Title
Computationally Efficient Low Power Neuron Model for Digital Brain
Abstract
Modern supercomputers are capable of performing elementary operations of the order of petaflops. This power is comparable with the computational power of a human brain. The power supply required by supercomputers is in megawatts while human brain requires few watts to perform the same elementary operations as the modern supercomputers can. Thus the power consumption of supercomputers can be reduced (about 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">5</sup> times) by considering the neuromorphic structure of a brain and attempt to mimic it on hardware chip to construct the computer that can function in the same manner as the human brain. In this paper we have proposed to develop the model for single brain cell, that can be replicated to built the whole brain model having computational power of supercomputers but the energy supply comparable with human brain.
Year
DOI
Venue
2018
10.1109/CIS2018.2018.00009
2018 14th International Conference on Computational Intelligence and Security (CIS)
Keywords
Field
DocType
Neuron, Excitable cell, Action Potential (AP), Ion Channels, Hodgkin Huxley Model (HH model), Action Potential Duration, Diastolic Interval, Refractory Period, Restitution Function, Cardiac myocyte
Biological neuron model,Computer science,Parallel computing,Action potential duration,Neuromorphic engineering,Chip,Artificial intelligence,Brain Cell,Brain model,Machine learning,Power consumption
Conference
ISBN
Citations 
PageRank 
978-1-7281-0170-5
0
0.34
References 
Authors
7
4
Name
Order
Citations
PageRank
Asif Mehmood100.34
Muhammad Javed Iqbal200.34
Hassan Dawood36714.45
Ping Guo460185.05