Principles for generation of reverberation

TL;DR

This paper investigates the mechanisms of neural reverberation and sequence generation, revealing neuron properties, network topology effects, and STDP learning rules that enable memory of spike sequences in neural circuits.

Contribution

It introduces a new understanding of reverberation mechanisms, a pipeline for designing sequential firing networks, and a STDP-based learning rule for sequence memory.

Findings

Neuron property essential for burst generation identified

Network topology and neurotransmitter release explain sequence patterns

STDP rule enables networks to learn and remember sequences

Abstract

In modern neuroscience, memory has been postulated to stored in neural circuits as sequential spike train and Reverberation is one of the specific example.Former research has made much progress on phenomenon description. However, the mechanism of reverberation has been unclear yet. In this study, combining electrophysiological record and numerical simulation, we confirmed a formerly unrealized neuron property that is necessary for the burst generation in reverberation. Secondly, we find out the mechanism of sequential pattern generation which clearly explained by network topology and asynchronous neurotransmitter release. In addition, we also developed a pipeline that could design the network fire in manually set order. Thirdly, we explored the dynamics of STDP learning and chased down the effects of STDP Rule in reverberation. With these understandings, we developed a STDP based learning rule which could drive the network to remember any presupposed sequence. These results indicated that neuron circuit can remember malformation through STDP rule. Those information are stored in synapse connections. By this way, animals remember information as spike sequence pattern.