Neural computation of flexible behaviours

Behrens Lab

Banner image representing actions
Research Area

Human behaviour is flexible. Whilst we can act by simply repeating behaviours from the past, we typically use experiences that are only loosely related. We can even imagine the consequences of entirely novel choices. Our research focuses on the neural mechanisms that support this flexible goal-directed behaviour. In doing so, we build new bridges between human and animal neuroscience, between biological and artificial intelligence, and new methods for integrating across scales of neural activity. 

Research Topics

How does neural activity represent models of the world to facilitate flexible behaviour?

These internal models underlie human ability to understand the relationships between events and predict the consequences of actions. We know broadly which parts of the brain support these relational world-models, but we have limited understanding of the computations involved or how they are performed by neurons. 

How are these representations generalised to new scenarios to facilitate rapid learning and inference? 

This generalisation enables humans to make flexible decisions without direct experience. It is fundamental to human cognition. Patterns of cellular activity are abstracted from sensory experience and applied to new situations, but it is unclear how this abstract knowledge is represented or generalised. 

Structuring complex behaviour. 

How do we build rich plans about the future? The state of the art in neuroscience considers learning and planning over sequences of low-level actions, but humans create, represent and execute complex, heterarchical, plans. Our notion of a foreign holiday includes a plan to fly and rent a car.  These representational plans, and our ability to compose them into hierarchies, are central to our rich behaviour, but there is currently no mechanistic neuroscience in this domain. 

Building representations for flexible cognition.  

How do these representations arise? One critical factor is likely to be rest.  Rest is important for consolidating learning,  but critically also for new insights. There is currently no mechanistic theory of how this happens.  How can knowledge representations be reconfigured in rest to generate new insights and abstractions? How can different abstractions be combined?

Lab members

  • Tim Behrens
  • Diksha Gupta
  • Mathias Sable Meyer
  • Will Dorrell
  • Jo Warren
  • Chongyu (Xiao) Qin
  • Lauren Bennett
  • Avital Hahamy
  • James Whittington
  • Jacob Bakermans
  • Beatriz Godinho
  • Mohamady El Gaby
  • Anna Shpektor
  • Alon Baram
  • Peter Doohan
  • Svenja Kuchenoff
  • Jiali Zhang
  • Adam Harris
  • Arya Bhomick
  • Emma Muller-Seydlitz
  • Daniel Shani
Tim Behrens
Part-time Group Leader
Ryan Cini
Research Technician
Diksha Gupta
Senior Research Fellow
Kris Jensen
Senior Research Fellow
Karyna Mishchanchuk
Senior Research Fellow
Sandra Reinert
Senior Research Fellow
Mathias Sablé-Meyer
Senior Research Fellow
Joseph Warren
PhD Student
Selected publications

Generative replay underlies compositional inference in the hippocampal-prefrontal circuit

Philipp Schwartenbeck, Alon Baram, Yunzhe Liu, Shirley Mark, Timothy Muller, Raymond Dolan, Matthew Botvinick, Zeb Kurth-Nelson, Timothy Behrens
Published by:
Cell (Volume 186, Issue 22, 4885-4897.e14) (doi: 10.1016/j.cell.2023.09.004)
26 October 2023

The human brain reactivates context-specific past information at event boundaries of naturalistic experiences

Avital Hahamy, Haim Dubossarsky, Timothy EJ Behrens
Published by:
Nature Neuroscience (Vol 26, 1080-1089) (doi: 10.1038/s41593-023-01331-6)
29 May 2023

Constructing future behaviour in the hippocampal formation through composition and replay

Jacob JW Bakermans, Joseph Warren, James CR Whittington, Timothy EJ Behrens
Published by:
bioRxiv (doi: 10.1101/2023.04.07.536053)
07 April 2023

Actionable Neural Representations: Grid Cells from Minimal Constraints

William Dorrell, Peter E. Latham, Timothy E.J. Behrens, James C.R. Whittington
Published by:
arXiv (doi: 10.48550/arXiv.2209.15563)
28 February 2023

Disentanglement with biological constraints: A theory of functional cell types

James CR Whittington, Will Dorrell, Surya Ganguli, Timothy Behrens
Published by:
The Eleventh International Conference on Learning Representations
01 February 2023

Complementary task representations in hippocampus and prefrontal cortex for generalizing the structure of problems

Veronika Samborska, James L Butler, Mark E Walton, Timothy EJ Behrens, Thomas Akam
Published by:
Nature Neuroscience (Vol 25, 1314-1326) (doi: 10.1038/s41593-022-01149-8)
28 September 2022

How to build a cognitive map

James CR Whittington, David McCaffary, Jacob JW Bakermans, Timothy EJ Behrens
Published by:
Nature Neuroscience (Vol 25, 1257-1272) (doi: 10.1038/s41593-022-01153-y)
26 September 2022

The Tolman-Eichenbaum machine: unifying space and relational memory through generalization in the hippocampal formation

James CR Whittington, Timothy H Muller, Shirley Mark, Guifen Chen, Caswell Barry, Neil Burgess, Timothy EJ Behrens
Published by:
Cell (Vol 183, Issue 5, 1249-1263.e23) (doi: 10.1016/j.cell.2020.10.024)
25 November 2020

Human replay spontaneously reorganizes experience

Yunzhe Liu, Raymond J Dolan, Zeb Kurth-Nelson, Timothy EJ Behrens
Published by:
Cell (Vol 178, Issue 3, 640-652.e14) ( 10.1016/j.cell.2019.06.012)
25 July 2019

A map of abstract relational knowledge in the human hippocampal–entorhinal cortex

Mona M Garvert, Raymond J Dolan, Timothy EJ Behrens
Published by:
eLife (doi: 10.7554/eLife.17086)
27 April 2017

Organizing conceptual knowledge in humans with a gridlike code

Alexandra O Constantinescu, Jill X O’Reilly, Timothy EJ Behrens
Published by:
Science (Vol 352, Issue 6292, 1464-1468) (doi: 10.1126/science.aaf0941)
17 June 2016