A framework for plasticity implementation on the SpiNNaker neural architecture

Many of the precise biological mechanisms of synaptic plasticity remain elusive, but simulations of neural networks have greatly enhanced our understandin g of how specific global functions arise from the massively parallel computation o f neurons and local Hebbian or spike-timing dependent plasticity rules. For simula ting large portions of neural tissue, this has created an increasingly strong need for large scale simulations of plastic neural networks on special purpose hardwar e platforms, because synaptic transmissions and updates are badly matched to compu ting style supported by current architectures. Because of the great diversity of b iological plasticity phenomena and the corresponding diversity of models, there is a great need for testing various hypotheses about plasticity before committing to one hardware implementation. Here we present a novel framework for investigating different plasticity approaches on the SpiNNaker distributed digital neural simula tion platform. The key innovation of the proposed architecture is to exploit the r econfigurability of the ARM processors inside SpiNNaker, dedicating a subset of th em exclusively to process synaptic plasticity updates, while the rest perform the usual neural and synaptic simulations. We demonstrate the flexibility of the propo sed approach by showing the implementation of a variety of spike- and rate-based l earning rules, including standard Spike-Timing dependent plasticity (STDP), voltag e-dependent STDP, and the rate-based BCM rule. We analyze their performance and va lidate them by running classical learning experiments in real time on a 4-chip Spi NNaker board. The result is an efficient, modular, flexible and scalable framework , which provides a valuable tool for the fast and easy exploration of learning mod els of very different kinds on the parallel and reconfigurable SpiNNaker system.