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Found 6 papers, 2 papers with code
Firing rate distributions in plastic networks of spiking neurons
In recurrent networks of leaky integrate-and-fire (LIF) neurons, mean-field theory has proven successful in describing various statistical properties of neuronal activity at equilibrium, such as firing rate distributions.
The determining role of covariances in large networks of stochastic neurons
We tackle this problem by constructing a dynamical model of both the expectations and covariances of the fractions of active and refractory neurons in the network's populations.
Beyond Wilson-Cowan dynamics: oscillations and chaos without inhibition
Fifty years ago, Wilson and Cowan developed a mathematical model to describe the activity of neural populations.
Universality of the stochastic block model
Mesoscopic pattern extraction (MPE) is the problem of finding a partition of the nodes of a complex network that maximizes some objective function.
Phase transition in the recoverability of network history
Network growth processes can be understood as generative models of the structure and history of complex networks.
Finite size analysis of the detectability limit of the stochastic block model
We then distinguish the concept of average detectability from the concept of instance-by-instance detectability and give explicit formulas for both definitions.
Physics and Society Information Theory Information Theory
