Biology subjects

Muller, T. H.

Publications and source records attributed to Muller, T. H..

8 recordsLinked to original sources

bioRxiv2026

Rapid value learning reveals generalized and context-dependent codes in frontal cortex

Studying how the brain represents value spans distinct methods and training histories, from neuroimaging in task-naive humans to single-neuron recordings in extensively trained non-human primates. Similar findings across fields have encouraged the untested assumption that rapidly emerging and overtrained value representations are equivalent. Here we recorded single-neuron activity in anterior cingulate cortex (ACC) and orbitofrontal cortex (OFC) as macaques learned novel cue values and chose between novel and overtrained cues. Value responses emerged within 4-7 cue presentations, matching behavioural adaptation. Yet ACC and OFC used distinct codes. ACC encoded value in a common format that generalised across training history. OFC coding was more context-dependent, with distinct subpopulations recruited by choice experience. Choice novelty was also encoded independently of value before chosen value signals emerged. During learning, ACC responses shifted from overtrained secondary reinforcers to newly predictive cues. These findings establish the necessary (rapid acquisition) and sufficient (generalised format) conditions for comparing value representations across methods, species, and training histories.

neuroscience↗

bioRxiv2025

Reasoning with programs in replay

Reasoning flexibly composes known elements to solve novel problems. Recent theories suggest the brain uses the axis of time to compose elements for reasoning. In this view, elements are packaged into fast neural sequences, with each sequence exploring the implications of a different composition. Using magnetoencephalography, we tested this idea while participants mentally executed programs. Each program contained a set of steps linked to operations, the implications of which had to be computed. We found that behavioral performance scaled with program complexity, and by the end of execution, inferred program solutions were represented in prefrontal and parietal cortices. We identified a possible mechanism by which these solutions were computed. During reasoning, representations of steps in the program reactivated in fast sequences, consistent with sampling candidate partial solutions. Further evidence suggested these reactivations were accompanied by representations of their operations, and were followed by neural patterns reflecting their computed implications. Together, these results suggest replaying sequences supports program execution and reveal a highly organized temporal microarchitecture of reasoning.

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.

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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.

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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.

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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.

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bioRxiv2018

What is a cognitive map? Organising knowledge for flexible behaviour

It is proposed that a cognitive map encoding the relationships between entities in the world supports flexible behaviour, but the majority of the neural evidence for such a system comes from studies of spatial navigation. Recent work describing neuronal parallels between spatial and non-spatial behaviours has rekindled the notion of a systematic organisation of knowledge across multiple domains. We review experimental evidence and theoretical frameworks that point to principles unifying these apparently disparate functions. These principles describe how to learn and use abstract, generalisable knowledge and suggest map-like representations observed in a spatial context may be an instance of general coding mechanisms capable of organising knowledge of all kinds. We highlight how artificial agents endowed with such principles exhibit flexible behaviour and learn map-like representations observed in the brain. Finally, we speculate on how these principles may offer insight into the extreme generalisations, abstractions and inferences that characterise human cognition.

neuroscience↗