit with only S1 cells receiving local excitatory input.
  • Our results challenge the classical view of a canonical microcircuit repeated through the neocortex.
  • Instead we propose that cell-type specific circuit motifs, such as the Martinotti/pyramidal pair, are optionally used across the cortex as building blocks to assemble cortical circuits.
  • Note preponderance of axons.
  • Classifications:
    • Pyr pyramidal cells
    • BC Basket cells
    • MC Martinotti cells
    • BPC bipolar cells
    • NFC neurogliaform cells
    • SC shrub cells
    • DBC double bouquet cells
    • HEC horizontally elongated cells.
  • Using Patch-seq

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ref: -2019 tags: three photon imaging visual cortex THG chirp NOPA mice GCaMP6 MIT date: 03-01-2019 18:46 gmt revision:2 [1] [0] [head]

PMID-30635577 Functional imaging of visual cortical layers and subplate in awake mice with optimized three photon microscopy

  • Murat Yildirim, Hiroki Sugihara, Peter T.C. So & Mriganka Sur'
  • Used a fs Ti:Saphirre 16W pump into a non-colinear optical parametric amplifier (both from Spectra-Physics) to generate the 1300nm light.
  • Used pulse compensation to get the pulse width at the output of the objective to 40 fS.
    • Three-photon cross section is inverse quadratic in pulse width:
    • NP 3δ(τR) 2(NA 22hcλ) 3 N \sim \frac{P^3 \delta}{(\tau R)^2} (\frac{NA^2}{2hc\lambda})^3
    • P is power, δ\delta is 3p cross-section, τ\tau is pulse width, R repetition rate, NA is the numerical aperture (sixth power of NA!!!), h c and λ\lambda Planks constant, speed of light, and wavelength respectively.
  • Optimized excitation per depth by monitoring damage levels. varied from 0.5nJ to 5 nJ.
  • Imaged up to 1.5mm deep! All the way to the white matter / subplate.
  • Allegedly used a custom scan and tube lens to minimize aberrations in the excitation path (hence improve 3p excitation)
  • Layer 5 neurons are more broadly tuned for orientation than other layers. But the data is not dramatic.
  • Used straightforward metrics for tuning, using a positive and negative bump gaussian fit, then vector averaging to get global orientation selectivity.
  • Interesting that the variance between layers seems higher than between mice.

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ref: -2017 tags: calcium imaging seeded iterative demixing light field microscopy mouse cortex hippocampus date: 02-13-2019 22:44 gmt revision:1 [0] [head]

PMID-28650477 Video rate volumetric Ca2+ imaging across cortex using seeded iterative demixing (SID) microscopy

  • Tobias Nöbauer, Oliver Skocek, Alejandro J Pernía-Andrade, Lukas Weilguny, Francisca Martínez Traub, Maxim I Molodtsov & Alipasha Vaziri
  • Cell-scale imaging at video rates of hundreds of GCaMP6 labeled neurons with light-field imaging followed by computationally-efficient deconvolution and iterative demixing based on non-negative factorization in space and time.
  • Utilized a hybrid light-field and 2p microscope, but didn't use the latter to inform the SID algorithm.
  • Algorithm:
    • Remove motion artifacts
    • Time iteration:
      • Compute the standard deviation versus time (subtract mean over time, measure standard deviance)
      • Deconvolve standard deviation image using Richardson-Lucy algo, with non-negativity, sparsity constraints, and a simulated PSF.
      • Yields hotspots of activity, putative neurons.
      • These neuron lcoations are convolved with the PSF, thereby estimating its ballistic image on the LFM.
      • This is converted to a binary mask of pixels which contribute information to the activity of a given neuron, a 'footprint'
        • Form a matrix of these footprints, p * n, S 0S_0 (p pixels, n neurons)
      • Also get the corresponding image data YY , p * t, (t time)
      • Solve: minimize over T ||YST|| 2|| Y - ST||_2 subject to T0T \geq 0
        • That is, find a non-negative matrix of temporal components TT which predicts data YY from masks SS .
    • Space iteration:
      • Start with the masks again, SS , find all sets O kO^k of spatially overlapping components s is_i (e.g. where footprints overlap)
      • Extract the corresponding data columns t it_i of T (from temporal step above) from O kO^k to yield T kT^k . Each column corresponds to temporal data corresponding to the spatial overlap sets. (additively?)
      • Also get the data matrix Y kY^k that is image data in the overlapping regions in the same way.
      • Minimize over S kS^k ||Y kS kT k|| 2|| Y^k - S^k T^k||_2
      • Subject to S k>=0S^k >= 0
        • That is, solve over the footprints S kS^k to best predict the data from the corresponding temporal components T kT^k .
        • They also impose spatial constraints on this non-negative least squares problem (not explained).
    • This process repeats.
    • allegedly 1000x better than existing deconvolution / blind source segmentation algorithms, such as those used in CaImAn

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ref: -0 tags: Kato fear conditioning GABA auditory cortex mice optogenetics SOM PV date: 02-04-2019 19:09 gmt revision:0 [head]

PMID-29375323 Fear learning regulates cortical sensory representation by suppressing habituation

  • Trained mice on CS+ and CS --> lick task.
    • CS+ = auditory tone followed by tailshock
    • CS- = auditory tone (both FM modulated, separated by 0.5 - 1.0 octave).
    • US = licking.
  • VGAT2-ChR2 or PV-ChR2
  • GABA-ergic silencing of auditory cortex through blue light illumination abolished behavior difference following CS+ and CS-.
  • Used intrinsic imaging to locate A1 cortex, then AAV - GCaMP6 imaging to lcoated pyramidal cells.
  • In contrast to reports of enhanced tone responses following simple fear conditioning (Quirk et al., 1997; Weinberger, 2004, 2015), discriminative learning under our conditions caused no change in the average fraction of pyramidal cells responsive to the CS+ tone.
    • Seemed to be an increase in suppression, and reduced cortical responses, which is consistent with habituation.
  • Whereas -- and this is by no means surprising -- cortical responses to CS+ were sustained at end of tone following fear conditioning.
  • ----
  • Then examined this effect relative to the two populations of interneurons, using PV-cre and SOM-cre mice.
    • In PV cells, fear conditioning resulted in a decreased fraction of cells responsive, and a decreased magnitude of responses.
    • In SOM cells, CS- responses were enhanced, while CS+ were less enhanced (the main text seems like an exaggeration c.f. figure 6E)
  • This is possibly the more interesting result of the paper, but even then the result is not super strong.

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ref: -0 tags: microstimulation rat cortex measurement ICMS spread date: 01-26-2017 02:52 gmt revision:0 [head]

PMID-12878710 Spatiotemporal effects of microstimulation in rat neocortex: a parametric study using multielectrode recordings.

  • Measure using extracellular ephys a spread of ~ 1.3mm from near-threshold microstimulation.
  • Study seems thorough despite limited techniques.

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ref: -0 tags: Kleinfeld vasculature cortex review ischemia perfusion date: 01-22-2017 19:40 gmt revision:3 [2] [1] [0] [head]

PMID-25705966 Robust and fragile aspects of cortical blood flow in relation to the underlying angioarchitecture.

  • "The penetrating arterioles that connect the pial network to the subsurface network are bottlenecks to flow; occlusion of even a single penetrating arteriole results in the death of a 500 μm diameter cylinder of cortical tissue despite the potential for collateral flow through microvessels."
  • The pioneering work of Fox and Raichle [7] suggest that there is simply not enough blood to go around if all areas of the cortex were activated at once.
  • There is strong if only partially understood coupling between neuronal and vascular dysfunction [15]. In particular, vascular disease leads to neurological decline and diminished cognition and memory [16].
  • A single microliter of cortex holds nearly one meter of total vasculature length wow! PMID-23749145
  • Subsurface micro vasculature (not arterioles or venules) is relatively robust to occlusion; figure 4.

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ref: -0 tags: David Kleinfeld penetrating arterioles perfusion cortex vasculature date: 10-17-2016 23:24 gmt revision:1 [0] [head]

PMID-17190804 Penetrating arterioles are a bottleneck in the perfusion of neocortex.

  • Focal photothrombosis was used to occlude single penetrating arterioles in rat parietal cortex, and the resultant changes in flow of red blood cells were measured with two-photon laser-scanning microscopy in individual subsurface microvessels that surround the occlusion.
  • We observed that the average flow of red blood cells nearly stalls adjacent to the occlusion and remains within 30% of its baseline value in vessels as far as 10 branch points downstream from the occlusion.
  • Preservation of average flow emerges 350 mum away; this length scale is consistent with the spatial distribution of penetrating arterioles
  • Rose bengal photosensitizer.
  • 2p laser scanning microscopy.
  • Downstream and connected arterioles show a dramatic reduction in blood flow, even 1-4 branches in; there is little reduncancy (figure 2)
  • Measured a good number of vessels (and look at their density!); results are satisfactorily quantitative.
  • Vessel leakiness extends up to 1.1mm away (!) (figure 5).

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ref: -0 tags: buszaki watson oscillations review gamma theta hippocampus cortex date: 09-30-2013 18:32 gmt revision:2 [1] [0] [head]

PMID-23393413 Brain rhythms and neural syntax: implications for efficient coding of cognitive content and neuropsychiatric disease.

