Biology subjects

Jensen, K. T.

Publications and source records attributed to Jensen, K. T..

12 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↗

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

The evolutionary genomics of incipient endosymbiosis in wild rhizobia bacteria

The advent of endosymbiosis underlies evolutionary innovation and ecosystem function. However, whether free-living partners tend to benefit or exploit each other during incipient endosymbiosis remains a dilemma. Rhizobia bacteria are plant endosymbionts capable of initiating root nodules and fixing nitrogen due to genes carried on mobile genetic elements (MGEs) such as the symbiosis island (SI). We conjugated marked SIs into the genomes of nonnodulating strains, which was sufficient to generate de novo root nodule-forming endosymbionts. Most novel endosymbionts originated as commensals that incurred no detectable costs to host plants, in contrast to predictions of exploitation. In fact, a third of endosymbionts originated as nitrogen fixing mutualists. Consistent with phylogenetic limits to transfer of MGE function, novel endosymbionts derived from more closely related SI donor and recipient strains showed greater nitrogen fixation. However, we did not detect phylogenetic limits to SI transmission, which could reflect selfish selection for generalized horizontal transfer of this MGE. In fact, the SI was able to displace other genomic elements residing at its characteristic tRNA gene insertion site. We thus provide genetic, genomic, and functional evidence of how MGEs can potentiate and constrain major evolutionary transitions to expand bacterial niches, with cascading effects on host organisms.

evolutionary biology↗

bioRxiv2025

Mobile genetic elements shape the evolution of wild bacterial pangenomes in the face of biotic and abiotic selective pressures in the soil

Mobile genetic elements (MGEs) provide a critical reservoir of diversity that potentiates bacterial local adaptation. However, the degree to which natural populations of MGEs and the chromosomes that host them share congruent or divergent evolutionary histories remains an open question. MGE and host chromosome phylogenies could show congruence or independence, depending on rates of MGE gain and loss, costs and benefit of MGE carriage, compatibility, and gene flow. We investigated phylogenetic congruence for two critical niche-expanding MGEs and host chromosomes using 302 genomes of wild symbiotic nitrogen-fixing Mesorhizobium bacteria. The MGEs comprise: 1) a nickel resistance island (NRI), which confers adaptation to nickel-enriched serpentine soils across replicated natural populations, and 2) a more extensive symbiosis island (SI), which enables symbiotic nitrogen fixation with a host legume. We demonstrate that the NRI, like the SI, is transmissible and its acquisition confers nickel adaptation. NRI acquisition often incurs no detectable cost, consistent with conditional neutrality during MGE-mediated adaptation. Phylogenies for the NRI, SI, and chromosome are congruent across wild strains, consistent with a history of vertical co-inheritance and/or MGE transmission primarily among close relatives. This congruence does not appear to result from barriers to gene flow, as diverse MGE and host chromosome haplotypes are broadly distributed and co-occur. Thus, despite ample ecological opportunity for admixture, distant relatives rarely transmitted MGEs, consistent with selection to maintain co-adapted genomic compartments. The SI has stronger phylogenetic congruence with the chromosome than does the smaller NRI, supporting the hypothesis that MGE complexity limits horizontal transfer among more divergent lineages. However, rare horizontal NRI transmission events among distant relatives and NRI loss appear to occur when haplotypes migrate across environments. Thus, environmentally dependent selection on migrants may periodically disrupt co-adapted MGE and chromosome haplotypes and serve as an engine of diversity.

evolutionary biology↗

bioRxiv2025

A neural network model of free recall learns multiple memory strategies

Humans exhibit structured patterns of memory recall, including a tendency to recall more recent information and to recall events in the same order they were experienced. Classic computational models explain these patterns by positing that memories incorporate the ongoing "temporal context", formed by smoothly integrating the stimulus history. However, it is unclear whether a single mechanism can account for the full repertoire of human memory strategies, as the optimal approach may be task-dependent. For example, human memory experts widely apply the "memory palace" strategy, which is empirically better but not captured by temporal context models. Here we show that neural networks optimized for free recall develop diverse retrieval strategies, with only some of them resembling temporal context models. The best-performing models discovered a stimulus-invariant index code that emphasizes the studied position of each list item, instead of its temporal context. This creates a stable scaffold for forward recall akin to the memory palace technique. This index code was more likely to emerge when networks were i) encouraged to recall all studied items rather than prioritizing only the next few items, and ii) prevented from relying on recency. Our findings demonstrate that human-like recall patterns can arise from multiple distinct computational mechanisms, and that sequential retrieval using item index is an optimal strategy that explains expert-level recall performance.

