Deep Learning in Computational Neuroscience

Deep Learning in Computational Neuroscience

Sanjay Saxena, Sudip Paul, Adhesh Garg, Angana Saikia, Amitava Datta
Copyright: © 2020 |Pages: 21
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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Similarity Of Deep Neural Network And Brain

Deep neural networks are a set of algorithms that are modeled loosely after the human brain. They have an input layer, an output layer, and a minimum of one hidden layer in between. Each layer aims to extract features from the input to recognize patterns. The general structure of the deep neural network given in figure 1.

The hidden layer sums up the inputs, taking their respective weights into account, and makes a non-linear decision to activate a feature by identifying it and sends the activation calculation further to another layer of neurons as input. In terms of the brain, a neuron is the fundamental processing unit of mind as well as a deep neural network. Inside the brain, these are the cells responsible for every action/function in the body with just simple inputs from the external world.

Figure 1.

Deep neural network

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