Camps, O.; Stavrinides, S.G.; de Benito, C.; Picos, R. Implementation of the Hindmarsh–Rose Model Using Stochastic Computing. Mathematics2022, 10, 4628.
Camps, O.; Stavrinides, S.G.; de Benito, C.; Picos, R. Implementation of the Hindmarsh–Rose Model Using Stochastic Computing. Mathematics 2022, 10, 4628.
Camps, O.; Stavrinides, S.G.; de Benito, C.; Picos, R. Implementation of the Hindmarsh–Rose Model Using Stochastic Computing. Mathematics2022, 10, 4628.
Camps, O.; Stavrinides, S.G.; de Benito, C.; Picos, R. Implementation of the Hindmarsh–Rose Model Using Stochastic Computing. Mathematics 2022, 10, 4628.
Abstract
In this paper we present a successful implementation of the Hindmarsh–Rose model within a SC environment. The merits of the proposed approach are design simplicity, due to stochastic computing, and the ease of implementation. Simulation results showed that the approximation achieved is equivalent to introducing a noise source into the original model. A study for the level of noise introduced, according to the number of bits in the stochastic sequence, has been performed. Additionally, we demonstrate that such an approach, even though it is noisy, it reproduces the behaviour of biological systems, which are intrinsically noisy. It is also demonstrated that a speedup of x2 compared to biological systems is easily achievable, with a very small number of gates, thus paving the road for the in silico implementation of large neuron networks.
Keywords
stochastic logic; chaotic systems; approximate computing; Hindmarsh Rose system
Subject
Computer Science and Mathematics, Computational Mathematics
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