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↗

bioRxiv2023

Ignorance is bliss: effects of noise and metaboliccost on cortical task representations

Cognitive flexibility requires both the encoding of task-relevant and the ignoring of task-irrelevant stimuli. While the neural coding of task-relevant stimuli is increasingly well understood, the mechanisms for ignoring task-irrelevant stimuli remain poorly understood. Here, we study how task performance and biological constraints jointly determine the coding of relevant and irrelevant stimuli in neural circuits. Using mathematical analyses and task-optimized recurrent neural networks, we show that neural circuits can exhibit a range of representational geometries depending on the strength of neural noise and metabolic cost. By comparing these results with recordings from primate prefrontal cortex (PFC) over the course of learning, we show that neural activity in PFC changes in line with a minimal representational strategy. Specifically, our analyses reveal that the suppression of dynamically irrelevant stimuli is achieved by activity-silent, sub-threshold dynamics. Our results provide a normative explanation as to why PFC implements an adaptive, minimal representational strategy.

neuroscience↗

bioRxiv2023

A recurrent network model of planning explains hippocampal replay and human behavior

When faced with a novel situation, humans often spend substantial periods of time contemplating possible futures. For such planning to be rational, the benefits to behavior must compensate for the time spent thinking. Here we capture these features of human behavior by developing a neural network model where planning itself is controlled by prefrontal cortex. This model consists of a meta-reinforcement learning agent augmented with the ability to plan by sampling imagined action sequences from its own policy, which we call rollouts. The agent learns to plan when planning is beneficial, explaining empirical variability in human thinking times. Additionally, the patterns of policy rollouts employed by the artificial agent closely resemble patterns of rodent hippocampal replays recently recorded during spatial navigation. Our work provides a new theory of how the brain could implement planning through prefrontal-hippocampal interactions, where hippocampal replays are triggered by - and adaptively affect - prefrontal dynamics.

neuroscience↗

bioRxiv2022

Beyond the Euclidean brain: inferring non-Euclidean latent trajectories from spike trains

Neuroscience faces a growing need for scalable data analysis methods that reduce the dimensionality of population recordings yet retain key aspects of the computation or behaviour. To extract interpretable latent trajectories from neural data, it is critical to embrace the inherent topology of the features of interest: head direction evolves on a ring or torus, 3D body rotations on the special orthogonal group, and navigation is best described in the intrinsic coordinates of the environment. Accordingly, we recently proposed the manifold Gaussian process latent variable model (mGPLVM) to simultaneously infer latent representations on non-Euclidean manifolds and how neurons are tuned to these representations. This probabilistic method generalizes previous Euclidean models and allows principled selection between candidate latent topologies. While powerful, mGPLVM makes two unjustified approximations that limit its practical applicability to neural datasets. First, consecutive latent states are assumed independent a priori, whereas behaviour is continuous in time. Second, its Gaussian noise model is inappropriate for positive integer spike counts. Previous work in Euclidean LVMs such as GPFA has shown significant improvements in performance when modeling such features appropriately (Jensen et al., 2021). Here, we extend mGPLVM by incorporating temporally continuous priors over latent states and flexible count-based noise models. This improves inference on synthetic data, avoiding negative spike count predictions and discontinuous jumps in latent trajectories. On real data, we also mitigate these pathologies while improving model fit compared to the original mGPLVM formulation. In summary, our extended mGPLVM provides a widely applicable tool for inferring (non-)Euclidean neural representations from large-scale, heterogeneous population recordings. We provide an efficient implementation in python, relying on recent advances in approximate inference to e.g. fit 10,000 time bins of recording for 100 neurons in five minutes on a single GPU.

