A High-Accuracy and Energy-Efficient CORDIC Based Izhikevich Neuron With Error Suppression and Compensation

Efficient digital design of ganglion cells in the retinal pathway

Spiking networks, the third generation of neural networks, are presented as low-power consumption machines with higher cognitive ability, one of the main concerns in intelligence machines. In fact, neuromorphic systems are hardware implementations of spiking networks with minimum resource, area, and power consumption while preserve maximum working frequency. Here, the focus is on the digital implementation of Retinal Ganglion Cell (RGC) based on the linear approximation of non-linear terms which is called Linear Retinal Ganglion Cell (LRGC). The low-cost hardware design of biological cells is acceptable when the digital model of the cell has the same phase and time domain behavior as the original model and follows the dynamic behavior of the original model accurately, which is discussed and confirmed with different analyzes in this paper. The low-cost hardware design of biological cells allows the optimal implementation of a neural population on the hardware, provided that the collective behavior of the digital model matches the original model which is approved by the large-scale simulation of RGC and LRGC models. Cognitive processes are performed in the nervous system at a very low cost, which neuromorphic systems are trying to achieve this important. In this regard, the behavior of RGC and LRGC models in the reconstruction of the image through the retina pathway was examined and a high agreement between the performance of the two models was achieved. Finally, the high functional compatibility of RGC, LRGC models proves that the proposed model is a good candidate of the main model in neuromorphic systems with low hardware cost.

Efficient digital design of the nonlinear behavior of Hindmarsh-Rose neuron model in large-scale neural population

Spiking networks, as the third generation of neural networks, are of great interest today due to their low power consumption in cognitive processes. This important characteristic has caused the hardware implementation techniques of spiking networks in the form of neuromorphic systems attract a lot of attention. For the first time, the focus is on the digital implementation based on CORDIC approximation of the Hindmarsh-Rose (HR) neuron so that the hardware implementation cost is lower than previous studies. If the digital design of a neuron is done efficient, the possibility of implementing a population of neurons is provided for the feasibility of low-consumption implementation of high-level cognitive processes in hardware, which is considered in this paper through edge detector, noise removal and image magnification spiking networks based on the proposed CORDIC_HR model. While using less hardware resources, the proposed HR neuron model follows the behavior of the original neuron model in the time domain with much less error than previous study. Also, the complex nonlinear behavior of the original and the proposed model of HR neuron through the bifurcation diagram, phase space and nullcline space analysis under different system parameters was investigated and the good follow-up of the proposed model was confirmed from the original model. In addition to the fact that the individual behavior of the original and the proposed neurons is the same, the functional and behavioral performance of the randomly connected neuronal population of original and proposed neuron model is equal. In general, the main contribution of the paper is in presenting an efficient hardware model, which consumes less hardware resources, follows the behavior of the original model with high accuracy, and has an acceptable performance in image processing applications such as noise removal and edge detection.

Investigation on the Wilson Neuronal Model: Optimized Approximation and Digital Multiplierless Implementation

Neuromorphic engineering is an essential science field which incorporates the basic aspects of issues together such as: physics, mathematics, electronics, etc. The primary block in the Central Nervous System (CNS) is neurons that have functional roles such as: receiving, processing, and transmitting data in the brain. This paper presents Wilson Multiplierless Neuron (WMN) model which is a modified version of the original model. This model uses power-2 based functions, Look-Up Table (LUT) approach and shifters to apply a multiplierless digital realization leads to overhead costs reduction and increases in the final system frequency. The proposed model specifically follows the original neuron model in case of spiking patterns and also dynamical pathways. To validate the proposed model in digital hardware implementation, the FPGA board (Xilinx Virtex II XC2VP30) can be used. Hardware results show the increasing in the system frequency compared with the original model and other similar papers. Numerical results demonstrate that the proposed system speed-up is 210 MHz that is higher than the original one, 85 MHz. Additionally, the overall saving in FPGA resources for the proposed model is 96.86 % that is more than the original model, 95.13 %. From case study viewpoint for CNS consideration, a network consisting of Wilson neurons, synapses, and astrocytes have been considered to test the controlling effects on LTP and LTD processes for investigating the neuronal diseases (medical approaches) such as Epilepsy.