  • His frequency band standards:
    • delta: 1.5 - 4Hz
    • theta: 4 - 10Hz
    • beta: 10 - 30 Hz
    • gamma: 30 - 80Hz
    • fast: 80 - 200 Hz
    • ultra fast: 200 - 600 Hz.
  • comodugram: power-power correlelogram
  • Reviews current understanding of important rhythms:
    • How gamma is preserved amongs mammals, owing to the same fundamental mechanisms (membrane time constant, GABA transmission, AMPA receptior latency) all around 25ms; suggests that this is a means of tieing neurons into meaningful groups. or symbols; (solves the binding problem?)
    • Theta rhythm, in comparison, varies between species, inversely based on the size of the hippocampus. Larger hippocampus -> greater axonal delay.
    • These and other the critical step is to break neurons into symbols (as part of a 'language' or sequenced computation), not arbitrarily long trains of spikes which are arbitrarily difficult to parse.
  • Reviews the potential role of oscillations in active sensing, though with a rather conjectorial voice: suggests that sensory systems
  • Suggests that neocortical slow-wave oscillations during sleep are critical for transferring information from the hippocampus to the cortex: the cortex become excitable at particular phases of SWS, which biases the fast ripples from the hippocampus. During wakefulness, the direction is reversed -- the hippocampus 'requests' information from the neocortex by gating gamma with theta rhythms.
  • "Typically, when oscillators of different freqencies are coupled, the network oscillation frequency is determined by the fastest one. (??)
  • I actually find figure 3 to be rather weak -- the couplings are not that obvious, espeically if this is the cherry-picked example.
  • Cross phasing-coupling, or n:m coupling: one observes m events associated with the “driven” cycle of one frequency occurring at n different times or phases in the “stimulus” cycle of the other.
    • The mechanism of cross-frequency coupling may for the backbone of neural syntax, which allows for both segmentation and linking of cell assemblies into assemblies (leters) and sequences (words). Hmm. this seems like a stretch, but I am ever cautious.
  • Brain oscillations for quantifiable phenotypes! e.g. you can mono-zygotic twins apart from di-zygotic twins.

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ref: Hashimoto-2003.03 tags: cortex striatum learning carmena costa basal ganglia date: 03-07-2012 23:18 gmt revision:3 [2] [1] [0] [head]

PMID-22388818 Corticostriatal plasticity is necessary for learning intentional neuroprosthetic skills.

  • Trained a mouse to control an auditory cursor, as in Kipke's task {99}. Did not cite that paper, claimed it was 'novel'. oops.
  • Summed neuronal firing rate of groups of 2 or 4 M1 neurons.
    • One grou increased the frequenxy with increased firing rate; the other decreased tone with increasing FR.
  • Removal of striatal NMDA receptors impairs the ability to learn neuroprosthetic skills.
    • Hence, they argue, cortico-striatal plastciity is required to learn abstract skills, such as this tone to firing rate target acquisition task.
  • Auditory feedback was essential for operant learning.
  • Controlled by recording EMG of the vibrissae + injection of lidocane into the whisker pad.
  • One reward was sucrose solution; the other was a food pellet. When the rat was satiated on one modality, they showed increased preference for the opposite reward. Clever control.
  • Noticed pronounced oscillatory spike coupling, the coherence of which was increased in low-frequency bands in late learning relative to early learning (figure 3).
  • Genetic manipulations: knockin line that expresses Cre recombinase in both striatonigral and striatopallidal medium spiny neurons, crossed with mice carrying a floxed allele of the NMDAR1 gene.
    • These animals are relatively normal, and can learn to perform rapid sequential movements, but are unable to learn precise motor sequences.
    • Acute pharmacological blockade of NMDAR did not affect performance of the neuroprosthetic skill.
    • HEnce the deficits in the transgenic mice are due to an inability to perform the skill.

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ref: Costa-2006.1 tags: Rui Costa Miguel Nicolelis Dopamine depletion excess cortex striatum hyperkinesia akinesia parkinsons DAT-KO date: 03-02-2012 01:03 gmt revision:8 [7] [6] [5] [4] [3] [2] [head]

PMID-17046697 Rapid alterations in corticostriatal ensemble coordination during acute dopamine-dependent motor dysfunction.

  • used rats where they could rapidly switch between dopamine depletion (0.2%) and overexpression (500%)
  • most cortical and striatal neurons ( approximately 70%) changed firing rate during the transition between dopamine-related hyperkinesia and akinesia,
    • buuut the overall cortical firing rate remained unchanged
  • repeated dopamine depletion is accompanied by the loss of glutamergic synapses in striatopallidal neurons (Day et al 2006) PMID-16415865 (Kaneda et al 2005). PMID-16367790
  • with Marc Caron
  • Dopamine is believed to modulate positively the direct striatal pathway that contains predominantly D1-type receptors and disinhibits cortical neurons to modulate negatively the indirect pathway that predominantly contains D2-type receptors and increased crotical inhibition (Albin et al 1989 {1050}, Filion and Tremblay 1991; Gerfen 1992, Parr-Brownlie and Hyland, 2005).
  • According to the classical view (Albin et al 1989), lack of DA release should lead to inhibition of cortical activity and an inability to produce movement, while an excess of Dopamine should lead to increased cortical activity and hyperactivity (Gerfen, 1992).
    • mouse model: DDD PMID-17030735[] (dopamine transporter knockout)

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ref: Magill-2001.01 tags: dopamine STN globus_pallidus cortex parkinsons DBS 6OHDA date: 02-22-2012 15:31 gmt revision:6 [5] [4] [3] [2] [1] [0] [head]

PMID-11566503[0] Dopamine regulates the impact of the cerebral cortex on the subthalamic nucleus-globus pallidus network

  • Compared unit activity STN / GP and EEG in rats under urethane anesthesia in control and 6OHDA rats.
  • DA depletion:
    • increased FR of STN neurons.
    • caused oscillations in GP neurons.
  • dopamine depletion causes the STN-GP circuit to become more reactive to the influence of the activity of cortical inputs. also see PMID-10632612[1]
  • oscillatory activity in the STN-GP network in anaesthetised rats is phase-locked to rhythmic cortical activity and is abolished by transient cortical activation as well as cortical ablation.
    • 15-20% of the network still oscillated following cortex removal, suggesting that intrinsic properties pattern activity when dopamine levels are reduced.
  • cool figures - nice recordings, high SNR, clear oscillations in the firing and ECoG signal

____References____

[0] Magill PJ, Bolam JP, Bevan MD, Dopamine regulates the impact of the cerebral cortex on the subthalamic nucleus-globus pallidus network.Neuroscience 106:2, 313-30 (2001)
[1] Magill PJ, Bolam JP, Bevan MD, Relationship of activity in the subthalamic nucleus-globus pallidus network to cortical electroencephalogram.J Neurosci 20:2, 820-33 (2000 Jan 15)

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ref: Monakow-1978.11 tags: motor_cortex STN subthalamic nucleus anatomy DBS date: 01-26-2012 17:17 gmt revision:5 [4] [3] [2] [1] [0] [head]

PMID-83239[0] Projections of the precentral motor cortex and other cortical areas of the frontal lobe to the subthalamic nucleus in the monkey.

  • this paper is old and important!
  • The ipsilateral subthalamic nucleus receives a moderately strong and somatotopic organized projection from Woolsey's precentral motor cortex (PMd, M1 i guess)
    • No projections from the postcentral gyrus! (S1) (Is this still thought to be true?)
  • The remaining nucleus is occupied by less intensive projections from premotor and prefrontal areas
  • STN is a convergence site for pallidal and cortical motor/frontal projections.
  • autoradiography slices are damn hard for me to read.

____References____

[0] Monakow KH, Akert K, Künzle H, Projections of the precentral motor cortex and other cortical areas of the frontal lobe to the subthalamic nucleus in the monkey.Exp Brain Res 33:3-4, 395-403 (1978 Nov 15)

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ref: Hilker-2004.01 tags: STN subthalamic DBS stimulation cortex cerebellum PET PD parkinsons date: 01-24-2012 05:38 gmt revision:1 [0] [head]

PMID-14688612[0] Subthalamic Nucleus Stimulation Restores Glucose Metabolism in Associative and Limbic Cortices and in Cerebellum: Evidence from a FDG-PET Study in Advanced Parkinson's Disease

  • cortical depression of glucose metabolism
  • hypermetabolic state in the left rostral cerebellum (?!)
  • DBS generally remedies this imbalance, restoring glucose metabolism to the cortex associative/motor/frontal as well as to the thalamus distant from the stimulation site.

____References____

[0] Hilker R, Voges J, Weisenbach S, Kalbe E, Burghaus L, Ghaemi M, Lehrke R, Koulousakis A, Herholz K, Sturm V, Heiss WD, Subthalamic nucleus stimulation restores glucose metabolism in associative and limbic cortices and in cerebellum: evidence from a FDG-PET study in advanced Parkinson's disease.J Cereb Blood Flow Metab 24:1, 7-16 (2004 Jan)

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ref: Chestek-2007.1 tags: M1 cortex reaching tuning date: 01-15-2012 22:08 gmt revision:1 [0] [head]

PMID-17913908[0] Single-Neuron Stability during Repeated Reaching in Macaque Premotor Cortex

  • Neural activity was predominantly stable over time in all measured properties: firing rate, directional tuning, and contribution to a decoding model that predicted kinematics from neural activity. The small changes in neural activity that we did observe could be accounted for primarily by subtle changes in behavior. We conclude that the relationship between neural activity and practiced behavior is reasonably stable, at least on timescales of minutes up to 48 h.
    • Makes sense.
    • Good for neuroprosthetics.

____References____

[0] Chestek CA, Batista AP, Santhanam G, Yu BM, Afshar A, Cunningham JP, Gilja V, Ryu SI, Churchland MM, Shenoy KV, Single-neuron stability during repeated reaching in macaque premotor cortex.J Neurosci 27:40, 10742-50 (2007 Oct 3)

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ref: Parikh-2009.04 tags: BMI rats cortex layer depth date: 01-10-2012 01:09 gmt revision:2 [1] [0] [head]

PMID-19255460[0] Lower layers in the motor cortex are more effective targets for penetrating microelectrodes in cortical prostheses.

