Biology subjects

Witten, I. B.

Publications and source records attributed to Witten, I. B..

11 recordsLinked to original sources

bioRxiv2026

Lightning Pose 3D: an uncertainty-aware framework for data-efficient multi-view animal pose estimation

Multi-view pose estimation is essential for quantifying animal behavior in scientific research, yet current methods struggle to achieve accurate tracking with limited labeled data and suffer from poor uncertainty estimates. We address these challenges with a flexible framework that can operate with or without camera calibration, combining novel training and post-processing techniques with an uncertainty-aware pseudo-labeling distillation procedure. Our multi-view model processes all camera views jointly using a pretrained vision transformer backbone, and a simulated occlusion technique encourages the model to learn robust cross-view correspondences without requiring camera parameters. When camera parameters are available, 3D data augmentations and a triangulation-based loss further encourage geometric consistency. We extend the Ensemble Kalman Smoother (EKS) post-processor to the nonlinear case, leveraging camera geometry, and introduce a variance inflation technique that detects cross-view inconsistencies and corrects overconfident predictions. We validate our approach on five datasets spanning three species (fly, mouse, bird), including a multi-animal dataset with two visually distinct individuals; the proposed pipeline consistently outperforms existing methods across datasets. We demonstrate how these improvements translate to downstream scientific analyses using data from the International Brain Laboratory, showing improved unsupervised behavioral clustering and neural decoding of paw kinematics with just 200 labeled frames. To facilitate adoption, we developed a browser-based, cloud-compatible user interface that supports the full life cycle of multi-view pose estimation, from labeling and model training to post-processing with EKS and diagnostic visualizations.

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bioRxiv2025

Stress resilience is associated with transcriptional remodeling in the VTA

Individual responses to chronic stress vary, with some individuals remaining resilient while others exhibit susceptibility. The ventral tegmental area (VTA), a region involved in reward learning, and the lateral habenula (LHb), a region involved in aversive learning, have been implicated in the pathophysiology of stress-related mood disorders. Here, we seek to understand the molecular adaptations in these regions at the level of single cells that mediate susceptibility versus resilience. In particular, it remains unclear whether, at the level of gene expression, different cell types within different brain regions mediate stress susceptibility versus resilience, or if these phenotypes are mediated by distinct trajectories within the same cell types. To address this gap, we performed single-nucleus RNA-sequencing of LHb and VTA of mice subjected to chronic social defeat stress. While we found minimal gene expression changes in the LHb after stress, the VTA exhibited widespread, cell type-specific transcriptional remodeling in resilient individuals and few gene expression changes in susceptible individuals. Across VTA cell types, resilience was associated with the coordinated upregulation of genes involved in intercellular signaling and neural communication, with maintenance of receptor-ligand interaction strength in resilience that was not present in susceptibility. Within VTA neurons, gene expression changes were most prominent in glutamatergic and dopaminergic clusters. Multivariate analyses of dopamine and glutamate subclusters showed that resilient neurons diverged more from control than susceptible neurons, but along a similar trajectory, supporting a model in which resilience reflects greater stress-related adaptations in these cell types. Together, these findings highlight the VTA as a key site of molecular plasticity in stress resilience and therefore a potential therapeutic target.

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bioRxiv2025

VTA dopamine neuron activity produces spatially organized value representations

What is the neural architecture by which dopamine (DA) determines choice? Reinforcement learning (RL) has suggested an algorithmic chain: prediction errors based on reward train predicted values for stimuli and actions, and thereby determine choice. Elements of this chain have been identified, but have yet to be convincingly assembled. Here we used optogenetic stimulation of putative prediction error neurons at the top of this chain - i.e., DA neurons in the ventral tegmental area (VTA) - and recorded the activity of thousands of neurons in their target structure - the striatum - in mice performing a task that dissociates VTA DA signalling, stimulus value, action value, and choice. Conventional RL models captured some features of the data, including DA-driven choice reinforcement and value correlates, but failed on two key observations: i) action and state value correlates were relatively weak in the VTAs main target, the nucleus accumbens (NAc), and ii) VTA DA could not reinforce actions in the absence of a reward-predictive cue (CS+). Instead, we find that VTA DA produces stimulus value representations in the NAc, which in turn generates action value representations downstream (e.g. the CP). We formalize these observations in a Conditioned Reinforcer model, where VTA DA specifically acts on stimuli rather than actions, and these stimulus value representations are used to drive choice reinforcement.

