Biology subjects

Burrell, M.

Publications and source records attributed to Burrell, M..

9 recordsLinked to original sources

bioRxiv2026

Dynamic belief representation and updating through learned attractor-like dynamics in the frontal cortex

To act adaptively, animals must infer hidden states of the world from incomplete sensory information and update beliefs as new observations accrue. While dopamine signals are well explained by reinforcement learning models that incorporate belief states, how the brain implements belief-state inference remains unknown. Prior modeling work showed that recurrent neural networks trained to predict value (Value-RNN) develop task-specific, attractor-like dynamics that mirror evolution of beliefs. Here we performed high-density electrophysiological recordings from orbitofrontal cortex (OFC) and other brain areas of mice performing two variants of a Pavlovian task that differ in reward probability, which produce different within-trial belief dynamics. We find that OFC population activity exhibits task-specific attractor-like dynamics that mirror the within-trial dynamics in the Value-RNN. These dynamics are absent in motor and olfactory regions, are not explained by behavioral differences, and emerge progressively with learning. Our findings indicate that the brain approximates belief-state inference through learned, task-specific attractor-like dynamics.

neuroscience↗

bioRxiv2026

Phasic dopamine drives conditioned responding beyond its role in learning

Animals exposed to pairings of a neutral stimulus with reward acquire a conditioned response to the neutral stimulus. A prominent hypothesis, formalized in the Temporal Difference (TD) learning algorithm, is that animals learn to predict the future reward associated with the neutral stimulus ("value"). Though the TD algorithm does not explicitly specify what drives conditioned responding, a typical assumption is that it reflects the animals estimate of value. In TD learning, value estimates are updated using reward prediction error (RPE, the discrepancy between observed and predicted reward), and are thought to be signaled by the phasic activity of midbrain dopamine neurons. This hypothesis posits that dopamines effects on conditioned responding are mediated entirely by its effects on learning. However, recent experimental and theoretical evidence suggests that dopamine may play a more direct role in modulating conditioned responding. We use a combination of data analysis and computational modeling to probe the relationship between dopamine and conditioned responding. Our results suggest that dopamine directly modulates conditioned responding, in addition to its role in learning. These findings can be captured by a model in which dopamine RPE acts both indirectly (via learning) and directly on conditioned responding.

neuroscience↗

bioRxiv2025

STAMPS: Signal-peptide Transformer for Augmenting Mammalian Protein Secretion

While secretion plays a key role in the diverse applications of cell engineering, to date only a handful of mammalian signal peptides have been characterised in depth and systematic efforts to build novel variants remain sporadic. We present STAMPS (Signal-peptide Transformer for Augmenting Mammalian Protein Secretion), a generative, autoregressive transformer fine-tuned on [~]6,000 mammalian signal peptides to design de novo sequences that modulate and enhance secretion across proteins and hosts. We show that STAMPS can be used to identify candidate signal peptides that outperform the widely used IgG {kappa} light-chain leader (IgKL) when used to secrete EGFP in HEK293T and CHO cells, and hEPO in HEK293T cells. When incorporated in an industrial cell line development framework, STAMPS leads to the generation of signal peptides that yield [~]2.3-fold gain in secretion of a VHH-Fc compared to the internal industrial benchmark in CHO G22 cells, with the same candidates ranking highly in both CHO and HEK293T hosts. Sequence-to-function analysis highlights a longer, strongly hydrophobic core and a tightly positioned cleavage site as drivers of this strong performance. Together, these results establish data-driven, generative design of mammalian signal peptides as a practical route to tune and improve secretion for bioproduction and cell engineering applications.

