Literature DB >> 17983670

What can we learn from synaptic weight distributions?

Boris Barbour1, Nicolas Brunel, Vincent Hakim, Jean-Pierre Nadal.   

Abstract

Much research effort into synaptic plasticity has been motivated by the idea that modifications of synaptic weights (or strengths or efficacies) underlie learning and memory. Here, we examine the possibility of exploiting the statistics of experimentally measured synaptic weights to deduce information about the learning process. Analysing distributions of synaptic weights requires a theoretical framework to interpret the experimental measurements, but the results can be unexpectedly powerful, yielding strong constraints on possible learning theories as well as information that is difficult to obtain by other means, such as the information storage capacity of a cell. We review the available experimental and theoretical techniques as well as important open issues.

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Year:  2007        PMID: 17983670     DOI: 10.1016/j.tins.2007.09.005

Source DB:  PubMed          Journal:  Trends Neurosci        ISSN: 0166-2236            Impact factor:   13.837


  53 in total

1.  Formation and disruption of tonotopy in a large-scale model of the auditory cortex.

Authors:  Markéta Tomková; Jakub Tomek; Ondřej Novák; Ondřej Zelenka; Josef Syka; Cyril Brom
Journal:  J Comput Neurosci       Date:  2015-09-07       Impact factor: 1.621

2.  Fast state-space methods for inferring dendritic synaptic connectivity.

Authors:  Ari Pakman; Jonathan Huggins; Carl Smith; Liam Paninski
Journal:  J Comput Neurosci       Date:  2014-06       Impact factor: 1.621

3.  The biophysical basis underlying the maintenance of early phase long-term potentiation.

Authors:  Moritz F P Becker; Christian Tetzlaff
Journal:  PLoS Comput Biol       Date:  2021-03-22       Impact factor: 4.475

4.  Efficient associative memory storage in cortical circuits of inhibitory and excitatory neurons.

Authors:  Julio Chapeton; Tarec Fares; Darin LaSota; Armen Stepanyants
Journal:  Proc Natl Acad Sci U S A       Date:  2012-12-03       Impact factor: 11.205

5.  Ultra-rapid axon-axon ephaptic inhibition of cerebellar Purkinje cells by the pinceau.

Authors:  Antonin Blot; Boris Barbour
Journal:  Nat Neurosci       Date:  2014-01-12       Impact factor: 24.884

6.  Feedback inhibition and its control in an insect olfactory circuit.

Authors:  Subhasis Ray; Zane N Aldworth; Mark A Stopfer
Journal:  Elife       Date:  2020-03-12       Impact factor: 8.140

7.  Hebbian Learning in a Random Network Captures Selectivity Properties of the Prefrontal Cortex.

Authors:  Grace W Lindsay; Mattia Rigotti; Melissa R Warden; Earl K Miller; Stefano Fusi
Journal:  J Neurosci       Date:  2017-10-06       Impact factor: 6.167

8.  A neural circuit mechanism for regulating vocal variability during song learning in zebra finches.

Authors:  Jonathan Garst-Orozco; Baktash Babadi; Bence P Ölveczky
Journal:  Elife       Date:  2014-12-15       Impact factor: 8.140

9.  Sensitivity to perturbations in vivo implies high noise and suggests rate coding in cortex.

Authors:  Michael London; Arnd Roth; Lisa Beeren; Michael Häusser; Peter E Latham
Journal:  Nature       Date:  2010-07-01       Impact factor: 49.962

10.  A few strong connections: optimizing information retention in neuronal avalanches.

Authors:  Wei Chen; Jon P Hobbs; Aonan Tang; John M Beggs
Journal:  BMC Neurosci       Date:  2010-01-06       Impact factor: 3.288

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