A Λ‐type neuron model using enhancement‐mode MOSFETs

Sekine, Yoshifumi; Sumiyama, Masami; Saeki, Katsutoshi; Aihara, Kazuyuki

doi:10.1002/ecjb.10020

Cited by 18 publications

(13 citation statements)

References 8 publications

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“…A + and A -, which are both positive, determine the maximum amounts of synaptic modification, which occur when Δt is close to zero. To correspond to the continuous firing patterns which the P-HNM outputs, Song's STDP rule was converted into equations (6) - (8).…”

Section: B Stdpmentioning

confidence: 99%

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Extraction of Phase Information Buried in Fluctuation of a Pulse-type Hardware Neuron Model Using STDP

Saeki

Hayashi²,

Sekine³

2006

The 2006 IEEE International Joint Conference on Neural Network Proceedings

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Since neural networks have superior information processing functions, many investigators attempt to model biological neurons and their networks. Furthermore, a number of studies of neural networks have recently been made with the purpose of applying engineering to the brain. In this study, we investigate the effect of STDP on the ability to extract phase information buried in fluctuation. We focus on spike timing dependent synaptic plasticity (STDP), and we construct neural networks from a pulse-type hardware neuron model using STDP. We show that phase information buried in fluctuation is revealed by the effect of STDP, making it possible to decode the synaptic weight. Moreover, we show that it is possible to extract the phase difference buried in fluctuation representing the reinforcement part of the synaptic weight, using neural networks with STDP.

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Section: B Stdpmentioning

confidence: 99%

“…On the other hand, we proposed a pulse-type hardware neuron model (P-HNM) that approximately simulates pulse signal as the information transmission means in the brain [8].…”

Section: Introductionmentioning

confidence: 99%

Extraction of Phase Information Buried in Fluctuation of a Pulse-type Hardware Neuron Model Using STDP

Saeki

Hayashi²,

Sekine³

2006

The 2006 IEEE International Joint Conference on Neural Network Proceedings

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“…For example, Sekine et al proposed a Bonhoefer-van der Pol (BVP) equivalent model [30], which is based on negative differential resistance of a FET circuitry called a lambda transistor. It is also shown that some extensions of this model result in bursting action potentials and chaotic responses [31]- [35]. The BVP model is based on the Hodgkin-Huxley equations and is reduced to two dimensions focusing on qualitative description of nerve dynamics.…”

Section: Introductionmentioning

confidence: 99%

A MOSFET-Based Model of a Class 2 Nerve Membrane

Kohno

Aihara

2005

IEEE Trans. Neural Netw.

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We have constructed a nerve membrane using MOSFET circuitry, which can be a basic element of an FET-based neural system. Its mechanism of action potentials generation is designed to reproduce that of the Hodgkin-Huxley equations. The responses to singlet, doublet, repetitive pulse, and sustained stimuli are analyzed to show that it exhibits similar properties to the Hodgkin-Huxley equations; namely, 1) excitable dynamics with generation of action potentials, 2) the existence of a chaotic response to periodic stimuli, and 3) Class 2 excitability. It is known that Class 2 excitability is generated by an inverted Hopf bifurcation. We have applied Hopf bifurcation theory to our nerve membrane's system equations and have shown a routine for ascertaining whether a certain parameter set generates an inverted Hopf bifurcation.

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“…Especially in these days, the studies on CA2 region in hippocampus, which is difficult to study because its existence domain is very small, has been proceeded [6,7]. For construction of a model with the features of cell body in living body, the cell body model using Λ-type negative resistance elements can be constructed with simple circuits [8][9][10]. However, currently, the analysis cannot be conducted for large scale of NN analysis because of numerous parameters.…”

Section: Introductionmentioning

confidence: 99%

A Study on Cell Body Model for Highly Integrated Circuits

Okuyama

Saeki

Sekine

2017

Elect Comm in Japan

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A number of recent studies on neural networks (NNs) have been conducted with the purpose of applying engineering to the brain function. One approach which focuses on the dynamics of the living organism, various Artificial Neural Networks (ANNs) has been proposed. However, the NN of a living body is very large in scale. Therefore, when we analyze the dynamics using the hardware NN model, the number of devices and the circuit size should be smaller. In this paper, we aim to reduce the area of a cell body model, and look into the cell body model made up of only NMOS. As a result, it is clearly shown that the packaging density of the proposed model can reduce the size of a traditional model by more than half. C⃝ 2017 Wiley Periodicals, Inc. Electron Comm Jpn, 100(3): 3-9, 2017; Published online in Wiley Online Library (wileyonlinelibrary.com).

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A Λ‐type neuron model using enhancement‐mode MOSFETs

Cited by 18 publications

References 8 publications

Extraction of Phase Information Buried in Fluctuation of a Pulse-type Hardware Neuron Model Using STDP

Extraction of Phase Information Buried in Fluctuation of a Pulse-type Hardware Neuron Model Using STDP

A MOSFET-Based Model of a Class 2 Nerve Membrane

A Study on Cell Body Model for Highly Integrated Circuits

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