Deep Learning in Computational Neuroscience

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.

Brain Tumour Segmentation in FLAIR MRI Using Sliding Window Texture Feature Extraction Followed by Fuzzy C-Means Clustering

“…The classification techniques can include SVM, KNN (distance = Euclidean, City-block and Minkowski & k = 1, 3, 5, 7), Binary Decision Trees, Random Forest and Ensemble methods, for example, Adaboost, Gentleboost, Logitboost, LPboost, Robustboost, Rusboost and Totalboost. Aside from these approaches, deep learning CNN model may also be deployed as in the current case being reported (Saxena, Paul, Garg, Saikia & Datta, 2020).…”

Convolutional neural network and its pretrained models for image classification and object detection: A survey

“…It can learn both in supervised and/or unsupervised mode. The classification problems are examples of supervised learning, while pattern analysis problems are examples of unsupervised learning 5 …”