Memristive Izhikevich Spiking Neuron Model and Its Application in Oscillatory Associative Memory

Fang, Xiaoyan; Duan, Shukai; Wang, Lidan

doi:10.3389/fnins.2022.885322

Cited by 7 publications

(3 citation statements)

References 50 publications

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“…Once the filament is weak enough and can be thermally dissolved again, the device is switched to the HRS and initiate a new round of burst firing (state 3). Therefore, the burst firing characteristics can be understood as the filament rupture/generation, whose dynamics are regulated by the V O concentration at the grain boundaries, consistent with the mathematic model of bursting neurons. − …”

Section: Resultssupporting

confidence: 58%

Artificial Spiking Neuron with Bursting Dynamics for Noise-Resistant Neuromorphic Coding

Liu

Zhu

Guo

et al. 2023

ACS Appl. Electron. Mater.

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Emulating complex neuronal functionalities in human brains using emerging biomimetic electronics is critical for next-generation brain-like chips and artificial general intelligence. Burst firing, featured with the repeated firing of discrete groups of spikes, is high-order neuronal dynamics for reliable signal transmission, coding, and processing. Here, we report a memristor (Pt/ Co 3 O 4−x /ITO) that can natively produce periodic voltage oscillation groups under current stimulation, capturing the burst-firing features of actual biological neurons. This behavior stems from the dynamic formation/rupture of conductive filaments regulated by the V O concentration at the grain boundaries. The burst frequency can be tuned by adjusting the driving current intensity and used to implement burst frequency coding. The bursting neuron-based artificial neural system achieves a recognition accuracy of 86% for the Fashion-MNIST tasks, under the disturbance of random noise spikes. This work provides a promising platform to realize complex neuronal dynamics and to construct robust brain-inspired neuromorphic systems.

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Section: Resultssupporting

confidence: 58%

Artificial Spiking Neuron with Bursting Dynamics for Noise-Resistant Neuromorphic Coding

Liu

Zhu

Guo

et al. 2023

ACS Appl. Electron. Mater.

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“…The Izhikevic model (Izhikevich, 2003) is a commonly found non-linear example, which we can use to demonstrate the applicability of SHIP to modeling non-linear components. It also finds hardware implementation examples (see e.g., Fang et al, 2022a). As the ODE solution is not trivial, we base our implementation on the Forward-Euler method.…”

Section: Izhikevic Neuron Modelmentioning

confidence: 99%

SHIP: a computational framework for simulating and validating novel technologies in hardware spiking neural networks

Gemo,

Spiga,

Brivio

2024

Front. Neurosci.

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Investigations in the field of spiking neural networks (SNNs) encompass diverse, yet overlapping, scientific disciplines. Examples range from purely neuroscientific investigations, researches on computational aspects of neuroscience, or applicative-oriented studies aiming to improve SNNs performance or to develop artificial hardware counterparts. However, the simulation of SNNs is a complex task that can not be adequately addressed with a single platform applicable to all scenarios. The optimization of a simulation environment to meet specific metrics often entails compromises in other aspects. This computational challenge has led to an apparent dichotomy of approaches, with model-driven algorithms dedicated to the detailed simulation of biological networks, and data-driven algorithms designed for efficient processing of large input datasets. Nevertheless, material scientists, device physicists, and neuromorphic engineers who develop new technologies for spiking neuromorphic hardware solutions would find benefit in a simulation environment that borrows aspects from both approaches, thus facilitating modeling, analysis, and training of prospective SNN systems. This manuscript explores the numerical challenges deriving from the simulation of spiking neural networks, and introduces SHIP, Spiking (neural network) Hardware In PyTorch, a numerical tool that supports the investigation and/or validation of materials, devices, small circuit blocks within SNN architectures. SHIP facilitates the algorithmic definition of the models for the components of a network, the monitoring of states and output of the modeled systems, and the training of the synaptic weights of the network, by way of user-defined unsupervised learning rules or supervised training techniques derived from conventional machine learning. SHIP offers a valuable tool for researchers and developers in the field of hardware-based spiking neural networks, enabling efficient simulation and validation of novel technologies.

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“…This observation aligns with the findings of the Izhikevich neuron model, a computational model that suggests diverse responses to external stimuli can be generated by varying the parameter values. These responses can span from excitatory to inhibitory in nature [29].…”

Section: Catalystsmentioning

confidence: 99%

On interaction of proteinoids with simulated neural networks

Mougkogiannis,

Adamatzky

2023

Preprint

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Proteinoids are thermal proteins which swell into microspheres in solution. The proteinoid microspheres show spiking of electrical potential similar to that to that of living neurons. Rich spectrum of proteinoids’ spiking responses to optical and electrical stimulation makes them promising candidates for neuromorphic unconventional computing devices. We decided to evaluate neuron-like activity of proteinoids in the experimental setups of their interaction with simulate neuronal network of Izhikevich neurons. The simulated neural networks stimulate and modulate electrical activity of proteinoid ensembles by interacting with them via arbitrary form programmable function generator. Different amino-acid compositions of proteinoids responded uniquely to input spiking from simulated neurons. We demonstrated that patterns of electrical spiking activity of proteinoids and complexity of the activity can be tuned by patterns of spikes generated by simulated neurons. The research opens novel venues to establishing interacting between nanobrains – brain-like organoids made from molecules, not animal cells — and real nervous systems.

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Memristive Izhikevich Spiking Neuron Model and Its Application in Oscillatory Associative Memory

Cited by 7 publications

References 50 publications

Artificial Spiking Neuron with Bursting Dynamics for Noise-Resistant Neuromorphic Coding

Artificial Spiking Neuron with Bursting Dynamics for Noise-Resistant Neuromorphic Coding

SHIP: a computational framework for simulating and validating novel technologies in hardware spiking neural networks

On interaction of proteinoids with simulated neural networks

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