Improvement of signal transmission through spike-timing-dependent plasticity in neural networks

Abstract

We explore the effects of spike-timing-dependent plasticity (STDP) on weak signal transmission in a noisy neural network. We first consider the network where an ensemble of independent neurons, which are subjected to a common weak signal, are connected in parallel to a single postsynaptic neuron via excitatory synapses. STDP can make the signal transmission more efficient, and this effect is more prominent when the presynaptic activities exhibit some correlations. We further consider a two-layer network where there are only couplings between two layers and find that postsynaptic neurons can fire synchronously under suitable conditions. Both the reliability and timing precision of neuronal firing in the output layer are remarkably improved with STDP. These results indicate that STDP can play crucial roles in information processing in nervous systems.