Computational Neuroscience: The Science of Thought From Synapses to Supercomputers

The brain is not just an organ-it is a computational system, processing vast amounts of information through dynamic neural networks. How does the brain encode, store, and retrieve information? How do electrical and chemical signals translate into thought, perception, and decision-making?

Computational neuroscience bridges the gap between biology and mathematics, offering a quantitative framework to study neural activity, learning, and cognition. This textbook provides an in-depth exploration of the mathematical, theoretical, and algorithmic principles governing the brain's computational processes.

With a focus on rigorous analysis and real-world applications, Computational Neuroscience: The Science of Thought From Synapses to Supercomputers equips students, researchers, and professionals with the essential knowledge to understand and model brain function.

• The Neuron as a Computational Unit - Understand the biophysical principles of synaptic transmission and action potentials.
• Mathematical Models of Neurons - Explore Hodgkin-Huxley equations, integrate-and-fire models, and stochastic neuron models.
• Information Theory & Neural Encoding - Learn how the brain processes and represents sensory information.

• Network Dynamics & Functional Connectivity - Discover how neurons interact to form large-scale networks.
• Hebbian Learning & Synaptic Plasticity - Examine how experience and adaptation shape neural circuits.
• Bayesian Inference in Neural Computation - Apply probabilistic models to decision-making and perception.
• Spiking Neural Networks & Temporal Coding - Analyze the role of precise timing in neural information processing.

• Neural Mechanisms of Perception - Understand how the brain processes vision, sound, and multisensory integration.
• Computational Models of Memory - Study working memory, long-term memory storage, and recall mechanisms.
• Decision-Making & Reinforcement Learning - Explore how neural circuits optimize choices and actions.

• Deep Learning & Neural Networks - Examine how biological computation has influenced AI and machine learning.
• Neuromorphic Engineering & Brain-Computer Interfaces - Discover how brain-inspired hardware is shaping the future of computing.
• Emerging Trends in Neurotechnology - Investigate advancements in brain simulation, cognitive augmentation, and neuroprosthetics.