Computational Neuroscience Explained

Computational Neuroscience Explained is a plain-English engineering and science guide for AI and ML engineers, neuroscientists in training, students, and technically curious readers who want to understand how brains actually compute and how that computation relates to modern artificial intelligence. Set inside the fictional Cortex Lab, the book uses a sustained narrative to carry readers from the biophysics of a single neuron all the way to whole-brain dynamics and the open questions at the frontier of the field.The opening chapters establish the foundational machinery: the membrane potential, ion channels and the Hodgkin-Huxley equations, the squid giant axon experiments, the McCulloch-Pitts neuron, and how a single biological neuron actually integrates and signals. From there the book turns to synapses, plasticity, and learning rules-LTP and LTD, spike-timing-dependent plasticity, three-factor rules, and the biological story behind the abstract learning rules that drive deep learning.The middle chapters cover neural coding (rate codes, temporal codes, and population codes), small circuits and network motifs, recurrent and attractor dynamics, oscillations and synchronization, and the large-scale architecture of the cortex. The book then turns to systems neuroscience: sensory processing in the visual and auditory systems, motor control, decision-making, memory, and the hippocampus.The later chapters cover the deep connections between computational neuroscience and AI: what artificial neural networks borrowed from biology, what they did not, what modern deep learning has taught us about the brain, predictive coding, free-energy and Bayesian brain theories, and the role of neuromorphic hardware and spiking networks. The final chapters cover the frontier: connectomics, large-scale brain simulation, brain-computer interfaces, neurodegenerative disease, and the open questions that will define the next decade of the field.Every chapter closes with a takeaway, so readers leave with both rigorous understanding of how biological circuits compute and a clearer view of what neuroscience and AI can actually learn from each other.