About the Book

Spanning neurons, plasticity,
consciousness, and silicon

The Brain spans the full arc from biological brains to engineered systems. It begins with the anatomy and physiology of the human brain, traces the developmental program that wires 86 billion neurons, formalizes the plasticity rules that enable learning, and examines theories of consciousness. The second half turns outward: connectome-to-function mapping, whole-brain emulation, biological computing with living neurons, brain–computer interfaces, and the specialization of silicon hardware from CPUs through GPUs to model-specific ASICs. Written for researchers, students, and practitioners working at the intersection of neuroscience and artificial intelligence.

Table of Contents

10 Chapters in Two Parts

1. 01
   Introduction

   Scope and themes; the organization of the brain sciences; the computational lens on neuroscience; how the book is structured

2. 02
   The Human Brain

   Anatomy and cells; action potentials and synaptic transmission; neurotransmitters and neuromodulators; cortical circuits; cognition, memory, and sensory processing; neural decoding; neuroethics

3. 03
   Developmental Intelligence

   The compressed developmental program; morphogen gradients and axon guidance; activity before experience; glial sculpting; critical periods; from local circuits to large-scale connectomes; parallels with AI

4. 04
   How Brains Learn

   Synaptic plasticity (Hebbian, LTP/LTD, STDP); dendritic computation and compartmentalized plasticity; three-factor learning rules; structural and homeostatic plasticity; sleep, replay, and memory consolidation; metaplasticity

5. 05
   Consciousness

   The self-scanning hypothesis; Global Neuronal Workspace; Integrated Information Theory; adversarial testing (COGITATE); the Causal Identity Theory; consciousness across species; theory-derived indicators for machine consciousness; AI safety

6. Part II — Translating Biology into Engineered Systems
7. 06
   From Connectome to Function

   Brain-wide maps of decision and action; structure–function connectomics at mammalian scale (MICrONS); from dataset to digital twin; neural foundation models; implications for emulation and AI

8. 07
   Brain Emulation

   Connectome acquisition and representation; alternative imaging modalities; neural dynamics models; the connectome-constrained simulation problem; embodiment and sensorimotor coupling; scaling laws; from flies to humans

9. 08
   Biological Computing

   From stem cells to computing substrates; closed-loop learning via active inference; reservoir computing with brain organoids; critical dynamics; scaling laws and energy efficiency

10. 09
    Brain–Computer Interfaces

    The signal hierarchy; electrode technologies; decoding from Kalman filters to deep networks; speech neuroprosthetics; bidirectional interfaces; biocompatibility; information-theoretic bandwidth limits

11. 10
    Silicon Intelligence

    The memory wall and inference bottleneck; the specialization spectrum; GPUs; AI accelerators; transformer-specific ASICs; model-specific silicon; the biological analogy; inference economics