Digital selection and analogue amplification coexist in a cortex-inspired silicon circuit

Digital circuits such as the flip-flop use feedback to achieve multistabili ty and nonlinearity to restore signals to logical levels, for example 0 and 1. Analogue feedback circuits are generally designed to operate linearly, so that signals are over a range, and the response is unique. By contrast, the response of cortical circuits to sensory stimulation can be both multis table and graded(1-4). We propose that the neocortex combines digital selec tion of an active set of neurons with analogue response by dynamically vary ing the positive feedback inherent in its recurrent connections. Strong pos itive feedback causes differential instabilities that drive the selection o f a set of active neurons under the constraints embedded in the synaptic we ights. Once selected, the active neurons generate weaker, stable feedback t hat provides analogue amplification of the input. Here we present our model of cortical processing as an electronic circuit that emulates this hybrid operation, and so is able to perform computations that are similar to stimu lus selection, gain modulation and spatiotemporal pattern generation in the neocortex.