Two-Dimensional Variation of Bursting Properties in a Silicon-Neuron Half-Center Oscillator

Simoni, Mario; DeWeerth, Stephen P.

doi:10.1109/tnsre.2006.881537

Cited by 17 publications

(17 citation statements)

References 13 publications

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“…5a, 7a and 9a the an equivalent manner, the results of the paper can be used for the assistance and rehabilitation of the human motion. Indicative results on the use of Central Patterns Generators for coordination of the motion of human joints in limbs or in controlling spinal cord locomotion can be found in Amini et al (2005); Guevremont et al (2006); Noble et al (2011);Simoni and DeWeerth (2006) and Vogelstein et al (2006). The results presented in the previous sections can also find similar application in the assistance and correction of the motor functions of the human body and in the treatment of neurological diseases (e.g.…”

Section: State and Disturbances Estimation With The Derivativefree Nomentioning

confidence: 80%

Robust synchronization of coupled neural oscillators using the derivative-free nonlinear Kalman Filter

Rigatos

2014

Cogn Neurodyn

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A synchronizing control scheme for coupled neural oscillators of the FitzHugh-Nagumo type is proposed. Using differential flatness theory the dynamical model of two coupled neural oscillators is transformed into an equivalent model in the linear canonical (Brunovsky) form. A similar linearized description is succeeded using differential geometry methods and the computation of Lie derivatives. For such a model it becomes possible to design a state feedback controller that assures the synchronization of the membrane's voltage variations for the two neurons. To compensate for disturbances that affect the neurons' model as well as for parametric uncertainties and variations a disturbance observer is designed based on Kalman Filtering. This consists of implementation of the standard Kalman Filter recursion on the linearized equivalent model of the coupled neurons and computation of state and disturbance estimates using the diffeomorphism (relations about state variables transformation) provided by differential flatness theory. After estimating the disturbance terms in the neurons' model their compensation becomes possible. The performance of the synchronization control loop is tested through simulation experiments.

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Section: State and Disturbances Estimation With The Derivativefree Nomentioning

confidence: 80%

Robust synchronization of coupled neural oscillators using the derivative-free nonlinear Kalman Filter

Rigatos

2014

Cogn Neurodyn

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“…As described in introduction, this value is far lower than the power consumption of [1][2][3], but is more than one order of magnitude higher than that of the ultra-low power Class I and Class II SNs designed by a similar approach in [28,29]. To bridge this gap, we are developing an SN circuit that consumes about one order of magnitude lower [18].…”

Section: Discussionmentioning

confidence: 98%

“…A wide variety of complex neuronal activities in the nervous system can be modeled precisely by the differential equations that describe the dynamics of the ionic currents and the membrane potential (ionic conductance models). Their direct circuit implementation [1][2][3] can precisely reproduce the neuronal behavior, but complex and high-power consuming (over 100 μW) circuitry is required because their equations generally have many variables and are composed of complex formulae. For energy-efficient and/or high-density implementation, simpler models are required.…”

Section: Introductionmentioning

confidence: 99%

A configurable qualitative-modeling-based silicon neuron circuit

Kohno

Sekikawa

Aihara

2017

NOLTA

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Abstract:A silicon neuronal network is a neuro-mimetic system that aims to realize an electronic-circuit version of the nervous system by connecting silicon neuron circuits via silicon synapse circuits. In our previous works, we proposed a qualitative-modeling-based design approach that provides a solution to the trade off in the silicon neuron circuits between the power consumption and the variety of supported neuronal activities. By this approach, we developed an analog silicon neuron circuit that can be configured to Class I, Class II, regular spiking, elliptic bursting, and square-wave bursting modes with power consumption less than 72 nW. Simulation and experimental results for the first 4 modes are reported.

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“…Silicon neurons that implement ionic-conductance models by analog integrated circuits were reported to be able to precisely reproduce the original neuronal cell's activity [3][4][5][6]. However, because ionic-conductance models are in principle composed of complex nonlinear equations with a relatively large number of variables, it requires complex and high-power-consuming circuitry to solve their equations.…”

Section: Introductionmentioning

confidence: 99%

Silicon neuronal networks towards brain-morphic computers

Kohno

Aihara

2014

NOLTA

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Neuromorphic systems are designed by mimicking or being inspired by the nervous system, which realizes robust, autonomous, and power-efficient information processing by highly parallel architecture. It is a candidate of the next-generation computing system that is expected to have advanced information processing ability by power-efficient and parallel architecture. A silicon neuronal network is a neuromorphic system with a most detailed level of analogy to the nervous system. It is a network of silicon neurons connected via silicon synapses; they are electronic circuits to reproduce the electrophysiological activity of neuronal cells and synapses, respectively. There is a trade-off between the proximity to the neuronal and synaptic activities and simplicity and power-consumption of the circuit. Power-efficient and simple silicon neurons assume uniform spikes, but biophysical experimental data suggest the possibility that variety of spikes given to a synapse is playing a certain role in the information processing in the brain. In this article, we review our design approach of silicon neuronal networks where uniform spikes are not assumed. Simplicity of the circuits is brought by mathematical techniques of qualitative neuronal modeling. Though it is neither simpler nor low-power consuming than above silicon neurons, it is expected to be more appropriate for silicon neuronal networks applied to brain-morphic computing.

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Two-Dimensional Variation of Bursting Properties in a Silicon-Neuron Half-Center Oscillator

Cited by 17 publications

References 13 publications

Robust synchronization of coupled neural oscillators using the derivative-free nonlinear Kalman Filter

Robust synchronization of coupled neural oscillators using the derivative-free nonlinear Kalman Filter

A configurable qualitative-modeling-based silicon neuron circuit

Silicon neuronal networks towards brain-morphic computers

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