Biology subjects

Dudman, J. T.

Publications and source records attributed to Dudman, J. T..

14 recordsLinked to original sources

bioRxiv2026

Evolutionarily conserved neural dynamics across mice, monkeys, and humans

On evolutionary timescales, brain circuits adapt to support survival in each species ecological niche. While some anatomical aspects of neural circuitry are conserved across species with distant evolutionary origins, each species also exhibits specific circuit adaptations that enable its behavioral repertoire. It remains unclear whether homologous brain regions leverage analogous neural computations as different species perform common behaviors such as reaching and manipulating objects. Here, we directly assessed conservation of neural computations using intracortical recordings from mouse, monkey, and human motor cortex--a homologous region across many mammals--during motor behaviors crucial for survival. We hypothesized that, despite their phylogenetic distance, rodents and primates produce movements through conserved neural computations implemented by motor cortical population dynamics. Remarkably, we found that movement-related neural dynamics were highly conserved across species, while variations in behavioral output were uniquely captured in neural trajectory geometries. Strikingly, neural dynamics during movement across species were more conserved than those across brain regions in the same human and between motor preparation and execution in the same monkeys. Lastly, through manipulation of neural network models trained to perform reaching movements, we reinforce that conservation of neural dynamics across species likely stems from shared circuit constraints. We thus assert that evolution maintains neural computations across phylogeny even as behavioral repertoires expand.

neuroscience↗

bioRxiv2025

Composing trajectories for rapid inference of navigational goals

Animals efficiently learn to navigate their environment. In the laboratory, naive mice explore their environment via highly structured trajectories and can learn to localize new spatial targets in as few as a handful of trials. It is unclear how such efficient learning is possible, since existing computational models of spatial navigation require far more experience to achieve comparable performance and do not attempt to explain the evolving structure of animal behavior during learning. To inform a new algorithm for rapid learning of navigational goals, we took inspiration from the reliable structure of behavior as mice learned to intercept hidden spatial targets. We designed agents that generate behavioral trajectories by controlling the speed and angular velocity of smooth path segments between anchor points. To rapidly learn good anchors, we use Bayesian inference on the history of rewarded and unrewarded trajectories to infer the probability that an anchor will be successful, and active sampling to trim hypothesized anchors. Agents learn within tens of trials to generate compact trajectories that intercept a target, capturing the evolution of behavioral structure and matching the upper limits of learning efficiency observed in mice. We further show that this algorithm can explain how mice avoid obstacles and rapidly adapt to target switches. Finally, we show that this framework naturally encompasses both egocentric and allocentric strategies for navigation.

animal behavior and cognition↗

bioRxiv2025

Correlates of head-fixed orienting movements in mouse superior colliculus and substantia nigra pars reticulata

Orienting movements are a critical component of the natural behavioral repertoire, but their underlying neural bases are not well understood. The deep superior colliculus (dSC) integrates input from several brain regions that influence the selection of targets for orienting movements and coordinates activity among brainstem motor nuclei to initiate and execute movement. Evidence suggests that one prominent dSC input, the substantia nigra pars reticulata (SNr), permits movement by disinhibiting its targets, but much is unknown about the relationship between SNr activity, dSC activity, and movement. Building on increasing application of the head-fixed mouse model to elucidate the neural basis of behavior, we examined neural activity recorded in dSC and SNr with high-density probes in mice performing several variants of a sensorimotor orienting task, from our labs and in data sets curated by the International Brain Laboratory. We found that dSC and SNr were active preceding and throughout movement, across task variants, suggesting that they were engaged by the required movements. Prior to movement, SNr activity reflected the outcome of the previous trial, consistent with a role in biasing movements towards the highest value target. However, the dependence of dSC activity on movement direction was weaker than in other directional orienting behaviors, and we found little evidence for strong suppression of dSC by SNr. These results complement and extend previous findings from other orienting tasks and suggest diverse roles for modulatory input from SNr to dSC in shaping motor behavior.

