Название: Computational Techniques in NeuroscienceАвтор: Kamal Malik, Harsh Sadawarti, Moolchand Sharma, Umesh Gupta
Издательство: CRC Press
Год: 2024
Страниц: 243
Язык: английский
Формат: pdf (true)
Размер: 13.6 MB
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.
Neural modeling is a mathematical or computer methodology that utilizes a neural network, an Artificial Intelligence (AI) technology that trains computers to interpret data in a manner similar to that of the human brain. Deep Learning is a Machine Learning approach that engages linked nodes or neurons in a hetero-structure similar to the human brain. Precise neural models make certain assumptions according to the available explicit data, and the consequences of these suppositions are quantified. Recent advances in neural computation reflect multidisciplinary research in theory, statistics in neuroscience, modeling computation, design, and construction of neurally inspired information processing systems. Hence, this sector attracts psychologists, neuroscientists, physicists, computer scientists, and AI investigators functioning on neural systems underlying perception, cognition, emotion, and behavior and artificial neural systems that have similar capabilities. Thus, advanced experimental technologies being developed by brain initiatives will fabricate large, complex data sets and meticulous statistical analysis and theoretical insight for a better understanding of these data mean sets.
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.
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