Fast speed and 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 (SNN) is a significant approach in biological neural networks. This paper presents a Multiplierless Noisy Izhikevich Neuron (MNIN) model, which is used for 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 FPGA realization results demonstrated that the MNIN model follows the different spiking patterns, appropriately.

One of the appealing cases of the neuromorphic research area is the implementation of biological neural networks. The current study offers Multiplierless Hodgkin-Huxley Model (MHHM). This modified model may reproduce various spiking behaviors, like the biological HH neurons, with high accuracy. The presented modified model, in comparison to the original HH model, due to its exact similarity to the original model, has more top performances in the case of FPGA saving and more achievable frequency (speed-up). In this approach, the proposed model has a 69 % saving in FPGA resources and also the maximum frequency of 85 MHz that is more than other similar works. In this modification, all spiking behaviors of the original model have been generated with low error calculations. To validate the MHHM neuron, this proposed model has been implemented on digital hardware FPGA. This approach demonstrates that the original HH model and the proposed model have high similarity in terms of higher performance and digital hardware cost reduction.

The neuron can be called the main cell of a nervous system that can transmit messages from one neuron to another neuron or another cell through electrical signals. In neuromorphic engineering, the hardware realization and simulation of these neurons are crucial. To accomplish a proper digital implementation (i.e. reducing hardware resources and increasing speed and accuracy) of three important neuron models including Hodgkin-Huxley, Morris-Lecar, and Izhikevich, this study proposes a set of multiplier-less mathematical equations based on converting nonlinear functions to $2^{x}$ functions. Then optimizes the proposed equations based on reducing the number of different $2^{x}$ terms. The suggested model can accurately recreate the behavioral characteristics of the original neuron models. The suggested model was synthesized and implemented on the Zynq XC7Z010 (3CLG400) reconfigurable board (FPGA) to validate the mathematical simulation findings. The results of hardware synthesizing and implementations of the proposed model show that different biological behavior can be replicated with greater efficiency and at substantially reduced implementation costs. This method (implemented on the zynq board) can raise the frequency of the proposed models at least by up to 3.5 times that of the original model and reduce power consumption between 20% and 60% for different proposed models. Also, due to the reduction of hardware resources in the proposed model, it is possible to implement a much larger number of neurons (between 4 and 12 times) relative to the original model on a single zynq board.

The human brain is composed of 1011 neurons with a switching speed of about 1 ms. Studying spiking neural networks, including modeling, simulation and implementation of biological neuron models help to learn about the brain and related diseases or to design more efficient bio-mimic processors and smarter robots. Such applications have made this part of neuromorphic researches very popular. In this paper, Wilson neuron model has been implemented as an approximation of Hodgkin-Huxley biological model that is adjusted for efficient digital realization on the platforms. Results show that the proposed model can adequately reproduce neuron dynamical behaviors. Hardware implementation on FPGA shows that our modifications on Wilson original model imitate the biological behavior of neurons, besides using feasibility, targeting a low-cost and high efficiency. The modifications raised a 15% speed-up compared with the original model. The mean NRMSE, RMSE and the MAE parameters are 6.43, 0.44 and 0.31, respectively.

Neurons are the basic blocks in the Central Nervous System (CNS). Simulation and hardware realization of these blocks are vital in neuromorphic engineering. This paper presents a set of multiplierless mathematical equations based on 2^X terms to achieve a low-cost, high-speed, and high-accuracy digital implementation of Hodgkin-Huxley (HH) neuron model. The HH model is the most complicated and high-accuracy among the mathematical neuron models. The proposed model can reproduce spiking behaviors of the original HH model with high precision. To validate the mathematical simulation results, the proposed model has been synthesized and implemented on Field-Programmable Gate Array (FPGA) development board. Hardware synthesis and physical implementations reveal that the biological behavior of different spiking patterns can be reproduced with higher performance and significantly lower implementation costs compared with the original HH model. Also, in this approach the maximum frequency of 200 MHz is achievable which is valuable in comparison with other similar works.

In recent years, hardware modeling for various parts of the body's sensitive organs, including the brain and nervous system, heart and eyes, has been considered for the treatment of diseases and rehabilitation, as well as for moving towards the construction of artificial prostheses. The retina is a thin layer that is the innermost layer of the human eye. In this paper, low-cost hardware implementation for retinal cone cells is performed. Existing mathematical models for implementing the behavior of these cells include a series of nonlinear functions that, if implemented directly, would require a large amount of hardware and, in addition, would not have the desired speed. The proposed model uses multi-linear functions to approximate the nonlinear terms and eliminate the multiplication expressions. The simulation results show that the proposed model tracks the behavior of the original model with high precision. There is also a good match between the main model and the proposed model in terms of dynamic behaviors. The results of hardware implementation using the virtex5 XC5VLX20T (2FF323) reconfigurable board (FPGA) show that the proposed model is fully valid and has a lower hardware volume as well as a 4 times higher frequency, and 22% less power consumption than the original model.