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[0] Ferrari PF, Rozzi S, Fogassi L, Mirror neurons responding to observation of actions made with tools in monkey ventral premotor cortex.J Cogn Neurosci 17:2, 212-26 (2005 Feb)[1] Maravita A, Iriki A, Tools for the body (schema).Trends Cogn Sci 8:2, 79-86 (2004 Feb)[2] Sanchez J, Principe J, Carmena J, Lebedev M, Nicolelis MA, Simultaneus prediction of four kinematic variables for a brain-machine interface using a single recurrent neural network.Conf Proc IEEE Eng Med Biol Soc 7no Issue 5321-4 (2004)[3] Wood F, Fellows M, Donoghue J, Black M, Automatic spike sorting for neural decoding.Conf Proc IEEE Eng Med Biol Soc 6no Issue 4009-12 (2004)[4] 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)[5] Won DS, Wolf PD, A simulation study of information transmission by multi-unit microelectrode recordings.Network 15:1, 29-44 (2004 Feb)[6] 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)[7] Salcman M, Bak MJ, A new chronic recording intracortical microelectrode.Med Biol Eng 14:1, 42-50 (1976 Jan)[8] Patil PG, Carmena JM, Nicolelis MA, Turner DA, Ensemble recordings of human subcortical neurons as a source of motor control signals for a brain-machine interface.Neurosurgery 55:1, 27-35; discussion 35-8 (2004 Jul)[9] Santhanam G, Ryu SI, Yu BM, Afshar A, Shenoy KV, A high-performance brain-computer interface.Nature 442:7099, 195-8 (2006 Jul 13)[10] Brockwell AE, Rojas AL, Kass RE, Recursive bayesian decoding of motor cortical signals by particle filtering.J Neurophysiol 91:4, 1899-907 (2004 Apr)[11] 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)[12] Jackson A, Mavoori J, Fetz EE, Long-term motor cortex plasticity induced by an electronic neural implant.Nature 444:7115, 56-60 (2006 Nov 2)

[0] Maravita A, Iriki A, Tools for the body (schema).Trends Cogn Sci 8:2, 79-86 (2004 Feb)[1] Iriki A, Tanaka M, Iwamura Y, Coding of modified body schema during tool use by macaque postcentral neurones.Neuroreport 7:14, 2325-30 (1996 Oct 2)

[0] Ferrari PF, Rozzi S, Fogassi L, Mirror neurons responding to observation of actions made with tools in monkey ventral premotor cortex.J Cogn Neurosci 17:2, 212-26 (2005 Feb)

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ref: -0 tags: sparse coding reference list olshausen field date: 08-04-2021 01:07 gmt revision:5 [4] [3] [2] [1] [0] [head]

This was compiled from searching papers which referenced Olshausen and Field 1996 PMID-8637596 Emergence of simple-cell receptive field properties by learning a sparse code for natural images.

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ref: -2015 tags: olshausen redwood autoencoder VAE MNIST faces variation date: 11-27-2020 03:04 gmt revision:0 [head]

Discovering hidden factors of variation in deep networks

  • Well, they are not really that deep ...
  • Use a VAE to encode both a supervised signal (class labels) as well as unsupervised latents.
  • Penalize a combination of the MSE of reconstruction, logits of the classification error, and a special cross-covariance term to decorrelate the supervised and unsupervised latent vectors.
  • Cross-covariance penalty:
  • Tested on
    • MNIST -- discovered style / rotation of the characters
    • Toronto faces database -- seven expressions, many individuals; extracted eigen-emotions sorta.
    • Multi-PIE --many faces, many viewpoints ; was able to vary camera pose and illumination with the unsupervised latents.

