Formation of feedforward networks and frequency synchrony by spike-timing-dependent plasticity

Abstract

Spike-timing-dependent plasticity (STDP) with asymmetric learning windows is commonly found in the brain and useful for a variety of spike-based computations such as input filtering and associative memory. A natural consequence of STDP is establishment of causality in the sense that a neuron learns to fire with a lag after specific presynaptic neurons have fired. The effect of STDP on synchrony is elusive because spike synchrony implies unitary spike events of different neurons rather than a causal delayed relationship between neurons. We explore how synchrony can be facilitated by STDP in oscillator networks with a pacemaker. We show that STDP with asymmetric learning windows leads to self-organization of feedforward networks starting from the pacemaker. As a result, STDP drastically facilitates frequency synchrony. Even though differences in spike times are lessened as a result of synaptic plasticity, the finite time lag remains so that perfect spike synchrony is not realized. In contrast to traditional mechanisms of large-scale synchrony based on mutual interaction of coupled neurons, the route to synchrony discovered here is enslavement of downstream neurons by upstream ones. Facilitation of such feedforward synchrony does not occur for STDP with symmetric learning windows.

9 figures

References in corpus (8)

• Optimal Paths in Disordered Complex Networks
• Plasticity and learning in a network of coupled phase oscillators
• Slow Switching in Globally Coupled Oscillators: Robustness and Occurrence through Delayed Coupling
• Prevalence of unstable attractors in networks of pulse-coupled oscillators
• Entrainment of randomly coupled oscillator networks by a pacemaker
• Robustness and Enhancement of Neural Synchronization by Activity-Dependent Coupling
• Strong Effects of Network Architecture in the Entrainment of Coupled Oscillator Systems
• Slow switching in a population of delayed pulse-coupled oscillators

Cited by in corpus (7)

• A Biologically Plausible Supervised Learning Method for Spiking Neural Networks Using the Symmetric STDP Rule
• Self-organization of feedforward structure and entrainment in excitatory neural networks with spike-timing-dependent plasticity
• Adaptive coupling induced multi-stable states in complex networks
• Analysis of relative influence of nodes in directed networks
• STDP-driven networks and the \emph{C. elegans} neuronal network
• Spontaneous Formation of Dynamical Groups in an Adaptive Networked System
• Training and spontaneous reinforcement of neuronal assemblies by spike timing