neuroscience↗

bioRxiv2025

Cortical control of innate behavior from subcortical demonstration

Motor control in mammals is traditionally viewed as a hierarchy of descending spinal-targeting pathways, with frontal cortex at the top 1-3. Many redundant muscle patterns can solve a given task, and this high dimensionality allows flexibility but poses a problem for efficient learning 4. Although a feasible solution invokes subcortical innate motor patterns, or primitives, to reduce the dimensionality of the control problem, how cortex learns to utilize such primitives remains an open question 5-7. To address this, we studied cortical and subcortical interactions as head-fixed mice learned contextual control of innate hindlimb extension behavior. Naive mice performed reactive extensions to turn off a cold air stimulus within seconds and, using predictive cues, learned to avoid the stimulus altogether in tens of trials. Optogenetic inhibition of large areas of rostral cortex completely prevented avoidance behavior, but did not impair hindlimb extensions in reaction to the cold air stimulus. Remarkably, mice covertly learned to avoid the cold stimulus even without any prior experience of successful, cortically-mediated avoidance. These findings support a dynamic, heterarchical model in which the dominant locus of control can change, on the order of seconds, between cortical and subcortical brain areas. We propose that cortex can leverage periods when subcortex predominates as demonstrations, to learn parameterized control of innate behavioral primitives.

neuroscience↗

bioRxiv2024

Discovery of neuronal cell types by pairing whole cell reconstructions with RNA expression profiles

Effective classification of neuronal cell types requires both molecular and morphological descriptors to be collected in situ at single-cell resolution. However, current spatial transcriptomics techniques are not compatible with imaging workflows that successfully reconstruct the morphology of complete axonal projections. Here, we introduce a new method, which we call morphoFISH, that combines tissue clearing, submicron whole-brain two photon imaging, and Expansion-Assisted Iterative Fluorescence In Situ Hybridization (EASI-FISH) to assign molecular identities to fully reconstructed neurons in the mouse brain. We used morphoFISH to molecularly identify a previously unknown population of cingulate neurons projecting ipsilaterally to the dorsal striatum and contralaterally to higher-order thalamus. By pairing whole-brain morphometry, improved techniques for nucleic acid preservation and spatial gene expression, morphoFISH offers a quantitative solution for discovery of multimodal cell types and complements existing techniques for characterization of increasingly fine-grained cellular heterogeneity in brain circuits.

neuroscience↗

bioRxiv2024

A global dopaminergic learning rate enables adaptive foraging across many options

In natural environments, animals must efficiently allocate their choices across multiple concurrently available resources when foraging, a complex decision-making process not fully captured by existing models. To understand how rodents learn to navigate this challenge we developed a novel paradigm in which untrained, water-restricted mice were free to sample from six options rewarded at a range of deterministic intervals and positioned around the walls of a large ([~]2m) arena. Mice exhibited rapid learning, matching their choices to integrated reward ratios across six options within the first session. A reinforcement learning model with separate states for staying or leaving an option and a dynamic, global learning rate was able to accurately reproduce mouse learning and decision-making. Fiber photometry recordings revealed that dopamine in the nucleus accumbens core (NAcC), but not dorsomedial striatum (DMS), more closely reflected the global learning rate than local error-based updating. Altogether, our results provide insight into the neural substrate of a learning algorithm that allows mice to rapidly exploit multiple options when foraging in large spatial environments.

neuroscience↗

bioRxiv2023

Conjoint specification of action by neocortex and striatum

The interplay between two major forebrain structures - cortex and subcortical striatum - is critical for flexible, goal-directed action. Traditionally, it has been proposed that striatum is critical for selecting what type of action is initiated while the primary motor cortex is involved in the online control of movement execution. Recent data indicates that striatum may also be critical for specifying movement execution. These alternatives have been difficult to reconcile because when comparing very distinct actions, as in the vast majority of work to date, they make essentially indistinguishable predictions. Here, we develop quantitative models to reveal a somewhat paradoxical insight: only comparing neural activity during similar actions makes strongly distinguishing predictions. We thus developed a novel reach-to-pull task in which mice reliably selected between two similar, but distinct reach targets and pull forces. Simultaneous cortical and subcortical recordings were uniquely consistent with a model in which cortex and striatum jointly specify flexible parameters of action during movement execution. One sentence summaryMotor cortex and subcortical striatum act in concert to specify the movement parameters of a reach-to-pull action in mice.

