Spike timing-dependent plasticity induces non-trivial topology in the brain

被引:31
作者
Borges, R. R. [1 ,2 ]
Borges, F. S. [1 ]
Lameu, E. L. [1 ]
Batista, A. M. [1 ,3 ,4 ]
Iarosz, K. C. [4 ]
Caldas, I. L. [4 ]
Antonopoulos, C. G. [5 ]
Baptista, M. S. [6 ]
机构
[1] Univ Estadual Ponta Grossa, Posgrad Ciencias, Ponta Grossa, PR, Brazil
[2] Univ Tecnol Fed Parana, Dept Matemat, BR-86812460 Apucarana, PR, Brazil
[3] Univ Estadual Ponta Grossa, Dept Matemat & Estat, Ponta Grossa, PR, Brazil
[4] Univ Sao Paulo, Inst Fis, Sao Paulo, SP, Brazil
[5] Univ Essex, Dept Math Sci, Wivenhoe Pk, Colchester CO4 3SQ, Essex, England
[6] Univ Aberdeen, SUPA, Inst Complex Syst & Math Biol, Aberdeen, Scotland
基金
巴西圣保罗研究基金会; 英国工程与自然科学研究理事会;
关键词
Plasticity; Synchronization; Network; LONG-TERM POTENTIATION; NEURAL-NETWORKS; MODEL; NEUROPLASTICITY; DYNAMICS; SYNCHRONIZATION; INFORMATION; NEURONS;
D O I
10.1016/j.neunet.2017.01.010
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
We study the capacity of Hodgkin-Huxley neuron in a network to change temporarily or permanently their connections and behavior, the so called spike timing-dependent plasticity (STDP), as a function of their synchronous behavior. We consider STDP of excitatory and inhibitory synapses driven by Hebbian rules. We show that the final state of networks evolved by a STDP depend on the initial network configuration. Specifically, an initial all-to-all topology evolves to a complex topology. Moreover, external perturbations can induce co-existence of clusters, those whose neurons are synchronous and those whose neurons are desynchronous. This work reveals that STDP based on Hebbian rules leads to a change in the direction of the synapses between high and low frequency neurons, and therefore, Hebbian learning can be explained in terms of preferential attachment between these two diverse communities of neurons, those with low-frequency spiking neurons, and those with higher-frequency spiking neurons. (C) 2017 Elsevier Ltd. All rights reserved.
引用
收藏
页码:58 / 64
页数:7
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