Parameter and input estimation in Hindmarsh-Rose neuron by adaptive observer

Mukae, Jun; Totoki, Yusuke; Suemitsu, Haruo; Matsuo, Takami

doi:10.1109/sii.2011.6147601

Cited by 3 publications

(1 citation statement)

References 11 publications

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“…The value of the forcing current into the neuron I is around -10 to 10, and we have used I=3. Some procedures to fit these parameters to actual measured neuronal behaviour can be found in the literature [28][29][30].…”

Section: Original Modelmentioning

confidence: 99%

Implementation of the Hindmarsh–Rose Model Using Stochastic Computing

Camps¹,

Stavrinides²,

Benito³

et al. 2022

Preprint

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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.

show abstract

Section: Original Modelmentioning

confidence: 99%

Implementation of the Hindmarsh–Rose Model Using Stochastic Computing

Camps¹,

Stavrinides²,

Benito³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

A method for estimation of parameters in a neural model with noisy measurements

2016

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Implementation of the Hindmarsh–Rose Model Using Stochastic Computing

et al. 2022

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The Hindmarsh–Rose model is one of the most used models to reproduce spiking behaviour in biological neurons. However, since it is defined as a system of three coupled differential equations, its implementation can be burdensome and impractical for a large number of elements. In this paper, we present a successful implementation of this model within a stochastic computing environment. The merits of the proposed approach are design simplicity, due to stochastic computing, and the ease of implementation. Simulation results demonstrated that the approximation achieved is equivalent to introducing a noise source into the original model, in order to reproduce the actual observed behaviour of the biological systems. 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, reproduces the behaviour of biological systems, which are intrinsically noisy. It is also demonstrated that using some 18–19 bits are enough to provide a speedup of x2 compared to biological systems, with a very small number of gates, thus paving the road for the in silico implementation of large neuron networks.

show abstract

Parameter and input estimation in Hindmarsh-Rose neuron by adaptive observer

Cited by 3 publications

References 11 publications

Implementation of the Hindmarsh–Rose Model Using Stochastic Computing

Implementation of the Hindmarsh–Rose Model Using Stochastic Computing

A method for estimation of parameters in a neural model with noisy measurements

Implementation of the Hindmarsh–Rose Model Using Stochastic Computing

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