  • Aggregate analysis (633 neurons) and best session analysis (75 neurons) indicated that units in the lower layers (layers 5, 6) are more likely to encode direction information when compared to units in the upper layers (layers 2, 3) (p< 0.05).
  • DUH. Have we forgotten all anatomy?

____References____

[0] Parikh H, Marzullo TC, Kipke DR, Lower layers in the motor cortex are more effective targets for penetrating microelectrodes in cortical prostheses.J Neural Eng 6:2, 026004 (2009 Apr)

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ref: Hatsopoulos-2005.01 tags: BMI Hatsopoulos Donoghue cortex date: 01-03-2012 22:49 gmt revision:4 [3] [2] [1] [0] [head]

PMID-17282055[0][] Cortically controlled brain-machine interface

  • conference proceedings. describe the 6month teraplegic trial.
  • (above, monkey)
    • lets them record from 40% of electrodes.
    • 100-200uv units, 20uv noise.
    • one year to three years post implantation.
  • advocate hybrid multimodal control.
    • M1 = continuous control
    • PMd = discrete control
      • used a probabilistic model for this (poisson firing rate, individual neurons are independent)

____References____

[0] Hatsopoulos N, Mukand J, Polykoff G, Friehs G, Donoghue J, Cortically controlled brain-machine interface.Conf Proc IEEE Eng Med Biol Soc 7:1, 7660-7663 (2005)

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ref: Penfield-1937 tags: Penfield 1937 motor cortex stimulation ICMS human neurosurgery electrodes date: 01-03-2012 22:08 gmt revision:3 [2] [1] [0] [head]

No PMID / bibtex penfield-1937. Somatic motor and sensory representation in the cerebral cortex of man as studied by electrical stimulation

  • Fritsch and Hitzig (1870) [0] cited as the first paper in electrical excitation of the CNS.
  • Good review of the scientific experiments thereafter, including stimulation to S1 by Ferrier, work with apes etc.
  • Central sulcus called the 'Rolandic fissure'.
  • Interesting! quote:

The account of Bartholow (1874) is interesting to say the least and may be cited. His patient was a 30-year old-domestic. As an infant this unfortunate had chanced to fall into the fire, burning her scalp so badly that " hair was never reproduced." A piece of whale bone in the wig she was forced to wear irritated the scarred scalp and, by her statement, three months before she was admitted, an ulcer appeared. When she presented herself for relief, this had eroded the skull over a space 2 in. in diameter " where the pulsations of the brain are plainly seen." Although " rather feeble-minded " Bartholow observed that Mary returned replies to all questions and no sensory or motor loss could be made out in spite of the fact that brain substance apparently had been injured in the process of evacuation of pus from the infected area. The doctor believed, therefore, that fine insulated needles could be introduced without further damage.

While the electrodes were in the right side Bartholow decided to try the effect of more current. ' Her countenance exhibited great distress and she began to cry. Very soon the left hand was extended as if in the act of taking hold of some object in front of her; the arm presently was agitated with clonic spasms ; her eyes became fixed with pupils widely dilated ; the lips were blue and she frothed at the mouth ; her breathing became stertorous, she lost conscious-ness and was violently convulsed on the left side. This convulsion lasted for five minutes and was succeeded by coma. She returned to consciousness in twenty minutes from the beginning of the attack and complained of some weakness and vertigo." Three days after this stimulation, following a series of right-sided seizures, the patient died.

  • Relatively modern neurosurgical procedures.
  • They observe changes to blood circulation prior epileptic procedures. wow!
  • Very careful hand-drawn maps of what they have observed. Important, as you'll probably never get this trough an IRB. It pays to be meticulous.

____References____

[0] Fritsch G, Hitzig E, Electric excitability of the cerebrum (Uber die elektrische Erregbarkeit des Grosshirns).Epilepsy Behav 15:2, 123-30 (2009 Jun)

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ref: notes-2005.06 tags: Crick claustrum cortex telecephalon date: 01-03-2012 15:20 gmt revision:1 [0] [head]

http://www.klab.caltech.edu/news/crick-koch-05.pdf

  • small, sheetlike region, not very think 5mm in humans.
  • between the extreme capsule and the external capsule. in the sheep
  • there are remarkably few microelectrode investigations of claustral receptive fields in the claustrum & almost none in awake animals.
  • the claustrum is interconnected with the extrastriate visual areas (17 & 18), sensory cortex, and prefrontal cortex.
  • claustrum is highly vasularized and protected from stroke via multiple arteries.
    • hence, few selective lesions of the claustrum and so we don't know what effects its absence manifests.

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ref: Butovas-2007.04 tags: Butovas Schwarts ICMS stimulation rat barrel cortex date: 01-03-2012 06:55 gmt revision:2 [1] [0] [head]

PMID-17419757[0] Detection psychophysics of intracortical microstimulation in rat primary somatosensory cortex.

  • headposted rats, ICMS to barrel cortex
  • single pulse threshold = 2 nC, around the threshold for evocation of short-latency action potentials near an electrode.
  • one pulse saturated at 80% correct.
  • multiple pulses had a higher rate, though this saturated at 15 pulses.
  • double pulse optimal in terms of power / discrimination.

____References____

[0] Butovas S, Schwarz C, Detection psychophysics of intracortical microstimulation in rat primary somatosensory cortex.Eur J Neurosci 25:7, 2161-9 (2007 Apr)

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ref: Aflalo-2007.03 tags: Graziano motor cortex M1 SUA macaque monkey electrophysiology tuning date: 01-03-2012 03:37 gmt revision:1 [0] [head]

PMID-17360898[] Relationship between Unconstrained Arm Movements and Single-Neuron Firing in the Macaque Motor Cortex

  • the best explanation of neuronal firing was the final mulijoint configuration of the arm - it accounted for 36% of the SUA variance.
  • the search for the 'correct' motor parameter (that neurons are tuned to) is an ill-posed experimental question because motor parameters are very intercorrelated.
  • they knock experiments in which the animals are overtrained & the movements limited - and they are right!
  • single electrode recording with cronically implanted steel chamber - e.g. it took a damn long time!
    • imaged the central sulcus through the dura.
    • verified location with single unit responses to palpation of the contralateral hand/arm (in S1) & microstimulation-evoked movements in M1.
  • used optotrak to measure the position of the monkey.
  • occasionally, the monkey attemptted to scratch the experimenter with fast semi-ballistic arm movement. heh. :)
  • movements were seprarated based on speed analysis - that is, all the data were analyzed as discrete segments.
  • neurons were inactive during periods of hand stasis between movements.
  • tested the diversity of their training set in a clever way: they simulated neurons tuned to various parameters of the motion, and tested to see if their analysis could recover the tuning. it could.
    • however, they still used unvalidated regression analysis to test their hypothesis. regression analysis estimates how much variance is estimated by the cosine-tuning model - it returns an R^2.
  • either averaged the neuronal tuning over an entire movement or smoothed the firing rate using a 10hz upper cutoff.
  • Moran & Schwartz' old result seems to be as much a consequence of averaging across trials as it is a consequence of actual tuning...
    • whithout the averaging, only 3% of the variance could be attributed to speed tuning.
  • i think that they have a good point in all of this: when you eliminate sources of variance (e.g. starting position) from the behavior, either by mechanical restraint or simple omission of segments or even better averaging over trials, you will get a higher R^2. but it may be false, a compression of the space along an axis where they are not well correlated!
  • a model in which the final position matters little, but the velocity used to get there does, has been found to account for little of the neuronal variance.
    • instead, neurons are tuned to any of a number of movements that terminate near a preferred direction.
  • observational studies of of the normal psontaneous behavior of monkeys indicate that a high proportion of time is spent using the arm as a postural device.
    • therefore, they expect that neurons are tuned to endpoint posture.
    • modeled the neuronal firing as a gaussian surface in the 8-dimensional space of the arm posture.
  • in comparison to other studies, the offset between neural activity and behavior was not significantly different, over the entire population of recorded neurons, from zero. This may be due to the nature of the task, which was spontaneous and ongoing, not cue and reaction based, as in many other studies.
    • quote: This result suggests that the neuronal tuning to posture reflects reatively more and anticipation of the future state of the limb rather than a feedback signal about a recent state of the limb.

____References____

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ref: Moran-1999.11 tags: electrophysiology motor cortex Schwartz Moran M1 tuning date: 01-03-2012 03:36 gmt revision:2 [1] [0] [head]

PMID-10561437[0] Motor cortical representation of speed and direction during reaching

  • velocity is represented in the motor cortex.
  • they developed an equation relating firing rate to the position and velocity.
  • EMG direction had significantly different tuning from the cortical activity
    • the effect of speed on EMG was also different.
  • used single-electrode recording - 1,066 cells!!
  • introduce the square-root transformation of the firing rate (from Ashe and Georgopolous 1994)

____References____

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ref: Donoghue-1990.01 tags: Donoghue Suner Sanes rat motor cortex reorganization M1 tuning surprising date: 01-03-2012 03:30 gmt revision:4 [3] [2] [1] [0] [head]

PMID-2340869[0] Dynamic organization of primary motor cortex output to target muscles in adult rats. II. Rapid reorganization following motor nerve lesions.

  1. Map out the motor cortex into vibrissa and forelimb areas using ICMS.
  2. Implant a simulating electrode in the vibrissa motor cortex.
  3. Implant EMG electrodes in the forearm.
  4. Sever the buccal and mandibular branches of the facial nerve.
  5. stimulate, and wait for forearm EMG to be elicited by ICMS. Usually occurs! Why? Large horizontal axons in motor cortex? Uncovering of silent synapses, and homeostatic modulation of firing rates?

