Literature DB >> 16988038

Triplets of spikes in a model of spike timing-dependent plasticity.

Jean-Pascal Pfister1, Wulfram Gerstner.   

Abstract

Classical experiments on spike timing-dependent plasticity (STDP) use a protocol based on pairs of presynaptic and postsynaptic spikes repeated at a given frequency to induce synaptic potentiation or depression. Therefore, standard STDP models have expressed the weight change as a function of pairs of presynaptic and postsynaptic spike. Unfortunately, those paired-based STDP models cannot account for the dependence on the repetition frequency of the pairs of spike. Moreover, those STDP models cannot reproduce recent triplet and quadruplet experiments. Here, we examine a triplet rule (i.e., a rule which considers sets of three spikes, i.e., two pre and one post or one pre and two post) and compare it to classical pair-based STDP learning rules. With such a triplet rule, it is possible to fit experimental data from visual cortical slices as well as from hippocampal cultures. Moreover, when assuming stochastic spike trains, the triplet learning rule can be mapped to a Bienenstock-Cooper-Munro learning rule.

Entities:  

Mesh:

Year:  2006        PMID: 16988038      PMCID: PMC6674434          DOI: 10.1523/JNEUROSCI.1425-06.2006

Source DB:  PubMed          Journal:  J Neurosci        ISSN: 0270-6474            Impact factor:   6.167


  169 in total

1.  What is the appropriate description level for synaptic plasticity?

Authors:  Harel Z Shouval
Journal:  Proc Natl Acad Sci U S A       Date:  2011-11-16       Impact factor: 11.205

2.  A triplet spike-timing-dependent plasticity model generalizes the Bienenstock-Cooper-Munro rule to higher-order spatiotemporal correlations.

Authors:  Julijana Gjorgjieva; Claudia Clopath; Juliette Audet; Jean-Pascal Pfister
Journal:  Proc Natl Acad Sci U S A       Date:  2011-11-11       Impact factor: 11.205

3.  A biophysically-based neuromorphic model of spike rate- and timing-dependent plasticity.

Authors:  Guy Rachmuth; Harel Z Shouval; Mark F Bear; Chi-Sang Poon
Journal:  Proc Natl Acad Sci U S A       Date:  2011-11-16       Impact factor: 11.205

4.  Learning complex temporal patterns with resource-dependent spike timing-dependent plasticity.

Authors:  Jason F Hunzinger; Victor H Chan; Robert C Froemke
Journal:  J Neurophysiol       Date:  2012-04-11       Impact factor: 2.714

5.  Dynamic afferent synapses to decision-making networks improve performance in tasks requiring stimulus associations and discriminations.

Authors:  Mark A Bourjaily; Paul Miller
Journal:  J Neurophysiol       Date:  2012-03-28       Impact factor: 2.714

6.  Experimental and computational aspects of signaling mechanisms of spike-timing-dependent plasticity.

Authors:  Hidetoshi Urakubo; Minoru Honda; Keiko Tanaka; Shinya Kuroda
Journal:  HFSP J       Date:  2009-06-03

7.  Networks that learn the precise timing of event sequences.

Authors:  Alan Veliz-Cuba; Harel Z Shouval; Krešimir Josić; Zachary P Kilpatrick
Journal:  J Comput Neurosci       Date:  2015-09-03       Impact factor: 1.621

8.  Tunable low energy, compact and high performance neuromorphic circuit for spike-based synaptic plasticity.

Authors:  Mostafa Rahimi Azghadi; Nicolangelo Iannella; Said Al-Sarawi; Derek Abbott
Journal:  PLoS One       Date:  2014-02-13       Impact factor: 3.240

9.  Theta-modulation drives the emergence of connectivity patterns underlying replay in a network model of place cells.

Authors:  Panagiota Theodoni; Bernat Rovira; Yingxue Wang; Alex Roxin
Journal:  Elife       Date:  2018-10-25       Impact factor: 8.140

10.  Attractor Dynamics in Networks with Learning Rules Inferred from In Vivo Data.

Authors:  Ulises Pereira; Nicolas Brunel
Journal:  Neuron       Date:  2018-06-14       Impact factor: 17.173

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.