Biology subjects

Kurth-Nelson, Z.

Publications and source records attributed to Kurth-Nelson, Z..

13 recordsLinked to original sources

bioRxiv2026

Functional reorganization of motor cortex connectivity during learning

Learning new tasks requires the brain to reshape the flow of neural activity, but how these changes arise from dynamic neural connectivity remains unclear. Here, we used two-photon photostimulation and calcium imaging to map learning-related changes in connectivity in layer 2/3 of mouse motor cortex, induced by learning of an optical brain-computer interface (BCI) task. Mice rapidly (within minutes) learned to change activity in a conditioned neuron to earn rewards. Activity changes were sparse; the conditioned neuron increased activity more than surrounding neurons. Mapping connectivity before and after learning revealed changes in motor cortex connectivity, enriched in neurons that were active before trial initiation, analogous to motor cortex populations that are active preceding movement. Motor cortex plasticity reroutes preparatory activity to neurons that are active later and control the conditioned neuron. Our findings show how rapid learning can be achieved through structured changes in motor cortex connectivity.

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↗

bioRxiv2024

A neural mechanism for compositional generalization of structure in humans

A human ability to adapt to the dynamics of novel environments relies on abstracting and generalizing from past experiences. Previous research has focused on how humans generalize from isolated sequential processes, yet we know little about mechanisms that enable adaptation to more complex dynamics, including those that govern much everyday experience. Here, using a novel sequence learning task based on graph factorization, coupled with simultaneous magnetoencephalography (MEG) recordings, we ask whether reuse of experiential "building blocks" enable inference and generalization. Behavioral results were consistent with participants decomposing task experience into subprocesses, abstracting their dynamical structure away from their sensory specifics and transferring these to a new task environment. Neurally, this transfer was associated with a learning-induced, condition-specific, increase in neural similarity among stimuli conditional on their structural role in abstract subprocesses that were shared across task phases. Consistent with this, a complementary dynamical role decoding analysis revealed enhanced neural decodability for stimuli sharing identical structural transition types across experiential contexts, specifically when prior experience included the relevant graph structure. Decoding strength for these role representations was positively related to behavioral success in subprocess knowledge transfer, a relationship that will require future independent replication. These findings provide neural evidence consistent with the idea that a structural scaffolding mechanism supports generalization of experience to new contexts.

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bioRxiv2024

Neural mechanisms of credit assignment for delayed outcomes during contingent learning

Adaptive behavior in complex environments critically relies on the ability to appropriately link specific choices or actions to their outcomes. However, the neural mechanisms that support the ability to credit only those past choices believed to have caused the observed outcomes remain unclear. Here, we leverage multivariate pattern analyses of functional magnetic resonance imaging (fMRI) data and an adaptive learning task to shed light on the underlying neural mechanisms of such specific credit assignment. We find that the lateral orbitofrontal cortex (lOFC) and hippocampus (HC) code for the causal choice identity when credit needs to be assigned for choices that are separated from outcomes by a long delay, even when this delayed transition is punctuated by interim decisions. Further, we show when interim decisions must be made, learning is additionally supported by lateral frontopolar cortex (lFPC). Our results indicate that lFPC holds previous causal choices in a "pending" state until a relevant outcome is observed, and the fidelity of these representations predicts the fidelity of subsequent causal choice representations in lOFC and HC during credit assignment. Together, these results highlight the importance of the timely reinstatement of specific causes in lOFC and HC in learning choice-outcome relationships when delays and choices intervene, a critical component of real-world learning and decision making.

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bioRxiv2023

Cognitive Model Discovery via Disentangled RNNs

Computational cognitive models are a fundamental tool in behavioral neuroscience. They instantiate in software precise hypotheses about the cognitive mechanisms underlying a particular behavior. Constructing these models is typically a difficult iterative process that requires both inspiration from the literature and the creativity of an individual researcher. Here, we adopt an alternative approach to learn parsimonious cognitive models directly from data. We fit behavior data using a recurrent neural network that is penalized for carrying information forward in time, leading to sparse, interpretable representations and dynamics. When fitting synthetic behavioral data from known cognitive models, our method recovers the underlying form of those models. When fit to laboratory data from rats performing a reward learning task, our method recovers simple and interpretable models that make testable predictions about neural mechanisms.

