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ZENODO
Dataset . 2018
License: CC BY
Data sources: Datacite
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ZENODO
Dataset . 2018
License: CC BY
Data sources: Datacite
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
ZENODO
Dataset . 2018
License: CC BY
Data sources: ZENODO
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Closed-Loop Experiment In The Retina

Authors: Gardella, Christophe; Mora, Thierry; Marre, Olivier;

Closed-Loop Experiment In The Retina

Abstract

Data from closed-loop experiment in the retina See the related code on github: https://github.com/ChrisGll/RBM_TRBM This file explains the organization of data recorded in the closed-loop experiment performed by Christophe Gardella and used in the following articles: - Closed-loop estimation of retinal network sensitivity reveals signature of efficient coding, Ferrari, Gardella, Marre and Mora, eNeuro, 2017: http://www.eneuro.org/content/early/2018/01/16/ENEURO.0166-17.2017 - Blindfold learning of an accurate neural metric, Gardella, Marre and Mora, PNAS, 2018: http://www.pnas.org/content/early/2018/03/09/1718710115.long The stimulus consists in a series of 0.9 s snippets of bar trajectory. Each snippet is called a sequence. The bar has a smooth random motion, with each sequence trajectory beginning and ending at position 0, the center of the screen. In parentheses are the values specific to our data. Notations : Scalar variables: stim_rate: (=50) binning rate for the stimulus, in Hz Nne: (=60) number of neurons Nseq: (=17034) number of sequences Nb_seq: (=45) number of time bins per sequence Nreftj: (=2) number of reference trajectories Npertdir: (=16) number of perturbation directions per reference trajectory Indices: ne_i : index of neuron b_i : index of time bin seq_i : index of sequence (from 1 to Nseq) reftj_i : index of reference trajectory (1 or Nreftj) pertdir_i : index of perturbation direction (1 to Npertdir) In general, in the code: ..._i stands for index: ..._l stands for list ..._il stands for list of indices Stimulus: trajs: cell of size (Nseq,1) with trajs{seq_i}: vector of size (1, Nb_seq) with trajs{seq_i}(b_i) the bar position in time bin b_i, in µm. rand_seq_il: list of indices of sequences corresponding to random bar trajectories Example: rand_seq_il(1) is the index of the first sequence corresponding to a random trajectory. trajs{rand_seq_il(1)} is the corresponding random trajectory. ref_seq_il: cell of size (1, Nreftj) with ref_seq_il{reftj_i} the list of indices of sequences corresponding to repetitions of reference trajectory reftj_i. pert_seq_il: cell of size (Npertdir, Nreftj) with pert_seq_il{pertdir_i, reftj_i}: list of indices of sequences corresponding to a perturbation of reference trajectory reftj_i in direction pertdir_i. Indices are sorted by increasing perturbation amplitude. Example: All sequence trajectories are either random, a trajectory, or a perturbation. So the intersection between rand_seq_il, any ref_seq_il{reftj_i} or any pert_seq_il{pertdir_i, reftj_i} is always be empty. Example: The union between rand_seq_il, all ref_seq_il{reftj_i} and all pert_seq_il{pertdir_i, reftj_i} is the complete list of indices 1:Nseq. pert_amp_l: cell of size (Npertdir, Nreftj) with pert_amp_l{pertdir_i, reftj_i}: list of amplitudes of corresponding perturbations. pert_amp_l{pertdir_i, reftj_i}(n) is the amplitude of the perturbation in sequence pert_seq_il{pertdir_i, reftj_i}(n). Responses: spkb_rate : (=50) binning rate for the responses, in Hz sparse_spkb_all: cell of size (Nseq,1) with sparse_spkb_all{seq_i}: sparse matrix of size (Nne, Nb_seq): binned response during sequence seq_i. sparse_spkb_all{seq_i}(ne_i,b_i)=1 if neuron ne_i spiked at least once in time bin b_i. sparse_spkb_all is the representation of responses used for computing the linear discriminability and distances with the RBM and TRBM metrics. In order to compute distances, one usually only considers a subset of the Nb_seq time bins of the sequence. We note: b_il_s: (=23:37) list of indices of time bins used for distance computation. If one needs un-binned responses (for some metrics such as the van Rossum metric), spike times can be found in the variable spkt_all: spkt_all: cell of size (Nseq,1) with spkt_all{seq_i}: cell of size (Nne, 1) with spkt_all{seq_i}{ne_i}: list of spike times (in UNIT) of neuron ne_i in sequence seq_i. The list is empty if there is no spike. These times are in s, and are relative, with t=0 the beginning of the sequence. pert_lindiscrim_l: cell of size (Npertdir, Nreftj) with pert_lindiscrim_l{pertdir_i, reftj_i} the linear discriminability of responses to the perturbation of reference trajectory reftj_i in direction pertdir_i. pert_lindiscrim_l{pertdir_i, reftj_i}(n) is the linear discriminability of responses in sequence pert_seq_il{pertdir_i, reftj_i}(n).

Keywords

restricted boltzmann machine, perturbations, closed-loop experiment, neural metric, Retina

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