Biology subjects

Harvey, C. D.

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

17 recordsLinked to original sources

bioRxiv2026

Inferring the evolving objectives that organize animal behavior

Understanding how the brain supports flexible, goal-directed behavior is a central aim of neuroscience. Yet such behavior is highly variable and unfolds over long timescales, making it challenging to understand its organization. Advances in behavioral tracking quantify movements but do not reveal the objectives that guide them. Motivated by this, we develop inverse reinforcement learning (IRL) into a general framework for inferring these objectives from naturalistic behavior. Our approach represents an animal's objectives as internal rewards, and infers these rewards as well as the policies they induce from observed behavior. It applies across diverse behavioral settings, from freely moving behaviors to structured tasks. It can infer objectives that switch over time or gradually change with experience. In mice capturing crickets, it identified a small set of objectives that the animals switched between over the course of a trial. In mice learning a cued labyrinth, it inferred how the animals' internal rewards changed as learning progressed. Across both datasets, the models predicted held-out actions and generated extended behavior that reproduced key features of animal performance. By transforming long, variable behavioral sequences into time-resolved estimates of the animal's objectives, our approach provides a quantitative basis for studying how the brain organizes natural behavior.

neuroscience↗

bioRxiv2026

Data-derived agents reveal dynamical reservoirs in mouse cortex for adaptive behavior

Animals generate behaviors that are robust to perturbations yet adaptable to changing conditions. How neural population dynamics support this balance between robustness and flexibility remains unclear. We address this question in goal-directed navigation by combining large-scale calcium imaging from mouse cortex with a data-derived modeling framework. We trained agents to navigate in a simulative environment while recapitulating mouse neural and behavioral data trial-by-trial. Data-derived agents discovered novel dynamics of chaotic attractors, characterized by intrinsically variable trajectories confined within overall goal-specific attracting landscapes. These dynamics support reliable goal achievement while maintaining a structured distribution of navigational trajectories. Circuit-level analyses and perturbations reveal mechanisms that stabilize chaos and enhance behavioral adaptability in the data-derived agents. Thus, through our new modeling approach that emphasizes closed-loop interactions between behavior and neural dynamics, we reveal chaos as a functional principle for flexible behavior.

neuroscience↗

bioRxiv2024

An optical brain-machine interface reveals a causal role of posterior parietal cortex in goal-directed navigation

Cortical circuits contain diverse sensory, motor, and cognitive signals, and form densely recurrent networks. This creates challenges for identifying causal relationships between neural populations and behavior. We developed a calcium imaging-based brain-machine interface (BMI) to study the role of posterior parietal cortex (PPC) in controlling navigation in virtual reality. By training a decoder to estimate navigational heading and velocity from PPC activity during virtual navigation, we discovered that mice could immediately navigate toward goal locations when control was switched to BMI. No learning or adaptation was observed during BMI, indicating that naturally occurring PPC activity patterns are sufficient to drive navigational trajectories in real time. During successful BMI trials, decoded trajectories decoupled from the mouses physical movements, suggesting that PPC activity relates to intended trajectories. Our work demonstrates a role for PPC in navigation and offers a BMI approach for investigating causal links between neural activity and behavior.

neuroscience↗

bioRxiv2023

Specialized structure of neural population codes in parietal cortex outputs

Do cortical neurons that send axonal projections to the same target area form specialized population codes for transmitting information? We used calcium imaging in mouse posterior parietal cortex (PPC), retrograde labeling, and statistical multivariate models to address this question during a delayed match-to-sample task. We found that PPC broadcasts sensory, choice, and locomotion signals widely, but sensory information is enriched in the output to anterior cingulate cortex. Neurons projecting to the same area have elevated pairwise activity correlations. These correlations are structured as information-limiting and information-enhancing interaction networks that collectively enhance information levels. This network structure is unique to sub-populations projecting to the same target and strikingly absent in surrounding neural populations with unidentified projections. Furthermore, this structure is only present when mice make correct, but not incorrect, behavioral choices. Therefore, cortical neurons comprising an output pathway form uniquely structured population codes that enhance information transmission to guide accurate behavior.

neuroscience↗

bioRxiv2023

ArMo: An Articulated Mesh Approach for Mouse 3D Reconstruction

Characterizing animal behavior requires methods to distill 3D movements from video data. Though keypoint tracking has emerged as a widely used solution to this problem, it only provides a limited view of pose, reducing the body of an animal to a sparse set of experimenter-defined points. To more completely capture 3D pose, recent studies have fit 3D mesh models to subjects in image and video data. However, despite the importance of mice as a model organism in neuroscience research, these methods have not been applied to the 3D reconstruction of mouse behavior. Here, we present ArMo, an articulated mesh model of the laboratory mouse, and demonstrate its application to multi-camera recordings of head-fixed mice running on a spherical treadmill. Using an end-to-end gradient based optimization procedure, we fit the shape and pose of a dense 3D mouse model to data-derived keypoint and point cloud observations. The resulting reconstructions capture the shape of the animals surface while compactly summarizing its movements as a time series of 3D skeletal joint angles. ArMo therefore provides a novel alternative to the sparse representations of pose more commonly used in neuroscience research.

