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Showing 2 results for Analog Circuit
Multi-Objective Learning Automata for Design and Optimization a Two-Stage CMOS Operational Amplifier
N. Sayyadi Shahraki, S. H. Zahiri,
Volume 16, Issue 2 (6-2020)
Abstract
In this paper, we propose an efficient approach to design optimization of analog circuits that is based on the reinforcement learning method. In this work, Multi-Objective Learning Automata (MOLA) is used to design a two-stage CMOS operational amplifier (op-amp) in 0.25μm technology. The aim is optimizing power consumption and area so as to achieve minimum Total Optimality Index (TOI), as a new and comprehensive proposed criterion, and also meet different design specifications such as DC gain, Gain-Band Width product (GBW), Phase Margin (PM), Slew Rate (SR), Common Mode Rejection Ratio (CMRR), Power Supply Rejection Ratio (PSRR), etc. The proposed MOLA contains several automata and each automaton is responsible for searching one dimension. The workability of the proposed approach is evaluated in comparison with the most well-known category of intelligent meta-heuristic Multi-Objective Optimization (MOO) methods such as Particle Swarm Optimization (PSO), Inclined Planes system Optimization (IPO), Gray Wolf Optimization (GWO) and Non-dominated Sorting Genetic Algorithm II (NSGA-II). The performance of the proposed MOLA is demonstrated in finding optimal Pareto fronts with two criteria Overall Non-dominated Vector Generation (ONVG) and Spacing (SP). In simulations, for the desired application, it has been shown through Computer-Aided Design (CAD) tool that MOLA-based solutions produce better results.

Role of Astrocyte in Desynchronization Analogue Neural Network
Z. Najafniya, Gh. Karimi, Mahnaz Ranjbar,
Volume 17, Issue 3 (9-2021)
Abstract
Neural synchronization is considered as a key role in several neurological diseases, such as Parkinson’s and Epilepsy’s disease. During these diseases, there is increased synchronization of massive numbers of neurons. In addition, evidences show that astrocytes modulate the synaptic interactions of the neuronal population. The Astrocyte is an important part of a neural network that can be involved in the desynchronization of the neuronal population. In this paper, we design a new analog neuromorphic circuit to implement the effect of astrocyte in the desynchronization of neural networks. The simulation results demonstrate that the astrocyte circuit as a feedback path can be desynchronized to a synchronized neural population. In this circuit, as a first step, the population of twenty neurons is synchronized with the same input currents. Next, by involving an astrocyte feedback circuit, the synchronization of the neural network is disturbed. Then, the neuronal population will be desynchronized. The proposed circuit is designed and simulated using HSPICE simulator in 0.35 μm standard CMOS technology.

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© 2022 by the authors. Licensee IUST, Tehran, Iran. This is an open access journal distributed under the terms and conditions of the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) license.