@article {70118, title = {Composing trajectories for rapid inference of navigational goals}, journal = {bioRxiv}, year = {2025}, month = {2025 Sep 24}, abstract = {

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.

}, doi = {10.1101/2025.09.24.678123}, url = {https://www.biorxiv.org/content/early/2025/09/24/2025.09.24.678123}, author = {AbdelRahman, Nada Y and Jiang, Wanchen and Coddington, Luke T and Gong, Sheng and Dudman, Joshua T and Hermundstad, Ann M} } @article {68586, title = {Mesolimbic dopamine adapts the rate of learning from action.}, journal = {Nature}, year = {2023}, month = {2023 Jan 18}, abstract = {

Recent success in training artificial agents and robots derives from a combination of direct learning of behavioural policies and indirect learning through value functions. Policy learning and value learning use distinct algorithms that optimize behavioural performance and reward prediction, respectively. In animals, behavioural learning and the role of mesolimbic dopamine signalling have been extensively evaluated with respect to reward prediction; however, so far 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 behavioural policies evolved as naive, head-restrained mice learned a trace conditioning paradigm. Individual differences in initial dopaminergic reward responses correlated with the emergence of learned behavioural policy, but not the emergence of putative value encoding for a predictive cue. Likewise, physiologically calibrated manipulations of mesolimbic dopamine produced several 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 behavioural policy learning. This work provides strong evidence that phasic dopamine activity can regulate direct learning of behavioural policies, expanding the explanatory power of reinforcement learning models for animal learning.

}, issn = {1476-4687}, doi = {10.1038/s41586-022-05614-z}, author = {Coddington, Luke T and Lindo, Sarah E and Dudman, Joshua T} } @article {67723, title = {Dissociable contributions of phasic dopamine activity to reward and prediction.}, journal = {Cell Reports}, volume = {36}, year = {2021}, month = {2021 Sep 07}, pages = {109684}, abstract = {

Sensory cues that precede reward acquire predictive (expected value) and incentive (drive reward-seeking action) properties. Mesolimbic dopamine neurons{\textquoteright} responses to sensory cues correlate with both expected value and reward-seeking action. This has led to the proposal that phasic dopamine responses may be sufficient to inform value-based decisions, elicit actions, and/or induce motivational states; however, causal tests are incomplete. Here, we show that direct dopamine neuron stimulation, both calibrated to physiological and greater intensities, at the time of reward can be sufficient to induce and maintain reward seeking (reinforcing) although replacement of a cue with stimulation is insufficient to induce reward seeking or act as an informative cue. Stimulation of descending cortical inputs, one synapse upstream, are sufficient for reinforcement and cues to future reward. Thus, physiological activation of mesolimbic dopamine neurons can be sufficient for reinforcing properties of reward without being sufficient for the predictive and incentive properties of cues.

}, issn = {2211-1247}, doi = {10.1016/j.celrep.2021.109684}, author = {Pan, Wei-Xing and Coddington, Luke T and Dudman, Joshua T} } @article {65815, title = {In vivo optogenetics with stimulus calibration.}, journal = {Methods in Molecular Biology}, volume = {2188}, year = {2021}, month = {2020 Nov 06}, pages = {273-283}, abstract = {

Optogenetic reagents allow for depolarization and hyperpolarization of cells with light. This provides unprecedented spatial and temporal resolution to the control of neuronal activity both in vitro and in vivo. In the intact animal this requires strategies to deliver light deep into the highly scattering tissue of the brain. A general approach that we describe here is to implant optical fibers just above brain regions targeted for light delivery. In part due to the fact that expression of optogenetic proteins is accomplished by techniques with inherent variability (e.g., viral expression\ levels), it also requires strategies to measure and calibrate the effect of stimulation. Here we describe general procedures that allow one to simultaneously stimulate neurons and use photometry with genetically encoded activity indicators to precisely calibrate stimulation.