synthetic biology↗

bioRxiv2025

Billion-Scale Deciphering of Human Gene Regulatory Grammar

Predicting how DNA sequence specifies gene expression remains a core challenge across regulatory genomics. Most predictive assays and models depend on native genomic DNA, constraining the full biochemical engineering space for assessing and designing new sequences. Here, we address this gap with a scalable experimental-computational platform that rapidly generates million-scale sequence-to-expression datasets that directly link degenerate sequences to their function in human cells. We built degenerate libraries of 200-bp promoter cassettes and performed pooled stable integration of up to 1012 unique constructs, enabling the curation of million-scale sequence-to-expression datasets by fluorescently sorting billions of human cells. Biophysical modeling of transcription-factor occupancy on the data using position weight matrices reveals a broad spectrum of correlations between factor abundance and expression levels, with some co-abundances reaching Pearsons r {approx} 0.99, consistent with cooperative and probabilistic regulation. Leveraging the dataset, we trained sequence-to-expression deep learning models that predict held-out expression with Pearson r {approx} 0.4, converge on shared sequence determinants, and agree strongly with each other (Pearsons r = 0.93), indicating reproducible sequence-expression relationships. Finally, with minimal retraining the models generalize to an independently generated dataset collected under distinct sorting conditions, transferring sequence rules across contexts. Our platform enables repeated, rapid studies and supports deeper mechanistic insight while providing baseline models for forward design of human regulatory elements, advancing prediction beyond genomic-DNA-anchored methods.

synthetic biology↗

bioRxiv2025

A hardwired neural circuit for temporal difference learning

The neurotransmitter dopamine plays a major role in learning by acting as a teaching signal to update the brains predictions about rewards. A leading theory proposes that this process is analogous to a reinforcement learning algorithm called temporal difference (TD) learning, and that dopamine acts as the error term within the TD algorithm (TD error). Although many studies have demonstrated similarities between dopamine activity and TD errors1-5, the mechanistic basis for dopaminergic TD learning remains unknown. Here, we combined large-scale neural recordings with patterned optogenetic stimulation to examine whether and how the key steps in TD learning are accomplished by the circuitry connecting dopamine neurons and their targets. Replacing natural rewards with optogenetic stimulation of dopamine axons in the nucleus accumbens (NAc) in a classical conditioning task gradually generated TD error-like activity patterns in dopamine neurons by specifically modifying the task-related activity of NAc neurons expressing the D1 dopamine receptor (D1 neurons). In turn, patterned optogenetic stimulation of NAc D1 neurons in naive animals drove dopamine neuron spiking according to the TD error of the stimulation pattern, indicating that TD computations are hardwired into this circuit. The transformation from D1 neurons to dopamine neurons could be described by a biphasic linear filter, with a rapid positive and delayed negative phase, that effectively computes a temporal difference. This finding suggests that the time horizon over which the TD algorithm operates--the temporal discount factor--is set by the balance of the positive and negative components of the linear filter, pointing to a circuit-level mechanism for temporal discounting. These results provide a new conceptual framework for understanding how the computations and parameters governing animal learning arise from neurobiological components.

neuroscience↗

bioRxiv2024

Repix: reliable, reusable, versatile chronic Neuropixels implants using minimal components

Neuropixels probes represent the state-of-the-art for high-yield electrophysiology in neuroscience: the simultaneous recording of hundreds of neurons is now routinely carried out in head-restrained animals. In contrast, neural recording in unrestrained animals, as well as recording and tracking neurons over days, remains challenging, though it is possible using chronic implants. A major challenge is the availability of simple methods that can be implemented with limited or no prior experience with Neuropixels probes, while achieving reliable, reusable, versatile high-density electrophysiology. Here we developed, deployed, and evaluated the real-world performance of Repix, a chronic implantation system that permits the repeated re-use of Neuropixels probes. The lightweight system allows implanted animals to express a full range of natural behaviors, including social behaviors. We show that Repix allows the recording of hundreds of neurons across many months, up to a year, with implants across cortical and subcortical brain regions. Probes can be reused repeatedly with stable yield. Repix has been used by 16 researchers in 10 laboratories to date, and we evaluated the real-world performance of Repix in a variety of chronic recording paradigms in both mice and rats with a combined 209 implantations. We found that the key advantage of Repix is robustness and simplicity. Adopters of Repix became proficient at five procedures on average, regardless of prior experience with in vivo electrophysiology. With the companion protocol alongside this article, the performance and user-friendliness of Repix should facilitate a wide uptake of chronic Neuropixels recordings.

