Topic: nonlinear manifold

Deep feedforward and recurrent networks have become popular choices for models of neural computation, but they do not provide a clear link to the details of the underlying biophysical processes. Here we argue that the reason this link has been difficult to establish is because biological circuits operate in a fundamentally different way. To illustrate this, we present a new theory of neural computation in excitatory-inhibitory spiking networks, which captures the precise computational role of each neuron and every spike. By assuming low-rank recurrent connectivity, we show that spiking populat