Spatial-temporal patterns in hardware oriented oscillatory CNN architectures

The analysis and the detection of spatial-temporal patterns are extremely important to unfold the main features of numerous biological phenomena. It is also essential to conceive hardware oriented architectures in order to realize VLSI platforms that are able to process and recognize spatial-temporal patterns without breaking them into frames. Oscillatory networks, whose dynamical behavior is described by large system of ordinary differential equation, represent a suitable paradigm to describe many spatial-temporal time-periodic patterns. The aim of the manuscript is to show that locally connected oscillatory networks (oscillatory CNNs) with linear memoryless and space-invariant interactions act as globally connected oscillatory networks with linear dynamical interactions under the constraint that the couplings involve at least two components of the oscillator state vector. The space-invariant local connectivity permits to realize simple prototype hardware platforms for processing spatial-temporal patterns.