Open AccessNew analogue stop‐learning control module using astrocyte for neuromorphic learning
Author(s) -
Almasi Milad,
Karimi Gholamreza,
Ranjbar Mahnaz,
Azghadi Mostafa Rahimi
Publication year - 2020
Publication title -
iet circuits, devices and systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.251
H-Index - 49
eISSN - 1751-8598
pISSN - 1751-858X
DOI - 10.1049/iet-cds.2019.0297
Subject(s) - neuromorphic engineering , computer science , very large scale integration , artificial neural network , astrocyte , cmos , transistor , key (lock) , spike (software development) , artificial intelligence , computer architecture , neuroscience , electronic engineering , embedded system , engineering , electrical engineering , biology , software engineering , computer security , voltage , central nervous system
Learning algorithms and devices are an essential part of neural networks and neuromorphic architectures. Astrocyte, as an important element in the learning of neural networks, is believed to play a key role in long‐term synaptic plasticity and memory. In addition, recent experimental observations indicate that astrocytes are active elements in learning in complex networks. In this study, the authors propose a new analogue astrocyte circuit that consumes fewer numbers of transistors compared to its previous counterparts. The authors then use this astrocyte to design a novel analogue circuit to implement a stop‐learning mechanism in a spike‐based learning algorithm and present its neuromorphic very large scale integration (VLSI) simulations. Experimental results demonstrate that the designed circuit can precisely implement the learning mechanisms shown by previous studies implementing spike‐based learning rules without astrocyte. The proposed circuit proposes the first analogue stop‐learning control algorithm that uses astrocytes. It has been designed and simulated in Taiwan semiconductor manufacturing company (TSMC) 0.35 µm complementary metal‐oxide semiconductor (CMOS) technology.