Sparse decoding of neural activity in a spiking neuron model of V1

We investigate using a previously developed spiking neuron model of layer 4 of primary visual cortex (V1) [1] as a recurrent network whose activity is consequently linearly decoded, given a set of complex visual stimuli. Our motivation is based on the following: 1) Linear decoders have proven useful in analyzing a variety of neural signals, including spikes, firing rates, local field potentials, voltage sensitive dye imaging, and scalp EEG, 2) linear decoding of activity generated from highly recurrent, nonlinear networks with fixed connections has been shown to provide universal computational capabilities, with such methods termed liquid state machines (LSM) [2] and echo state networks (ESN) [3], 3) in LSMs or ESNs often little is assumed about the recurrent network architecture. However it is likely that for a given type of stimulus/input, the architecture of a biologically constrained recurrent network is important since it shapes the spatio-temporal correlations across the neuronal population, which can potentially be exploited efficiently by an appropriate decoder.