Self-sustained activity in attractor networks using neuromorphic VLSI

We describe and demonstrate the implementation of attractor neural network dynamics in analog VLSI chips. The on-chip network is composed of an excitatory and an inhibitory population of recurrently connected linear integrate-and-fire neurons. Besides the recurrent input these two populations receive external input in the form of spike trains from an Address-Event-Representation (AER) based system. External AER input stimulates the attractor network and provides also an adequate background activity for the on-chip populations. We use the mean-field approximation of a model attractor neural network to identify regions of parameter space allowing for attractor states, matching hardware constraints. Consistency between theoretical predictions and the observed collective behaviour of the network on chip is checked using the `effective transfer function´ (ETF). We demonstrate that the silicon network can support two equilibrium states of sustained firing activity that are attractors of the dynamics, and that external stimulation can provoke a transition from the lower to the higher state.