Form specifies function: robust spike-based computation in analog VLSI without precise synaptic weights
Recent studies demonstrated that sophisticated information processing can occur in spike-based computational systems that make use of synapses with only two states (potentiated or depressed). Here we present the hybrid software/hardware implementation of a model of the mammalian olfactory bulb using an analog VLSI device comprising an array of integrate and fire neurons with bistable synapses. Our implementation incorporates both software and hardware components, integrated using an asynchronous event-based spike representation. The model is able to perform highly selective simulated odor recognition, using induced synchronization within a population of neurons as the key to computation. The success of this scheme shows that the analog VLSI circuits used can perform sophisticated computation, taking advantage of the neuron dynamics and the topology of the network, without requiring precise analog synaptic weights.