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[0] Mehring C, Rickert J, Vaadia E, Cardosa de Oliveira S, Aertsen A, Rotter S, Inference of hand movements from local field potentials in monkey motor cortex.Nat Neurosci 6:12, 1253-4 (2003 Dec)

[0] Schmidt EM, Single neuron recording from motor cortex as a possible source of signals for control of external devices.Ann Biomed Eng 8:4-6, 339-49 (1980)[1] 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)[2] Salcman M, Bak MJ, A new chronic recording intracortical microelectrode.Med Biol Eng 14:1, 42-50 (1976 Jan)

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

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

[0] Ashe J, Georgopoulos AP, Movement parameters and neural activity in motor cortex and area 5.Cereb Cortex 4:6, 590-600 (1994 Nov-Dec)

[0] Sergio LE, Kalaska JF, Systematic changes in motor cortex cell activity with arm posture during directional isometric force generation.J Neurophysiol 89:1, 212-28 (2003 Jan)

[0] Taira M, Boline J, Smyrnis N, Georgopoulos AP, Ashe J, On the relations between single cell activity in the motor cortex and the direction and magnitude of three-dimensional static isometric force.Exp Brain Res 109:3, 367-76 (1996 Jun)

[0] Fu QG, Suarez JI, Ebner TJ, Neuronal specification of direction and distance during reaching movements in the superior precentral premotor area and primary motor cortex of monkeys.J Neurophysiol 70:5, 2097-116 (1993 Nov)

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

[0] Teich MC, Heneghan C, Lowen SB, Ozaki T, Kaplan E, Fractal character of the neural spike train in the visual system of the cat.J Opt Soc Am A Opt Image Sci Vis 14:3, 529-46 (1997 Mar)

[0] Vyssotski AL, Serkov AN, Itskov PM, Dell'Omo G, Latanov AV, Wolfer DP, Lipp HP, Miniature neurologgers for flying pigeons: multichannel EEG and action and field potentials in combination with GPS recording.J Neurophysiol 95:2, 1263-73 (2006 Feb)[1] Otto KJ, Johnson MD, Kipke DR, Voltage pulses change neural interface properties and improve unit recordings with chronically implanted microelectrodes.IEEE Trans Biomed Eng 53:2, 333-40 (2006 Feb)

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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: -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: -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: Mehring-2003.12 tags: BMI LFP MUA SUA Mehring Vaadia date: 07-24-2012 15:54 gmt revision:3 [2] [1] [0] [head]

PMID-14634657[0]Inference of hand movements from local field potentials in monkey motor cortex

  • idea: you get equally good predictions from SUA, LFP, or MUA in decoding a 8-target center-out task.
  • c.f. {1167}

____References____

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ref: -0 tags: SUA LFP BMI decoding Donoghue date: 07-24-2012 15:54 gmt revision:0 [head]

PMID-22157115 Decoding 3D reach and grasp from hybrid signals in motor and premotor cortices: spikes, multiunit activity, and local field potentials.

  • Idea: you get more information from SUA (what they call SA) activity than broadband LFPS for predicting reach direction / position for a freely moving monkey.
  • C.F. {253}

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ref: Schmidt-1980.01 tags: BMI 1980 SUA M1 prosthetics Schmidt MEA date: 01-04-2012 22:59 gmt revision:14 [13] [12] [11] [10] [9] [8] [head]

PMID-6794389[0] Single neuron recording from motor cortex as a possible source of signals for control of external devices