neuroscience↗

bioRxiv2024

The role of prospective contingency in the control of behavior and dopamine signals during associative learning

Associative learning depends on contingency, the degree to which a stimulus predicts an outcome. Despite its importance, the neural mechanisms linking contingency to behavior remain elusive. Here we examined the dopamine activity in the ventral striatum - a signal implicated in associative learning - in a Pavlovian contingency degradation task in mice. We show that both anticipatory licking and dopamine responses to a conditioned stimulus decreased when additional rewards were delivered uncued, but remained unchanged if additional rewards were cued. These results conflict with contingency-based accounts using a traditional definition of contingency or a novel causal learning model (ANCCR), but can be explained by temporal difference (TD) learning models equipped with an appropriate inter-trial-interval (ITI) state representation. Recurrent neural networks trained within a TD framework develop state representations like our best handcrafted model. Our findings suggest that the TD error can be a measure that describes both contingency and dopaminergic activity.

neuroscience↗

bioRxiv2023

Worth the work? Monkeys discount rewards by a subjective adapting effort cost

All life must solve how to allocate limited energy resources to maximise benefits from scarce opportunities. Economic theory posits decision makers optimise choice by maximising the subjective benefit (utility) of reward minus the subjective cost (disutility) of the required effort. While successful in many settings, this model does not fully account for how experience can alter reward-effort trade-offs. Here we test how well the subtractive model of effort disutility explains the behavior of two non-human primates (Macaca mulatta) in a binary choice task in which reward quantity and physical effort to obtain were varied.Applying random utility modelling to independently estimate reward utility and effort disutility, we show the subtractive effort model better explains out-of-sample choice behavior when compared to parabolic and exponential effort discounting. Furthermore, we demonstrate that effort disutility is dependent on previous experience of effort: in analogy to work from behavioral labour economics, we develop a model of reference-dependent effort disutility to explain the increased willingness to expend effort following previous experience of effortful options in a session. The result of this analysis suggests that monkeys discount reward by an effort cost that is measured relative to an expected effort learned from previous trials. When this subjective cost of effort, a function of context and experience, is accounted for, trial-by-trial choice behavior can be explained by the subtractive cost model of effort.Therefore, in searching for net utility signals that may underpin effort-based decision-making in the brain, careful measurement of subjective effort costs is an essential first step. SignificanceAll decision-makers need to consider how much effort they need to expend when evaluating potential options. Economic theories suggest that the optimal way to choose is by cost-benefit analysis of reward against effort. To be able to do this efficiently over many decision contexts, this needs to be done flexibly, with appropriate adaptation to context and experience. Therefore, in aiming to understand how this might be achieved in the brain, it is important to first carefully measure the subjective cost of effort. Here we show monkeys make reward-effort cost-benefit decisions, subtracting the subjective cost of effort from the subjective value of rewards. Moreover, the subjective cost of effort is dependent on the monkeys experience of effort in previous trials.

neuroscience↗

bioRxiv2021

Systemic antibody-oligonucleotide delivery to the central nervous system ameliorates mouse models of spinal muscular atrophy

Antisense oligonucleotides (ASOs) have emerged as one of the most innovative new genetic drug modalities, however, the high molecular weight limits their bioavailability for otherwise treatable neurological disorders. We investigated conjugation of ASOs to an antibody against the murine transferrin receptor (TfR), 8D3130, and evaluated it via systemic administration in mouse models of the neurodegenerative disease, spinal muscular atrophy (SMA). SMA like several other neurological and neuromuscular diseases, is treatable with single-stranded ASOs, inducing splice modulation of the survival motor neuron 2 (SMN2) gene. Administration of 8D3130-ASO conjugate resulted in bioavailability of 2.7% of the injected dose in brain. Additionally, 8D3130-ASO yielded therapeutically high levels of SMN2 splicing in the central nervous system of mildly affected adult SMA mice and resulted in extended survival of severe SMA mice. Systemic delivery of nucleic acid therapies with brain targeting antibodies offers powerful translational potential for future treatments of neuromuscular and neurodegenerative diseases.

molecular biology↗