Unlocking the Power of Computational Neuroscience with Artificial Intelligence and Machine Learning

Written by Kamal Malik, this comprehensive reference text delves into the cutting-edge techniques of deep learning and machine learning in the field of neuroscience, exploring innovative engineering approaches to study the brain’s structure and dynamics.

Exploring the Frontiers of Neural Circuits and Biologically Inspired Intelligence

• Neuron modeling, development, and direction of neural circuits to explain perception, behavior, and decision making
• Human behavior prediction using smart technologies and understanding the modeling of nervous systems
• Nature-inspired algorithms such as swarm intelligence, ant colony optimization, and multi-agent systems

Information-Theoretic, Control-Theoretic, and Decision-Theoretic Approaches in Neuroscience

This text presents a detailed examination of various approaches, including:

• Convolutional networks for fast, energy-efficient neuromorphic computing
• Reinforcement learning in feedback control
• Functional magnetic resonance imaging (fMRI) and neural data analysis case studies

Real-World Applications of Computational Neuroscience

The book showcases the power of computational neurosciences in solving real-life problems, including:

• Neural rehabilitation
• Brain-computer interfacing
• Neural control and system analysis
• Neurobiologically inspired self-monitoring systems

This reference text is an ideal resource for graduate students and academic researchers in the fields of electrical engineering, electronics and communication engineering, computer engineering, information technology, and biomedical engineering.