Learning and Computation in Brains, Minds, and Machines
Nima Dehghani
This repository is the computational companion to NeuroAI: Theoretical Foundations of Dynamics, Learning and Computation in Brains, Minds, and Machines by Nima Dehghani. It contains the chapter-by-chapter code used for computational reconstructions, numerical experiments, analyses, and figures.
The manuscript files are not hosted here. For each released chapter, the arXiv badge opens its abstract page and the PDF badge opens the chapter PDF directly.
Author
Nima Dehghani
McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
The NSF AI Institute for Artificial Intelligence and Fundamental Interactions (IAIFI), Cambridge, Massachusetts, USA
☑ released ☐ forthcoming
Almost all chapters are written. It will take me some time to polish it to a clean document. But I promise to release them in a fairly fast pace.
- ☐ Chapter 1: NeuroAI—Subject, Method, and Boundary
- ☑ Chapter 2: Neural Logic, Invariance, and the Retina—McCulloch and Pitts
- ☐ Chapter 3: Learning a Decision Boundary—Rosenblatt and the Perceptron
- ☐ Chapter 4: Amari—Neural Fields, Associative Dynamics, and the Geometry of Learning
- ☑ Chapter 5: Memory as an Energy Landscape—Hopfield
- ☐ Chapter 6: Capacity and Disorder—Statistical Mechanics of Associative Memory
- ☐ Chapter 7: Chaos in Random Neural Networks—Dynamic Mean-Field Theory
- ☐ Chapter 8: Abbott—Dynamic Synapses, STDP, Sequence Learning, and Multiscale Memory
- ☐ Chapter 9: Abbott—Random Networks, FORCE Learning, and Population Computation
- ☐ Chapter 10: Hinton—Boltzmann Machines, Backpropagation, and Distributed Representation
- ☐ Chapter 11: Sejnowski—NETtalk, Infomax, and Independent Components
- ☐ Chapter 12: Poggio—From Ill-Posed Vision to Compositional Intelligence
- ☐ Chapter 13: Efficient, Sparse, and Predictive Coding—From Redundancy Reduction to Hierarchical Generative Inference
- ☐ Chapter 14: When Does Matter Compute?—Physical Realization, Coarse-Graining, and Autonomous Closure
- ☐ Chapter 15: What Does Recurrence Buy?—Temporal Irreducibility, Adaptive Reservoirs, and Compositional Motifs
- ☐ Chapter 16: Where Does Biological Computation Live?—Scale, Cellular Information, and Effective Neural Theories
Each released chapter has its own directory containing the corresponding code, generated data, and figure-reproduction material. Chapter-specific instructions, dependencies, and expected outputs are documented within that directory.
For book updates and additional material, visit the official NeuroAI book website.