Stochastic spike synchronization in a small-world neural network with spike-timing-dependent plasticity

Area of Science:

• Computational neuroscience
• Complex systems

Background:

• Watts-Strogatz small-world networks (SWN) of subthreshold neurons exhibit noise-induced spiking.
• Stochastic spike synchronization (SSS) occurs in these networks at intermediate noise intensities without synaptic plasticity.

Purpose of the Study:

• Investigate the impact of additive spike-timing-dependent plasticity (STDP) on SSS in SWNs.
• Analyze the mechanisms of synaptic potentiation and depression induced by STDP.
• Compare additive STDP with multiplicative STDP and network structures.

Main Methods:

• Simulated Watts-Strogatz small-world networks with subthreshold neurons.
• Incorporated additive and multiplicative spike-timing-dependent plasticity (STDP).
• Analyzed pair-correlations of instantaneous individual spike rates (IISRs) and spike time delays.

Main Results:

• Additive STDP induces a 'Matthew' effect, strengthening good synchronization and weakening bad synchronization via a positive feedback loop.
• Long-term potentiation and depression of synaptic strengths were observed and microscopically studied.
• Multiplicative STDP effects were compared to additive STDP and different network topologies (regular lattice, random graph).

Conclusions:

• STDP significantly modulates stochastic spike synchronization in small-world neuronal networks.
• Synaptic plasticity, driven by STDP, can lead to self-reinforcing synchronization patterns.
• The findings offer insights into activity-dependent synaptic plasticity and network dynamics.