Controlling Effects of Astrocyte on Neuron Behavior in Tripartite Synapse Using VHDL–AMS

Abstract

Astrocyte cells form the largest cell population in the brain and can influence neuron behavior. These cells provide appropriate feedback control in regulating neuronal activities in the Central Nervous System (CNS). This paper presents a set of equations as a model to describe the interactions between neurons and astrocyte. A VHDL–AMS-based tripartite synapse model that includes a pre-synaptic neuron, the synaptic terminal, a post-synaptic neuron, and an astrocyte cell is presented. In this model, the astrocyte acts as a controller module for neurons and can regulates the spiking activity of them. Simulation results show that by regulating the coupling coefficients of astrocytes, spiking frequency of neurons can be reduced and the activity of neuronal cells is modulated.

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

“…The Central Nervous System (CNS) is a basic biological system which includes three vital organs: neurons, synapses, and glias [ 1 , 2 ]. In this network, neurons are responsible for information processing and transformation of data in different parts of the human brain.…”

“…Among the above list, the Izhikevich model is a computationally reasonable neuronal model that is capable of reproducing all spiking patterns. This model generates all patterns of brain spikes, which is a very significant issue in the neural networks [ 1 , 3 , 4 ]. On the other hand, the HH model is a full-biological neuron model with a high number of equations and terms and is a high-cost neuronal model [ 2 ].…”

“…This process is high performance in case of speed, but long development time is its disadvantage. Moreover, two factors in digital implementation, high-silicon area and power are required, but this procedure is efficient compared by other approaches [ 1 , 2 , 3 , 4 , 5 , 6 ]. 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.…”

“…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.…”