COMPUTATIONAL NEUROSCIENCE FOR BEGINNERS

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.