Real-Time FPGA Simulation of Surrogate Models of Large Spiking Networks

Models of neural systems often use idealized inputs and outputs, but there is also much to learn by forcing a neural model to interact with a complex simulated or physical environment. Unfortunately, sophisticated interactions require models of large neural systems, which are difficult to run in real time. We have prototyped a system that can simulate efficient surrogate models of a wide range of neural circuits in real time, with a field programmable gate array (FPGA). The scale of the simulations is increased by avoiding simulation of individual neurons, and instead simulating approximations of the collective activity of groups of neurons. The system can approximate roughly a million spiking neurons in a wide range of configurations.