Biology subjects

Whittington, J. C.

Publications and source records attributed to Whittington, J. C..

3 recordsLinked to original sources

bioRxiv2026

A Mechanistic Theory Unifying Value and Schema in the Prefrontal Cortex

Animals exhibit behavioural flexibility by making reward-optimal decisions in unfamiliar situations. Computing value from minimal reward feedback requires leveraging known structure in the world. The Prefrontal Cortex (PFC) plays a pivotal role in both value computation and learning world structure, however mechanistic explanations for each---within the frameworks of Reinforcement Learning and Cognitive Maps---are largely distinct. We develop a mechanistic model of PFC that unifies value and structure through behavioural flexibility. In this model, value acts as a control signal that gates structured working memory representations of the current context in order to ultimately control behaviour. This is a significant departure from the conventional perspective of value, in which it is used for action readout. Critically, this model generalises to novel contexts without the need for additional synaptic plasticity. We demonstrate that recurrent neural networks (RNNs) meta-trained with either reinforcement learning or supervised learning recapitulate this exact model on various value-based tasks. These results not only provide a parsimonious explanation of both the value and schema frameworks of PFC function, but also propose a new mechanistic interpretation of the role of PFC in flexible decision making.

neuroscience↗

bioRxiv2023

A Cellular Basis for Mapping Behavioural Structure

To flexibly adapt to new situations, our brains must understand the regularities in the world, but also in our own patterns of behaviour. A wealth of findings is beginning to reveal the algorithms we use to map the outside world1-6. In contrast, the biological algorithms that map the complex structured behaviours we compose to reach our goals remain enigmatic. Here we reveal a neuronal implementation of an algorithm for mapping abstract behavioural structure and transferring it to new scenarios. We trained mice on many tasks which shared a common structure organising a sequence of goals, but differed in the specific goal locations. Animals discovered the underlying task structure, enabling zero-shot inferences on the first trial of new tasks. The activity of most neurons in the medial Frontal cortex tiled progress-to-goal, akin to how place cells map physical space. These "goal-progress cells" generalised, stretching and compressing their tiling to accommodate different goal distances. In contrast, progress along the overall sequence of goals was not encoded explicitly. Instead a subset of goal-progress cells was further tuned such that individual neurons fired with a fixed task-lag from a particular behavioural step. Together these cells implemented an algorithm that instantaneously encoded the entire sequence of future behavioural steps, and whose dynamics automatically retrieved the appropriate action at each step. These dynamics mirrored the abstract task structure both on-task and during offline sleep. Our findings suggest that goal-progress cells in the medial frontal cortex may be elemental building blocks of schemata that can be sculpted to represent complex behavioural structures.

neuroscience↗

bioRxiv2019

The Tolman-Eichenbaum Machine: Unifying space and relational memory through generalisation in the hippocampal formation

The hippocampal-entorhinal system is important for spatial and relational memory tasks. We formally link these domains; provide a mechanistic understanding of the hippocampal role in generalisation; and offer unifying principles underlying many entorhinal and hippocampal cell-types. We propose medial entorhinal cells form a basis describing structural knowledge, and hippocampal cells link this basis with sensory representations. Adopting these principles, we introduce the Tolman-Eichenbaum machine (TEM). After learning, TEM entorhinal cells include grid, band, border and object-vector cells. Hippocampal cells include place and landmark cells, remapping between environments. Crucially, TEM also predicts empirically recorded representations in complex non-spatial tasks. TEM predicts hippocampal remapping is not random as previously believed. Rather structural knowledge is preserved across environments. We confirm this in simultaneously recorded place and grid cells.\n\nOne Sentence SummarySimple principles of representation and generalisation unify spatial and non-spatial accounts of hippocampus and explain many cell representations.

neuroscience↗