neuroscience↗

bioRxiv2023

Nonlinear manifolds underlie neural population activity during behaviour

There is rich variety in the activity of single neurons recorded during behaviour. Yet, these diverse single neuron responses can be well described by relatively few patterns of neural co-modulation. The study of such low-dimensional structure of neural population activity has provided important insights into how the brain generates behaviour. Virtually all of these studies have used linear dimensionality reduction techniques to estimate these population-wide co-modulation patterns, constraining them to a flat "neural manifold". Here, we hypothesised that since neurons have nonlinear responses and make thousands of distributed and recurrent connections that likely amplify such nonlinearities, neural manifolds should be intrinsically nonlinear. Combining neural population recordings from monkey, mouse, and human motor cortex, and mouse striatum, we show that: 1) neural manifolds are intrinsically nonlinear; 2) their nonlinearity becomes more evident during complex tasks that require more varied activity patterns; and 3) manifold nonlinearity varies across architecturally distinct brain regions. Simulations using recurrent neural network models confirmed the proposed relationship between circuit connectivity and manifold nonlinearity, including the differences across architecturally distinct regions. Thus, neural manifolds underlying the generation of behaviour are inherently nonlinear, and properly accounting for such nonlinearities will be critical as neuroscientists move towards studying numerous brain regions involved in increasingly complex and naturalistic behaviours.

neuroscience↗

bioRxiv2022

Preserved neural population dynamics across animals performing similar behaviour

Animals of the same species often exhibit similar behaviours that are advantageously adapted to their body and their environment. These behaviours are shaped by selection pressures over evolutionary timescales at the species level, yet each individual produces these behaviours using a different, uniquely constructed brain. It remains unclear how these common behavioural adaptations emerge from the idiosyncratic neural circuitry of a given individual. Here, we hypothesised that the adaptive behaviour of a species requires specific neural population latent dynamics. These latent dynamics should thus be preserved and identifiable across individuals within a species, regardless of the idiosyncratic aspects of each individuals brain. Using recordings of neural populations from monkey and mouse motor cortex, we show that individuals from the same species share surprisingly similar neural dynamics when they perform the same behaviour. The similarity in neural population dynamics extends beyond cortical regions to the dorsal striatum, an evolutionarily older structure, and also holds when animals con-sciously plan future movements without overt behaviour. These preserved dynamics are behaviourally-relevant, allowing decoding of intended and ongoing movements across individuals. We posit that these emergent neural population dynamics result from evolutionarily-imposed constraints on brain development, and reflect a fundamental property of the neural basis of behaviour.

neuroscience↗

bioRxiv2021

Mesolimbic dopamine adapts the rate of learning from errors in performance

Recent success in training artificial agents and robots derives from a combination of direct learning of behavioral policies and indirect learning via value functions. Policy learning and value learning employ distinct algorithms that optimize behavioral performance and reward prediction, respectively. In animals, behavioral learning and the role of mesolimbic dopamine signaling have been extensively evaluated with respect to reward prediction; however, to date there has been little consideration of how direct policy learning might inform our understanding. Here we used a comprehensive dataset of orofacial and body movements to understand how behavioral policies evolve as naive, head-restrained mice learned a trace conditioning paradigm. Individual differences in initial dopaminergic reward responses correlated with the emergence of learned behavioral policy, but not the emergence of putative value encoding for a predictive cue. Likewise, physiologically-calibrated manipulations of mesolimbic dopamine produced multiple effects inconsistent with value learning but predicted by a neural network-based model that used dopamine signals to set an adaptive rate, not an error signal, for behavioral policy learning. This work provides strong evidence that phasic dopamine activity can regulate direct learning of behavioral policies, expanding the explanatory power of reinforcement learning models for animal learning.

