Deep Learning in Computational Neuroscience

Sanjay Saxena, Sudip Paul, Adhesh Garg, Angana Saikia, Amitava Datta

Research output: Chapter in Book/Conference paperChapter

Abstract

Computational neuroscience is inspired by the mechanism of the human brain. Neural networks have reformed machine learning and artificial intelligence. Deep learning is a type of machine learning that teaches computers to do what comes naturally to individuals: acquire by example. It is inspired by biological brains and became the essential class of models in the field of machine learning. Deep learning involves several layers of computation. In the current scenario, researchers and scientists around the world are focusing on the implementation of different deep models and architectures. This chapter consists the information about major architectures of deep network. That will give the information about convolutional neural network, recurrent neural network, multilayer perceptron, and many more. Further, it discusses CNN (convolutional neural network) and its different pretrained models due to its major requirements in visual imaginary. This chapter also deliberates about the similarity of deep model and architectures with the human brain.
Original languageEnglish
Title of host publicationChallenges and applications for implementing machine learning in computer vision
EditorsRamgopal Kashyap, A.V. Senthil Kumar
PublisherIGI Global
Chapter2
Pages43-63
ISBN (Electronic)9781799801849
ISBN (Print) 9781799801825
DOIs
Publication statusPublished - 2020

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    Saxena, S., Paul, S., Garg, A., Saikia, A., & Datta, A. (2020). Deep Learning in Computational Neuroscience. In R. Kashyap, & A. V. S. Kumar (Eds.), Challenges and applications for implementing machine learning in computer vision (pp. 43-63). IGI Global. https://doi.org/10.4018/978-1-7998-0182-5.ch002