Binding sparse spatiotemporal patterns in spiking computation

Imagine a two-dimensional spatial array of detectors temporally driven via an unknown number of mutually overlapping, unknown patterns. One at a time, these patterns are randomly, partially, sparsely and repeatedly presented, superimposed with omnipresent noise. The challenge is to design a scheme for detecting and recalling these patterns in an unsupervised, online and computationally efficient fashion. As our main contribution, we propose a network of spiking neurons consisting of two reciprocally connected layers. The bottom layer receives stimulus from the detector array and serves as input/output. The top layer encodes, detects and recalls specific patterns. Feedforward projections are data-driven, bottom-up, and analytic, while feedback projections are model-driven, top-down, and synthetic. We judiciously select neuron dynamics and spike-timing dependent synaptic learning rules such that these feedforward and feedback views eventually converge to bind together the spatial extent of each pattern into a coherent, temporary assembly. We present simulations demonstrating that our system is able to detect repeating patterns in an input stream with an impressive degree of tolerance to noise and pattern characteristics.