neuroscience↗

bioRxiv2021

Long-term stability of neural activity in the motor system

How an established behavior is retained and stably produced by a nervous system in constant flux remains a mystery. One possible solution is to fix the activity patterns of single neurons in the relevant circuits. Alternatively, activity in single cells could drift over time provided that the population dynamics are constrained to produce stable behavior. To arbitrate between these possibilities, we recorded single unit activity in motor cortex and striatum continuously for several weeks as rats performed stereotyped motor behaviors - both learned and innate. We found long-term stability in single neuron activity patterns across both brain regions. A small amount of drift in neural activity, observed over weeks of recording, could be explained by concomitant changes in task-irrelevant behavioral output. These results suggest that stereotyped behaviors are generated by stable single neuron activity patterns.

neuroscience↗

bioRxiv2021

iLQR-VAE : control-based learning of input-driven dynamics with applications to neural data

AO_SCPLOWBSTRACTC_SCPLOWUnderstanding how neural dynamics give rise to behaviour is one of the most fundamental questions in systems neuroscience. To achieve this, a common approach is to record neural populations in behaving animals, and model these data as emanating from a latent dynamical system whose state trajectories can then be related back to behavioural observations via some form of decoding. As recordings are typically performed in localized circuits that form only a part of the wider implicated network, it is important to simultaneously learn the local dynamics and infer any unobserved external input that might drive them. Here, we introduce iLQR-VAE, a control-based approach to variational inference in nonlinear dynamical systems, capable of learning both latent dynamics, initial conditions, and ongoing external inputs. As in recent deep learning approaches, our method is based on an input-driven sequential variational autoencoder (VAE). The main novelty lies in the use of the powerful iterative linear quadratic regulator algorithm (iLQR) in the recognition model. Optimization of the standard evidence lower-bound requires differentiating through iLQR solutions, which is made possible by recent advances in differentiable control. Importantly, the recognition model is naturally tied to the generative model, greatly reducing the number of free parameters and ensuring high-quality inference throughout the course of learning. Moreover, iLQR can be used to perform inference flexibly on heterogeneous trials of varying lengths. This allows for instance to evaluate the model on a single long trial after training on smaller chunks. We demonstrate the effectiveness of iLQR-VAE on a range of synthetic systems, with autonomous as well as input-driven dynamics. We further apply it to neural and behavioural recordings in non-human primates performing two different reaching tasks, and show that iLQR-VAE yields high-quality kinematic reconstructions from the neural data.

neuroscience↗

bioRxiv2021

Scalable Bayesian GPFA with automatic relevance determination and discrete noise models

Latent variable models are ubiquitous in the exploratory analysis of neural population recordings, where they allow researchers to summarize the activity of large populations of neurons in lower dimensional latent spaces. Existing methods can generally be categorized into (i) Bayesian methods that facilitate flexible incorporation of prior knowledge and uncertainty estimation, but which typically do not scale to large datasets; and (ii) highly parameterized methods without explicit priors that scale better but often struggle in the low-data regime. Here, we bridge this gap by developing a fully Bayesian yet scalable version of Gaussian process factor analysis (bGPFA), which models neural data as arising from a set of inferred latent processes with a prior that encourages smoothness over time. Additionally, bGPFA uses automatic relevance determination to infer the dimensionality of neural activity directly from the training data during optimization. To enable the analysis of continuous recordings without trial structure, we introduce a novel variational inference strategy that scales near-linearly in time and also allows for non-Gaussian noise models appropriate for electrophysiological recordings. We apply bGPFA to continuous recordings spanning 30 minutes with over 14 million data points from primate motor and somatosensory cortices during a self-paced reaching task. We show that neural activity progresses from an initial state at target onset to a reach-specific preparatory state well before movement onset. The distance between these initial and preparatory latent states is predictive of reaction times across reaches, suggesting that such preparatory dynamics have behavioral relevance despite the lack of externally imposed delay periods. Additionally, bGPFA discovers latent processes that evolve over slow timescales on the order of several seconds and contain complementary information about reaction time. These timescales are longer than those revealed by methods which focus on individual movement epochs and may reflect fluctuations in e.g. task engagement.

neuroscience↗