Biology subjects

Behrens, T. E. J.

Publications and source records attributed to Behrens, T. E. J..

At least 19 recordsLinked to original sources

bioRxiv2026

Structured and flexible representations in medial-frontal cortex support goal-directed navigation

Humans and animals plan actions to achieve goals in worlds that are complex and continually changing. While planning is critically dependent on the prefrontal cortex in humans, little is known about its cellular underpinnings. Mechanistic understanding has been limited by a scarcity of controlled animal experiments in which subjects must flexibly plan novel behaviours on every trial. Here we characterise the neural representations and dynamics of mouse medial frontal cortex (mFC) during flexible navigation in structured environments. We trained mice to navigate complex mazes, to goals that changed location on every trial. Optogenetic silencing established that mFC was necessary for efficient navigation. mFC activity was dominated by two factorised components: (i) a structured representation of subjects position within the maze that formed an efficient code for behavioural trajectories, and (ii) a flexible representation of the shortest path-distance to the current goal. Both representations oscillated within local field potential (LFP) theta cycles, processing from further to closer to the goal at a systematic offset. These data suggest a computation in which mFC evaluates possible futures by their distance-to-goal to update a structured behavioural policy.

neuroscience↗

bioRxiv2025

A mechanistic theory of planning in prefrontal cortex

Planning is critical for adaptive behaviour in a changing world, because it lets us anticipate the future and adjust our actions accordingly. While prefrontal cortex is crucial for this process, it remains unknown how planning is implemented in neural circuits. Prefrontal representations were recently discovered in simpler sequence memory tasks, where different populations of neurons represent different future time points. We demonstrate that combining such representations with the ubiquitous principle of neural attractor dynamics allows circuits to solve much richer problems including planning. This is achieved by embedding the environment structure directly in synaptic connections to implement an attractor network that infers desirable futures. The resulting 'spacetime attractor' excels at planning in challenging tasks known to depend on prefrontal cortex. Recurrent neural networks trained by gradient descent on such tasks learn a solution that precisely recapitulates the spacetime attractor - in representation, in dynamics, and in connectivity. Analyses of networks trained across different environment structures reveal a generalisation mechanism that rapidly reconfigures the world model used for planning, without the need for synaptic plasticity. The spacetime attractor is a testable mechanistic theory of planning. If true, it would provide a path towards detailed mechanistic understanding of how prefrontal cortex structures adaptive behaviour.

neuroscience↗

bioRxiv2025

Neural signatures of model-based and model-free reinforcement learning across prefrontal cortex and striatum

Animals integrate knowledge about how the state of the environment evolves to choose actions that maximise reward. Such goal-directed behaviour - or model-based (MB) reinforcement learning (RL) - can flexibly adapt choice to changes, being thus distinct from simpler habitual - or model-free (MF) RL - strategies. Previous inactivation and neuroimaging work implicates prefrontal cortex (PFC) and the caudate striatal region in MB-RL; however, details are scarce about its implementation at the single-neuron level. Here, we recorded from two PFC regions - the dorsal anterior cingulate cortex (ACC) and dorsolateral PFC (DLPFC), and two striatal regions, caudate and putamen - while two rhesus macaques performed a sequential decision-making (two-step) task in which MB-RL involves knowledge about the statistics of reward and state transitions. All four regions, but particularly the ACC, encoded the rewards received and tracked the probabilistic state transitions that occurred. However, ACC (and to a lesser extent caudate) encoded the key variables of the task - namely the interaction between reward, transition and choice - which underlies MB decision-making. ACC and caudate neurons also encoded MB-derived estimates of choice values. Moreover, caudate value estimates of the choice options flipped when a rare transition occurred, demonstrating value update based on structural knowledge of the task. The striatal regions were unique (relative to PFC) in encoding the current and previous rewards with opposing polarities, reminiscent of dopaminergic neurons, and indicative of a MF prediction error. Our findings provide a deeper understanding of selective and temporally dissociable neural mechanisms underlying goal-directed behaviour.

neuroscience↗

bioRxiv2024

A hierarchical coordinate system for sequence memory in human entorhinal cortex.

