Central Nervous System: Overall Considerations Based on Hardware Realization of Digital Spiking Silicon Neurons (DSSNs) and Synaptic Coupling

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.

An Optimization on the Neuronal Networks Based on the ADEX Biological Model in Terms of LUT-State Behaviors: Digital Design and Realization on FPGA Platforms

“…To validate the proposed neuron mode, it is required that the dynamical evaluations are considered. In addition to the spiking patterns similarities, the equilibrium points and Jacobean matrix for the original and proposed models must be examined in case of points matching [ 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 ]. Therefore, the nullcline of the two models (original and proposed) are considered for evaluating the dynamical behaviors [ 19 , 20 ].…”

“…These connections causes the coupling of two connected neurons. These coupled neurons are basic submodules of the CNS that can be realized in real states [ 7 , 8 , 9 , 10 ]. Furthermore, glias are the protection-based cells for neurons and regulate the coupling behaviors between different neuron connections.…”

“…The advantage of digital approach is its flexible attributes, time-down in processing and power supply. Using reconfigurable digital boards, such as FPGAs cause the speed-up and flexibility in this case [ 8 , 9 , 10 , 11 , 12 ]. In the field of neural networks implementation, digital approach may be a suitable procedure [ 1 , 3 , 4 , 5 , 6 , 7 , 8 ].…”

“…Indeed, because of high-speed switching in the neuronal networks, it is required to have a accelerate hardware, which is capable of regenerating the same behavior and performance of the human neuronal cells. Different approaches are considered for implementing the neural networks in hardware state in the literature [ 10 , 11 , 12 , 13 , 14 ]. In the field of neuronal realization, since we have high-frequency networks, the digital way is a better solution.…”

“…On the other hand, it is best suited for computing and digital electronics, and is less affected since noise response are analog in nature, digital hardware is flexible in implementation and digital instruments are free from observational errors such as parallax and approximation errors (compared to the analog approach). The implementation of the neuronal cells using configurable platforms such as FPGA is an attractive area in terms of low overhead costs and high-frequency digital systems [ 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 ]. FPGA is an integrated circuit designed to be configured by a customer or a designer after manufacturing, hence the term field-programmable.…”