Self-organization of feedforward structure and entrainment in excitatory neural networks with spike-timing-dependent plasticity

TL;DR

This paper investigates how spike-timing dependent plasticity (STDP) influences neural network organization, demonstrating that STDP promotes feedforward structures and synchrony, with pacemaker neurons emerging at the root of these networks.

Contribution

It reveals the role of STDP in shaping feedforward neural networks and synchrony, highlighting the emergence of pacemaker neurons in the process.

Findings

STDP prunes synapses and promotes feedforward network formation.

A pacemaker neuron emerges at the root of the feedforward structure.

Neural activity propagates along layers from the pacemaker.

Abstract

Spike-timing dependent plasticity (STDP) is an organizing principle of biological neural networks. While synchronous firing of neurons is considered to be an important functional block in the brain, how STDP shapes neural networks possibly toward synchrony is not entirely clear. We examine relations between STDP and synchronous firing in spontaneously firing neural populations. Using coupled heterogeneous phase oscillators placed on initial networks, we show numerically that STDP prunes some synapses and promotes formation of a feedforward network. Eventually a pacemaker, which is the neuron with the fastest inherent frequency in our numerical simulations, emerges at the root of the feedforward network. In each oscillatory cycle, a packet of neural activity is propagated from the pacemaker to downstream neurons along layers of the feedforward network. This event occurs above a clear-cut threshold value of the initial synaptic weight. Below the threshold, neurons are self-organized into separate clusters each of which is a feedforward network.