Implementation of Izhikevich neuron based on stochastic computing using a novel inspired Omega‐Flip stochastic number generator

Hedayatpour, Mohammad Ali; Karami, Mohammad Azim; Shamsi, Jafar

doi:10.1002/cta.3322

Cited by 3 publications

(1 citation statement)

References 34 publications

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“…In their work [27], astrocyte model is approximated using piece wise linear approximation method. Recently, a digital design for an optimized SC-based Izhikevich spiking neuron model is presented in [29].…”

Section: Related Workmentioning

confidence: 99%

A Digital Realization of Neuroglial Interaction Model and Its Network Structure

Seyedbarhagh

Ahmadi

2022

IEEE Access

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Astrocyte cells, the most existing abundant cells in central nervous system, play an essential role in modulating the neuronal activities, information processing, and regulating the synaptic plasticity through calcium (Ca2+) fluctuations of ions hemostasis by using a feedback mechanism. The pathophysiological and hypersynchronous neuronal activity can lead to the epileptic seizures, which is known as one of the neurodegenerative disorders in the field of neuroscience. This paper presents a modified neuron-astrocyte interaction (tripartite synapse) model based on leaky and integrate fire neuron and the astrocyte-synapse models using an area-efficient hardware approach called stochastic computing paradigm. The proposed model is synthesized physically on field-programmable gate array as a proof of concept. The implementation results of the presented model can mimic the bidirectional communication in biological minimal network of pre-postsynaptic and Ca2+-based model for astrocyte with considerably lower hardware cost. The influence of astrocytes on neural network behavioral has been investigated by providing a proper feedback mechanism and considering the role of gap junction coupling and the various coefficients on desynchronizing the impaired synchronization of the coupled neurons.INDEX TERMS Astrocytes, Desynchronization, Field programmable gate array (FPGA), Neural-Glial interaction.

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Section: Related Workmentioning

confidence: 99%

A Digital Realization of Neuroglial Interaction Model and Its Network Structure

Seyedbarhagh

Ahmadi

2022

IEEE Access

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FPGA Implementation of Nerve Cell Using Izhikevich Neuronal Model as Spike Generator (SG)

Islam,

Hazzazi,

Hoque

et al. 2024

IEEE Access

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The neuron is sometimes referred to as the "head" or "central" cell of the nervous system since it has the ability to communicate with other neurons or cells via electrical impulses. The hardware realization and simulation of these neurons are critical in neuromorphic engineering. In this paper, we made a device which generates 4 different spiking patterns of nervous system as a Spike Generator (SG) using a hybrid approximation of the target model called the Piece-Wised Power-2 Based Izhikevich Model (PWP2BIM). This proposed model works in a low-cost state to achieve a correct digital implementation of the Izhikevich model, one of the main neuron models (i.e. decreasing hardware resources and enhancing speed and accuracy). The proposed model successfully reproduces the behavioral traits of the initial neuron model. To verify the results of the mathematical simulation, the proposed model was synthesized and implemented on the Zynq XC7Z010 (3CLG400) reconfigurable board (FPGA). The findings of hardware synthesis and applications of the suggested paradigm demonstrate that certain biological behaviors may be duplicated more effectively and at a significantly lower cost. The suggested model's frequency can be increased using this technique (implemented on the Zynq board) at least by 3.6 times compared to the original model, and power consumption can be decreased by 28%. High-frequency design of neuronal models with low-cost attributes is required for application-based types of equipment in case of high-speed operations of these components. Thus, using our approach, the desired goals of application-based features are to be fulfilled. In addition, because the suggested model uses fewer hardware resources than the original model, it is feasible to construct a significantly higher number of neurons (approximately 5 times) on a single Zynq board.

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A Low-Cost and Fault-Tolerant Stochastic Architecture for the Bernsen Algorithm Using Bitstream Correlation

Wang,

Xie,

et al. 2023

J CIRCUIT SYST COMP

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Many algorithms for image processing do not require particularly high precision, but they rely on complicated arithmetic operations for every pixel in an image. The Bernsen algorithm is a typical local thresholding algorithm for solving the problem of uneven lighting. However, this algorithm requires a significant computing overhead and is extremely sensitive to noise. In this work, two stochastic computing architectures are proposed for implementing the Bernsen algorithm by using, respectively, uncorrelated and correlated input bitstreams. Experimental results show that both designs, especially the one using correlated bitstreams, present high fault tolerance of soft errors and low hardware cost in comparison with its conventional binary implementation. However, SC logic with uncorrelated inputs is not always superior to its corresponding binary circuit in energy consumption, especially the circuit that needs long input bitstreams. That means that a reasonable use of correlation can further optimize the SC circuit design.

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Implementation of Izhikevich neuron based on stochastic computing using a novel inspired Omega‐Flip stochastic number generator

Cited by 3 publications

References 34 publications

A Digital Realization of Neuroglial Interaction Model and Its Network Structure

A Digital Realization of Neuroglial Interaction Model and Its Network Structure

FPGA Implementation of Nerve Cell Using Izhikevich Neuronal Model as Spike Generator (SG)

A Low-Cost and Fault-Tolerant Stochastic Architecture for the Bernsen Algorithm Using Bitstream Correlation

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