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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 that generates 4 different spiking patterns of the 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.

The main required organ of the biological system is the Central Nervous System (CNS), which can influence the other basic organs in the human body. The basic elements of this important organ are neurons, synapses, and glias (such as astrocytes, which are the highest percentage of glias in the human brain). Investigating, modeling, simulation, and hardware implementation (realization) of different parts of the CNS are important in case of achieving a comprehensive neuronal system that is capable of emulating all aspects of the real nervous system. This paper uses a basic neuron model called the Izhikevich neuronal model to achieve a high copy of the primary nervous block, which is capable of regenerating the behaviors of the human brain. The proposed approach can regenerate all aspects of the Izhikevich neuron in high similarity degree and performances. The new model is based on Look-Up Table (LUT) modeling of the mathematical neuromorphic systems, which can be realized in a high degree of correlation with the original model. The proposed procedure is considered in three cases: 100 points LUT modeling, 1000 points LUT modeling, and 10,000 points LUT modeling. Indeed, by removing the high-cost functions in the original model, the presented model can be implemented in a low-error, high-speed, and low-area resources state in comparison with the original system. To test and validate the proposed final hardware, a digital FPGA board (Xilinx Virtex-II FPGA board) is used. Digital hardware synthesis illustrates that our presented approach can follow the Izhikevich neuron in a high-speed state (more than the original model), increase efficiency, and also reduce overhead costs. Implementation results show the overall saving of 84.30% in FPGA and also the higher frequency of the proposed model of about 264 MHz, which is significantly higher than the original model, 28 MHz.

The main required organ of the biological system is the Central Nervous System (CNS), which can influence the other basic organs in the human body. The basic elements of this important organ are neurons, synapses, and glias (such as astrocytes, which are the highest percentage of glias in the human brain). Investigating, modeling, simulation, and hardware implementation (realization) of different parts of the CNS are important in case of achieving a comprehensive neuronal system that is capable of emulating all aspects of the real nervous system. This paper uses a basic neuron model called the Izhikevich neuronal model to achieve a high copy of the primary nervous block, which is capable of regenerating the behaviors of the human brain. The proposed approach can regenerate all aspects of the Izhikevich neuron in high similarity degree and performances. The new model is based on Look-Up Table (LUT) modeling of the mathematical neuromorphic systems, which can be realized in a high degree of correlation with the original model. The proposed procedure is considered in three cases: 100 points LUT modeling, 1000 points LUT modeling, and 10,000 points LUT modeling. Indeed, by removing the high-cost functions in the original model, the presented model can be implemented in a low-error, high-speed, and low-area resources state in comparison with the original system. To test and validate the proposed final hardware, a digital FPGA board (Xilinx Virtex-II FPGA board) is used. Digital hardware synthesis illustrates that our presented approach can follow the Izhikevich neuron in a high-speed state (more than the original model), increase efficiency, and also reduce overhead costs. Implementation results show the overall saving of 84.30 % in FPGA and also the higher frequency of the proposed model of about 264 MHz, which is significantly higher than the original model, 28 MHz. [ABSTRACT FROM AUTHOR]