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Research ArticleOpen Access

Machine Learning and Computational Neuroscience: A Widespread Move towards Brain Computer Interface

Volume 3 - Issue 1

Alpana Upadhyay*1 and Monika Vinish2

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    • 1Associate Professor & Head Department of MCA, Gujarat Technological University, India
    • 2Postdoctoral Scientist, 301 University Blvd, Department of Anesthesiology, USA

    *Corresponding author: Alpana Upadhyay, Associate Professor & Head Department of MCA, Sunshine College, Gujarat Technological University, Gujarat, India

Received: February 28, 2018;   Published: March 13, 2018

DOI: 10.26717/BJSTR.2018.03.000846

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In current era of Information and Communication Technology (ICT), entire world has become vibrant and the capability these technologies have has dramatically changed human life and permeated all facets of modern life. From past many years neuroscience research has an outburst of opportunities for developing and designing brain based neuro-technologies Neuroscience research, now a day, has also been driven by such technological progression. Neuroscience is more centered on study of neural codes and computations using advance data analytics. Artificial neural network in machine learning, on the other hand has a propensity to disdain for designing codes, designing of circuits and dynamics to support optimization of brute force by means of consistent and simple preliminary architecture. This supple, adaptive and brain based neurotechnology incorporated with and capitalized on the capabilities of humans, is used to getting human brain-computer interaction better. BCIBrain Computer Interfaces is the major front runner of this notion that has been fundamentally intended on the enhancement of the quality of life particularly pertaining to clinical population.

Keywords: Machine Learning; Computational Neuroscience; Brain-Computer interface; Artificial Neural Network; Deep Learning

Abbreviations: ICT: Information and Communication Technology; BCI: Brain Computer Interfaces; BBCI: Berlin Brain-Computer Interface

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