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Complete Mean-Field Theory for Dynamics of Binary Recurrent Networks
被引:9
|作者:
Farkhooi, Farzad
[1
,2
]
Stannat, Wilhelm
[1
,2
]
机构:
[1] Tech Univ Berlin, Inst Math, D-10623 Berlin, Germany
[2] Bernstein Ctr Computat Neurosci, D-10115 Berlin, Germany
关键词:
CORTICAL CIRCUITS;
NEURONS;
CHAINS;
CORTEX;
STATE;
MODEL;
D O I:
10.1103/PhysRevLett.119.208301
中图分类号:
O4 [物理学];
学科分类号:
0702 ;
摘要:
We develop a unified theory that encompasses the macroscopic dynamics of recurrent interactions of binary units within arbitrary network architectures. Using the martingale theory, our mathematical analysis provides a complete description of nonequilibrium fluctuations in networks with a finite size and finite degree of interactions. Our approach allows the investigation of systems for which a deterministic mean-field theory breaks down. To demonstrate this, we uncover a novel dynamic state in which a recurrent network of binary units with statistically inhomogeneous interactions, along with an asynchronous behavior, also exhibits collective nontrivial stochastic fluctuations in the thermodynamical limit.
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页数:5
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