}, issn = {1940-6029}, doi = {10.1007/978-1-0716-0818-0_14}, author = {Coddington, Luke T and Dudman, Joshua T} } @article {65389, title = {Basal ganglia circuits for action specification.}, journal = {Annual Review Neuroscience}, volume = {43}, year = {2020}, month = {2020 Jul 8}, pages = {485-507}, abstract = {

Behavior is readily classified into patterns of movements with inferred common goals-actions. Goals may be discrete; movements are continuous. Through the careful study of isolated movements in laboratory settings, or via introspection, it has become clear that animals can exhibit exquisite graded specification to their movements. Moreover, graded control can be as fundamental to success as the selection of which action to perform under many naturalistic scenarios: a predator adjusting its speed to intercept moving prey, or a tool-user exerting the perfect amount of force to complete a delicate task. The basal ganglia are a collection of nuclei in vertebrates that extend from the forebrain (telencephalon) to the midbrain (mesencephalon), constituting a major descending extrapyramidal pathway for control over midbrain and brainstem premotor structures. Here we discuss how this pathway contributes to the continuous specification of movements that endows our voluntary actions with vigor and grace. Expected final online publication date for the , Volume 43 is July 8, 2020. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

}, issn = {1545-4126}, doi = {10.1146/annurev-neuro-070918-050452}, author = {Park, Junchol and Coddington, Luke T and Dudman, Joshua T} } @article {65278, title = {When does midbrain dopamine activity exert its effects on behavior?}, journal = {Nature Neuroscience}, volume = {23}, year = {2020}, month = {2020 Jan 13}, pages = {154-6}, issn = {1546-1726}, doi = {10.1038/s41593-019-0577-y}, author = {Coddington, Luke T} } @article {65171, title = {Learning from action: reconsidering movement signaling in midbrain dopamine neuron activity.}, journal = {Neuron}, volume = {104}, year = {2019}, month = {2019 Oct 09}, pages = {63-77}, abstract = {

Animals infer when and where a reward is available from experience with informative sensory stimuli and their own actions. In vertebrates, this is thought to depend upon the release of dopamine from midbrain dopaminergic neurons. Studies of the role of dopamine have focused almost exclusively on their encoding of informative sensory stimuli; however, many dopaminergic neurons are active just prior to movement initiation, even in the absence of sensory stimuli. How should current frameworks for understanding the role of dopamine incorporate these observations? To address this question, we review recent anatomical and functional evidence for action-related dopamine signaling. We conclude by proposing a framework in which dopaminergic neurons encode subjective signals of action initiation to solve an internal credit assignment problem.

}, issn = {1097-4199}, doi = {10.1016/j.neuron.2019.08.036}, author = {Coddington, Luke T and Dudman, Joshua T} } @article {49469, title = {The timing of action determines reward prediction signals in identified midbrain dopamine neurons.}, journal = {Nature Neuroscience}, volume = {21}, year = {2018}, month = {2018 Oct 15}, pages = {1563-73}, abstract = {

Animals adapt their behavior in response to informative sensory cues using multiple brain circuits. The activity of midbrain dopaminergic neurons is thought to convey a critical teaching signal: reward-prediction error. Although reward-prediction error signals are thought to be essential to learning, little is known about the dynamic changes in the activity of midbrain dopaminergic neurons as animals learn about novel sensory cues and appetitive rewards. Here we describe a large dataset of cell-attached recordings of identified dopaminergic neurons as naive mice learned a novel cue-reward association. During learning midbrain dopaminergic neuron activity results from the summation of sensory cue-related and movement initiation-related response components. These components are both a function of reward expectation yet they are dissociable. Learning produces an increasingly precise coordination of action initiation following sensory cues that results in apparent reward-prediction error correlates. Our data thus provide new insights into the circuit mechanisms that underlie a critical computation in a highly conserved learning circuit.

}, issn = {1546-1726}, doi = {10.1038/s41593-018-0245-7}, author = {Coddington, Luke T and Dudman, Joshua T} } @article {48994, title = {Emergence of reward expectation signals in identified dopamine neurons.}, journal = {bioRxiv}, year = {2017}, month = {2017 Dec 22}, abstract = {

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.

}, doi = {10.1101/238881}, author = {Coddington, Luke T and Dudman, Joshua T} }