____References____

[0] Donoghue JP, Suner S, Sanes JN, Dynamic organization of primary motor cortex output to target muscles in adult rats. II. Rapid reorganization following motor nerve lesions.Exp Brain Res 79:3, 492-503 (1990)

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ref: Shuler-2006.03 tags: reward V1 visual cortex timing reinforcement surprising date: 01-03-2012 02:33 gmt revision:4 [3] [2] [1] [0] [head]

PMID-16543459[0] Reward Timing in the Primary Visual Cortex

  • the responses of a substantial fraction of neurons in the primary visual cortex evolve from those that relate solely to the physical attributes of the stimuli to those that accurately predict the timing of reward.. wow!
  • rats. they put goggles on the rats to deliver full-fields retinal illumination for 400ms (isn't this cheating? full field?)
  • recorded from deep layers of V1
  • sensory processing does not seem to be reliable, stable, and reproducible...
  • rewarded only half of the trials, to see if the plasticity was a result of reward delivery or association of stimuli and reward.
  • after 5-7 sessions of training, neurons began to respond to the poststimulus reward time.
  • this was actually independent of reward delivery - only dependent on the time.
  • reward-related activity was only driven by the dominant eye.
  • individual neurons predict reward time quite accurately. (wha?)
  • responses continued even if the animal was no longer doing the task.
  • is this an artifact? of something else? what's going on? the suggest that it could be caused by subthreshold activity due to recurrent connections amplified by dopamine.

____References____

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ref: Shinkman-1974.06 tags: Shinkman Bruce Pfingst operant conditioning visual cortex cat ICMS 1974 stimulation date: 12-29-2011 05:13 gmt revision:4 [3] [2] [1] [0] [head]

PMID-4598035[0] Operant conditioning of single-unit response patterns in visual cortex.

  • In cat V1 -- suprising, this is usually considered to be sensory.
  • implanted bilater tripolar stimulating electrodes aimed at the lateral hypothalamus. These were tested for self-stimulation, and preferred locations/currents were selected for optimal ICS reinforcement.
    • 200 bar presses in 8 minute test.
  • Anesthetized, immobilized, head-restrained, contact-lens focused cats.
  • Back projected stimuli onto a screen 50 cm from eye ; dot, bar, or small spot was effective in triggering patterned response, as with many of these studies.
  • For conditioning: set a threshold at the third quartile (1/4 of trials exceeded threshold); comparator circuit counted the number of spikes during stimulus presentation, and if threshold was exceeded, reinforcing ICS was delivered.
    • Reinforcing ICS started 300ms after visual stimulus and lasted 500ms.
  • Conditioning was deemed successful if the mean trial firing rate for the last 50 conditioned trials had a mean firing rate > 30% larger than the first 50 control trials.
    • While recording some cells, ICS reinforcement was delivered at random as control.
  • Conditioning produced changes within stimulus presentation but not outside.
  • They consider the use of an immobilized subject is a pro -- better control, rules out alternative explanations based on motor feedback.

____References____

[0] Shinkman PG, Bruce CJ, Pfingst BE, Operant conditioning of single-unit response patterns in visual cortex.Science 184:4142, 1194-6 (1974 Jun 14)

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ref: Douglas-1991.01 tags: functional microcircuit cat visual cortex microstimulation date: 12-29-2011 05:12 gmt revision:3 [2] [1] [0] [head]

PMID-1666655[0] A functional microcircuit for cat visual cortex

  • Using in vivo stim and record, They describe what may be a 'cannonical' circuit for the cortex.
  • Not dominated by excitation / inhibition, but rather cell dynamics.
  • Thalamus weaker than poysynaptic inupt from the cortex for excitation.
  • Focuses on Hubel and Wiesel style stuffs. Cats, SUA.
  • Stimulated the geniculate body & observed the response using intracellular electrodes from 102 neurons.
  • Their traces show lots of long-duration inhibition.
  • Probably not relevant to my purposes.

____References____

[0] Douglas RJ, Martin KA, A functional microcircuit for cat visual cortex.J Physiol 440no Issue 735-69 (1991)

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ref: Schwartz-1988.08 tags: Georgopoulos 1988 motor coding cortex population vector date: 12-20-2011 00:49 gmt revision:3 [2] [1] [0] [head]

PMID-3411361[0] Primate motor cortex and free arm movements to visual targets in three-dimensional space. I. Relations between single cell discharge and direction of movement.

  • 475/568 (83%) of cells varied in an orderly fashion with movement -- tuned to a movement direction.
    • As before, binned the firing based on movement direction.
  • generalize 2-D results [1][2]
  • Totally awesome tracking system: a spark gap was attached to the monkey's wrist and was discharged every 20ms. The sonic signal was picked up by at least 3 of the 8 ultrasonic recievers placed at the corners of the workspace and the xyz coordinates were calculated from the sonic delays using a microprocessor-based system.
  • monkey(s) had to press lighted buttons (arcade buttons) within this workspace.
  • otherwise same materials / methods as before.
  • every effort was made to isolate initially negative-going action potentials, and indication that the neuron was less likely to be damaged.
    • fiber spikes are initially positive. Cite Mountcastle et al 1969.
  • EMG signals gained 3000 and bandpassed 100-500Hz. rather narrow, but normal I guess.
  • Neural data recorded as interspike intervals.
  • vectoral dot-product tuning of cells, with the coeficients set by multiple linear regression.
    • This is equivalent to cosine tuning.
  • rather complicated CUSUM for determining onset of activity - including inhibition.
  • as in the earlier study, 60% of cells were tuned in the reaction time, and 85% within the movement time.
  • EMG activity looks like it can be described with cosine tuning as well.
  • 3D tuning directed over the whole space.
  • Residuals of firing rates measured with respect to the tuning functions; residuals were mean zero and approximately the same spread, and were distributed equally over the 3D space.
  • movement latency about 300ms. pretty quick reaction time?
  • Got some pretty awesome graphics for 1986 :)
  • The discharge rate of motor cortical cells varies with the magnitude of force and that cells with higher thresholds are recruited at progressively higher forces (Hepp-Reymond et al 1978).
  • Murphy et al 1982 found that ICMS to M1 caused rotation about single joints, which is inconsistent with cosine tuning (would require complex tuning, or tuning to joints).
  • They argue that cosine tuning refects transformatino by the propriospinal system, which engages patterns of muscle activity.
    • Most PTNs can influence several motoneuron pools in the spinal cord. (Fetz and Finocchio 1975, Fetz and Cheney 1978, 1980 ... Lemon 1986, Cheney and Fetz 1985)
    • Suggest that PTNs related to the weighted combinations of muscles.

____References____

[0] Schwartz AB, Kettner RE, Georgopoulos AP, Primate motor cortex and free arm movements to visual targets in three-dimensional space. I. Relations between single cell discharge and direction of movement.J Neurosci 8:8, 2913-27 (1988 Aug)
[1] Georgopoulos AP, Kalaska JF, Caminiti R, Massey JT, On the relations between the direction of two-dimensional arm movements and cell discharge in primate motor cortex.J Neurosci 2:11, 1527-37 (1982 Nov)
[2] Thach WT, Correlation of neural discharge with pattern and force of muscular activity, joint position, and direction of intended next movement in motor cortex and cerebellum.J Neurophysiol 41:3, 654-76 (1978 May)

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ref: Georgopoulos-1982.11 tags: Georgopoulos 1982 motor tuning cortex M1 population vector date: 12-19-2011 23:52 gmt revision:1 [0] [head]

PMID-7143039 On the relations between the direction of two-dimensional arm movements and cell discharge in primate motor cortex.

  • eight directions 45deg intervals, 2D joystick, frictionless, LED tarkets in a blocked randomized experimental design.
    • MK made simultaneous saccades; saccade latency 150-170ms.
      • some motor cells responded to visual movement.
    • EMG activity began ~80ms before movement.
    • monkeys used both arms.
  • bell-shaped or cosine tuning in 75% of the cells.
    • This has also been described in the saccade system in the paramedian pontine reticular formation (Henn and Cohen 1976), the mesencelphatic reticular formation (Buttner eta la 1977) and the internal medullary lamina of the thalamus (Schlag and Schlag-Ney 1977)
  • cells tended to cluster by tuning in depth.
  • cells tended to respond to movement & small corrections to movement, but did not necessarily respond to non-task related movement. "Yet these same cells were frequently silent during other movements which also involved contraction of the same muscles [as used in the task]"
  • cell discharge was much stronger during active movements than during passive manipulations.
  • 64% of cells were activated before the earliest EMG changes; 87% before the onset of movement.
  • The famous one, where the population vector was formalized / conceived / validated.
  • most neurons begin firing ~ 100ms before movement begins.
  • useda PDP11/20 minicomputer to control the LEDs & data recording.
  • Thach 1978 -- approxmately equal proportions of motor cortical cells were related to muscle activity, hans position, and direction of intended movement Thach 1978) PMID-96223
  • single electrode Pt/Ir recording 2-3Mohm; recordings made for 6-7 hours.
  • cite georgopoulos 1983 -- they propose distributed population coding.
  • point out that the central problem -- upon which some progress has been made - is the translation between visual and motor coordinate frames.

