Memristive Imitation of Synaptic Transmission and Plasticity

Mannan, Zubaer Ibna; Adhikari, Shyam Prasad; Yang, Changju; Budhathoki, Ram Kaji; Kim, Hyongsuk; Chua, Leon O.

doi:10.1109/tnnls.2019.2892385

Cited by 49 publications

(26 citation statements)

References 38 publications

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“…Synapse is an important bridge for connecting the axon and the dendrite of different neurons, which plays a key role for receiving and transferring electrical signals between neurons. Based on some mathematical and biological neuron models [45,46], the patterns in electrical activities can be modulated by the synapse current. Autapse can connect the axon and the dendrite of the same neuron by a close loop, which is a type of special synapse.…”

Section: Model Establishmentmentioning

confidence: 99%

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Firing multistability in a locally active memristive neuron model

et al. 2020

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The theoretical, numerical and experimental demonstrations of firing dynamics in isolated neuron are of great significance for the understanding of neural function in human brain. In this paper, a new type of locally active and non-volatile memristor with three stable pinched hysteresis loops is presented. Then a novel locally active memristive neuron model is established by using the locally active memristor as a connecting autapse, both firing patterns and multistability in this neuronal system are investigated. We have confirmed that, on the one hand, the construced neuron can generate multiple firing patterns like periodic bursting, periodic spiking, chaotic bursting, chaotic spiking, stochastic bursting, transient chaotic bursting and transient stochastic bursting. On the other hand, the phenomenon of firing multistability with coexisting four kinds of firing patterns can be observed via changing its initial states. It is worth noting that the proposed neuron exhibits such firing multistability previously unobserved in single neuron model. Finally, an electric neuron is designed and implemented, which is extremely useful for the practical scientific and engineering applications. The results captured from neuron hardware experiments match well with the theoretical and numerical simulation results.

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Section: Model Establishmentmentioning

confidence: 99%

“…It is well known that memristor [43,44] is a natural nonlinear nano-electronic device. Due to excellent biomimetic characteristics like nano-scale, nonlinearity and memorability, memristors usually are used to imitate biological synapses [45,46]. Local activity is considered as the origin of complexity [47,48].…”

Section: Introductionmentioning

confidence: 99%

Firing multistability in a locally active memristive neuron model

et al. 2020

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“…e first nanomemristor device was created in 2008 by HP engineers [2]. Since then, it has been applied in artificial neural networks to simulate artificial synapse for biological signal processing in that it has a better memory than resistors and can change its performance in response to the voltage and current like a biological synapse [3][4][5]. As everyone knows, the memristive neural network is a memristor-based neural network.…”

Section: Introductionmentioning

confidence: 99%

Fixed-Time Synchronization of Delayed Memristive Neural Networks with Discontinuous Activations

2021

Journal of Mathematics

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In this paper, the fixed-time synchronization problem for a class of memristive neural networks with discontinuous neuron activation functions and mixed time-varying delays is investigated. With the help of the fixed-time stability theory, under the framework of Filippov solution and differential inclusion theory, several new and useful sufficient criteria for fixed-time synchronization are obtained by designing two types of energy-saving and simple controllers for the considered systems. Compared with the traditional fixed-time synchronization controller, the controllers used in this paper only have one power exponent term, which is a function of the system state error rather than a constant. Moreover, some previous relevant works are especially improved. Finally, two numerical examples are given to show the correctness and the effectiveness of the obtained theoretical results.

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“…The proposed neuro-memristive synapse requires less hardware and inputs than the excitatory synapses in [12]- [14]. Also, it is area efficient and consumes less power as differential amplifier is obsolete in this design.…”

Section: Introductionmentioning

confidence: 99%

Neuro-Memristive Circuit for Bio-Synaptic Plasticity

Mannan

Kim

2021

2021 IEEE International Symposium on Circuits and Systems (ISCAS)

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A neuro-memristive circuit that impersonates the synaptic plasticity of a biological synapse is proposed in this literature. The proposed memristive circuit effectively operates within the frequency bands of brainwaves and exhibits the similar plasticity attributes of brain states corresponding to these wave bands. Specifically, it efficiently imitates the short-term and longterm plasticity phenomena of a synapse. In addition, it exhibits the distinct bio-realistic attributes (i.e. exponentially decaying conductance trace of synapse, spike-frequency adaption and voltage dependent synaptic responses) of a neuron. The neuromemristive circuit is designed in SPICE and the bio-realistic functionalities are demonstrated via various simulations.

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Memristive Imitation of Synaptic Transmission and Plasticity

Cited by 49 publications

References 38 publications

Firing multistability in a locally active memristive neuron model

Firing multistability in a locally active memristive neuron model

Fixed-Time Synchronization of Delayed Memristive Neural Networks with Discontinuous Activations

Neuro-Memristive Circuit for Bio-Synaptic Plasticity

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