The Experts below are selected from a list of 69 Experts worldwide ranked by ideXlab platform
Kazuyuki Aihara - One of the best experts on this subject based on the ideXlab platform.
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a qualitative modeling based low power silicon Nerve Membrane
International Conference on Electronics Circuits and Systems, 2014Co-Authors: Takashi Kohno, Kazuyuki AiharaAbstract:The silicon neuronal network is an electronic circuit system that reproduces the electrophysiological activities of the nervous system in real-time or faster, which is composed of silicon neuron circuits connected via silicon synapse circuits. It is a candidate for the next-generation computing platform because it is expected to realize the low-power, autonomous, and intelligent information processing similar to the brain. The dynamical property of silicon neuron circuits is a most important factor for information processing in the silicon neuronal networks. In many silicon neuron circuits, however, their spike generation dynamics is drastically approximated by resetting of the state variables. We have developed a silicon Nerve Membrane circuit which is free of this approximation and configurable to Class I and II in the Hodgkin's classification after fabrication. By using mathematical techniques in the qualitative neuronal modeling, we accomplished low-power consumption around 3 nW, which is comparable to the leading-edge silicon neuron circuits. It was designed for TSMC 0.25μm CMOS process and all the transistors are in their subthreshold domain. In this article, its simulation results by Spectre software are reported.
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ICECS - A qualitative-modeling-based low-power silicon Nerve Membrane
2014 21st IEEE International Conference on Electronics Circuits and Systems (ICECS), 2014Co-Authors: Takashi Kohno, Kazuyuki AiharaAbstract:The silicon neuronal network is an electronic circuit system that reproduces the electrophysiological activities of the nervous system in real-time or faster, which is composed of silicon neuron circuits connected via silicon synapse circuits. It is a candidate for the next-generation computing platform because it is expected to realize the low-power, autonomous, and intelligent information processing similar to the brain. The dynamical property of silicon neuron circuits is a most important factor for information processing in the silicon neuronal networks. In many silicon neuron circuits, however, their spike generation dynamics is drastically approximated by resetting of the state variables. We have developed a silicon Nerve Membrane circuit which is free of this approximation and configurable to Class I and II in the Hodgkin's classification after fabrication. By using mathematical techniques in the qualitative neuronal modeling, we accomplished low-power consumption around 3 nW, which is comparable to the leading-edge silicon neuron circuits. It was designed for TSMC 0.25μm CMOS process and all the transistors are in their subthreshold domain. In this article, its simulation results by Spectre software are reported.
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Bottom-up design of Class 2 silicon Nerve Membrane
2007Co-Authors: Takashi Kohno, Kazuyuki AiharaAbstract:The two major principles in silicon neuron implementations are phenomenological and conductance-based. The former reproduces some properties perceived by the designers and does not claim mechanisms are consistent. The latter reproduces the dynamics of the ion channels on the Nerve Membranes. Although it makes the silicon neurons more similar to biological ones, the implementations tend to be complicated because it attempts to replicate the detailed dynamics of the biological components. In previous work [1], we proposed a simple and biologically realistic MOSFET-based Class 2 silicon Nerve Membrane. It reproduced basic mathematical structures that produce resting potential and threshold in the Hodgkin-Huxley equations [2,3]. In this paper, we focus on a method of designing such a silicon Nerve Membrane, which is based on mathematical analyses that have been applied to biological neuron models. The concept of the method is to reproduce the topological structure in phase portraits of biological Nerve Membrane models utilizing the characteristic curves of basic MOSFET circuitries as the elements of these for silicon Nerve Membranes. The design method also revealed how to tune their parameters up.
