Do the equations behind working memory, decision making, and cortical dynamics actually make sense to you, or do they stay locked inside journal notation you never fully unpack?

Graduate students and clinicians alike hit the same wall in theoretical neuroscience: the papers assume you already know the mean-field derivation, the drift-diffusion boundary conditions, or why a Hopfield network stores memories as attractors. Textbooks either stay too qualitative to build real intuition or bury the mechanism in dense proof-style notation with no clinical or experimental anchor.

This 2026 edition builds that intuition chapter by chapter, from single-neuron biophysics through the Hodgkin-Huxley equations, synaptic plasticity, and dendritic cable theory, up through network-level phenomena: balanced excitation-inhibition dynamics, oscillations, attractor networks, and working-memory bump models. It carries the same rigor into systems-level cognition, covering drift-diffusion decision models, reinforcement learning and dopaminergic reward prediction, the Bayesian brain and predictive coding, and computational models of vision, motor control, and hippocampal navigation. Two full chapters translate this machinery directly into computational psychiatry, modeling circuit-level dysfunction in schizophrenia, mood disorders, anxiety, and addiction.

Each of the 25 chapters includes:

Worked mathematical derivations explained in plain clinical language, not just cited
Case vignettes and clinical decision points connecting theory to real diagnostic reasoning
Two custom clinical illustrations per chapter (50 total) with precisely labeled anatomy and circuit diagrams
A quick-reference sidebar and differential comparison table for fast review

A companion video course of more than 100 curated lecture and tutorial videos, accessed through a QR code printed in the book, extends every chapter beyond the page.

Stop re-deriving the same equations from scattered PDFs. Start building a working model of the brain you can actually reason with.