The entorhinal cortex (EC) supports a coordinate system for spatial memories, organised in a hierarchy along the EC dorso-ventral axis. Recent theories suggest that a similar coordinate system could scaffold non-spatial memories. Here we show that an abstract hierarchical coordinate system supports arbitrary sequence memories in the human medial temporal lobe (MTL). In single-unit recordings from MTL, we find abstract, coordinate-like coding in a simple sequential memory task. In fMRI we find that abstract coordinate representations are arranged hierarchically along the entorhinal cortex, mirroring the anatomical gradient of grid cells in the rodent EC but now for non-spatial sequences. We replicate this finding in an independent cohort of participants. These data suggest that memories are scaffolded on a hierarchical coordinate system aligned to preserved anatomy across domains and species.

neuroscience↗

bioRxiv2023

On prefrontal working memory and hippocampal episodic memory: Unifying memories stored in weights and activation slots

Remembering events in the past is crucial to intelligent behaviour. Flexible memory retrieval, beyond simple recall, requires a cognitive map, or model of how sensations, actions, and latent environmental or task states are all related to one another. Two key brain systems are implicated in this process: the hippocampal episodic memory (EM) system and the prefrontal working memory (WM) system. While an understanding of the hippocampal system, from computation to algorithm and representation, is emerging, less is understood about how the prefrontal WM system can give rise to flexible computations beyond simple memory retrieval, and even less is understood about how the two systems relate to each other. Here we develop a mathematical theory relating the algorithms and representations of EM and WM by unveiling a duality between storing memories in synapses versus neural activity. In doing so, we develop a formal theory of the algorithms and representations of prefrontal WM in terms of structured, and controllable, neural subspaces (termed activity slots) that together can represent a dynamic cognitive map without any need for synaptic plasticity. By building models using this formalism, we elucidate the differences, similarities, and trade-offs between the hippocampal and prefrontal algorithms. Lastly, we show that several prefrontal representations in tasks ranging from list learning to cue dependent recall are unified as controllable activity slots. Our results unify frontal and temporal representations of memory, and offer a new basis for understanding dynamic prefrontal representations of WM.

neuroscience↗

bioRxiv2023

Flexible and abstract neural representations of structural knowledge

Humans ability for generalisation is outstanding. It is flexible enough to identify cases where knowledge from prior tasks is relevant, even when many features of the current task are different, such as the sensory stimuli or the size of the task state space. We have previously shown that in abstract tasks, humans can generalise knowledge in cases where the only cross-task shared feature is the statistical rules that govern the tasks state-state relationships. Here, we hypothesized that this capacity is associated with generalisable representations in the entorhinal cortex (EC). This hypothesis was based on the ECs generalisable representations in spatial tasks and recent discoveries about its role in the representation of abstract tasks. We first develop an analysis method capable of testing for such representations in fMRI data, explain why other common methods would have failed for our task, and validate our method through a combination of electrophysiological data analysis, simulations and fMRI sanity checks. We then show with fMRI that EC representations generalise across complex non-spatial tasks that share a hexagonal grid structural form but differ in their size and sensory stimuli, i.e. their only shared feature is the rules governing their statistical structure. There was no clear evidence for such generalisation in EC for non-spatial tasks with clustered, as opposed to planar, structure.

neuroscience↗

bioRxiv2023

Constructing future behaviour in the hippocampal formation through composition and replay

Hippocampus is critical for memory, imagination and constructive reasoning. However, recent models have suggested that its neuronal responses can be well explained by graphs, or state-spaces, that model the transitions between experiences. Here we use simulations and hippocampal recordings to reconcile these views. We show that if state-spaces are constructed compositionally from existing building blocks, or primitives, hippocampal responses can be interpreted as compositional memories, binding these primitives together. Critically, this enables agents to behave optimally in novel environments with no new learning, inferring behaviour directly from the composition. We predict a role for hippocampal replay in building and consolidating these compositional memories. Importantly, due to their compositional nature, replay can construct states the agent has never experienced - effectively building memories of the future. We test these predictions in two datasets by showing that replay events from newly discovered landmarks induce and strengthen new remote firing fields. These memories, built in replay, are compositional. When the landmark is moved, replay builds a new firing field at the same vector to the new location. Together, these findings provide a framework for reasoning about compositional memories, and demonstrate that such memories are formed in hippocampal replay.

neuroscience↗

bioRxiv2022

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

Although we perceive the world in a continuous manner, our experience is partitioned into discrete events. However, to make sense of these events, they must be stitched together into an overarching narrative - a model of unfolding events. It has been proposed that such a stitching process happens in offline neural reactivations when rodents build models of spatial environments. Here we show that, whilst understanding a natural narrative, humans reactivate neural representations of past events. Similar to offline replay, these reactivations occur in hippocampus and default mode network, where reactivations are selective to relevant past events. However, these reactivations occur, not during prolonged offline periods, but at the boundaries between ongoing narrative events. These results, replicated across two datasets, suggest reactivations as a candidate mechanism for binding temporally distant information into a coherent understanding of ongoing experience.

