Investigating STDP and LTP in a Spiking Neural Network

摘要

The idea that synaptic plasticity holds the key to the neural basis of learning and memory is now widely accepted in neuroscience. The precise mechanism of changes in synaptic strength has, however, remained elusive. Neurobiological research has led to the postulation of many models of plasticity, and among the most contemporary are spike-timing dependent plasticity (STDP) and long-term potentiation (LTP). The STDP model is based on the observation of single, distinct pairs of pre- and post- synaptic spikes, but it is less clear how it evolves dynamically under the input of long trains of spikes, which characterise normal brain activity. This research explores the emergent properties of a spiking artificial neural network which incorporates both STDP and LTP. Previous findings are replicated in most instances, and some interesting additional observations are made. These highlight the profound influence which initial conditions and synaptic input have on the evolution of synaptic weights.