  • also [1]
  • I guess this was the first published article claiming that motorneurons could be used to drive a prosthesis, and first clear attempt at long-term array recording (?)
  • recorded via arrays for up to 37 months!
    • only 2 of the 11 eelctrodes were recording at the time of sacrifice.
  • trained the monkey to perform an 8 target tracking task
    • with cortical neurons: 2.45 bits/second
    • with wrist flexion/extension: 4.48 bits/second
  • electrodes: {946} A new chronic recording intracortical microelectrode (1976!)
    • 25um iridium wires electropolished to a 1um tip; 1.5mm long.
    • electrodes float on the cortex; signals transmitted through 25um gold wire, which is in turn connected to a head-mounted connector.
    • iridium and gold are insulated with vapor-deposited parylene-C
    • electrode tips are exposed with a HV arc. (does this dull them? from the electromicrograph, it seems that it just makes them rougher.)
    • arrays of 12.
    • 1M impedance (average)
  • interesting: neural activity was recorded from at least 8 different neurons with this electrode during the course of the implant, indicating that it was migrating through cortical tissue.
    • the average recording time from the same electrode was 8 days; max 23 days.
  • second implant was more successful: maximium time recording from the same neuron was 108 days.
  • failure is associated with cracks in the parylene insulation (which apparently occurred on the grain boundaries of the iridium). "still only marginally reliable" (and still.. and still..)
  • they have operantly trained cortical units in another, earlier study.
  • have, effectively, 8 levels of activity, with feedback monkey has to match the proscribed firing rate.
  • > 50% rewarded trials = success for them; 26/28 of the neurons tested were eventually conditioned successfully.
  • looks like the monkey can track the target firing rate rather accurately. "the output of cortical cells can provide information output rates moderately less precise than the intact motor system. "
  • Monkey can also activated sequences of neurons: A, then AB, then B.
  • people have also tried conditioning individual EMG units; it is sometimes possible to control 2 different motor units in the same muscle independently, but in general only a single channel of information can be obtained from one muscle, and gross EMGs are fine for this.
    • Thus surface EMG is preferred.
    • you can get ~ 2.73 bits/sec with gross EMG on a human; 2.99 bits/sec (max) with a monkey.
  • they remind us, of course, that an enormous amount of work remains to be done.

____References____

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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: 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: Dagnelie-2008.01 tags: visual BMI prosthesis review Dagnelie date: 12-17-2011 02:25 gmt revision:0 [head]

PMID-18429703 Psychophysical evaluation for visual prosthesis.

  • Visual prostheses are clinical and preclinical trials!
  • cochlear implants function with 16-20 electrodes; retina is 120e6 photoreceptors and 1.2 optic nerve fibers.
  • Argus 2 retinal implant has 60 electrodes. visual information impoverished.
  • In the heyday of prewar German scientific discovery, Foerster (3) established that electrical stimulation of the visual cortex in an awake patient during a neurosurgical intervention produced the percept of dots of light, called phosphenes, and that the location of a phosphene changed with that of the electrical stimulus.
  • people originally thought that loss of the photoreceptors would lead to degradation of the RGCs; this appears not to be true.
  • There is broad consensus that functional vision restoration is predicated on prior visual experience; this is different than cochlera prostheses, which work on congenitally deaf people.
    • Visual development depends on nearly a decade of high-resolution perception, and cannot be emulated later in life through a low-bw prosthesis.
  • There are at the present time at least 20 distinct research groups in at least 8 countries actively engaged in visual prosthesis development.
  • discuss a lot of pre-clinical testing & all the nitty-grity details, e.g. how to make a low res prosthesis work for reading.

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ref: Carlton-1981.1 tags: visual feedback 1981 error correction movement motor control reaction time date: 12-06-2011 06:35 gmt revision:1 [0] [head]

PMID-6457106 Processing visual feedback information for movement control.

  • Vusual feedback can correct movement within 135ms.
  • Measured this by simply timing the latency from presentation of visual error to initiation of corrective movement.

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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: 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: 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: 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: bookmark-0 tags: software visualization C++ sgv date: 03-17-2008 19:44 gmt revision:9 [8] [7] [6] [5] [4] [3] [head]

http://www.sgvsarc.com/demo.htm

  • looks like a good way to understand a large amount of code quickly. I've been waiting for a product like this for some time now!
    • I secretly hoped, given the name, that it would produce SVG files. It turns out otherwise: I'm just dyslexic :)

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ref: Ashe-1994.12 tags: Georgoplous motor control M1 S1 SUA electrophysiology 2D date: 04-09-2007 20:27 gmt revision:2 [1] [0] [head]

