Abstract
The Central Nervous System (CNS) is the part of the nervous system including the brain and spinal cord. The CNS is so named because the brain integrates the received information and influences the activity of different sections of the bodies. The basic elements of this important organ are: neurons, synapses, and glias. Neuronal modeling approach and hardware realization design for the nervous system of the brain is an important issue in the case of reproducing the same biological neuronal behaviors. This work applies a quadratic-based modeling called Digital Spiking Silicon Neuron (DSSN) to propose a modified version of the neuronal model which is capable of imitating the basic behaviors of the original model. The proposed neuron is modeled based on the primary hyperbolic functions, which can be realized in high correlation state with the main model (original one). Really, if the high-cost terms of the original model, and its functions were removed, a low-error and high-performance (in case of frequency and speed-up) new model will be extracted compared to the original model. For testing and validating the new model in hardware state, Xilinx Spartan-3 FPGA board has been considered and used. Hardware results show the high-degree of similarity between the original and proposed models (in terms of neuronal behaviors) and also higher frequency and low-cost condition have been achieved. The implementation results show that the overall saving is more than other papers and also the original model. Moreover, frequency of the proposed neuronal model is about 168 MHz, which is significantly higher than the original model frequency, 63 MHz.
Highlights
Spiking Neural Networks (SNNs) are a very attractive research area based on neuronal brain cells
The original and proposed neuron models are implemented on Xilinx Spartan-3 FPGA Board (Model: XC3S50-TQ144 Package) for validating the proposed method
The proposed neuron model is compared by the Digital Spiking Silicon Neuron (DSSN) model that is implemented in other similar papers [96]
Summary
In the past recent decades, a variety of mathematical computational approaches have been implemented in different research fields such as fluid mechanic engineering [1–9], chemical engineering [10–18], electrical engineering [19–28], telecommunication engineering [29–34], computer engineering [35–39], petroleum engineering [40–47], energy engineering [48–50], mathematics [51–59], environmental engineering [60–62], health and medical sciences [63–65], industrial engineering [66], etc. In coupled neurons, when the presynaptic neuron is triggered by an applied stimulus current, this can release the voltage signals, this voltage can trigger the synaptic gap, and the additional current is injected to the postsynaptic neurons, which is illustrated as a train of spiking behaviors in the post neurons. This behavior of neurons can be described by the spiking neuron models [67–72]. The mathematical equations of the model are given by following statements: dV φ
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