neuroscience↗

bioRxiv2021

Covert valuation for information sampling and choice

We use our eyes to assess the value of objects around us and carefully fixate options that we are about to choose. Neurons in the prefrontal cortex reliably encode the value of fixated options, which is essential for decision making. Yet as a decision unfolds, it remains unclear how prefrontal regions determine which option should be fixated next. Here we show that anterior cingulate cortex (ACC) encodes the value of options in the periphery to guide subsequent fixations during economic choice. In an economic decision-making task involving four simultaneously presented cues, we found rhesus macaques evaluated cues using their peripheral vision. This served two distinct purposes: subjects were more likely to fixate valuable peripheral cues, and more likely to choose valuable options whose cues were never even fixated. ACC, orbitofrontal cortex, dorsolateral prefrontal cortex, and ventromedial prefrontal cortex neurons all encoded cue value post-fixation. ACC was unique, however, in also encoding the value of cues before fixation and even cues that were never fixated. This pre-saccadic value encoding by ACC predicted which cue was next fixated during the decision process. ACC therefore conducts simultaneous processing of peripheral information to guide information sampling and choice during decision making.

neuroscience↗

bioRxiv2021

Distributional reinforcement learning in prefrontal cortex

Prefrontal cortex is crucial for learning and decision-making. Classic reinforcement learning (RL) theories centre on learning the expectation of potential rewarding outcomes and explain a wealth of neural data in prefrontal cortex. Distributional RL, on the other hand, learns the full distribution of rewarding outcomes and better explains dopamine responses. Here we show distributional RL also better explains prefrontal cortical responses, suggesting it is a ubiquitous mechanism for reward-guided learning.

neuroscience↗

bioRxiv2021

Complementary task representationsin hippocampus and prefrontal cortexfor generalising the structure of problems

Few situations in life are completely novel. We effortlessly generalise prior knowledge to solve novel problems, abstracting common structure and mapping it onto new sensorimotor specifics. Here we trained mice on a series of reversal learning problems that shared the same structure but had different physical implementations. Performance improved across problems, demonstrating transfer of knowledge. Neurons in medial prefrontal cortex (mPFC) maintained similar representations across multiple problems, despite their different sensorimotor correlates, whereas hippocampal (dCA1) representations were more strongly influenced by the specifics of each problem. Critically, this was true both for representations of the events that comprised each trial, and those that integrated choices and outcomes over multiple trials to guide subjects decisions. These data suggest that PFC and hippocampus play complementary roles in generalisation of knowledge, with the former abstracting the common structure among related problems, and the latter mapping this structure onto the specifics of the current situation.

neuroscience↗

bioRxiv2020

Replay bursts coincide with activation of the default mode and parietal alpha network

Our brains at rest spontaneously replay recently acquired information, but how this process is orchestrated to avoid interference with ongoing cognition is an open question. We investigated whether replay coincided with spontaneous patterns of whole brain activity. We found, in two separate datasets, that replay sequences were packaged into transient bursts occurring selectively during activation of the default mode network (DMN) and parietal alpha network. These networks were characterized by widespread synchronized oscillations coupled to increases in ripple band power, mechanisms that coordinate information flow between disparate cortical areas. Our data show a tight correspondence between two widely studied phenomena of neural physiology and suggest the DMN may coordinate replay bursts in a manner that minimizes interference with ongoing cognition.

neuroscience↗

bioRxiv2020

Measuring Sequences of Representations with Temporally Delayed Linear Modelling

There are rich structures in off-task neural activity. For example, task related neural codes are thought to be reactivated in a systematic way during rest. This reactivation is hypothesised to reflect a fundamental computation that supports a variety of cognitive functions. Here, we introduce an analysis toolkit (TDLM) for analysing this activity. TDLM combines nonlinear classification and linear temporal modelling to testing for statistical regularities in sequences of neural representations. It is developed using non-invasive neuroimaging data and is designed to take care of confounds and maximize sequence detection ability. The method can be extended to rodent electrophysiological recordings. We outline how TDLM can successfully reveal human replay during rest, based upon non-invasive magnetoencephalography (MEG) measurements, with strong parallels to rodent hippocampal replay. TDLM can therefore advance our understanding of sequential computation and promote a richer convergence between animal and human neuroscience research.

neuroscience↗

bioRxiv2020

Projections of non-invasive human recordings into state space show unfolding of spontaneous and over-trained choice