PMID-7703686[0] Movement parameters and neural activity in motor cortex and area 5

  • 290 cells in the motor cortex and 207 cells in area 5 (S1)
  • median R^2 = 0.581 & 0.530 in motor cortex
  • most prominent representation of target direction; least prominent representation of acceleration. (though statistically significant correlations were observed for all behavioral parameters)

Duke does not have online access to the article :(

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ref: Sergio-2003.01 tags: M1 isometric force posture direction SUA Kalaska date: 04-09-2007 15:22 gmt revision:1 [0] [head]

PMID-12522173[0] Systematic changes in motor cortex cell activity with arm posture during directional isometric force generation.

  • isometric joystick was positioned at 5-9 different locations in a plane in the monkey's workspace.
  • discharge of all cells varied with position and force.
    • Cell directional tuning tended to shift systematically with hand location even though the direction of static force output at the hand remained constant
      • would this be true if the forces were directed in muscle coordinates?
  • "provides further evidence that MI contributes to the transformation between extrinsic and intrinsic representations of motor output during isometric force production."

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ref: Taira-1996.06 tags: 3D Georgopoulos SUA M1 force motor control direction tuning date: 04-09-2007 15:16 gmt revision:1 [0] [head]

PMID-8817266[0] On the relations between single cell activity in the motor cortex and the direction and magnitude of three-dimensional static isometric force.

  • 3D isometric joystick.
  • stepwise multiple linear regression.
  • direction of force is a signal especially prominent in the motor cortex.
    • the pure directional effect was 1.8 times more prevalent in the cells than in the muscles studied (!)

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ref: Fu-1993.11 tags: premotor M1 PMd PMv SUA date: 04-05-2007 17:12 gmt revision:0 [head]

PMID-8294972[0] Neuronal specification of direction and distance during reaching movements in the superior precentral premotor area and primary motor cortex of monkeys.

  • key thing: distance to target is represented in the motor & premotor corticies.

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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: Teich-1997.03 tags: fractal LGN RGC electrophysiology SUA fano_factor date: 02-05-2007 19:00 gmt revision:0 [head]

PMID-9058948[0] Fractal character of the neural spike train in the visual system of the cat

  • excellent description of several analyses of point-process spike trains (here RGC and LGN cells): interevent-interval histogram, rescaled range analysis, the event-number histogram, the Fano factor, the Allan factor, and the periodogram.

http://hardcarve.com/wikipic/Teich1997_fanofactor.gif

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ref: Vyssotski-2006.02 tags: neurologger neural_recording recording_technology EEG SUA LFP electrical engineering date: 02-05-2007 06:21 gmt revision:6 [5] [4] [3] [2] [1] [0] [head]

PMID-16236777[0] Miniature neurologgers for flying pigeons: multichannel EEG and action and field potentials in combination with GPS recording.

Recording neuronal activity of animals moving through their natural habitat is difficult to achieve by means of conventional radiotelemetry. This illustration shows a new approach, exemplified by a homing pigeon carrying both a small GPS path recorder and a miniaturized action and field potential logger (“neurologger”), the entire assembly weighing maximally 35 g, a load carried easily by a pigeon over a distance of up to 50 km. Before release at a distant location, the devices are activated and store both positional and neuronal activity data during the entire flight. On return to the loft, all data are downloaded and can be analyzed using software for path analysis and electrical brain activity. Thus single unit activity or EEG patterns can be matched to the flight path superimposed on topographical maps. Such neurologgers may also be useful for a variety of studies using unrestrained laboratory animals in different environments or test apparatuses. The prototype on the hand-held pigeon records and stores EEG simultaneously from eight channels up to 47 h, or single unit activity from two channels during 9 h, but the number of channels can be increased without much gain in weight by sandwiching several of these devices. Further miniaturization can be expected. For details, see Vyssotski AL, Serkov AN, Itskov PM, Dell Omo G, Latanov AV, Wolfer DP, and Lipp H-P. Miniature neurologgers for flying pigeons: multichannel EEG and action and field potentials in combination with GPS recording. [1]

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