Computational Techniques in Neuroscience

Harsh Sadawarti editor Umesh Gupta editor Moolchand Sharma editor Prayag Tiwari editor Kamal Malik editor

Format:Hardback

Publisher:Taylor & Francis Ltd

Published:14th Nov '23

Currently unavailable, and unfortunately no date known when it will be back

Computational Techniques in Neuroscience cover

The text discusses the techniques of deep learning and machine learning in the field of neuroscience, engineering approaches to study the brain structure and dynamics, convolutional networks for fast, energy-efficient neuromorphic computing, and reinforcement learning in feedback control. It showcases case studies in neural data analysis.

Features:

  • Focuses on neuron modeling, development, and direction of neural circuits to explain perception, behavior, and biologically inspired intelligent agents for decision making
  • Showcases important aspects such as human behavior prediction using smart technologies and understanding the modeling of nervous systems
  • Discusses nature-inspired algorithms such as swarm intelligence, ant colony optimization, and multi-agent systems
  • Presents information-theoretic, control-theoretic, and decision-theoretic approaches in neuroscience.
    Includes case studies in functional magnetic resonance imaging (fMRI) and neural data analysis

This reference text addresses different applications of computational neuro-sciences using artificial intelligence, deep learning, and other machine learning techniques to fine-tune the models, thereby solving the real-life problems prominently. It will further discuss important topics such as neural rehabili-tation, brain-computer interfacing, neural control, neural system analysis, and neurobiologically inspired self-monitoring systems. It will serve as an ideal reference text for graduate students and academic researchers in the fields of electrical engineering, electronics and communication engineering, computer engineering, information technology, and biomedical engineering.

ISBN: 9781032461281

Dimensions: unknown

Weight: 607g

224 pages