Phase transitions and self-organized criticality in networks of stochastic spiking neurons

被引:78
作者
Brochini, Ludmila [1 ]
Costa, Ariadne de Andrade [2 ]
Abadi, Miguel [1 ]
Roque, Antonio C. [3 ]
Stolfi, Jorge [2 ]
Kinouchi, Osame [3 ]
机构
[1] Univ Sao Paulo, Dept Estat IME, BR-05508090 Sao Paulo, SP, Brazil
[2] Univ Estadual Campinas, Inst Comp, BR-13083852 Campinas, SP, Brazil
[3] Univ Sao Paulo, Dept Fis FFCLRP, BR-14040901 Ribeirao Preto, SP, Brazil
基金
巴西圣保罗研究基金会;
关键词
CORTICAL NETWORKS; NEURAL-NETWORKS; MODEL; AVALANCHES; ADAPTATION; RANGE;
D O I
10.1038/srep35831
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
Phase transitions and critical behavior are crucial issues both in theoretical and experimental neuroscience. We report analytic and computational results about phase transitions and self-organized criticality (SOC) in networks with general stochastic neurons. The stochastic neuron has a firing probability given by a smooth monotonic function Phi(V) of the membrane potential V, rather than a sharp firing threshold. We find that such networks can operate in several dynamic regimes (phases) depending on the average synaptic weight and the shape of the firing function F. In particular, we encounter both continuous and discontinuous phase transitions to absorbing states. At the continuous transition critical boundary, neuronal avalanches occur whose distributions of size and duration are given by power laws, as observed in biological neural networks. We also propose and test a new mechanism to produce SOC: the use of dynamic neuronal gains - a form of short-term plasticity probably located at the axon initial segment (AIS) - instead of depressing synapses at the dendrites (as previously studied in the literature). The new self-organization mechanism produces a slightly supercritical state, that we called SOSC, in accord to some intuitions of Alan Turing.
引用
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页数:15
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