Silicon Synaptic Homeostasis

Synaptic homeostasis is a mechanism present in biological neural systems used to stabilize the network’s activity. It acts by scaling the synaptic weights in order to keep the neurons firing rate within a functional range, in face of chronic changes of their activity level, while preserving the relative differences between individual synapses. In analog VLSI spike based neural networks, homeostasis is an appealing biologically inspired means to solve technological issues such as mismatch, temperature drifts or long lasting dramatic changes in the input activity level. Here we present a new synaptic circuit, the Diff-PairIntegrator, designed to reproduce the biological temporal evolution of post-synaptic currents, and compatible with implementation of spike-based learning and homeostasis. We describe the silicon synapse and show how it can be used in conjunction with a software control algorithm to model synaptic scaling homeostatic mechanisms.