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bioRxiv2023

A neural mechanism for learning from delayed postingestive feedback

Animals learn the value of foods based on their postingestive effects and thereby develop aversions to foods that are toxic1-6 and preferences to those that are nutritious7-14. However, it remains unclear how the brain is able to assign credit to flavors experienced during a meal with postingestive feedback signals that can arise after a substantial delay. Here, we reveal an unexpected role for postingestive reactivation of neural flavor representations in this temporal credit assignment process. To begin, we leverage the fact that mice learn to associate novel15-18, but not familiar, flavors with delayed gastric malaise signals to investigate how the brain represents flavors that support aversive postingestive learning. Surveying cellular resolution brainwide activation patterns reveals that a network of amygdala regions is unique in being preferentially activated by novel flavors across every stage of the learning process: the initial meal, delayed malaise, and memory retrieval. By combining high-density recordings in the amygdala with optogenetic stimulation of genetically defined hindbrain malaise cells, we find that postingestive malaise signals potently and specifically reactivate amygdalar novel flavor representations from a recent meal. The degree of malaise-driven reactivation of individual neurons predicts strengthening of flavor responses upon memory retrieval, leading to stabilization of the population-level representation of the recently consumed flavor. In contrast, meals without postingestive consequences degrade neural flavor representations as flavors become familiar and safe. Thus, our findings demonstrate that interoceptive reactivation of amygdalar flavor representations provides a neural mechanism to resolve the temporal credit assignment problem inherent to postingestive learning.

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bioRxiv2023

Neural circuit models for evidence accumulation through choice-selective sequences

Decision making is traditionally thought to be mediated by neurons that accumulate evidence through persistent activity. However, recent decision-making experiments in rodents have observed neurons across the brain that fire sequentially, rather than persistently, with the subset of neurons in the sequence depending on the animals choice. We developed two new candidate circuit models in which neurons are active sequentially and transfer evidence faithfully to the next active population. One model encodes evidence in the relative firing of two competing chains of neurons, and the other in the network location of a stereotyped pattern ("bump") of neural activity. Neural recordings from four brain regions during an evidence accumulation task revealed that different regions displayed evidence tuning consistent with different candidate models. This work provides a mechanistic explanation for how graded information may be precisely accumulated within and transferred between neural populations, and suggests that different brain regions may accumulate evidence through different circuit mechanisms.

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bioRxiv2022

A neural substrate of sex-dependent modulation of motivation by value

While there is emerging evidence of sex differences in decision-making behavior, the neural substrates that underlie such differences remain largely unknown. Here, we demonstrate that in mice performing a value-based decision-making task, while choices are similar between the sexes, motivation to engage in the task is modulated by action value in females more strongly than in males. Inhibition of activity in anterior cingulate cortex (ACC) neurons that project to the dorsomedial striatum (DMS) disrupts this relationship between value and motivation preferentially in females, without affecting choice in either sex. In line with these effects, in females compared to males, ACC-DMS neurons have stronger representations of negative outcomes, and more neurons are active when the value of the chosen option is low. In contrast, the representation of each choice is similar between the sexes. Thus, we identify a neural substrate that contributes to sex-specific modulation of motivation by value.

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bioRxiv2022

A vector reward prediction error model explains dopaminergic heterogeneity

The hypothesis that midbrain dopamine (DA) neurons broadcast an error for the prediction of reward (reward prediction error, RPE) is among the great successes of computational neuroscience1-3. However, recent results contradict a core aspect of this theory: that the neurons uniformly convey a scalar, global signal. For instance, when animals are placed in a high-dimensional environment, DA neurons in the ventral tegmental area (VTA) display substantial heterogeneity in the features to which they respond, while also having more consistent RPE-like responses at the time of reward4. We argue that the previously predominant family of extensions to the RPE model, which replicate the classic model in multiple parallel circuits, are ill-suited to explaining these and other results concerning DA heterogeneity within the VTA. Instead, we introduce a complementary "feature-specific RPE" model positing that DA neurons within VTA report individual RPEs for different elements of a population vector code for an animals state (moment-to-moment situation). To investigate this claim, we train a deep reinforcement learning model on a navigation and decision-making task and compare the feature-specific RPE derived from the network to population recordings from DA neurons during the same task. The model recapitulates key aspects of VTA DA neuron heterogeneity. Further, we show how our framework can be extended to explain patterns of heterogeneity in action responses reported among SNc DA neurons5. Thus, our work provides a path to reconcile new observations of DA neuron heterogeneity with classic ideas about RPE coding, while also providing a new perspective on how the brain performs reinforcement learning in high dimensional environments.