animal behavior and cognition↗

bioRxiv2022

Mechanoreceptor signal convergence and transformation in the dorsal horn flexibly shape a diversity of outputs to the brain

The encoding of touch in the spinal cord dorsal horn (DH) and its influence on tactile representations in the brain are poorly understood. Using a range of mechanical stimuli applied to the skin, large scale in vivo electrophysiological recordings, and genetic manipulations, here we show that neurons in the mouse spinal cord DH receive convergent inputs from both low- and high-threshold mechanoreceptor subtypes and exhibit one of six functionally distinct mechanical response profiles. Genetic disruption of DH feedforward or feedback inhibitory motifs, comprised of interneurons with distinct mechanical response profiles, revealed an extensively interconnected DH network that enables dynamic, flexible tuning of postsynaptic dorsal column (PSDC) output neurons and dictates how neurons in primary somatosensory cortex respond to touch. Thus, mechanoreceptor subtype convergence and nonlinear transformations at the earliest stage of the somatosensory hierarchy shape how touch of the skin is represented in the brain.

neuroscience↗

bioRxiv2022

Synaptic wiring motifs in posterior parietal cortex support decision-making

The posterior parietal cortex (PPC) exhibits choice-selective activity during perceptual decision-making tasks. However, it is not known how this selective activity arises from the underlying synaptic connectivity. Here, we combined virtual reality behavior, two-photon calcium imaging, high throughput electron microscopy, and circuit modeling to analyze how synaptic connectivity between neurons in PPC relates to their selective activity. We found that excitatory pyramidal neurons preferentially target inhibitory interneurons with the same selectivity. In turn, inhibitory interneurons preferentially target pyramidal neurons with opposite selectivity, forming an opponent inhibition motif. Using circuit models, we show that opponent inhibition amplifies selective inputs and induces competition between neural populations with opposite selectivity, thereby improving the encoding of trial-type information. These results provide evidence for how synaptic connectivity in cortical circuits supports a learned decision-making task.

neuroscience↗

bioRxiv2022

A distributed and efficient population code of mixed selectivity neurons for flexible navigation decisions

Decision-making requires flexibility to rapidly switch sensorimotor associations depending on behavioral goals stored in memory. We identified cortical areas and neural activity patterns that mediate this flexibility during virtual-navigation, where mice switched navigation toward or away from a visual cue depending on its match to a remembered cue. An optogenetics screen identified V1, posterior parietal cortex (PPC), and retrosplenial cortex (RSC) as necessary for accurate decisions. Calcium imaging revealed neurons that can mediate rapid sensorimotor switching by encoding a conjunction of a current and remembered visual cue that predicted the mouses navigational choice from trial-to-trial. Their activity formed efficient population codes before correct, but not incorrect, choices. These neurons were distributed across posterior cortex, even V1, but were densest in RSC and sparsest in PPC. We propose the flexibility of navigation decisions arises from neurons that mix visual and memory information within a visual-parietal-retrosplenial network, centered in RSC.

neuroscience↗

bioRxiv2021

Cognitive experience alters cortical involvement in navigation decisions

The neural correlates of decision-making have been investigated extensively, and recent work aims to identify under what conditions cortex is actually necessary for making accurate decisions. We discovered that mice with distinct cognitive experiences, beyond sensory and motor learning, use different cortical areas and neural activity patterns to solve the same task, revealing past learning as a critical determinant of whether cortex is necessary for decision-making. We used optogenetics and calcium imaging to study the necessity and neural activity of multiple cortical areas in mice with different training histories. Posterior parietal cortex and retrosplenial cortex were mostly dispensable for accurate decision-making in mice performing a simple navigation-based decision task. In contrast, these areas were essential for the same simple task when mice were previously trained on complex tasks with delay periods or association switches. Multi-area calcium imaging showed that, in mice with complex-task experience, single-neuron activity had higher selectivity and neuron-neuron correlations were weaker, leading to codes with higher task information. Therefore, past experience sets the landscape for how future tasks are solved by the brain and is a key factor in determining whether cortical areas have a causal role in decision-making.

neuroscience↗

bioRxiv2021

Mechanoreceptor synapses in the brainstem shape the central representation of touch

