Sensing Temporal Codes and Probing System Responses with Spikes: An Active Pixel Approach

Measuring the similarity of spike-trains is an essential operation for temporal neural coding, spike-based local learning rules, stereo vision and audio, for matching neuronal responses to their evoking stimuli, or for use as a loss function in supervised learning tasks in spiking neural networks. We present a novel spike-based temporal correlator and its accompanying analog subthreshold VLSI implementation, which can perform this operation accurately and efficiently. We discuss two potential applications of this primitive: (1) In event-based neuromorphic systems, it can be used to correlate spike-trains from multiple sources, e.g. pre- and post-synaptic spikes for spike-timing-dependent analog on-chip learning, or event-streams from multiple sensors like dynamic vision sensors and silicon cochleae for stereo-perception. (2) In (neuromorphic) neural interfaces the same framework can be used to correlate neural tissue activity with event-based stimuli, allowing us to identify the characteristic time-constant of the system, assuming integrative dynamics. We verify our results via schematic simulations in a 180nm process.