Embodied Learning: Investigating Synaptic Plasticity in a spiking neural network
Dan Bush
Simulation of Adaptive Behavior 2006 (SAB 2006)
Rome, Italy, 25-29 September 2006
Summary
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. The direction of future work, which will include the addition of a volume signalling element based on the postulated actions of nitric oxide in neural learning mechanisms, is then outlined.