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ref: OReilly-2006.02 tags: computational model prefrontal_cortex basal_ganglia date: 12-07-2011 04:11 gmt revision:1 [0] [head]

PMID-16378516[0] Making Working Memory Work: A Computational Model of Learning in the Prefrontal Cortex and Basal Ganglia

found via: http://www.citeulike.org/tag/basal-ganglia

____References____

[0] O'Reilly RC, Frank MJ, Making working memory work: a computational model of learning in the prefrontal cortex and basal ganglia.Neural Comput 18:2, 283-328 (2006 Feb)

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ref: Fletcher-2005.07 tags: explicit implicit learning fMRI frontal_cortex MT date: 12-07-2011 03:58 gmt revision:1 [0] [head]

PMID-15537672[0] On the Benefits of not Trying: Brain Activity and Connectivity Reflecting the Interactions of Explicit and Implicit Sequence Learning

quote: ünder certain curcumstances, automatic learning may be attenuated by explicit memory processes" : expicit attemps to learn a difficult sequence (compared to a control) produces a failure in implicit learning, and this failure is caused by the supression of learning rather than the expression. There is a deleterious effect of explicit search on implicit learning.

  • implicit learning is hampered by explicit search.
  • Compare this to the known benefits of coginive effort on motor learning ... (?)

____References____

[0] Fletcher PC, Zafiris O, Frith CD, Honey RA, Corlett PR, Zilles K, Fink GR, On the benefits of not trying: brain activity and connectivity reflecting the interactions of explicit and implicit sequence learning.Cereb Cortex 15:7, 1002-15 (2005 Jul)

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ref: Harris-2008.03 tags: retroaxonal retrosynaptic Harris learning cortex backprop date: 12-07-2011 02:34 gmt revision:2 [1] [0] [head]

PMID-18255165[0] Stability of the fittest: organizing learning through retroaxonal signals

  • the central hypothesis: strengthening of a neuron's output synapses stabilizes recent changes in the same neuron's inputs.
    • this causes representations (as are arrived at with backprop) that are tuned to task features.
  • Retroaxonal signaling in the brain is too slow for an instructive (says at least the sign of the error wrt a current neuron's output) backprop algorithm
  • hence, retroaxonal signals are not instructive but selective.
  • At SFN Harris was looking for people to test this in a model; as (yet) unmodeled and untested, I'm suspicious of it.
  • Seems plausible, yet it also just seems to be a way of moving the responsibility for learning computation to the postsynaptic neuron (which is then propagated back to the present neuron). The theory does not immediately suggest what neurons are doing to learn their stuff; rather how they may be learning.
    • If this stabilization is based on some sort of feedback (attention? reward?), which may guide learning (except for the cortex, which does not have many (any?) DA receptors...), then I may be more willing to accept it.
    • It seems likely that the cortex is doing a lot of unsupervised learning: predicting what sensory info will come next based on present sensory info (ICA, PCA).

____References____

[0] Harris KD, Stability of the fittest: organizing learning through retroaxonal signals.Trends Neurosci 31:3, 130-6 (2008 Mar)

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ref: BuzsAki-1996.04 tags: hippocampus neocortex theta gamma consolidation sleep Buzsaki review learning memory date: 12-07-2011 02:31 gmt revision:6 [5] [4] [3] [2] [1] [0] [head]

PMID-8670641[0] The hippocampo-neocortical dialogue.

  • the entorhinal ctx is bidirectionally conneted to nearly all areas of the neocortical mantle.
  • Buzsaki correctly predicts that information gathered during exploration is played back at a faster scale during synchronous population busts during (comnsummatory) behaviors.
  • looks like a good review of the hippocampus, but don't have time to read it now.
  • excellent explanation of the anatomy (with some omissions, click through to read the caption):
  • SPW = sharp waves, 40-120ms in duration. caused by synchronous firing in much of the cortex ; occur 0.02 - 3 times/sec in daily activity & during slow wave sleep.
    • BUzsaki thinks that this may be related to memory consolidation.
  • check the cited-by articles : http://cercor.oxfordjournals.org/cgi/content/abstract/6/2/8
____References____
[0] Buzsaiki G, The hippocampo-neocortical dialogue.Cereb Cortex 6:2, 81-92 (1996 Mar-Apr)

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ref: Shinkman-1974.06 tags: operant conditioning visual cortex Shinkman date: 11-26-2011 00:40 gmt revision:0 [head]

PMID-4598035 Operant conditioning of single-unit response patterns in visual cortex

  • They successfully conditioned cells in the visual cortex to increase firing response to visual patterns (sensory stimulus).
    • This is conditional response, not conditioning behavior directly.
  • Reinforced using electrical stimulation of the lateral hypothalamus.
    • Optimal reinforcement electrodes were determined via self-stimulation.
  • Immobilized V1 recording appears hardcore. Cats were immobilized but not anesthetized for recording / reinforcement.
  • Delivered fixed ICMS pulse train when threshold number of spikes was exceeded.
  • Data analysis without matlab must have been hard. Actually, the data doesn't look that good, but this may be an artifact of presentation.
  • Controlled for eye movements using a paralytic.

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ref: Shadmehr-1997.01 tags: Shadmehr human long term memory learning motor M1 cortex date: 03-25-2009 15:29 gmt revision:2 [1] [0] [head]

PMID-8987766[0] Functional Stages in the Formation of Human Long-Term Motor Memory

  • We demonstrate that two motor maps may be learned and retained, but only if the training sessions in the tasks are separated by an interval of ~5 hr.
  • Analysis of the after-effects suggests that with a short temporal distance, learning of the second task leads to an unlearning of the internal model for the first.
  • many many citations!

____References____

[0] Shadmehr R, Brashers-Krug T, Functional stages in the formation of human long-term motor memory.J Neurosci 17:1, 409-19 (1997 Jan 1)

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ref: MAPlle-2009.03 tags: sleep spindles learning ripples LFP hippocampus neocortex synchrony SWS REM date: 03-25-2009 15:05 gmt revision:2 [1] [0] [head]

PMID-19245368[0] The influence of learning on sleep slow oscillations and associated spindles and ripples in humans and rats

  • Here we examined whether slow oscillations also group learning-induced increases in spindle and ripple activity, thereby providing time-frames of facilitated hippocampus-to-neocortical information transfer underlying the conversion of temporary into long-term memories.
  • No apparent grouping effect between slow oscillations and learning-induced spindles and ripples in rats.
  • Stronger effect of learning on spindles (neocortex) and ripples (hippocampus) ; less or little effect of learning on slow waves in the neocortex.
  • have a good plot showing their time-series analysis:

____References____

[0] Mölle M, Eschenko O, Gais S, Sara SJ, Born J, The influence of learning on sleep slow oscillations and associated spindles and ripples in humans and rats.Eur J Neurosci 29:5, 1071-81 (2009 Mar)

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ref: Matsuzaka-2007.02 tags: skill learning M1 motor control practice cortex date: 03-20-2009 18:31 gmt revision:1 [0] [head]

PMID-17182912[0] Skill Representation in the Primary Motor Cortex After Long-Term Practice

  • The acquisition of motor skills can lead to profound changes in the functional organization of the primary motor cortex (M1) yes
  • 2 task modes: random target acquisition, and one of 2 repeating sequences (predictable, repeating mode)
  • 2 years of training -> 40% of units were differentially active during the two task modes
  • variations in movement types in the two classes did not fully explain the difference in activity between the 2 tasks
    • M1 neurons are more influence by the task than the actual kinematics.

____References____

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ref: Hoffman-2007.1 tags: up down states neocortex SWS date: 03-20-2009 01:27 gmt revision:1 [0] [head]

PMID-17978020[0] The Upshot of Up States in the Neocortex: From Slow Oscillations to Memory Formation

  • slow waves are caused by spreading synchronous up/down depolarizations in the neocortex during SWS
    • the slow waves are thought to be generated intrinsically (?)
  • cortex is insensitive in up states, but highly sensitive to thalamic stimulation in down states? humm, need to see the data for that - from slices.
  • quote: "According to some theories of memory consolidation (Marr, 1971Go; Buzsáki, 1989Go; Squire, 1992Go; McClelland et al., 1995Go), memories are thought to be minted rapidly in the hippocampus during behavior and transferred to the neocortex during slow-wave sleep for long-term storage."
  • there is other stuff about 50-150 Hz activation in the hippocampus leading to neocortical activation, and that this is associated with transfer from labile hippocampus to long-term neocortex.
  • the review gives an impression of not being as concrete as, say, Buzsaki.

____References____

[0] Hoffman KL, Battaglia FP, Harris K, MacLean JN, Marshall L, Mehta MR, The upshot of up states in the neocortex: from slow oscillations to memory formation.J Neurosci 27:44, 11838-41 (2007 Oct 31)

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ref: Diedrichsen-2005.1 tags: Shadmehr error learning basal ganglia cerebellum motor cortex date: 03-09-2009 19:26 gmt revision:0 [head]

PMID-16251440[0] Neural correlates of reach errors.

  • Abstract:
  • Reach errors may be broadly classified into errors arising from unpredictable changes in target location, called target errors, and errors arising from miscalibration of internal models (e.g., when prisms alter visual feedback or a force field alters limb dynamics), called execution errors.
    • Execution errors may be caused by miscalibration of dynamics (e.g., when a force field alters limb dynamics) or by miscalibration of kinematics (e.g., when prisms alter visual feedback).
  • Although all types of errors lead to similar on-line corrections, we found that the motor system showed strong trial-by-trial adaptation in response to random execution errors but not in response to random target errors.
  • We used functional magnetic resonance imaging and a compatible robot to study brain regions involved in processing each kind of error.
  • Both kinematic and dynamic execution errors activated regions along the central and the postcentral sulci and in lobules V, VI, and VIII of the cerebellum, making these areas possible sites of plastic changes in internal models for reaching.
    • Only activity related to kinematic errors extended into parietal area 5.
    • These results are inconsistent with the idea that kinematics and dynamics of reaching are computed in separate neural entities.
  • In contrast, only target errors caused increased activity in the striatum and the posterior superior parietal lobule.
  • The cerebellum and motor cortex were as strongly activated as with execution errors. These findings indicate a neural and behavioral dissociation between errors that lead to switching of behavioral goals and errors that lead to adaptation of internal models of limb dynamics and kinematics.

