Neural Learning: Can we Make it a Little More Bio-Inspired!

Date of Submission

December 2018

Date of Award

Winter 12-12-2019

Institute Name (Publisher)

Indian Statistical Institute

Document Type

Master's Dissertation

Degree Name

Master of Technology

Subject Name

Computer Science


Electronics and Communication Sciences Unit (ECSU-Kolkata)


Pal, Nikhil Ranjan (ECSU-Kolkata; ISI)

Abstract (Summary of the Work)

Though neural networks are inspired by human brains, recent studies have reported several differences between them in terms of their working principles. Specifically, some investigations on animal brains have shown that, in an animal brain, for different stimuli, different clusters neurons get activated. For example, when an animal visualizes different images, neurons from different parts of the brain gets activated. Being inspired from such an observation, here we propose a multilayered model of neural network that incorporates an idea of local activation of neurons for different group of objects (classes). In order to realize activation of distinct spatial clusters of neurons for different types of stimuli, the proposed model makes an interesting integration of a multi-layer perceptron and a self-organizing map. When compared to a conventional multilayer perceptron, the proposed model produces distinct locally activated regions for different classes and at the same time it learns to discriminate between classes.


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Creative Commons License

Creative Commons Attribution 4.0 International License
This work is licensed under a Creative Commons Attribution 4.0 International License.


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