Mammals use glabrous (hairless) skin of their hands and feet to navigate and manipulate their environment. Cortical maps of the body surface across species contain disproportionately large numbers of neurons dedicated to glabrous skin sensation, potentially reflecting a higher density of mechanoreceptors that innervate these skin regions. Here, we find that disproportionate representation of glabrous skin emerges over postnatal development at the first synapse between peripheral mechanoreceptors and their central targets in the brainstem. Mechanoreceptor synapses undergo developmental refinement that depends on proximity of their terminals to glabrous skin, such that those innervating glabrous skin make synaptic connections that expand their central representation. In mice that do not sense gentle touch, mechanoreceptors innervating glabrous skin still make more powerful synaptic connections in the brainstem. We propose that the skin region a mechanoreceptor innervates controls refinement of its central synapses over development to shape the representation of touch in the brain.

neuroscience↗

bioRxiv2020

Inferring brain-wide interactions using data-constrained recurrent neural network models

Behavior arises from the coordinated activity of numerous anatomically and functionally distinct brain regions. Modern experimental tools allow unprecedented access to large neural populations spanning many interacting regions brain-wide. Yet, understanding such large-scale datasets necessitates both scalable computational models to extract meaningful features of inter-region communication and principled theories to interpret those features. Here, we introduce Current-Based Decomposition (CURBD), an approach for inferring brain-wide interactions using data-constrained recurrent neural network models that directly reproduce experimentally-obtained neural data. CURBD leverages the functional interactions inferred by such models to reveal directional currents between multiple brain regions. We first show that CURBD accurately isolates inter-region currents in simulated networks with known dynamics. We then apply CURBD to multi-region neural recordings obtained from mice during running, macaques during Pavlovian conditioning, and humans during memory retrieval to demonstrate the widespread applicability of CURBD to untangle brain-wide interactions underlying behavior from a variety of neural datasets.

neuroscience↗

bioRxiv2020

DeepEthogram: a machine learning pipeline for supervised behavior classification from raw pixels

Researchers commonly acquire videos of animal behavior and quantify the prevalence of behaviors of interest to study nervous system function, the effects of gene mutations, and the efficacy of pharmacological therapies. This analysis is typically performed manually and is therefore immensely time consuming, often limited to a small number of behaviors, and variable across researchers. Here, we created DeepEthogram: software that takes raw pixel values of videos as input and uses machine learning to output an ethogram, the set of user-defined behaviors of interest present in each frame of a video. We used convolutional neural network models that compute motion in a video, extract features from motion and single frames, and classify these features into behaviors. These models classified behaviors with greater than 90% accuracy on single frames in videos of flies and mice, matching expert-level human performance. The models accurately predicted even extremely rare behaviors, required little training data, and generalized to new videos and subjects. DeepEthogram runs rapidly on common scientific computer hardware and has a graphical user interface that does not require programming by the end-user. We anticipate DeepEthogram will enable the rapid, automated, and reproducible assignment of behavior labels to every frame of a video, thus accelerating all those studies that quantify behaviors of interest. Code is available at: https://github.com/jbohnslav/deepethogram

neuroscience↗

bioRxiv2020

Correlations enhance the behavioral readout of neural population activity in association cortex

The spatiotemporal structure of activity in populations of neurons is critical for accurate perception and behavior. Experimental and theoretical studies have focused on "noise" correlations - trial-to-trial covariations in neural activity for a given stimulus - as a key feature of population activity structure. Much work has shown that these correlations limit the stimulus information encoded by a population of neurons, leading to the widely-held prediction that correlations are detrimental for perceptual discrimination behaviors. However, this prediction relies on an untested assumption: that the neural mechanisms that read out sensory information to inform behavior depend only on a populations total stimulus information independently of how correlations constrain this information across neurons or time. Here we make the critical advance of simultaneously studying how correlations affect both the encoding and the readout of sensory information. We analyzed calcium imaging data from mouse posterior parietal cortex during two perceptual discrimination tasks. Correlations limited the ability to encode stimulus information, but (seemingly paradoxically) correlations were higher when mice made correct choices than when they made errors. On a single-trial basis, a mouses behavioral choice depended not only on the stimulus information in the activity of the population as a whole, but unexpectedly also on the consistency of information across neurons and time. Because correlations increased information consistency, sensory information was more efficiently converted into a behavioral choice in the presence of correlations. Given this enhanced-by-consistency readout, we estimated that correlations produced a behavioral benefit that compensated or overcame their detrimental information-limiting effects. These results call for a re-evaluation of the role of correlated neural activity, and suggest that correlations in association cortex can benefit task performance even if they decrease sensory information.

neuroscience↗

bioRxiv2020

Scaling of information in large neural populations reveals signatures of information-limiting correlations