____References____

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ref: Mehta-2007.01 tags: hippocampus visual cortex wilson replay sleep learning states date: 03-09-2009 18:53 gmt revision:1 [0] [head]

PMID-17189946[0] Cortico-hippocampal interaction during up-down states and memory consolidation.

  • (from the associated review) Good pictorial description of how the hippocampus may impinge order upon the cortex:
    • During sleep the cortex is spontaneously and randomly active. Hippocampal activity is similarly disorganized.
    • During waking, the mouse/rat moves about in the environment, activating a sequence of place cells. The weights of the associated place cells are modified to reflect this sequence.
    • When the rat falls back to sleep, the hippocampus is still not random, and replays a compressed copy of the day's events to the cortex, which can then (and with other help, eg. ACh), learn/consolidate it.
  • see [1].

____References____

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ref: Ji-2007.01 tags: hippocampus visual cortex wilson replay sleep date: 03-09-2009 18:48 gmt revision:3 [2] [1] [0] [head]

PMID-17173043[0] Coordinated memory replay in the visual cortex and hippocampus during sleep.

  • EEG from Layer 5 of the visual cortex.
  • used tetrodes.
  • rats were trained to alternate loops in a figure-8 maze to get at food.
  • the walls of the maze were lined with high-contrast cues.
  • data for correlated activity between ctx and hippocampus weak - they just show that the frame ('up' period in cellular activity) start & end between the two regions are correlated. No surprise - they are in the same brain after all!
  • Found that cells in the deep visual cortex (V1 & V2) had localized firing fields. Rat vision is geared for navigation? (mostly?)
  • From this, they could show offline replay of the same sequence; these offline sequences were compressed by about 5-10.
    • shuffle tests on the replayed frames look pretty good - respectable degree of significance here.
    • Aside: possibly some of the noise of the recordings is reflective not of the noise of the system, but the noise / high dimensionality of the sensory input driving the visual ctx.
  • Also found some visual and some hippocampal cells that replayed sequences simultaneously; shuffle test here looks ok too.
  • picture from associated review, {692}

____References____

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ref: Vasilaki-2009.02 tags: associative learning prefrontal cortex model hebbian date: 02-17-2009 03:37 gmt revision:2 [1] [0] [head]

PMID-19153762 Learning flexible sensori-motor mappings in a complex network.

  • Were looking at a task, presented to monkeys over 10 years ago, where two images were presented to the monkeys, and they had to associate left and rightward saccades with both.
  • The associations between saccade direction and image was periodically reversed. Unlike humans, who probably could very quickly change the association, the monkeys required on the order of 30 trials to learn the new association.
  • Interestingly, whenever the monkeys made a mistake, they effectively forgot previous pairings. That is, after an error, the monkeys were as likely to make another error as they were to choose correctly, independent of the number of correct trials preceding the error. Strange!
  • They implement and test reward-modulated hebbian learning (RAH), where:
    • The synaptic weights are changed based on the presynaptic activity, the postsynaptic activity minus the probability of both presynaptic and postsynaptic activity. This 'minus' effect seems similar to that of TD learning?
    • The synaptic weights are soft-bounded,
    • There is a stop-learning criteria, where the weights are not positively updated if the total neuron activity is strongly positive or strongly negative. This allows the network to ultimately obtain perfection (at some point the weights are no longer changed upon reward), and explains some of the asymmetry of the reward / punishment.
  • Their model perhaps does not scale well for large / very complicated tasks... given the presence of only a single reward signal. And the lack of attention / recall? Still, it fits the experimental data quite well.
  • They also note that for all the problems they study, adding more layers to the network does not significantly affect learning - neither the rate nor the eventual performance.

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ref: Schicknick-2008.11 tags: dopamine plasticity auditory cortex date: 12-15-2008 04:13 gmt revision:1 [0] [head]

PMID-18321872[0] Dopaminergic Modulation of Auditory Cortex-Dependent Memory Consolidation through mTOR.

  • I will annotate this paper later, after winter break.

____References____

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ref: Froemke-2007.11 tags: nucleus basalis basal forebrain acetylcholine auditory cortex potentiation voltage clamp date: 10-08-2008 22:44 gmt revision:2 [1] [0] [head]

PMID-18004384[0] A synaptic memory trace for cortical receptive field plasticity.

  • nucleus basalis = basal forebrain!
  • stimulation of the nucleus basalis caused a reorganization of the auditory cortex tuning curves hours after the few minutes of training.
  • used whole-cell current-clamp recording to reveal tone-evoked excitatory and inhibitory postsynaptyic currents.
  • pairing of nucleus basalis and auditory tone presentation (2-5 minutes) increased excitatory currents and decreased inhibitory currents as compared to other (control) frequencies.
  • tuning changes required simultaneous tone presentation and nucleus basalis stimulation. (Could they indiscriminately stimulate the NB? did they have to target a certain region of it? Seems like it.)
    • did not require postsynaptic spiking!
  • Pairing caused a dramatic (>7-fold) increase in the probability of firing bursts of 2+ spikes
  • Cortical application of atropine, an acetylcholine receptor antagonist, prevented the effects of nucleus basalis pairing.
  • the net effects of nucleus basalis pairing are suppression of inhibition (20 sec) followed by enhancement of excitation (60 sec)
  • also tested microstimulation of the thalamus and cortex; NB pairing increased EPSC response from intracortical microstim, but not from thalamic stimulation. Both cortical and thalamic stimulation elicited an effect in the voltage-clamped recorded neuron.
  • by recording from the same site (but different cells), they showed that while exitation persisted hours after pairing, inhibition gradually increased commensurate with the excitation.
  • Thus, NB stimulation leaves a tag of reduced inhibition (at the circuit level!), specifically for neurons that are active at the time of pairing.

____References____

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ref: Pawlak-2008.03 tags: dopamine striatum cortex STDP plasticity NMDAR date: 10-08-2008 17:24 gmt revision:1 [0] [head]

PMID-18322089[0] Dopamine Receptor Activation Is Required for Corticostriatal Spike-Timing-Dependent Plasticity

  • Single action potentials (APs) backpropagate into the higher-order dendrites of striatal spiny projection neurons during cortically driven "up" states (Kerr and Plenz, 2004)
    • note: many 'up' states in the striatum do not contain an AP.
  • Blocking dopamine D1/D5 receptors prevented both LTD and LTP induction.
  • first paragraph has a ton of references! They note that burst spiking in cortical and striatal projection neurons is infrequent - mostly, there are single spikes - and so STDP investigations are more applicable than high frequency stimulation LTP induction.
  • tested in vitro -- para-horizontal sections into the dorsolateral striatum of young rat brain, whole-cell current clamp, GABA_A currents blocked.
  • striatal projection neurons (SPNs) have a strange mode of AP generation - their membrane potential rises for 120ms after current injection, followed by a spike. They used this and infrared differential microscopy of morphology to locate the projection neurons.
  • stimulated using extracellular current to layer 5 of the cortex or nearby white matter. kept microstim current to a minimum.
  • paired this with AP generation in the SPNs at varying time delays, both at low frequency (0.1Hz)
  • there are a few cholinergic neurons in the striatum, apparently.
  • demonstrated STDP: "synaptic strength is maximally enhanced when cortically evoked EPSPs lead a spike by 10 ms, whereas synaptic strength is maximally depressed when EPSPs follow a spike by 30 ms"
  • also tried eliciting bursts in the SPN, but: "the timing of EPSPs with single APs is as efficient in inducing synaptic plasticity as the timing of EPSPs with AP bursts"
  • the STDP / LTP / LTD was NMDA-R dependent.
  • blocked D1/D5 with SCH-23390, and found that they could not induce LTP / LTD.
  • block of D2 receptor advanced the onset of LTP and delayed the onset of LTD, to a less dramatic degree than the D1/D5 block. Long-term LTP/LTD magnitude was not effected.
  • why did these guys get in J. Neuroscience where as this is in Science? because the Science article studied medium spiny neurons, with GFP labeling the D1/D2 receptors?

____References____

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ref: Schultz-2000.03 tags: review orbitofrontal cortex basal ganglia dopamine reward reinforcement learning striatum date: 10-07-2008 03:53 gmt revision:1 [0] [head]

PMID-10731222[0] Reward processing in primate orbitofrontal cortex and basal ganglia

  • Orbitofrontal neurons showed three principal forms of reward-related activity during the performance of delayed response tasks,
    • responses to reward-predicting instructions,
    • activations during the expectation period immediately preceding reward and
    • responses following reward
    • above, reward-predicting stimulus in a dopamine neuron. Left: the animal received a small quantity of apple juice at irregular intervals without performing in any behavioral task. Right: the animal performed in an operant lever-pressing task in which it released a touch-sensitive resting key and touched a small lever in reaction to an auditory trigger signal. The dopamine neuron lost its response to the primary reward and responded to the reward-predicting sound.
  • for the other figures, read the excellent paper!

____References____

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ref: Recanzone-1993.01 tags: plasticity cortex learning auditory owl monkeys SUA date: 10-06-2008 22:46 gmt revision:1 [0] [head]

PMID-8423485[0] Plasticity in the frequency representation of primary auditory cortex following discrimination training in adult owl monkeys

  • Measured tonotopic organization (hence plasticity) in the owl monkey auditory cortex following training on a frequency discrimination task.
  • improvement in performance correlates with an improvement in neuronal tuning.
  • two controls:
    • monkeys that were engaged in a tactile discrimination task
    • monkeys that received the same auditory stimuli but had no reason to attend to it
  • lots of delicious behavior graphs

____References____

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ref: Nakahara-2001.07 tags: basal ganglia model cerebral cortex motor learning date: 10-05-2008 02:38 gmt revision:0 [head]

PMID-11506661[0] Parallel cortico-basal ganglia mechanisms for acquisition and execution of visuomotor sequences - a computational approach.

