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Deep Learning in Computational Neuroscience
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Author(s): Sanjay Saxena (Department of Computer Science and Engineering, IIIT Bhubaneswar, Bhubaneswar, India), Sudip Paul (North-Eastern Hill University, India), Adhesh Garg (Department of Computer Science and Engineering, IIIT Bhubaneswar, Bhubaneswar, India), Angana Saikia (North-Eastern Hill University, India)and Amitava Datta (The University of Western Australia, Australia)
Copyright: 2020
Pages: 21
Source title:
Challenges and Applications for Implementing Machine Learning in Computer Vision
Source Author(s)/Editor(s): Ramgopal Kashyap (Amity University, Raipur, India)and A.V. Senthil Kumar (Hindusthan College of Arts and Science, India)
DOI: 10.4018/978-1-7998-0182-5.ch002
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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.
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