An eight layer cellular neural network for spatio-temporal image filtering

Spatio-temporal filters are critical components of biologically inspired or neuromorphic algorithms for image motion analysis. In this paper, we describe eight layer cellular neural network architectures that can be used to implement these filters. Despite the apparently large number of layers, we describe how these architectures can be implemented efficiently using weak inversion transistor circuits. Integrating both spatial and temporal filtering into a single network reduces hardware complexity in comparison with an architecture that cascades separate spatial and temporal filtering stages. In addition, by considering spatial and temporal filtering jointly, we can obtain filters with enhanced velocity selectivity, as well as more robust population responses to moving image input.