Neuronal avalanches of a self-organized neural network with active-neuron-dominant structure

TL;DR

This paper investigates a self-organized neural network with an active-neuron-dominant structure, demonstrating that neuronal avalanches emerge under certain conditions and enhance information transmission and network complexity.

Contribution

It introduces a novel self-organized neural network model with active-neuron dominance and shows how neuronal avalanches improve information capacity and transmission efficiency.

Findings

Neuronal avalanches occur in the network with proper input intensity.

Spike-timing dependent plasticity leads to active-neuron-dominant connectivity.

Network avalanches maximize activity pattern entropy and input complexity.

Abstract

Neuronal avalanche is a spontaneous neuronal activity which obeys a power-law distribution of population event sizes with an exponent of -3/2. It has been observed in the superficial layers of cortex both \emph{in vivo} and \emph{in vitro}. In this paper we analyze the information transmission of a novel self-organized neural network with active-neuron-dominant structure. Neuronal avalanches can be observed in this network with appropriate input intensity. We find that the process of network learning via spike-timing dependent plasticity dramatically increases the complexity of network structure, which is finally self-organized to be active-neuron-dominant connectivity. Both the entropy of activity patterns and the complexity of their resulting post-synaptic inputs are maximized when the network dynamics are propagated as neuronal avalanches. This emergent topology is beneficial for information transmission with high efficiency and also could be responsible for the large information capacity of this network compared with alternative archetypal networks with different neural connectivity.