Literature DB >> 24487341

Emerging principles of population coding: in search for the neural code.

Maoz Shamir1.   

Abstract

Population coding theory aims to provide quantitative tests for hypotheses concerning the neural code. Over the last two decades theory has focused on analyzing the ways in which various parameters that characterize neuronal responses to external stimuli affect the information content of these responses. This article reviews and provides an intuitive explanation for the major effects of noise correlations and neuronal heterogeneity, and discusses their implications for our ability to investigate the neural code. It is argued that to test neural code hypotheses further, additional constraints are required, including relating trial-to-trial variation in neuronal population responses to behavioral decisions and specifying how information is decoded by downstream networks.
Copyright © 2014 Elsevier Ltd. All rights reserved.

Mesh:

Year:  2014        PMID: 24487341     DOI: 10.1016/j.conb.2014.01.002

Source DB:  PubMed          Journal:  Curr Opin Neurobiol        ISSN: 0959-4388            Impact factor:   6.627


  23 in total

1.  Dynamics of robust pattern separability in the hippocampal dentate gyrus.

Authors:  Joel Zylberberg; Robert A Hyde; Ben W Strowbridge
Journal:  Hippocampus       Date:  2015-11-05       Impact factor: 3.899

2.  Long-range intralaminar noise correlations in the barrel cortex.

Authors:  Vicente Reyes-Puerta; Yael Amitai; Jyh-Jang Sun; Itamar Shani; Heiko J Luhmann; Maoz Shamir
Journal:  J Neurophysiol       Date:  2015-03-18       Impact factor: 2.714

Review 3.  Cracking the Neural Code for Sensory Perception by Combining Statistics, Intervention, and Behavior.

Authors:  Stefano Panzeri; Christopher D Harvey; Eugenio Piasini; Peter E Latham; Tommaso Fellin
Journal:  Neuron       Date:  2017-02-08       Impact factor: 17.173

4.  Nonlinear dendritic integration of electrical and chemical synaptic inputs drives fine-scale correlations.

Authors:  Stuart Trenholm; Amanda J McLaughlin; David J Schwab; Maxwell H Turner; Robert G Smith; Fred Rieke; Gautam B Awatramani
Journal:  Nat Neurosci       Date:  2014-10-26       Impact factor: 24.884

5.  Global Motion Processing by Populations of Direction-Selective Retinal Ganglion Cells.

Authors:  Jon Cafaro; Joel Zylberberg; Greg D Field
Journal:  J Neurosci       Date:  2020-06-19       Impact factor: 6.167

6.  Representational untangling by the firing rate nonlinearity in V1 simple cells.

Authors:  Merse E Gáspár; Pierre-Olivier Polack; Peyman Golshani; Máté Lengyel; Gergő Orbán
Journal:  Elife       Date:  2019-09-10       Impact factor: 8.140

7.  Sequential Nonlinear Filtering of Local Motion Cues by Global Motion Circuits.

Authors:  Erin L Barnhart; Irving E Wang; Huayi Wei; Claude Desplan; Thomas R Clandinin
Journal:  Neuron       Date:  2018-09-13       Impact factor: 17.173

8.  Complementary Functional Organization of Neuronal Activity Patterns in the Perirhinal, Lateral Entorhinal, and Medial Entorhinal Cortices.

Authors:  Christopher S Keene; John Bladon; Sam McKenzie; Cindy D Liu; Joseph O'Keefe; Howard Eichenbaum
Journal:  J Neurosci       Date:  2016-03-30       Impact factor: 6.167

9.  Visual coding with a population of direction-selective neurons.

Authors:  Michele Fiscella; Felix Franke; Karl Farrow; Jan Müller; Botond Roska; Rava Azeredo da Silveira; Andreas Hierlemann
Journal:  J Neurophysiol       Date:  2015-08-19       Impact factor: 2.714

10.  Direction-Selective Circuits Shape Noise to Ensure a Precise Population Code.

Authors:  Eric Shea-Brown; Fred Rieke; Joel Zylberberg; Jon Cafaro; Maxwell H Turner
Journal:  Neuron       Date:  2016-01-20       Impact factor: 17.173

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