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bioRxiv2021

Cholinergic interneurons mediate cocaine extinction through similar plasticity across medium spiny neuron subtypes

Cholinergic interneurons (ChINs) in the nucleus accumbens (NAc) have been implicated in the acquisition and extinction of drug associations, as well as related plasticity in medium spiny neurons (MSNs). However, since most previous work has relied on artificial manipulations, if and how endogenous patterns of cholinergic signaling relate to drug associations is unknown. Moreover, despite great interest in the opposing effects of dopamine on MSN subtypes, whether ChIN-mediated effects are similar or different across MSN subtypes is also unknown. Here, we find that endogenous acetylcholine event frequency during extinction negatively correlates with the strength and persistence of cocaine-context associations across individuals, consistent with effects of artificial manipulation of ChIN activity during extinction. Moreover, ChIN activation during extinction produces a reduction in excitatory synaptic strength on both MSN subtypes, similar to the effect of multiple extinction sessions in the absence of ChIN manipulations. Together, our findings indicate that natural variation in NAc acetylcholine may contribute to individual differences in drug-context extinction by modulating glutamatergic presynaptic strength similarly at both D1R and D2R MSN subtypes. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=146 SRC="FIGDIR/small/458113v1_ufig1.gif" ALT="Figure 1"> View larger version (30K): org.highwire.dtl.DTLVardef@14b4cc1org.highwire.dtl.DTLVardef@9c5224org.highwire.dtl.DTLVardef@d4bff3org.highwire.dtl.DTLVardef@1862150_HPS_FORMAT_FIGEXP M_FIG C_FIG

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bioRxiv2020

Distinct signals in medial and lateral VTA dopamine neurons modulate fear extinction at different times

Dopamine (DA) neurons are known to encode reward prediction error (RPE), in addition to other signals, such as salience. While RPE is known to support learning, the role of salience in supporting learning remains less clear. To address this, we recorded and manipulated VTA DA neurons in mice during fear extinction, a behavior we observed to generate spatially segregated RPE and salience signals. We applied deep learning to classify mouse freezing behavior, eliminating the need for human scoring. Our fiber photometry recordings showed that DA neurons in medial and lateral VTA have distinct activity profiles during fear extinction: medial VTA activity more closely reflected RPE, while lateral VTA activity more closely reflected a salience-like signal. Optogenetic inhibition of DA neurons in either region slowed fear extinction, with the relevant time period for inhibition differing across regions. Our results indicate that salience-like signals can have similar downstream consequences to RPE-like signals, although with different temporal dependencies.

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bioRxiv2019

Choice-selective sequences in cortical inputs to the NAc provide a potential substrate for credit assignment

How are actions linked with subsequent outcomes to guide choices? The nucleus accumbens, which is implicated in this process, receives glutamatergic inputs from the prelimbic cortex and midline regions of the thalamus. However, little is known about whether and how representations differ across these input pathways. By comparing these inputs during a reinforcement learning task in mice, we discovered that prelimbic cortical inputs preferentially represent actions and choices, whereas midline thalamic inputs preferentially represent cues. Choice-selective activity in the prelimbic cortical inputs is organized in sequences that persist beyond the outcome. Through computational modeling, we demonstrate that these sequences can support the neural implementation of reinforcement learning algorithms, both in a circuit model based on synaptic plasticity, and one based on neural dynamics. Finally, we test and confirm predictions of our circuit models by direct manipulation of nucleus accumbens input neurons. Thus, we integrate experiment and modeling to suggest neural solutions for credit assignment.

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bioRxiv2018

Value representations do not explain movement selectivity in DMS-projecting dopamine neurons

Although midbrain dopamine (DA) neurons have been thought to primarily encode reward prediction error (RPE), recent studies have also found movement-related DAergic signals. For example, we recently reported that DA neurons in mice projecting to dorsomedial striatum are modulated by choices contralateral to the recording side. Here, we introduce, and ultimately reject, a candidate resolution for the puzzling RPE vs movement dichotomy, by showing how seemingly movement-related activity might be explained by an action-specific RPE. By considering both choice and RPE on a trial-by-trial basis, we find that DA signals are modulated by contralateral choice in a manner that is distinct from RPE, implying that choice encoding is better explained by movement direction. This fundamental separation between RPE and movement encoding may help shed light on the diversity of functions and dysfunctions of the DA system.

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