In the quest of efficient hardware implementations of dynamic neural fields: An experimental study on the influence of the kernel shape

Dynamic neural field (DNF) is a popular mesoscopic model for cortical column interactions. It is widely studied analytically and successfully applied to physiological modelling, bioinspired computation and robotics. DNF behavior emerges from distributed and decentralized interactions between computing units which makes it an interesting candidate as a cellular building-block for unconventional computations. That is why we are studying the hardware implementation of DNF on digital substratum (eg. FPGA). As shown in previous papers, this implementation requires several modifications to the equations in order to obtain decent hardware surface utilisation and clock speed. Here we show that the modification of the lateral weights kernel function is possible as long as certain conditions, enumerated in Amari's seminal work are respected. Thank to metaheuristic optimisation it is possible to find the right parameters for two behavioral scenarii of bio-inspired computation interest. We show that the two most hardware-friendly kernels (difference of linear functions and piece-wise function) are as easy to tune as the traditional Mexican hat kernel. However the difference of exponential kernel is more difficult to tune.