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ref: -2015 tags: PaRAC1 photoactivatable Rac1 synapse memory optogenetics 2p imaging mouse motor skill learning date: 10-30-2019 20:35 gmt revision:1 [0] [head]

PMID-26352471 Labelling and optical erasure of synaptic memory traces in the motor cortex

  • Idea: use Rac1, which has been shown to induce spine shrinkage, coupled to a light-activated domain to allow for optogenetic manipulation of active synapses.
  • PaRac1 was coupled to a deletion mutant of PSD95, PSD delta 1.2, which concentrates at the postsynaptic site, but cannot bind to postsynaptic proteins, thus minimizing the undesirable effects of PSD-95 overexpression.
    • PSD-95 is rapidly degraded by proteosomes
    • This gives spatial selectivity.
  • They then exploited the dendritic targeting element (DTE) of Arc mRNA which is selectively targeted and translated in activiated dendritic segments in response to synaptic activation in an an NMDA receptor dependent manner.
    • Thereby giving temporal selectivity.
  • Construct is then PSD-PaRac1-DTE; this was tested on hippocampal slice cultures.
  • Improved sparsity and labelling further by driving it with the Arc promoter.
  • Motor learning is impaired in Arc KO mice; hence inferred that the induction of AS-PaRac1 by the Arc promoter would enhance labeling during learning-induced potentiation.
  • Delivered construct via in-utero electroporation.
  • Observed rotarod-induced learning; the PaRac signal decayed after two days, but the spine volume persisted in spines that showed Arc / DTE hence PA labeled activity.
  • Now, since they had a good label, performed rotarod training followed by (at variable delay) light pulses to activate Rac, thereby suppressing recently-active synapses.
    • Observed both a depression of behavioral performance.
    • Controlled with a second task; could selectively impair performance on one of the tasks based on ordering/timing of light activation.
  • The localized probe also allowed them to image the synapse populations active for each task, which were largely non-overlapping.

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ref: -2019 tags: meta learning feature reuse deepmind date: 10-06-2019 04:14 gmt revision:1 [0] [head]

Rapid learning or feature reuse? Towards understanding the effectiveness of MAML

  • It's feature re-use!
  • Show this by freezing the weights of a 5-layer convolutional network when training on Mini-imagenet, either 5shot 1 way, or 5shot 5 way.
  • From this derive ANIL, where only the last network layer is updated in task-specific training.
  • Show that ANIL works for basic RL learning tasks.
  • This means that roughly the network does not benefit much from join encoding -- encoding both the task at hand and the feature set. Features can be learned independently from the task (at least these tasks), with little loss.

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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: -2004 tags: Olshausen sparse coding review date: 03-08-2019 07:02 gmt revision:0 [head]

PMID-15321069 Sparse coding of sensory inputs

  • Classic review, Olshausen and Field. 15 years old now!
  • Note the sparsity here is in neuronal activation, not synaptic activity (though one should follow the other).
  • References Lewicki's auditory studies, Efficient coding of natural sounds 2002; properties of early auditory neurons are well suited for producing a sparse independent code.
    • Studies have found near binary encoding of stimuli in rat auditory cortex -- e.g. one spike per noise.
  • Suggests that overcomplete representations (e.g. where there are more 'second layer' neurons than inputs or pixels) are useful for flattening manifolds in the input space, making feature extraction easier.
    • But then you have an under-determined problem, where presumably sparsity metrics step in to restrict the actual coding space. Authors mention that this could lead to degeneracy.
    • Example is the early visual cortex, where axons to higher layers exceed those from the LGN by a factor of 25. Which, they say, may be a compromise between over-representation and degeneracy.
  • Sparse coding is a necessity from an energy standpoint -- only one in 50 neurons can be active at any given time.
  • Sparsity increases when classical receptive field stimuli in V1 is expanded with a real-world-statistics surround. (Gallant 2002).

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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: photoacoustic tomography mouse imaging q-switched laser date: 05-11-2017 05:23 gmt revision:1 [0] [head]

Single-impulse panoramic photoacoustic computed tomography of small-animal whole-body dynamics at high spatiotemporal resolution

  • Used Q-switched Nd:YAG and Ti:Sapphire lasers to illuminate mice axially (from the top, through a diffuser and conical lens), exciting the photoacuostic effect, from which they were able to image at 125um resolution a full slice of the mouse.
    • I'm surprised at their mode of illumination -- how do they eliminate the out-of-plane photoacoustic effect?
  • Images look low contrast, but structures, e.g. cortical vasculature, are visible.
  • Can image at the rep rate of the laser (50 Hz), and thereby record cardiac and pulmonary rhythms.
  • Suggest that the photoacoustic effect can be used to image brain activity, but spatial and temporal resolution are limited.

