How to build a brain from scratch

This advanced option course discusses the search for a general theory of learning and inference in biological brains. It draws upon diverse themes in the fields of psychology, neuroscience, machine learning and artificial intelligence research. We begin by posing broad questions. What are brains for, and what does it mean to ask how they “work”? Then, over a series of lectures, we discuss parallel computational approaches in machine learning/AI and psychology/neuroscience, including reinforcement learning, deep learning, and Bayesian methods. We contrast computational and representational approaches to understanding neuroscience data. We ask whether current approaches in machine learning are feasible and scaleable, and which methods – if any – resemble the computations observed in biological brains. We review how high-level cognitive functions – attention, episodic memory, concept formation, reasoning and executive control – are being instantiated in artificial agents, and how their implementation draws upon what we know about the mammalian brain. Finally, we contemplate the outlook for the future, and whether AI will be “solved” in the near future.

Lecture 1: Building and understanding brains.

Introduction; Recent advances in AI research; Biological and Artifical Brains; The computational approach; Definitions of intelligence; Good old-fashioned AI

Lecture 2: Model-free reinforcement learning

Why do we have a brain; Classical and operant conditioning; reinforcement learning and the Bellman equation; Temporal difference learning; Q-learning, eligibility traces, actor-critic methods

Lecture 3: Feedforward networks and object categorisation

Parametric models for object recognition; Critiques of pure representationalism; Perceptrons and sigmoid neurons; Depth: the multilayer perceptron; Challenges: optimisation, generalisation and overfitting

Lecture 4: Structuring information in space and time

Convnets and translational invariance; Convnets and the primate ventral stream; Limitations of feedforward deep networks; Hierarchies of temporal integration in the brain; Temporal integration in perceptual decision-making; Recurrent neural networks and the parietal cortex

Lecture 5: Computation and modular memory systems

Modular memory systems; working memory gating in the PFC; LSTMs; The differentiable neural computer; The problem of continual learning

Lecture 6: Complementary learning systems theory

Dual process memory models; the hippocampus as a parametric storage devide; experience-dependent replay and consolidation; the deep Q-network; knowledge partitioning and resource allocation

Lecture 7: Unsupervised and generative models

Unsupervised learning: knowing that a thing is a thing; Encoding models: Hebbian learning and sparse coding; Variational autoencoders; The Bayesian approach; Predictive coding

Lecture 8: Building a model of the world for planning and reasoning

Temporal abstraction in RL and the cingulate cortex; Multiple controllers for behaviour; Cognitive maps and the hippocampus; Hierarchical planning; Grid cells and conceptual knowledge

Reading list


Summerfield C., Saxe A., & Nelli, S. If deep learning is the answer, what is the question? Nat. Rev. Neurosci 22 55-67 (2020).

Marcus, G. The Next Decade in AI: Four Steps Towards Robust Artificial Intelligence. https://arxiv.org/abs/2002.06177

Hadsell, R., Rao, D., Rusu, A., Pascanu, R. Embracing change: Continual Learning in Deep Networks. Trends in Cognitive Sciences, 24, p1028-1040 (2020).

Shanahan M., Crosby M., Beyret B., Cheke, L. Artificial Intelligence and the Common Sense of Animals. Trends in Cognitive Sciences 24 p862-872 (2020).

Lindsay, G. W. Convolutional neural networks as a model of the visual system: past, present, and future. J. Cogn. Neurosci. https://doi.org/10.1162/jocn_a_01544 (2020).30.

Hasson, U., Nastase, S. A. & Goldstein, A. Direct fit to nature: an evolutionary perspective on biological and artificial neural networks. Neuron 105, 416–434 (2020).33.

Drummond N, Niv Y. Curr Biol. Model-based decision making and model-free learning. Aug 3;30(15):R860-R865 (2020)

Summerfield C, Luyckx F, Sheahan H. Prog Neurobiol. Structure learning and the posterior parietal cortex. Jan;184:101717 (2020).


Richards, B. A. et al. A deep learning framework for neuroscience. Nat. Neurosci.22, 1761–1770 (2019).34.

Sinz, F. H., Pitkow, X., Reimer, J., Bethge, M. & Tolias, A. S. Engineering a less artificial intelligence. Neuron 103, 967–979 (2019).23.

Kell, A. J. & McDermott, J. H. Deep neural network models of sensory systems: windows onto the role of task constraints. Curr. Opin. Neurobiol. 55, 121–132 (2019).25.

Cichy, R. M. & Kaiser, D. Deep neural networks as scientific models. Trends Cogn. Sci.23, 305–317 (2019).28.

Zador, A. M. A critique of pure learning and what artificial neural networks can learn from animal brains. Nat. Commun. 10, 3770 (2019).31.

Lillicrap, T. P. & Kording, K. P. What does it mean to understand a neural network? Preprint at arXiv https://arxiv.org/abs/1907.06374 (2019)


Behrens TEJ, Muller TH, Whittington JCR, Mark S, Baram AB, Stachenfeld KL, Kurth-Nelson Z. What Is a Cognitive Map? Organizing Knowledge for Flexible Behavior.  Neuron. Oct 24;100(2):490-509 (2018)


van Gerven M. Computational Foundations of Natural Intelligence.  Front Comput Neurosci. 11:112 (2017).

Bowers, J. S. Parallel distributed processing theory in the age of deep networks. Trends Cogn. Sci.21, 950–961 (2017).27.

Hassabis D, Kumaran D, Summerfield C, Botvinick M. Neuroscience-Inspired Artificial Intelligence.  Neuron. 19;95(2):245-258 (2017).

Lake, B. M., Ullman, T. D., Tenenbaum, J. B. & Gershman, S. J. Building machines that learn and think like people. Behav. Brain Sci. 40, e253 (2017).29.


Ullman, S., Assif, L., Fetaya, E. & Harari, D. Atoms of recognition in human and computer vision. Proc. Natl Acad. Sci. USA 11 3, 2744–2749 (2016).

Marblestone, A. H., Wayne, G. & Kording, K. P. Toward an integration of deep learning and neuroscience. Front. Comput. Neurosci.10, 1–61 (2016).24.

Yamins, D. L. K. & DiCarlo, J. J. Using goal- driven deep learning models to understand sensory cortex. Nat. Neurosci.19, 356–365 (2016).


Kriegeskorte, N. Deep neural networks: a new framework for modeling biological vision and brain information processing. Annu. Rev. Vis. Sci.1, 417–446 (2015).26.

Rogers, T. T. & Mcclelland, J. L. Parallel distributed processing at 25: further explorations in the microstructure of cognition. Cogn. Sci.38, 1024–1077 (2014).32.