Potentiation Decay of Synapses and the Length Distributions of Synfire Chains Self-organized in Recurrent Neural Networks

TL;DR

This paper investigates how potentiation decay influences the formation and length distribution of synfire chains in recurrent neural networks, revealing that decay rates control chain length and stability.

Contribution

It introduces the role of potentiation decay in self-organizing synfire chains, linking decay rates to chain length and network stability in models with STDP.

Findings

Fast decay leads to longer, wider chains.

Slow decay results in shorter, narrower chains.

Potentiation decay regulates neural circuit formation.

Abstract

Synfire chains are thought to underlie precisely-timed sequences of spikes observed in various brain regions and across species. How they are formed is not understood. Here we analyze self-organization of synfire chains through the spike-timing dependent plasticity (STDP) of the synapses, axon remodeling, and potentiation decay of synaptic weights in networks of neurons driven by noisy external inputs and subject to dominant feedback inhibition. Potentiation decay is the gradual, activity-independent reduction of synaptic weights over time. We show that potentiation decay enables a dynamic and statistically stable network connectivity when neurons spike spontaneously. Periodic stimulation of a subset of neurons leads to formation of synfire chains through a random recruitment process, which terminates when the chain connects to itself and forms a loop. We demonstrate that chain length distributions depend on the potentiation decay. Fast potentiation decay leads to long chains with wide distributions, while slow potentiation decay leads to short chains with narrow distributions. We suggest that the potentiation decay, which corresponds to the decay of early long-term potentiation of synapses (E-LTP), is an important synaptic plasticity rule in regulating formation of neural circuity through STDP.