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Introduction: Simulation of biological neural networks is a computationally intensive task due to the number of neurons, various communication pathways, and non-linear terms in the differential equations of the neuron. Method: This study proposes an original modification to optimize performance and power consumption in systems, simulating or implementing spiking neural networks. First, the proposed modified models were simulated for validation. Furthermore, digital hardware was designed, and both the original and proposed models were implemented on a Field-Programmable Gate Array (FPGA). Results and discussion: Moreover, the impact of the proposed modification on performance metrics was studied. The implementation results confirmed that the proposedmodels are considerably faster and require less energy to generate a spike compared with unmodified neurons. [ABSTRACT FROM AUTHOR]

Realization of biological neuron models is an essential research area in the field of neuromorphic. This brief presents a novel implementation of adaptive-exponential (AdEx) neuron model based on a complete-matching method called Power-2 Based AdEx Model (PBAM). This model can precisely reproduce different spiking behaviors, similar to the biological neurons with lower implementation costs compared with previous works. To validate the PBAM neuron, the proposed model is physically realized on FPGA as a proof of concept. Experimental results demonstrate high similarity with the original model, high computational performance and lower hardware cost. The proposed PBAM neuron implementation on FPGA requires considerably lower hardware resources compared with the original AdEx neuron. In comparison with similar works, this model has a higher performance and can operate at higher frequencies. In this modification, time domain spiking and dynamical behaviors of the original model are regenerated with very low computational errors using low-cost fixed-point calculations. This makes the proposed model an ideal candidate for large scale neuromorphic and biologically inspired neural network implementations targeting low-cost hardware platforms. [ABSTRACT FROM AUTHOR]

Summary: Astrocytes are the most abundant type of glial cells in the central nervous system (CNS). These non‐neuronal cells are able to regulate the neurons activity in the different parts of brain tissue by calcium waves generation in its internal space. Moreover, astrocytes interact with neurons and modulate the spiking activity of them. In this paper, a set of piecewise linear estimations of a three‐dimensional spontaneous astrocyte model are presented for digital FPGA realization. This leads to achieve a high‐speed and low‐cost system in large‐scale implementation. In this approach, the three‐dimensional original model is converted to a two‐dimensional one and the hardware overhead have been reduced, significantly due to eliminating the large number of multiplications in the original astrocyte model. Simulation results in MATLAB demonstrate that our method can mimic the original calcium waves in high degree of similarity. To validate our method in case of hardware, the proposed model has been tested and simulated in Modelsim software and also implemented on Spartan3 XC3S50 (TQ144) FPGA board. Hardware realization results show that the proposed model has high similarity by the simulation outputs. Consequently, this reduced‐model of astrocyte can be used in large‐scale networks because of its low‐cost hardware and high‐speed system. [ABSTRACT FROM AUTHOR]

Real-time, large-scale simulation of biological systems is challenging due to different types of nonlinear functions describing biochemical reactions in the cells. The promise of the high speed, cost effectiveness, and power efficiency in addition to parallel processing has made application-specific hardware an attractive simulation platform. This paper proposes high-speed and low-cost digital hardware to emulate a biological-plausible astrocyte and glutamate-release mechanism. The nonlinear terms of these models were calculated using a high-precision and cost-effective algorithm. Subsequently, the modified models were simulated to study and validate their functions. We developed several hardware versions by setting different constraints to investigate trade-offs and find the best possible design. FPGA implementation results confirmed the ability of the design to emulate biological cell behaviours in detail with high accuracy. As for performance, the proposed design turned out to be faster and more efficient than previously published works that targeted digital hardware for biological-plausible astrocytes. [ABSTRACT FROM AUTHOR]

Fast speed and a high accuracy implementation of biological plausible neural networks are vital key objectives to achieve new solutions to model, simulate and cure the brain diseases. Efficient hardware implementation of spiking neural networks is a significant approach in biological neural networks. This paper presents a multiplierless noisy Izhikevich neuron (MNIN) model, which is used for the digital implementation of biological neural networks in large scale. Simulation results show that the MNIN model reproduces the same operations of the original noisy Izhikevich neuron. The proposed model has a low-cost hardware implementation property compared with the original neuron model. The field-programmable gate array realization results demonstrated that the MNIN model follows the different spiking patterns appropriately. [ABSTRACT FROM AUTHOR]