Biology subjects

Denovellis, E. L.

Publications and source records attributed to Denovellis, E. L..

11 recordsLinked to original sources

bioRxiv2026

Naturalistic behavior and self-generated neural activity predictive of self-correction.

Changing ones mind -- revising a past decision independently of external cues -- can lead to better outcomes, but its neural basis remains poorly understood. We therefore developed a challenging spatial task for rats where a conflict between innate foraging biases and task rules leads to abundant, spontaneous, and characteristically corrective changes-of-mind (COMs). Neural recordings in the hippocampus, a brain region implicated in counterfactual thinking, revealed two distinct stages wherein more local representations gave way to representations of distant alternatives after animals had begun to reverse course. These representations predicted their eventual choice, and often serially encoded trial start and end locations. Our novel task paradigm reveals distinct representational phases engaged during self-correction and uncovers a rich repertoire of hippocampal spatial representations tied to behavior.

neuroscience↗

bioRxiv2026

Meta-learning is expressed through altered prefrontal cortical dynamics

Learning where and when rewards like food and water are available is essential for survival1,2. In the simplest cases where resource availability is stable, animals can learn reward contingencies by integrating outcomes across repeated samples of each possible action. In more natural settings, however, reward availability is governed by structured higher-order rules such as depletion and repletion over time. To adapt flexibly to such changing environments, optimal choices require meta-learning wherein animals learn how to learn from external feedback, ultimately enabling them to infer the underlying reward structure from abstract, generalizable rules rather than relying solely on recent outcomes3,4. The existence of meta-learning in animal behavior is well established3-8, yet the neural circuits and computations that implement it remain poorly understood9-11. Here we investigated meta-learning using a spatial foraging task in which rats acquired a depletion-repletion rule that regulated reward availability, and carried out longitudinal, high-density recordings from the medial prefrontal cortex (mPFC). We show that meta-learning engages specific, systematic changes in mPFC neural dynamics that embed the learned rule and thereby alter how the network learns action values from reward outcomes. These dynamics are based on mixed coding of task structure and value in individual mPFC neurons. At the population level, this coding organizes into low-dimensional dynamical motifs that generalize across task conditions. As meta-learning progresses, these motifs are reshaped to instantiate both rule-guided inference of future states before outcome delivery and rule-based value updating during the outcome period. These results indicate that meta-learning sculpts pre-existing prefrontal dynamics to support the acquisition of new, generalizable reward-learning strategies.

neuroscience↗

bioRxiv2024

Hippocampal representations of alternative possibilities are flexibly generated to meet cognitive demands

The cognitive ability to go beyond the present to consider alternative possibilities, including potential futures and counterfactual pasts, can support adaptive decision making. Complex and changing real-world environments, however, have many possible alternatives. Whether and how the brain can select among them to represent alternatives that meet current cognitive needs remains unknown. We therefore examined neural representations of alternative spatial locations in the rat hippocampus during navigation in a complex patch foraging environment with changing reward probabilities. We found representations of multiple alternatives along paths ahead and behind the animal, including in distant alternative patches. Critically, these representations were modulated in distinct patterns across successive trials: alternative paths were represented proportionate to their evolving relative value and predicted subsequent decisions, whereas distant alternatives were prevalent during value updating. These results demonstrate that the brain modulates the generation of alternative possibilities in patterns that meet changing cognitive needs for adaptive behavior.

neuroscience↗

bioRxiv2024

Closed-loop modulation of remote hippocampal representations with neurofeedback

Humans can remember specific remote events without acting on them and influence which memories are retrieved based on internal goals. However, animal models typically present sensory cues to trigger memory retrieval and then assess retrieval based on action. Thus, it is difficult to determine whether measured neural activity patterns relate to the cue(s), the memory, or the behavior. We therefore asked whether retrieval-related neural activity could be generated in animals without cues or a behavioral report. We focused on hippocampal "place cells" which primarily represent the animals current location (local representations) but can also represent locations away from the animal (remote representations). We developed a neurofeedback system to reward expression of remote representations and found that rats could learn to generate specific spatial representations that often jumped directly to the experimenter-defined target location. Thus, animals can deliberately engage remote representations, enabling direct study of retrieval-related activity in the brain.