neuroscience↗

bioRxiv2021

Neuron-type specificity of dorsal raphe projections to ventral tegmental area

The midbrain dorsal raphe (DR) and ventral tegmental area (VTA) contain two of the brains main ascending neuromodulatory transmitters: serotonin and dopamine. We studied the pathway from DR to VTA using single-cell RNA sequencing, anatomical tracing, and electrophysiology and behavior in mice. Single-cell sequencing confirmed a differential distribution of dopamine cell types between medial and lateral aspects of the VTA. This molecular diversity included differential expression of a subset of glutamatergic and serotonergic receptors. Anatomical data showed that distinct serotonergic and glutamatergic populations of DR neurons project to distinct medial-lateral locations in VTA. Physiological data showed that serotonergic neurons are positioned to excite putative dopaminergic neurons in lateral VTA on short timescales (within trial), and inhibit them on long timescales (on the next trial). Our results reveal precise anatomical specificity of DR projections to VTA, and suggest a functional role for serotonergic modulation of dopaminergic function across multiple timescales.

neuroscience↗

bioRxiv2020

Neuropixels 2.0: A miniaturized high-density probe for stable, long-term brain recordings

To study the dynamics of neural processing across timescales, we require the ability to follow the spiking of thousands of individually separable neurons over weeks and months, during unrestrained behavior. To address this need, we introduce the Neuropixels 2.0 probe together with novel analysis algorithms. The new probe has over 5,000 sites and is miniaturized such that two probes plus a headstage, recording 768 sites at once, weigh just over 1 g, suitable for implanting chronically in small mammals. Recordings with high quality signals persisting for at least two months were reliably obtained in two species and six different labs. Improved site density and arrangement combined with new data processing methods enable automatic post-hoc stabilization of data despite brain movements during behavior and across days, allowing recording from the same neurons in the mouse visual cortex for over 2 months. Additionally, an optional configuration allows for recording from multiple sites per available channel, with a penalty to signal-to-noise ratio. These probes and algorithms enable stable recordings from >10,000 sites during free behavior in small animals such as mice.

neuroscience↗

bioRxiv2019

Flexible routing of motor control signals through neocortical projection neuron classes

The interaction of descending neocortical outputs and subcortical premotor circuits is critical for shaping the skilled movements. Two broad classes of motor cortical output projection neurons provide input to many subcortical motor areas: pyramidal tract neurons (PT), which project throughout the neuraxis; and intratelencephalic neurons (IT), which project within cortex and subcortical striatum. It is unclear whether these classes are functionally in series or whether separable components of descending motor control signals are distributed across these distinct classes of projection neurons. Here we combine large-scale neural recordings across all layers of motor cortex with cell-type specific perturbations to study cortically-dependent mouse motor behaviors: kinematically-variable manipulation of a joystick and a kinematically-precise reach-to-grasp. We find that striatum-projecting IT neuron activity preferentially represents amplitude whereas pons-projecting PT neurons preferentially represent the variable direction of forelimb movements. Thus, separable components of descending motor cortical commands are distributed across motor cortical projection cell classes. One-sentence summarySeparable components of cortical motor commands are distributed across distinct glutamatergic projection neuron cell-types.

neuroscience↗

bioRxiv2017

Emergence of reward expectation signals in identified dopamine neurons

Coherent control of purposive actions emerges from the coordination of multiple brain circuits during learning. Dissociable brain circuits and cell-types are thought to preferentially participate in distinct learning mechanisms. For example, the activity of midbrain dopamine (mDA) neurons is proposed to primarily, or even exclusively, reflect reward prediction error signals in well-trained animals. To study the specific contribution of individual circuits requires observing changes before tight functional coordination is achieved. However, little is known about the detailed timing of the emergence of reward-related representations in dopaminergic neurons. Here we recorded activity of identified dopaminergic neurons as naive mice learned a novel stimulus-reward association. We found that at early stages of learning mDA neuron activity reflected both external (sensory) and internal (action initiation) causes of reward expectation. The increasingly precise correlation of action initiation with sensory stimuli rather than an evaluation of outcomes governed mDA neuron activity. Thus, our data demonstrate that mDA neuron activity early in learning does not reflect errors, but is more akin to a Hebbian learning signal - providing new insight into a critical computation in a highly conserved, essential learning circuit.

neuroscience↗