Biology subjects

Salzman, C. D.

Publications and source records attributed to Salzman, C. D..

9 recordsLinked to original sources

bioRxiv2026

Lasting aversive consequences of a single dose of 4-hydroxytamoxifen

4-hydroxytamoxifen (4-OHT) is commonly used to facilitate activity-dependent recombination in neurons. Here we show that one dose of 4-OHT devalues alcohol, contexts, and tastants, indicating that experimental designs using 4-OHT should control for its aversiveness. Further, reactivation of 4-OHT-responsive neurons causes conditioned taste avoidance. An activity screen coupled with graph theoretical analysis identifies candidate brain areas mediating these effects. We suggest these areas could be therapeutic targets for treating addiction.

neuroscience↗

bioRxiv2026

Neural representations supporting generalization under continual learning

Abstraction and generalization are essential for flexible decision-making in novel situations. Recent work in humans and monkeys has shown how abstract variables are encoded by the representational geometry of neural population activity. However, these observations--which are typically made after learning has converged--demonstrate the product of abstraction, but not the process by which abstract knowledge is learned: how are the inputs from concrete experiences transformed into abstract knowledge, and how do neural circuits perform these operations and relay this knowledge? To address these questions, we developed a factorized model of temporal abstraction that builds on the successor representation. The model disentangles the contributions of different levels of abstract learning--from stimulus-stimulus associations to a generalizable task schema--in the form of a factorized prediction error that relates the change in relational knowledge to a predicted change in representational geometry on each trial. We fit the model to the behavior of human participants performing a context-dependent decision task during fMRI. The model captured the learning dynamics at multiple timescales, including the increasing contribution of generalization as participants transferred abstracted relational knowledge between novel task instances. In fMRI, BOLD activity in hippocampus--where, in past work, abstract knowledge was represented after learning--was increasingly attributed to the acquisition of abstract knowledge based on generalization. A similar temporal pattern was observed in entorhinal cortex, a putative source of low-dimensional structural information, and orbitofrontal cortex (OFC), which may depend on relational knowledge to represent state relationships as a cognitive map that guides choices. Indeed, individual variation in the generalization signal in OFC correlated with behavioral performance on key trials that required relational knowledge. Our findings show how the brain regions previously shown to represent abstract knowledge after learning also support the process of abstraction as it evolves from learning concrete associations to a generalizable schema. Our approach offers a computational framework for disentangling the operations driving abstract learning and probing their neural correlates in the dynamics of representational geometry.

neuroscience↗

bioRxiv2025

Two-timepoint assays of neural responses increase the sensitivity and specificity of single-cell whole-brain activity screens

Current approaches for surveying whole brains for neurons activated during a particular state typically rely on immediate early gene (IEG) expression. However, IEG expression is variable across subjects and brain areas, demanding large sample sizes. Further, it cannot determine if the same or different neurons respond to two events. To overcome these issues, we present a whole-brain screening method utilizing transgenic mice to label neurons activated at two timepoints. An imaging and analysis pipeline surveys activity in ~500 brain areas in different conditions. Compared to IEG methods, this approach reduces required sample sizes and enhances sensitivity and specificity. Finally, graph theoretical analyses are utilized to identify key circuit nodes - brain areas whose activity correlates with activity in other areas in a state-dependent manner. We validate this method by surveying whole-brain activity during hunger and satiety, and by investigating neural circuits activated by the GLP1 agonist semaglutide used to treat obesity.

neuroscience↗

bioRxiv2023

Abstract representations emerge in human hippocampal neurons during inference behavior

Humans have the remarkable cognitive capacity to rapidly adapt to changing environments. Central to this capacity is the ability to form high-level, abstract representations that take advantage of regularities in the world to support generalization1. However, little is known about how these representations are encoded in populations of neurons, how they emerge through learning, and how they relate to behavior2,3. Here we characterized the representational geometry of populations of neurons (single-units) recorded in the hippocampus, amygdala, medial frontal cortex, and ventral temporal cortex of neurosurgical patients who are performing an inferential reasoning task. We find that only the neural representations formed in the hippocampus simultaneously encode multiple task variables in an abstract, or disentangled, format. This representational geometry is uniquely observed after patients learn to perform inference, and consisted of disentangled directly observable and discovered latent task variables. Interestingly, learning to perform inference by trial and error or through verbal instructions led to the formation of hippocampal representations with similar geometric properties. The observed relation between representational format and inference behavior suggests that abstract/disentangled representational geometries are important for complex cognition.