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Mathematical-model-based design of silicon burst neurons
Neurocomputing, 2007Co-Authors: Takashi Kohno, Kazuyuki AiharaAbstract:Conventionally, silicon neurons have been designed based on two major principles, namely phenomenological and conductance-based principles. In previous studies [T. Kohno, K. Aihara, Parameter tuning of a MOSFET-based Nerve Membrane, in: Proceedings of the 10th International Symposium on Artificial Life and Robotics 2005, 2005, pp. 91-94; T. Kohno, K. Aihara, A MOSFET-based model of a Class 2 Nerve Membrane, IEEE Trans. Neural Networks 16 (3) (2005) 754-773; T. Kohno, K. Aihara, Bottom-up design of Class 2 silicon Nerve Membrane, J. Intell. Fuzzy Syst., in press], we proposed a mathematical-model-based design principle that is based on phase plane and bifurcation analyses. It reproduces the mathematical structures of biological neuron models, thus making the silicon neurons simple and biologically realistic. In this study, we demonstrate that square-wave and another type of silicon bursters can be constructed by adding simple circuitries and tuning the system parameters for the silicon Nerve Membrane designed in our previous studies. Our simple square-wave burster exhibits various firing patterns, including chaotic spiking and bursting.
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A MOSFET-based model of a class 2 Nerve Membrane
IEEE transactions on neural networks, 2005Co-Authors: Takashi Kohno, Kazuyuki AiharaAbstract: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.
Takashi Kohno - One of the best experts on this subject based on the ideXlab platform.
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a qualitative modeling based low power silicon Nerve Membrane
International Conference on Electronics Circuits and Systems, 2014Co-Authors: Takashi Kohno, Kazuyuki AiharaAbstract:The silicon neuronal network is an electronic circuit system that reproduces the electrophysiological activities of the nervous system in real-time or faster, which is composed of silicon neuron circuits connected via silicon synapse circuits. It is a candidate for the next-generation computing platform because it is expected to realize the low-power, autonomous, and intelligent information processing similar to the brain. The dynamical property of silicon neuron circuits is a most important factor for information processing in the silicon neuronal networks. In many silicon neuron circuits, however, their spike generation dynamics is drastically approximated by resetting of the state variables. We have developed a silicon Nerve Membrane circuit which is free of this approximation and configurable to Class I and II in the Hodgkin's classification after fabrication. By using mathematical techniques in the qualitative neuronal modeling, we accomplished low-power consumption around 3 nW, which is comparable to the leading-edge silicon neuron circuits. It was designed for TSMC 0.25μm CMOS process and all the transistors are in their subthreshold domain. In this article, its simulation results by Spectre software are reported.
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ICECS - A qualitative-modeling-based low-power silicon Nerve Membrane
2014 21st IEEE International Conference on Electronics Circuits and Systems (ICECS), 2014Co-Authors: Takashi Kohno, Kazuyuki AiharaAbstract:The silicon neuronal network is an electronic circuit system that reproduces the electrophysiological activities of the nervous system in real-time or faster, which is composed of silicon neuron circuits connected via silicon synapse circuits. It is a candidate for the next-generation computing platform because it is expected to realize the low-power, autonomous, and intelligent information processing similar to the brain. The dynamical property of silicon neuron circuits is a most important factor for information processing in the silicon neuronal networks. In many silicon neuron circuits, however, their spike generation dynamics is drastically approximated by resetting of the state variables. We have developed a silicon Nerve Membrane circuit which is free of this approximation and configurable to Class I and II in the Hodgkin's classification after fabrication. By using mathematical techniques in the qualitative neuronal modeling, we accomplished low-power consumption around 3 nW, which is comparable to the leading-edge silicon neuron circuits. It was designed for TSMC 0.25μm CMOS process and all the transistors are in their subthreshold domain. In this article, its simulation results by Spectre software are reported.
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Bottom-up design of Class 2 silicon Nerve Membrane
2007Co-Authors: Takashi Kohno, Kazuyuki AiharaAbstract:The two major principles in silicon neuron implementations are phenomenological and conductance-based. The former reproduces some properties perceived by the designers and does not claim mechanisms are consistent. The latter reproduces the dynamics of the ion channels on the Nerve Membranes. Although it makes the silicon neurons more similar to biological ones, the implementations tend to be complicated because it attempts to replicate the detailed dynamics of the biological components. In previous work [1], we proposed a simple and biologically realistic MOSFET-based Class 2 silicon Nerve Membrane. It reproduced basic mathematical structures that produce resting potential and threshold in the Hodgkin-Huxley equations [2,3]. In this paper, we focus on a method of designing such a silicon Nerve Membrane, which is based on mathematical analyses that have been applied to biological neuron models. The concept of the method is to reproduce the topological structure in phase portraits of biological Nerve Membrane models utilizing the characteristic curves of basic MOSFET circuitries as the elements of these for silicon Nerve Membranes. The design method also revealed how to tune their parameters up.