  • Interesting model of parallel motor/visual learning, the motor through the posterior BG (the middle posterior part of the putamen) and supplementary motor areas, and the visual through the dorsolateral prefrontal cortex and the anterior BG (caudate head and rostral putamen).
  • visual tasks are learned quicker due to the simplicity of their transform.
  • require a 'coordinator' to adjust control of the visual and motor loops.
  • basal ganglia-thalamacortical loops are highly topographic; motor, oculomotor, prefrontal and limbic loops have been found.
  • pre-SMA, not the SMA, is connected to the prefrontal cortex.
  • pre-SMA receives connections from the rostral cingulate motor area.
  • used actor-critic architecture, where the critic learns to predict cumulative future rewards from state and the actor produces movements to maximize reward (motor) or transformations (sensory). visual and motor networks are actors in visual and motor representations, respectively.
  • used TD learning, where TD error is encoded via SNc.
  • more later, not finished writing (need dinner!)

____References____

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ref: Isoda-2007.02 tags: SMA saccade basal_forebrain executive function 2007 microstimulation SUA cortex sclin date: 10-03-2008 17:12 gmt revision:2 [1] [0] [head]

PMID-17237780[0] Switching from automatic to controlled action by monkey medial frontal cortex.

  • SCLIN's blog entry
  • task: two monkeys were trained to saccade to one of two targets, left/right pink/yellow. the choice was cued by the color of the central fixation target; when it changed, they should saccade to the same-colored target.
    • usually, the saccade direction remained the same; sometimes, it switched.
    • the switch could either occur to the same side as the SUA recording (ipsilateral) or to the opposite (contralateral).
  • found cells in the pre-SMA that would fire when the monkey had to change his adapted behavior
    • both cells that increased firing upon an ipsi-switch and contra-switch
  • microstimulated in SMA, and increased the number of correct trials!
    • 60ua, 0.2ms, cathodal only,
    • design: stimulation simulated adaptive-response related activity in a slightly advanced manner
    • don't actually have that many trials of this. humm?
  • they also did some go-nogo (no saccade) work, in which there were neurons responsive to inhibiting as well as facilitating saccades on both sides.
    • not a hell of a lot of neurons here nor trials, either - but i guess proper statistical design obviates the need for this.
  • I think if you recast this in tems of reward expectation it will make more sense and be less magical.
  • would like to do shadlen-similar type stuff in the STN
questions
  1. how long did it take to train the monkeys to do this?
  2. what part of the nervous system looked at the planned action with visual context, and realized that the normal habitual basal-ganglia output would be wrong?
    1. probably the whole brain is involved in this.
    2. hypothetical path of error trials: visual system -> cortico-cortico projections + context activation -> preparatory motor activity -> basal ganglia + visual context (is there anatomical basis for this?) -> activation of some region that detects the motor plan is unlikely to result in reward -> SMA?

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ref: Graybiel-2005.12 tags: graybiel motor_learning reinforcement_learning basal ganglia striatum thalamus cortex date: 10-03-2008 17:04 gmt revision:3 [2] [1] [0] [head]

PMID-16271465[] The basal ganglia: Learning new tricks and loving it

  • learning-related changes occur significantly earlier in the striatum than the cortex in a cue-reversal task. she says that this is because the basal ganglia instruct the cortex. I rather think that they select output dimensions from that variance-generator, the cortex.
  • dopamine agonist treatment improves learning with positive reinforcers but not learning with negative reinforcers.
  • there is a strong hyperkinetic pathway that projects directly to the subthalamic nucleus from the motor cortex. this controls output of the inhibitor pathway (GPi)
  • GABA input from the GPi to the thalamus can induce rebound spikes with precise timing. (the outputs are therefore not only inhibitory).
  • striatal neurons have up and down states. recommended action: simultaneous on-line recording of dopamine release and spike activity.
  • interesting generalization: cerebellum = supervised learning, striatum = reinforcement learning. yet yet! the cerebellum has a strong disynaptic projection to the putamen. of course, there is a continuous gradient between fully-supervised and fully-reinforcement models. the question is how to formulate both in a stable loop.
  • striosomal = striatum to the SNc
  • http://en.wikipedia.org/wiki/Substantia_nigra SNc is not an disorganized mass: the dopamergic neurons from the pars compacta project to the cortex in a topological map, dopaminergic neurons of the fringes (the lowest) go to the sensorimotor striatum and the highest to the associative striatum

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ref: Scott-2004.07 tags: Scott motor control optimal feedback cortex reaching dynamics review date: 04-09-2007 22:40 gmt revision:1 [0] [head]

PMID-15208695[0] PDF HTML summary Optimal feedback control and the neural basis of volitional motor control by Stephen S. Scott

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ref: Boline-2005.11 tags: electrophysiology motor cortex force isometric Ashe 2005 date: 04-09-2007 22:39 gmt revision:3 [2] [1] [0] [head]

this seems to be the same as {339}, with a different pubmed id & different author list. bug in the system!

PMID-16193273[0] On the relations between single cell activity in the motor cortex and the direction and magnitude of three-dimensional dynamic isometric force* the majority of cells responded to direction

  • few to the magnitude,
  • and ~10% to the direction & magnitude
  • control of static and dynamic motor systems is based on a common control process!
  • 2d task, monkeys, single-unit recording, regression analysis.

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ref: Ojakangas-2006.12 tags: BMI Donoghue prosthetic DBS prefrontal cortex planning date: 04-09-2007 22:32 gmt revision:3 [2] [1] [0] [head]

PMID-17143147[0] Decoding movement intent from human premotor cortex neurons for neural prosthetic applications

  • they suggest using additional frontal areas beyond M1 to provide signal sources for human neuromotor prosthesis.
    • did recording in prefrontal cortex during DBS surgeries.
    • these neurons were able to provide information about movement planning production, and decision-making.
  • unusual for BMI studies, their significance levels are near 0.02 - they show distros of % correct based on a ML decoding scheme.

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ref: DeLong-1974.05 tags: motor control basal ganglia cerebellum motor cortex DeLong putamen original date: 04-09-2007 01:51 gmt revision:1 [0] [head]

PMID-4219745[0] Relation of basal ganglia, cerebellum, and motor cortex units to ramp and ballistic limb movements.

  • monkey trained to make both ballistic movement and slow, pulling movements by pulling a manipulandum between three targets.
  • cells in the putamen discharged preferentially during slow movements.
    • consistent with a sequence / temporal scaling (?) role.
    • also consistent with the cerebellum creating rapid/feedforward trajectories.
  • cells in the motor cortex discharged for both types of movements, though a bit more for ballisic type movements (where the forces were higher).
  • paper is thankfully short and concise.
    • and also humble: "the mere correlation of unit discharge with some aspect of a movement without knowledge of the peripheral site influenced by the unit under study can only provide grounds for speculation".

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ref: Ashe-1997.09 tags: motor control force direction magnitude M1 cortex date: 04-09-2007 01:10 gmt revision:0 [head]

PMID-9331494[0] Force and the motor cortex.

  • most M1 cells seem to be related to the direction of static force; fewer related to direction and magnitude; fewer yet to only magnitude.
  • dynamic forces: there is a stron correlation between the rate of change of force and the motor cortex firing
    • dynamic force seems to determine firing rate moreso than static force (e.g. resisting gravity)
    • I have definantly seen evidence of this with the kinarm experiments.

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ref: Bair-1996.08 tags: precise spike timing cortex behavior Sejnowski date: 04-09-2007 00:57 gmt revision:0 [head]

PMID-8768391[0] Temporal precision of spike trains in extrastriate cortex of the behaving macaque monkey

  • This temporal modulation is stimulus dependent, being present for highly dynamic random motion but absent when the stimulus translates rigidly -- that is, the response is markedly reproducable and precise to a few milliseconds.

PMID-16339894[1] Neurons of the cerebral cortex exhibit precise interspike timing in correspondence to behavior.

  • in the cortex, spikes can be very precise.
  • this was a slice investigation.

PMID-7770778[2] Reliability of spike timing in neocortical neurons.

  • neocortex of rats
  • suggest low intrinsic noise level in spike generation, allowing accurate transformation of synaptic input into spike generation

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ref: Amirikian-2000.01 tags: Georgopulos directional tuning motor cortex SUA electrophysiology date: 04-05-2007 16:34 gmt revision:2 [1] [0] [head]

PMID-10678534[0] Directional tuning profiles of motor cortical cells

  • trained the monkeys to move to 20 targets in a horizontal plane
    • the larger number of targets allowed a more accurate estimation of the tuning properties of the cells
    • measured tuning based on the spike count during movement.
  • typical r^2 = 0.7 for a modified cosine fit

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ref: Ostry-2003.12 tags: force motor control review cortex M1 date: 04-05-2007 15:21 gmt revision:0 [head]

PMID-14610628[0] A critical evaluation of the force control hypothesis in motor control.

  • the target of this review is the inverse dynamics model of motor control, which is very successful in robots. however, it seems that the mammalian nervous system does things a bit more complicated than this.
  • they agree that motor learning is most likely the defining feature of the cortex (i think that the critical and essential element of the cortex is not what control solution it arrives at, but rather how it learns that solution given the anatomical connections development has endowed it with.
  • they also find issue with the failure to incorporate realistic spinal reflexes into inverse-dynamics models.
  • However, we find little empirical evidence that specifically supports the inverse dynamics or forward internal model proposals per se.
  • We further conclude that the central idea of the force control hypothesis--that control levels operate through the central specification of forces--is flawed.

