FPGA-based architecture for real-time synaptic plasticity computation

Synaptic plasticity provides the basis for most models of learning, memory and development in neural networks. The challenge for neuromorphic system designers is to find out efficient architectures to process accurately and speedily plasticity rules for a large number of synaptic connections. In this work, we propose a configurable architecture for real-time synaptic plasticity computation. Based on a dedicated plasticity processor, the architecture runs plasticity rules after a predefined configuration. As proof of concept, we implement on a commercial FPGA a biologically inspired form of spike timing-dependent plasticity (STDP) with complex time dependencies between pairs of pre- and post-synaptic spikes. Experimental results evaluate computation accuracy and speed as well as the number of synaptic connections we can process.