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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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bioRxiv2021

Generative replay for compositional visual understanding in the prefrontal-hippocampal circuit

Understanding the visual world is a constructive process. Whilst a frontal-hippocampal circuit is known to be essential for this task, little is known about the associated neuronal computations. Visual understanding appears superficially distinct from other known functions of this circuit, such as spatial reasoning and model-based planning, but recent models suggest deeper computational similarities. Here, using fMRI, we show that representations of a simple visual scene in these brain regions are relational and compositional - key computational properties theorised to support rapid construction of hippocampal maps. Using MEG, we show that rapid sequences of representations, akin to replay in spatial navigation and planning problems, are also engaged in visual construction. Whilst these sequences have previously been proposed as mechanisms to plan possible futures or learn from the past, here they are used to understand the present. Replay sequences form constructive hypotheses about possible scene configurations. These hypotheses play out in an optimal order for relational inference, progressing from predictable to uncertain scene elements, gradually constraining possible configurations, and converging on the correct scene configuration. Together, these results suggest a computational bridge between apparently distinct functions of hippocampal-prefrontal circuitry, and a role for generative replay in constructive inference and hypothesis testing.

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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.

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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.

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bioRxiv2019

Hippocampal-midbrain circuit enhances the pleasure of anticipation in the prefrontal cortex

Having something to look forward to is a keystone of well-being. Anticipation of a future reward, like an upcoming vacation, can often be more gratifying than the very experience itself. Theories of anticipation have described how it induces behaviors ranging from beneficial information-seeking through to harmful addiction. However, it remains unclear how neural systems compute an attractive value from anticipation, instead of from the reward itself. To address this gap, we administered a decision-making task to human participants that allowed us to analyze brain activity during receipt of information predictive of future pleasant outcomes. Using a computational model of anticipatory value that captures participants decisions, we show that an anticipatory value signal is orchestrated by influences from three brain regions. Ventromedial prefrontal cortex (vmPFC) tracks the value of anticipation; dopaminergic midbrain responds to information that enhances anticipation, while sustained hippocampal activity provides a functional coupling between these regions. This coordinating function of the hippocampus is consistent with its known role in episodic future thinking. Our findings shed new light on the neural underpinnings of anticipations influence over decision-making, while also unifying a range of phenomena associated with risk and time-delay preference.

neuroscience↗

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.

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bioRxiv2018

Episodic Control as Meta-Reinforcement Learning

Recent research has placed episodic reinforcement learning (RL) alongside model-free and model-based RL on the list of processes centrally involved in human reward-based learning. In the present work, we extend the unified account of model-free and model-based RL developed by Wang et al. (2018) to further integrate episodic learning. In this account, a generic model-free \"meta-learner\" learns to deploy and coordinate among all of these learning algorithms. The meta-learner learns through brief encounters with many novel tasks, so that it learns to learn about new tasks. We show that when equipped with an episodic memory system inspired by theories of reinstatement and gating, the meta-learner learns to use the episodic and model-based learning algorithms observed in humans in a task designed to dissociate among the influences of various learning strategies. We discuss implications and predictions of the model.

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bioRxiv2018

Prefrontal cortex as a meta-reinforcement learning system

Over the past twenty years, neuroscience research on reward-based learning has converged on a canonical model, under which the neurotransmitter dopamine stamps in associations between situations, actions and rewards by modulating the strength of synaptic connections between neurons. However, a growing number of recent findings have placed this standard model under strain. In the present work, we draw on recent advances in artificial intelligence to introduce a new theory of reward-based learning. Here, the dopamine system trains another part of the brain, the prefrontal cortex, to operate as its own free-standing learning system. This new perspective accommodates the findings that motivated the standard model, but also deals gracefully with a wider range of observations, providing a fresh foundation for future research.

neuroscience↗