Overview

Computational Neuroscience for Beginners: From Neurons to Networks with Python offers a clear and accessible introduction to the mathematical and computational principles that underlie brain function. Written in an engaging third-person narrative, the book guides readers from the biological foundations of neurons and synapses to the complexities of network connectivity, plasticity, and neural coding. Designed for students, self-learners, and early-stage researchers, the book balances biological accuracy with computational modeling, making it ideal for readers from diverse backgrounds-including neuroscience, psychology, computer science, engineering, and physics. With a focus on Python as a practical tool, the text lays the groundwork for hands-on simulations while remaining conceptually rich for those interested in theory. Key features include: A step-by-step journey from neurons to networks. Clear explanations of models such as Hodgkin-Huxley, integrate-and-fire, and spike-timing dependent plasticity. Detailed discussions of synaptic dynamics, network architecture, and emergent neural phenomena. Coverage of information processing and coding strategies (rate, temporal, population, and sparse coding). Conceptual bridges between biological neuroscience and artificial intelligence. By the end of the book, readers will not only understand how neurons and networks process information but also how to translate these insights into computational frameworks. This makes it a valuable resource for those who wish to explore the brain through the lens of computation and modeling.

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