Temporally evolving surround suppression helps decoding in a spiking model of motion processing

We find that a neural network with temporally evolving surround suppression improves the linear decodability of the network's population response. We present a novel model of motion processing that is fully implemented in a spiking neural network. We examine the role of lateral inhibition in V1 and MT. We model the response of the retina, V1 and MT, where each neuron is a single compartment conductance-based dynamical system. We apply a linear decoder to estimate the speed and direction of optic flow from a population response of a small spatial region in MT.

Before training the decoder on population vector responses from MT with labeled speeds, we allow the spiking neural network to adapt the weights of the recurrent inhibitory neurons with spike-timing dependent plasticity (STDP). This allows the individual cells to adapt their dynamic range to the statistics reflected in the activity of the excitatory feed-forward network. Also, we impose a random onset latency of 1-10 msec for each feed-forward neuron. The combination of the onset latency and the inhibitory STDP results in a surround suppression with a magnitude that modulates throughout the course of the response, balancing the incoming excitatory drive.

The temporally evolving surround suppression affects the activity of excitatory and inhibitory units in V1 and MT. The result is a population response of MT excitatory units that is more informative for decoding. The early response is less direction selective but drives the inhibition that sculpts the later responses. One source of improvement is that inhibition removes the non-selective response, but still preserves a robust selective response. Also the inhibition acts as gain control, which limits how much saturation corrupts the linear code. We measure decoding performance by calculating the sum squared error of an estimate of the direction and speed of optic flow.