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Mathematical-model-based design of silicon burst neurons
Neurocomputing, 2007Co-Authors: Takashi Kohno, Kazuyuki AiharaAbstract:Conventionally, silicon neurons have been designed based on two major principles, namely phenomenological and conductance-based principles. In previous studies [T. Kohno, K. Aihara, Parameter tuning of a MOSFET-based Nerve Membrane, in: Proceedings of the 10th International Symposium on Artificial Life and Robotics 2005, 2005, pp. 91-94; T. Kohno, K. Aihara, A MOSFET-based model of a Class 2 Nerve Membrane, IEEE Trans. Neural Networks 16 (3) (2005) 754-773; T. Kohno, K. Aihara, Bottom-up design of Class 2 silicon Nerve Membrane, J. Intell. Fuzzy Syst., in press], we proposed a mathematical-model-based design principle that is based on phase plane and bifurcation analyses. It reproduces the mathematical structures of biological neuron models, thus making the silicon neurons simple and biologically realistic. In this study, we demonstrate that square-wave and another type of silicon bursters can be constructed by adding simple circuitries and tuning the system parameters for the silicon Nerve Membrane designed in our previous studies. Our simple square-wave burster exhibits various firing patterns, including chaotic spiking and bursting.
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A MOSFET-based model of a class 2 Nerve Membrane
IEEE transactions on neural networks, 2005Co-Authors: Takashi Kohno, Kazuyuki AiharaAbstract: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.
Frederick W. Plapp - One of the best experts on this subject based on the ideXlab platform.
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Apparent reduction in the number of Nerve Membrane sodium channels in the house fly in relation to metabolic resistance to insecticides
Pesticide Biochemistry and Physiology, 1991Co-Authors: Ming-yie Liu, Frederick W. PlappAbstract:Abstract Nerve Membrane sodium channels were assayed by measuring [ 3 H]saxitoxin (STX) binding and its displacement by tetrodotoxin (TTX) from house fly head Membrane preparations. Activity was measured in a susceptible strain and in strains with single or combinations of genes for target site resistance to pyrethroids and metabolic resistance to insecticides. The affinities ( K d values) for STX binding were similar in all strains, indicating that qualitative differences in sodium channels associated with STX binding were not involved in resistance to pyrethroid insecticides. The amount of STX binding ( B max ) was the same in a susceptible strain and a strain with only target site resistance to pyrethroids, indicating that quantitative differences in sodium channels were not involved in resistance. In cotrast, the amount of STX binding was reduced in strains with metabolic resistance to insecticides. The results explain contradictions reported from several previous studies of STX binding in relation to pyrethroid resistance and suggest that the use of strains combining target site and metabolic resistance was responsible for the previous findings.
A. F. Huxley - One of the best experts on this subject based on the ideXlab platform.
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From overshoot to voltage clamp
Trends in neurosciences, 2002Co-Authors: A. F. HuxleyAbstract:In 1939, A.L. Hodgkin and I found that the Nerve action potential shows an "overshoot"--that is, the interior of the fibre becomes electrically positive during an action potential. In 1948, we did our first experiments with a voltage clamp to investigate the current-voltage relations of the Nerve Membrane. Between those dates, we spent much time speculating about the mechanism by which ions cross the Membrane and how the action potential is generated. This article summarizes these speculations, none of which has been previously published.
Toshio Narahashi - One of the best experts on this subject based on the ideXlab platform.
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Nerve Membrane ionic channels as the target site of insecticides
Mini-reviews in Medicinal Chemistry, 2002Co-Authors: Toshio NarahashiAbstract:Despite the much debated controversy over the continuing use of DDT as insecticide, and despite the fact that its use is now banned or restricted in some countries, there are reasons to believe that DDT will continue to be a subject of intense investigations in the years to come. In particular, the mode of action of DDT still remains a mystery and is a target of scientific curiosity because of its potent insecticidal activity with relatively low mammalian toxicity. The elucidation of the mechanism of action of DDT is urgently and much needed, since it will provide us with a clue to develop better insecticides with many of DDT’s advantages preserved while many of its disadvantages are eliminated.