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Randomly Spiking Dynamic Neural Fields Driven by a Shared Random Flow

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9886)

Abstract

Dynamic Neural Fields (DNF) is a well studied mean field model introduced by Amari. It is commonly used for high level bio-inspired cognitive architecture modeling or as a module for autonomous bio-inspired robotics. In a previous work we studied the feasibility of a purely cellular hardware implementation of this model in a digital substratum. We introduced the randomly spiking dynamic neural fields which successfully reproduced the DNF model’s behavior with local and decentralized computations implemented on FPGA. The lateral synaptic weights are computed with a random propagation of binary information generated with a cellular array of pseudo random number cellular automata. More than half of the area utilization was dedicated to the random numbers generation. In this paper we investigate two ways of reducing the surface of random number generators while keeping a cellular architecture.

Keywords

Normalize Root Mean Square Error Bernoulli Trial Inhibitory Layer Implementation Area Spike Diffusion 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Université de LorraineNancyFrance

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