How is information distributed across large neuronal populations within a given brain area? One possibility is that information is distributed roughly evenly across neurons, so that total information scales linearly with the number of recorded neurons. Alternatively, the neural code might be highly redundant, meaning that total information saturates. Here we investigated how information about the direction of a moving visual stimulus is distributed across hundreds of simultaneously recorded neurons in mouse primary visual cortex (V1). We found that information scales sublinearly, due to the presence of correlated noise in these populations. Using recent theoretical advances, we compartmentalized noise correlations into information-limiting and nonlimiting components, and then extrapolated to predict how information grows when neural populations are even larger. We predict that tens of thousands of neurons are required to encode 95% of the information about visual stimulus direction, a number much smaller than the number of neurons in V1. Overall, these findings suggest that the brain uses a widely distributed, but nonetheless redundant code that supports recovering most information from smaller subpopulations.

neuroscience↗

bioRxiv2019

Stable task information from an unstable neural population

Over days and weeks, neural activity representing an animals position and movement in sensorimotor cortex has been found to continually reconfigure or drift during repeated trials of learned tasks, with no obvious change in behavior. This challenges classical theories which assume stable engrams underlie stable behavior. However, it is not known whether this drift occurs systematically, allowing downstream circuits to extract consistent information. We show that drift is systematically constrained far above chance, facilitating a linear weighted readout of behavioural variables. However, a significant component of drift continually degrades a fixed readout, implying that drift is not confined to a null coding space. We calculate the amount of plasticity required to compensate drift independently of any learning rule, and find that this is within physiologically achievable bounds. We demonstrate that a simple, biologically plausible local learning rule can achieve these bounds, accurately decoding behavior over many days.

neuroscience↗

bioRxiv2019

The influence of visual cortex on perception is modulated by behavioural state

Our understanding of the link between neural activity and perception remains incomplete. Microstimulation and optogenetic experiments have shown that manipulating cortical activity can influence sensory-guided behaviour or elicit artificial percepts. And yet, some perceptual tasks can still be solved when sensory cortex is silenced or removed, suggesting that cortical activity may not always be essential. Reconciling these findings, and providing a quantitative framework linking cortical activity and behaviour, requires knowledge of the identity of the cells being activated during the behaviour, the engagement of the local and downstream networks, and the cortical and behavioural state. Here, we performed two-photon population calcium imaging in L2/3 primary visual cortex (V1) of headfixed mice performing a visual detection task while simultaneously activating specific groups of neurons using targeted two-photon optogenetics during low contrast visual stimulation. Only activation of groups of cells with similar tuning to the relevant visual stimulus led to a measurable bias of detection behaviour. Targeted photostimulation revealed signatures of centre-surround, predominantly inhibitory and like-to-like connectivity motifs in the local network which shaped the visual stimulus representation and partially explained the change in stimulus detectability. Moreover, the behavioural effects depended on overall performance: when the task was challenging for the mouse, V1 activity was more closely linked to performance, and cortical stimulation boosted perception. In contrast, when the task was easy, V1 activity was less informative about performance and cortical stimulation suppressed stimulus detection. Altogether, we find that both the selective routing of information through functionally specific circuits, and the prevailing cortical state, make similarly large contributions to explaining the behavioural response to photostimulation. Our results thus help to reconcile contradictory findings about the involvement of primary sensory cortex in behavioural tasks, suggesting that the influence of cortical activity on behaviour is dynamically reassigned depending on the demands of the task.

neuroscience↗

bioRxiv2018

All-optical electrophysiology reveals brain-state dependent changes in hippocampal subthreshold dynamics and excitability

A technology to record membrane potential from multiple neurons, simultaneously, in behaving animals will have a transformative impact on neuroscience research1. Parallel recordings could reveal the subthreshold potentials and intercellular correlations that underlie network behavior2. Paired stimulation and recording can further reveal the input-output properties of individual cells or networks in the context of different brain states3. Genetically encoded voltage indicators are a promising tool for these purposes, but were so far limited to single-cell recordings with marginal signal to noise ratio (SNR) in vivo4-6. We developed improved near infrared voltage indicators, high speed microscopes and targeted gene expression schemes which enabled recordings of supra- and subthreshold voltage dynamics from multiple neurons simultaneously in mouse hippocampus, in vivo. The reporters revealed sub-cellular details of back-propagating action potentials, correlations in sub-threshold voltage between multiple cells, and changes in dynamics associated with transitions from resting to locomotion. In combination with optogenetic stimulation, the reporters revealed brain state-dependent changes in neuronal excitability, reflecting the interplay of excitatory and inhibitory synaptic inputs. These tools open the possibility for detailed explorations of network dynamics in the context of behavior.

neuroscience↗