The time derivative neuron

We have developed the time derivative neuron to reduce the bandwidth needed to encode continuous-time input signals using spiking neuron models. Unlike conventional spiking neuron models, the time derivative neuron generates more spikes in regions of high change and is essentially invariant to DC shifts of the input. We describe the model and develop an efficient reconstruction algorithm to demonstrate its reconstruction accuracy and transmission bandwidth. The idea has both digital and analog hardware realizations and we show a SPICE transistor- level simulation of an efficient analog CMOS implementation.