Choices rely on a transformation of sensory inputs into motor responses. Using invasive single neuron recordings, the evolution of a choice process has been tracked by projecting population neural responses into state spaces. Here we develop an approach that allows us to recover state space trajectories on a millisecond timescale in non-invasive human recordings. We selectively suppress activity related to relevant and irrelevant sensory inputs and response direction in magnetoencephalography data acquired during context-dependent choices. Recordings from premotor cortex show a smooth progression from sensory input encoding to response encoding. In contrast to previous macaque recordings, information related to choice-irrelevant features is represented more weakly than choice-relevant sensory information. To test whether this mechanistic difference between species is caused by extensive overtraining common in non-human primate studies, we trained humans on >20,000 trials of the task. Choice-irrelevant features were still weaker than relevant features in premotor cortex after overtraining.

neuroscience↗

bioRxiv2019

Transferring structural knowledge across cognitive maps in humans and models

Relations between task elements often follow hidden underlying structural forms such as periodicities or hierarchies, whose inferences fosters performance. However, transferring structural knowledge to novel environments requires flexible representations that are generalizable over particularities of the current environment, such as its stimuli and size. We suggest that humans represent structural forms as abstract basis sets and that in novel tasks, the structural form is inferred and the relevant basis set is transferred. Using a computational model, we show that such representation allows inference of the underlying structural form, important task states, effective behavioural policies and the existence of unobserved state-trajectories. In two experiments, participants learned three abstract graphs during two successive days. We tested how structural knowledge acquired on Day-1 affected Day-2 performance. In line with our model, participants who had a correct structural prior were able to infer the existence of unobserved state-trajectories and appropriate behavioural policies.

neuroscience↗

bioRxiv2019

Combined model-free and model-sensitive reinforcement learning in non-human primates

Contemporary reinforcement learning (RL) theory suggests that potential choices can be evaluated by strategies that may or may not be sensitive to the computational structure of tasks. A paradigmatic model-free (MF) strategy simply repeats actions that have been rewarded in the past; by contrast, model-sensitive (MS) strategies exploit richer information associated with knowledge of task dynamics. MF and MS strategies should typically be combined, because they have complementary statistical and computational strengths; however, this tradeoff between MF/MS RL has mostly only been demonstrated in humans, often with only modest numbers of trials. We trained rhesus monkeys to perform a two-stage decision task designed to elicit and discriminate the use of MF and MS methods. A descriptive analysis of choice behaviour revealed directly that the structure of the task (of MS importance) and the reward history (of MF and MS importance) significantly influenced both choice and response vigour. A detailed, trial-by-trial computational analysis confirmed that choices were made according to a combination of strategies, with a dominant influence of a particular form of model sensitivity that persisted over weeks of testing. The residuals from this model necessitated development of a new combined RL model which incorporates a particular credit assignment weighting procedure. Finally, response vigor exhibited a subtly different collection of MF and MS influences. These results provide new illumination onto RL behavioural processes in non-human primates.

neuroscience↗

bioRxiv2019

Entorhinal and ventromedial prefrontal cortices abstract and generalise the structure of reinforcement learning problems

Knowledge of the structure of a problem, such as relationships between stimuli, enables rapid learning and flexible inference. Humans and other animals can abstract this structural knowledge and generalise it to solve new problems. For example, in spatial reasoning, shortest-path inferences are immediate in new environments. Spatial structural transfer is mediated by grid cells in entorhinal and (in humans) medial prefrontal cortices, which maintain their structure across different environments. Here, using fMRI, we show that entorhinal and ventromedial prefrontal cortex (vmPFC) representations perform a much broader role in generalising the structure of problems. We introduce a task-remapping paradigm, where subjects solve multiple reinforcement learning (RL) problems differing in structural or sensory properties. We show that, as with space, entorhinal representations are preserved across different RL problems only if task structure is preserved. In vmPFC, representations of standard RL signals such as prediction error also vary as a function of task structure.

neuroscience↗

bioRxiv2018

Intuitive planning: global navigation through cognitive maps based on grid-like codes

It is proposed that a cognitive map encoding the relationships between objects supports the ability to flexibly navigate the world. Place cells and grid cells provide evidence for such a map in a spatial context. Emerging evidence suggests analogous cells code for non-spatial information. Further, it has been shown that grid cells resemble the eigenvectors of the relationship between place cells and can be learnt from local inputs. Here we show that these locally-learnt eigenvectors contain not only local information but also global knowledge that can provide both distributions over future states as well as a global distance measure encoding approximate distances between every object in the world. By simply changing the weights in the grid cell population, it is possible to switch between computing these different measures. We demonstrate a simple algorithm can use these measures to globally navigate arbitrary topologies without searching more than one step ahead. We refer to this as intuitive planning.

neuroscience↗