Synthesis of Nonseparable 3-D Spatiotemporal Bandpass Filters on Analog Networks

Abstract

Linear cellular neural networks (CNNs) are capable of performing efficient spatiotemporal filtering operations as recursive infinite impulse response (IIR) filters. Particularly, linear CNNs can be characterized as a spatial frequency-dependent recursive temporal filter with complex coefficients. Based on a modified version of the CNN paradigm recently proposed by the authors, nonseparable spatiotemporal bandpass filters with tunable spatiotemporal passband volumes are synthesized. The filters reported here qualitatively resemble spatiotemporal receptive field models for the primary visual cortex. Numerical simulation results confirm the bandpass characteristics of our filtering network.