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ref: Shapovalova-2006.1 tags: dopamine learning neocortex rats russians D2 date: 03-12-2007 01:58 gmt revision:0 [head]

PMID-17216714 Motor and cognitive functions of the neostriatum during bilateral blocking of its dopamine receptors

  • systemic application of D1 selective blockers reduced learning in rats
    • probably this effect is not neostriatal:
  • local application of the same blocker on the cortex did not markedly affect learning, though it did effect initiation errors
  • D2 antagonist (raclopride) locally applied to the striatum blocked learning.

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ref: Lavin-2005.05 tags: dopamine PFC VTA prefrontal_cortex ventral_tegmentum 2005 date: 02-05-2007 20:37 gmt revision:1 [0] [head]

PMID-15901782[0]Mesocortical Dopamine Neurons Operate in Distinct Temporal Domains Using Multimodal Signaling

  • good paper, decent review of relevant infos in the introduction.
  • they suggest that the mesocortical system transmits fast signals about reward/salience via corelease of glutamate, whereas dopamine provides a more long-term modulator of cortical processing dynamics.
  • the ventral tegmental area provides dopamine to the prefrontal cortex.
  • DA levels in the PFC can increase ~10x above baseline for 10's of minutes.
    • these responses occur to both to unexpectedly rewarding stimuli as well as to aversive stimuli.
  • brief VTA stimulation invokes a short, transient (~200ms) inhibition of PFC in vivo, and this inhibition is typically blocked by DA antagonists. from: PMID-1436485[1]
    • transient inhibition begins ~20ms after VTA stimulation, which is barely enough time for activation of ionotropic receptors, let alone metabotropic DA receptors.
  • MFB stimulation evoked increased DA levels and an elevation in firing of nearby striatal neurons that outlasted the period of stimulation by > 300s.
  • strangely, the excitatory glutamergic response in the PFC to VTA stimulation is blocked by lesion of the MFB.
  • in suppport of co-release, TH-positive neurons in rats and primates are co-reactive for glutamate.
    • DA neurons can form glutamate synapses in vitro.
  • check it out:
    • midbrain DA neurons respond by firing a ~200ms burst of spikes to primary rewards, conditioned, or secondary rewards, rewards that are not predicted, and novel or unexpected stimuli.
    • DA neurons are activated by rewarding events that are better than predicted, remain unaffected by events that are as good as predicted, and are depressed by events that are worse than predicted (yet they do not cite any refs for this... there are a bunch of refs in the prev sentence. ) see:
    • stress can also increase PFC DA

____References____

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ref: Marzullo-2006.12 tags: kipke BMI cingulate cortex 2006 date: 02-05-2007 17:14 gmt revision:1 [0] [head]

PMID-17190032[0] http://hardm.ath.cx:88/pdf/Marzullo2006_CingulateCortexBMI.pdf

  • motivation: ALS or PLS (primary lateral sclerosis) can damage upper motor neurons.
  • cingulate cortex has both cognitive and motor properties. & is involved in reward-based motor planning.
  • they give a long list of things that the cingulate cortex has been found to be involved in, including:
    • reward-based motor planning and reward expectancy
    • behavioral inhibition
    • stimulus-reward association
    • trace-conditioning
    • attention in complex discrimination tasks
    • error detection in humans
    • pain perception in human, too.
  • seven rats were able to modulate activity of neurons in cingulate cortex in order to recieve reward.
    • 52-84% percent of cingulate cortex neurons can be trained for a BMI; each seem to be independent.
  • michigan electrode, 16 channels.
  • auditory feedback.
  • food reward.
  • set the threshold based on the mean firing rate of SUA / MUA + a scalar times the stdev of the firing rate. the scalar was varied to allow 30-40% correct or operant rates.
  • used monte carlo simulations to verify the animal was performing above chance.
  • rat cortex is smooth :)
  • some cells increased their firing rate, some decreased (gaussian smoothed mean firing rate)
    • verified cell status with autocorrelogram.
result: cingulate cortex, like probably anywhere else, can come under voluntary control.

____References____

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ref: notes-0 tags: insular cortex anterior cingulate spindle neurons date: 0-0-2007 0:0 revision:0 [head]

spindle neurons are found in the insular cortex as well as the anterior cingulate cortex, but only, apparently, in great apes. Activity in the insular cortex has been found to be correlated to feeling empathy.

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ref: van-2004.11 tags: anterior cingulate cortex error performance monitoring 2004 date: 0-0-2007 0:0 revision:0 [head]

PMID-15518940 Errors without conflict: implications for performance monitoring theories of anterior cingulate cortex.

  • did a event-locked fMRI to study whether the ACC would differentiate between correct and incorrect feedback stimuli in a time estimation task.
  • ACC seems to be not involved in error detection, just conflict.
----
  • according to one theory, ERN is generated as part of a reinforcement learning process. (Holroyd and Coles 2002): behavior is monitored by an 'adaptive critic' in the basal ganglia.
    • in this theory, the ACC is used to select between mental processes competing to access the motor system.
    • ERN corresponds to a decrease in dopamine.
    • ERN occurs when the stimulus indicates that an error has occured.
  • alternately, the ACC can monitor for the presence of conflict between simultaneously active but incompatible sensory/processing streams.
    • the ACC is active in correct trials in tasks that require conflict resolution. + it makes sense from a modeling strategy: high-energy state is equivalent to a state of conflit: many neurons are active at the same time.
    • that is, it is a stimuli resolver: e.g. the stroop task.
  • some studies localize (and the authors here indicate that the source-analysis that localizes dipole sources is inaccurate) the error potential to the posterior cingulate cortex.
    • fMRI solves this problem.
  • from their figures, it seems that the right putamen + bilateral caudate are involved in their time-estimation task (subjects has to press a button 1 second after a stimulus cue; feedback then guided/misguided them toward/away from 1000ms; subjects, of course, adjusted their behavior)
    • no sign of ACC activation was shown - as hard as they could look - despite identical (more or less) experimental design to the ERN studies.
      • hence, ERN is generated by areas other than the ACC.
  • in contrast, the stroop task fully engaged the anterior cingulate cortex.
  • cool: perhaps, then, error feedback negativity is better conceived as an (absence of) superimposed "correct feedback positivity" 'cause no area was more active in error than correct feedback.
  • of course, one is measuring brain activation through blood flow, and the other is measuring EEG signals.

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ref: Ito-2003.1 tags: anterior cingulate cortex ACC electrophysiology date: 0-0-2007 0:0 revision:0 [head]

PMID-14526085 Performance Monitoring by the Anterior Cingulate Cortex During Saccade Countermanding

locations of neurons http://www.sciencemag.org/content/vol302/issue5642/images/large/se3831902004.jpeg

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ref: Kerr-2004.01 tags: UP_DOWN states striatum cortex spike timing date: 0-0-2007 0:0 revision:0 [head]

PMID-14749432 Action Potential Timing Determines Dendritic Calcium during Striatal Up-States

  • striatum has up/down states too!
  • only read the abstract.

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ref: Lewis-2000.12 tags: UP_DOWN VTA dopamine D1 prefrontal cortex PFC date: 0-0-2007 0:0 revision:0 [head]

PMID-11073866 Ventral Tegmental Area Afferents to the Prefrontal Cortex Maintain Membrane Potential ‘Up’ States in Pyramidal Neurons via D1 Dopamine Receptors

  • need i say more?

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ref: notes-0 tags: cortex date: 0-0-2006 0:0 revision:0 [head]

  • eloquent cortex = cortex involved in motor or speech area. therefore, non-eloquent = not motor or speech.
  • DBS surgeries go through the middle frontal gyrus.

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ref: abstract-0 tags: tlh24 error signals in the cortex and basal ganglia reinforcement_learning gradient_descent motor_learning date: 0-0-2006 0:0 revision:0 [head]

Title: Error signals in the cortex and basal ganglia.

Abstract: Numerous studies have found correlations between measures of neural activity, from single unit recordings to aggregate measures such as EEG, to motor behavior. Two general themes have emerged from this research: neurons are generally broadly tuned and are often arrayed in spatial maps. It is hypothesized that these are two features of a larger hierarchal structure of spatial and temporal transforms that allow mappings to procure complex behaviors from abstract goals, or similarly, complex sensory information to produce simple percepts. Much theoretical work has proved the suitability of this organization to both generate behavior and extract relevant information from the world. It is generally agreed that most transforms enacted by the cortex and basal ganglia are learned rather than genetically encoded. Therefore, it is the characterization of the learning process that describes the computational nature of the brain; the descriptions of the basis functions themselves are more descriptive of the brain’s environment. Here we hypothesize that learning in the mammalian brain is a stochastic maximization of reward and transform predictability, and a minimization of transform complexity and latency. It is probable that the optimizations employed in learning include both components of gradient descent and competitive elimination, which are two large classes of algorithms explored extensively in the field of machine learning. The former method requires the existence of a vectoral error signal, while the latter is less restrictive, and requires at least a scalar evaluator. We will look for the existence of candidate error or evaluator signals in the cortex and basal ganglia during force-field learning where the motor error is task-relevant and explicitly provided to the subject. By simultaneously recording large populations of neurons from multiple brain areas we can probe the existence of error or evaluator signals by measuring the stochastic relationship and predictive ability of neural activity to the provided error signal. From this data we will also be able to track dependence of neural tuning trajectory on trial-by-trial success; if the cortex operates under minimization principles, then tuning change will have a temporal relationship to reward. The overarching goal of this research is to look for one aspect of motor learning – the error signal – with the hope of using this data to better understand the normal function of the cortex and basal ganglia, and how this normal function is related to the symptoms caused by disease and lesions of the brain.