neuroscience↗

bioRxiv2023

The representational geometry of emotional states in basolateral amygdala

Sensory stimuli associated with aversive outcomes cause multiple behavioral responses related to an animals evolving emotional state, but neural mechanisms underlying these processes remain unclear. Here aversive stimuli were presented to mice, eliciting two responses reflecting fear and flight to safety: tremble and ingress into a virtual burrow. Inactivation of basolateral amygdala (BLA) eliminated differential responses to aversive and neutral stimuli without eliminating responses themselves, suggesting BLA signals valence, not motor commands. However, two-photon imaging revealed that neurons typically exhibited mixed selectivity for stimulus identity, valence, tremble and/or ingress. Despite heterogeneous selectivity, BLA representational geometry was lower-dimensional when encoding valence, tremble and safety, enabling generalization of emotions across conditions. Further, tremble and valence coding directions were orthogonal, allowing linear readouts to specialize. Thus BLA representational geometry confers two computational properties that identify specialized neural circuits encoding variables describing emotional states: generalization across conditions, and readouts lacking interference from other readouts.

neuroscience↗

bioRxiv2021

Signatures of visuospatial information foraging during learning of complex environments

In the real world, making sequences of decisions to achieve goals often depends upon the ability to learn aspects of the environment that are not directly perceptible. Learning these so-called latent features requires seeking information about them, a process distinct from learning about near-term reward contingencies. Prior efforts to study latent feature learning often use single decisions, use few features, and fail to distinguish between reward-seeking and informationseeking. To overcome this, we designed a task in which humans and monkeys made a series of choices to search for shapes hidden on a grid. Reward and information outcomes from uncovering parts of shapes were not perfectly correlated and their effects could be disentangled. Members of both species adeptly learned the shapes and preferred to select informative tiles earlier in trials than rewarding ones, searching a part of the grid until their outcomes dropped below the average information outcome-a pattern consistent with foraging behavior. In addition, how quickly humans learned the shapes was predicted by how well their choice sequences matched the foraging pattern. This adaptive search for information may underlie the ability in humans and monkeys to learn latent features to support goal-directed behavior in the long run.

animal behavior and cognition↗

bioRxiv2020

Deep Graph Pose: a semi-supervised deep graphical model for improved animal pose tracking

Noninvasive behavioral tracking of animals is crucial for many scientific investigations. Recent transfer learning approaches for behavioral tracking have considerably advanced the state of the art. Typically these methods treat each video frame and each object to be tracked independently. In this work, we improve on these methods (particularly in the regime of few training labels) by leveraging the rich spatiotemporal structures pervasive in behavioral video -- specifically, the spatial statistics imposed by physical constraints (e.g., paw to elbow distance), and the temporal statistics imposed by smoothness from frame to frame. We propose a probabilistic graphical model built on top of deep neural networks, Deep Graph Pose (DGP), to leverage these useful spatial and temporal constraints, and develop an efficient structured variational approach to perform inference in this model. The resulting semi-supervised model exploits both labeled and unlabeled frames to achieve significantly more accurate and robust tracking while requiring users to label fewer training frames. In turn, these tracking improvements enhance performance on downstream applications, including robust unsupervised segmentation of behavioral "syllables," and estimation of interpretable "disentangled" low-dimensional representations of the full behavioral video. Open source code is available at https://github.com/paninski-lab/deepgraphpose.

animal behavior and cognition↗

bioRxiv2020

Prelimbic-dependent activation of amygdala somatostatin interneurons signals non-aversive cues to promote discrimination.

The amygdala and prelimbic cortex (PL) communicate during fear discrimination retrieval, but how they coordinate to discriminate a non-threatening stimulus is unknown. Here, we show that somatostatin interneurons (SOM) in the basolateral amygdala (BLA) become active specifically during learned non-threatening cues, when they block sensory-evoked phase resetting of theta-oscillations. Further, we show that SOM activation is PL-dependent, and promotes discrimination of non-threat. Thus, fear discrimination engages PL-dependent coordination of BLA SOM responses to non-threatening stimuli.

neuroscience↗

bioRxiv2018

Delay activity dynamics: task dependent time encoding and low dimensional trajectories

Our decisions often depend on multiple sensory experiences separated by time delays. The brain can remember these experiences and, simultaneously, estimate the timing between events. To understand the mechanisms underlying working memory and time encoding we analyze neural activity recorded during delays in four experiments on non-human primates. To disambiguate potential mechanisms, we propose two analyses, namely, decoding the passage of time from neural data, and computing the cumulative dimensionality of the neural trajectory over time. Time can be decoded with high precision in tasks where timing information is relevant and with lower precision when irrelevant for performing the task. Neural trajectories are always observed to be low dimensional. These constraints rule out working memory models that rely on constant, sustained activity, and neural networks with high dimensional trajectories, like reservoir networks. Instead, recurrent networks trained with backpropagation capture the time encoding properties and the dimensionality observed in the data.

neuroscience↗