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bioRxiv2024

RealtimeDecoder: A fast software module for online clusterless decoding

Decoding algorithms provide a powerful tool for understanding the firing patterns that underlie cognitive processes such as motor control, learning, and recall. When implemented in the context of a real-time system, decoders also make it possible to deliver feedback based on the representational content of ongoing neural activity. That in turn allows experimenters to test hypotheses about the role of that content in driving downstream activity patterns and behaviors. While multiple real-time systems have been developed, they are typically implemented in C++ and are locked to a specific data acquisition system, making them difficult to adapt to new experiments. Here we present a Python software system that implements online clusterless decoding using state space models in a manner independent of data acquisition systems. The parallelized system processes neural data with temporal resolution of 6 ms and median computational latency <50 ms for medium- to large-scale (32+ tetrodes) rodent hippocampus recordings without the need for spike sorting. It also executes auxiliary functions such as detecting sharp wave ripples from local field potential (LFP) data. Performance is similar to state-of-the-art solutions which use compiled programming languages. We demonstrate this system use in a rat behavior experiment in which the decoder allowed closed loop neurofeedback based on decoded hippocampal spatial representations . This system provides a powerful and easy-to-modify tool for real-time feedback experiments.

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bioRxiv2024

Regional specialization manifests in the reliability of neural population codes

The brain has the remarkable ability to learn and guide the performance of complex tasks. Decades of lesion studies suggest that different brain regions perform specialized functions in support of complex behaviors1-3. Yet recent large-scale studies of neural activity reveal similar patterns of activity and encoding distributed widely throughout the brain4-6. How these distributed patterns of activity and encoding are compatible with regional specialization of brain function remains unclear. Two frontal brain regions, the dorsal medial prefrontal cortex (dmPFC) and orbitofrontal cortex (OFC), are a paradigm of this conundrum. In the setting complex behaviors, the dmPFC is necessary for choosing optimal actions2,7,8, whereas the OFC is necessary for waiting for3,9 and learning from2,7,9-12 the outcomes of those actions. Yet both dmPFC and OFC encode both choice- and outcome-related quantities13-20. Here we show that while ensembles of neurons in the dmPFC and OFC of rats encode similar elements of a cognitive task with similar patterns of activity, the two regions differ in when that coding is consistent across trials ("reliable"). In line with the known critical functions of each region, dmPFC activity is more reliable when animals are making choices and less reliable preceding outcomes, whereas OFC activity shows the opposite pattern. Our findings identify the dynamic reliability of neural population codes as a mechanism whereby different brain regions may support distinct cognitive functions despite exhibiting similar patterns of activity and encoding similar quantities.

neuroscience↗

bioRxiv2024

Spyglass: a data analysis framework for reproducible and shareable neuroscience research

Scientific progress depends on reliable and reproducible results. Progress can be accelerated when data are shared and re-analyzed to address new questions. Current approaches to storing and analyzing neural data involve bespoke formats and software that make replication and reuse of data difficult. To address these challenges, we created Spyglass, an open-source data management and analysis framework written in Python. Spyglass provides reproducible pipelines for common neuroscience analyses and sharing of raw data, intermediate analyses, and final results within and across labs. Spyglass uses the Neurodata Without Borders (NWB) standard and includes pipelines for spectral filtering, spike sorting, pose tracking, and neural decoding. Spyglass can be extended to apply existing and newly developed pipelines to datasets from multiple sources. We demonstrate these features in the context of a cross-laboratory replication by applying advanced state space decoding algorithms to publicly available data. New users can try out Spyglass on a Jupyter Hub hosted by HHMI and 2i2c: https://spyglass.hhmi.2i2c.cloud/.

