Synaptic plasticity in spiking neural networks (SP/sup 2/INN): a system approach

In this paper, we present a digital system called (SP/sup 2/INN) for simulating very large-scale spiking neural networks (VLSNNs) comprising, e.g., 1000000 neurons with several million connections in total. SP/sup 2/INN makes it possible to simulate VLSNN with features such as synaptic short term plasticity, long term plasticity as well as configurable connections. For such VLSNN the computation of the connectivity including the synapses is the main challenging task besides computing the neuron model. We describe the configurable neuron model of SP/sup 2/INN, before we focus on the computation of the connectivity. Within SP/sup 2/INN, connectivity parameters are stored in an external memory, while the actual connections are computed online based on defined connectivity rules. The communication between the SP/sup 2/INN processor and the external memory represents a bottle-neck for the system performance. We show this problem is handled efficiently by introducing a tag scheme and a target-oriented addressing method. The SP/sup 2/INN processor is described in a high-level hardware description language. We present its implementation in a 0.35 (mu)m CMOS technology, but also discuss advantages and drawbacks of implementing it on a field programmable gate array.