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ref: -0 tags: photoacoustic tomography mouse imaging q-switched laser date: 05-11-2017 05:21 gmt revision:0 [head]

Single-impulse panoramic photoacoustic computed tomography of small-animal whole-body dynamics at high spatiotemporal resolution

  • Used Q-switched Nd:YAG and Ti:Sapphire lasers to illuminate mice axially, exciting the photoacuostic effect, from which they were able to image at 125um resolution a full slice of the mouse.
  • Images look low contrast, but structures, e.g. cortical vasculature, are visible.
  • Can image at the rep rate of the laser (50 Hz), and thereby record cardiac and pulmonary rhythms.
  • Suggest that the photoacoustic effect can be used to image brain activity, but spatial and temporal resolution are limited.

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ref: Zacksenhouse-2007.07 tags: Zacksenhouse 2007 Odoherty Nicolelis cortical adaptation BMI date: 01-06-2012 03:10 gmt revision:3 [2] [1] [0] [head]

PMID-17637835[0] Cortical modulations increase in early sessions with brain-machine interface.

  • "we demonstrate that the modulations of the firing-rates of cortical neurons increased abruptly after the monkeys started operating the BMI"
    • My hypothesis: is this like LMAN? Injection of noise for the purpose of exploration?
    • Their hypothesis: we are listening to the noise or effect of increased processing / congnitive load.
    • Alternative: decreased feedback / scrabled feedback makes the individual control signals themselves less controlled.
  • Describes spikes as inhomogeneous poisson processes, and breaks things down thusly.
  • Also develop a parametric model of neuronal firing based on tuning to movement, including velocity and acceleration.
  • Fano factor of recorded neurons increased during BCWH & BCWOH.
  • Percent overall modulation (POM) higher in brain control. That is, the variance explained not by the inhomogeneous poisson process, but rather by firing rate variations.
    • "[T]he ensemble-POM increased mainly due to an increase in the variance of the spike-count, which was not matched by the change in the mean spike-count."
  • Figure 6 is pretty convincing, actually.
  • PVM (percent velocity modulation) correlates strongly with POM, but with a fractional slope, indicating that veolocity tuning accounts for only a fraction of the variance.
    • "Since the increase in POM was not matched by increasing PVM or PKM, the higher neuronal rate modulations observed during brain control cannot be explained only by increased modulations due to the kinematics of the movement."

____References____

[0] Zacksenhouse M, Lebedev MA, Carmena JM, O'Doherty JE, Henriquez C, Nicolelis MA, Cortical modulations increase in early sessions with brain-machine interface.PLoS One 2:7, e619 (2007 Jul 18)

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ref: neuro-2005 tags: NRSA background BMI tool use date: 01-03-2012 15:21 gmt revision:2 [1] [0] [head]

  • tool use:
    • [0]
    • [1] varying neural responses following tool acquisition
  • BMI
    • [2] simultaneous prediction of 4 variables
  • spike sorting
    • [3] donoghue
    • [4] LFP
    • [5] MUA
    • [6,7] - 1980!!
    • [8] STN bmi (nahh)
    • [9] Shenoy, eye movement better, 6.5 bits/sec
    • [10] PF
    • [11] in rats, in the cinglate, still they didn't get the point.
    • [12] Fetz stimulation

____References____

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ref: Maravita-2004.02 tags: tool use monkey mirror neurons response learning date: 09-24-2008 17:02 gmt revision:2 [1] [0] [head]

PMID-15588812[0] Tools for the body schema

See also PMID-8951846[1] Coding of modified body schema during tool use by macaque postcentral neurones.

____References____

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ref: Ferrari-2005.02 tags: tool use monkey neural response leaning mirror neurons F5 date: 04-03-2007 22:44 gmt revision:1 [0] [head]

PMID-15811234[] Mirror Neurons Responding to Observation of Actions Made with Tools in Monkey Ventral Premotor Cortex

  • respond when the monkey sees a human using a tool!

____References____

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ref: life-0 tags: spooky house ana adventure grave date: 03-19-2007 01:11 gmt revision:0 [head]

http://maps.google.com/maps?f=q&hl=en&q=Durham,+NC&layer=&ie=UTF8&z=18&ll=35.970886,-79.148008&spn=0.002687,0.006781&t=h&om=0

go there!! be frightened!!