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bioRxiv2022

Neurofeedback training can modulate task-relevant memory replay in rats

Hippocampal replay - the time-compressed, sequential reactivation of ensembles of neurons related to past experience - is a key neural mechanism of memory consolidation. Replay typically coincides with a characteristic pattern of local field potential activity, the sharp-wave ripple (SWR). Reduced SWR rates are associated with cognitive impairment in multiple models of neurodegenerative disease, suggesting that a clinically viable intervention to promote SWRs and replay would prove beneficial. We therefore developed a neurofeedback paradigm for rat subjects in which SWR detection triggered rapid positive feedback in the context of a memory-dependent task. This training protocol increased the prevalence of task-relevant replay during the targeted neurofeedback period by changing the temporal dynamics of SWR occurrence. This increase was also associated with neural and behavioral forms of compensation after the targeted period. These findings reveal short-timescale regulation of SWR generation and demonstrate that neurofeedback is an effective strategy for modulating hippocampal replay.

neuroscience↗

bioRxiv2022

Dynamic Synchronization between Hippocampal Spatial Representations and the Stepping Rhythm

Summary paragraphThe hippocampus is a vertebrate brain structure that expresses spatial representations1 and is critical for navigation2,3. Navigation in turn intricately depends on locomotion; however, current accounts suggest a dissociation between hippocampal spatial representations and the details of locomotor processes. Specifically, the hippocampus is thought to primarily represent higher-order cognitive and locomotor variables like position, speed, and direction of movement4-7, while the limb movements that propel the animal are thought to be computed and represented primarily in subcortical circuits, including the spinal cord, brainstem, and cerebellum8-11. Whether hippocampal representations are actually decoupled from the detailed structure of locomotor processes remains unknown. To address this question, we simultaneously monitored hippocampal spatial representations and ongoing limb movements underlying locomotion at fast timescales. We found that the forelimb stepping cycle in freely behaving rats is rhythmic and peaks at ~8 Hz during movement, matching the ~8 Hz organization of information processing in the hippocampus during locomotion12. We also discovered precisely timed coordination between the time at which the forelimbs touch the ground ( plant times of the stepping cycle) and the hippocampal representation of space. Notably, plant times coincide with hippocampal representations closest to the actual position of the animal, while in-between these plant times, the hippocampal representation progresses towards possible future locations. This synchronization was specifically detectable when animals approached upcoming spatial decisions. Taken together, our results reveal profound and dynamic coordination on a timescale of tens of milliseconds between central cognitive representations and peripheral motor processes. This coordination engages and disengages rapidly in association with cognitive demands and is well suited to support rapid information exchange between cognitive and sensory-motor circuits.

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bioRxiv2021

Hippocampal replay reflects specific past experiences rather than a plan for subsequent choice

Executing memory-guided behavior requires both the storage of information about experience and the later recall of that information to inform choices. Awake hippocampal replay, when hippocampal neural ensembles briefly reactivate a representation related to prior experience, has been proposed to critically contribute to these memory-related processes. However, it remains unclear whether awake replay contributes to memory function by promoting the storage of past experiences, by facilitating planning based on an evaluation of those experiences, or both. We designed a dynamic spatial task which promotes replay before a memory-based choice and assessed how the content of replay related to past and future behavior. We found that replay content was decoupled from subsequent choice and instead was enriched for representations of previously rewarded locations and places that had not been recently visited, indicating a role in memory storage rather than in directly guiding subsequent behavior.

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bioRxiv2020

Hippocampal replay of experience at real-world speeds

Representations related to past experiences play a critical role in memory and decision-making processes. The rat hippocampus expresses these types of representations during sharp-wave ripple (SWR) events, and previous work identified a minority of SWRs that contain "replay" of spatial trajectories at ~20x the movement speed of the animal. Efforts to understand replay typically make multiple assumptions about which events to examine and what sorts of representations constitute replay. We therefore lack a clear understanding of both the prevalence and the range of representational dynamics associated with replay. Here we develop a state space model that uses a combination of movement dynamics of different speeds to capture the spatial content and time evolution of replay during SWRs. Using this model, we find that the large majority of replay events contain spatially coherent, interpretable content. Furthermore, many events progress at real-world, rather than accelerated, movement speeds, consistent with actual experiences.

neuroscience↗