Literature DB >> 18643104

Interplay between a phase response curve and spike-timing-dependent plasticity leading to wireless clustering.

Hideyuki Câteau1, Katsunori Kitano, Tomoki Fukai.   

Abstract

A phase response curve (PRC) characterizes the signal transduction between oscillators such as neurons on a fixed network in a minimal manner, while spike-timing-dependent plasiticity (STDP) characterizes the way of rewiring networks in an activity-dependent manner. This paper demonstrates that these two key properties both related to the interaction times of oscillators work synergetically to carve functionally useful circuits. STDP working on neurons that prefer asynchrony converts the initial asynchronous firing to clustered firing with synchrony within a cluster. They get synchronized within a cluster despite their preference to asynchrony because STDP selectively disrupts intracluster connections, which we call wireless clustering. Our PRC analysis reveals a triad mechanism: the network structure affects how the PRC is read out to determine the synchrony tendency, the synchrony tendency affects how the STDP works, and STDP affects the network structure, closing the loop.

Entities:  

Mesh:

Year:  2008        PMID: 18643104     DOI: 10.1103/PhysRevE.77.051909

Source DB:  PubMed          Journal:  Phys Rev E Stat Nonlin Soft Matter Phys        ISSN: 1539-3755


  13 in total

1.  STDP in Recurrent Neuronal Networks.

Authors:  Matthieu Gilson; Anthony Burkitt; Leo J van Hemmen
Journal:  Front Comput Neurosci       Date:  2010-09-10       Impact factor: 2.380

2.  Spectral analysis of input spike trains by spike-timing-dependent plasticity.

Authors:  Matthieu Gilson; Tomoki Fukai; Anthony N Burkitt
Journal:  PLoS Comput Biol       Date:  2012-07-05       Impact factor: 4.475

3.  Sensory feedback, error correction, and remapping in a multiple oscillator model of place-cell activity.

Authors:  Joseph D Monaco; James J Knierim; Kechen Zhang
Journal:  Front Comput Neurosci       Date:  2011-09-29       Impact factor: 2.380

4.  Excitatory, inhibitory, and structural plasticity produce correlated connectivity in random networks trained to solve paired-stimulus tasks.

Authors:  Mark A Bourjaily; Paul Miller
Journal:  Front Comput Neurosci       Date:  2011-09-12       Impact factor: 2.380

5.  Oscillations via Spike-Timing Dependent Plasticity in a Feed-Forward Model.

Authors:  Yotam Luz; Maoz Shamir
Journal:  PLoS Comput Biol       Date:  2016-04-15       Impact factor: 4.475

6.  Dendritic and Axonal Propagation Delays Determine Emergent Structures of Neuronal Networks with Plastic Synapses.

Authors:  Mojtaba Madadi Asl; Alireza Valizadeh; Peter A Tass
Journal:  Sci Rep       Date:  2017-01-03       Impact factor: 4.379

7.  Rhythmogenesis evolves as a consequence of long-term plasticity of inhibitory synapses.

Authors:  Sarit Soloduchin; Maoz Shamir
Journal:  Sci Rep       Date:  2018-08-29       Impact factor: 4.379

8.  Delay selection by spike-timing-dependent plasticity in recurrent networks of spiking neurons receiving oscillatory inputs.

Authors:  Robert R Kerr; Anthony N Burkitt; Doreen A Thomas; Matthieu Gilson; David B Grayden
Journal:  PLoS Comput Biol       Date:  2013-02-07       Impact factor: 4.475

9.  Stochastic variational learning in recurrent spiking networks.

Authors:  Danilo Jimenez Rezende; Wulfram Gerstner
Journal:  Front Comput Neurosci       Date:  2014-04-04       Impact factor: 2.380

10.  Oscillation, Conduction Delays, and Learning Cooperate to Establish Neural Competition in Recurrent Networks.

Authors:  Hideyuki Kato; Tohru Ikeguchi
Journal:  PLoS One       Date:  2016-02-03       Impact factor: 3.240

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