Biology subjects

Aronov, D.

Publications and source records attributed to Aronov, D..

12 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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bioRxiv2024

Remote activation of place codes by gaze in a highly visual animal

Vision enables many animals to perform spatial reasoning from remote locations. By viewing distant landmarks, animals recall spatial memories and plan future trajectories. Although these spatial functions depend on hippocampal place cells, the relationship between place cells and active visual behavior is unknown. Here, we studied a highly visual animal, the chickadee, in a behavior that required alternating between remote visual search and spatial navigation. We leveraged the unique head-directed nature of avian vision to track gaze in freely moving animals. We discovered a profound link between place coding and gaze. Place cells activated not only when the chickadee was in a specific location, but also when it simply gazed at that location from a distance. Gaze coding was precisely timed by fast, ballistic head movements called "head saccades". On each saccadic cycle, the hippocampus switched between encoding a prediction of what the bird was about to see and a reaction to what it actually saw. The temporal structure of these responses was coordinated by subclasses of interneurons that fired at different phases of the saccade. We suggest that place and gaze coding are components of a unified process by which the hippocampus represents the location that an animal is currently attending to. This process allows the hippocampus to implement both local and remote spatial functions.

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bioRxiv2024

Barcode activity in a recurrent network model of the hippocampus enables efficient memory binding

Forming an episodic memory requires binding together disparate elements that co-occur in a single experience. One model of this process is that neurons representing different components of a memory bind to an "index" -- a subset of neurons unique to that memory. Evidence for this model has recently been found in chickadees, which use hippocampal memory to store and recall locations of cached food. Chickadee hippocampus produces sparse, high-dimensional patterns ("barcodes") that uniquely specify each caching event. Unexpectedly, the same neurons that participate in barcodes also exhibit conventional place tuning. It is unknown how barcode activity is generated, and what role it plays in memory formation and retrieval. It is also unclear how a memory index (e.g. barcodes) could function in the same neural population that represents memory content (e.g. place). Here, we design a biologically plausible model that generates barcodes and uses them to bind experiential content. Our model generates barcodes from place inputs through the chaotic dynamics of a recurrent neural network and uses Hebbian plasticity to store barcodes as attractor states. The model matches experimental observations that memory indices (barcodes) and content signals (place tuning) are randomly intermixed in the activity of single neurons. We demonstrate that barcodes reduce memory interference between correlated experiences. We also show that place tuning plays a complementary role to barcodes, enabling flexible, contextually-appropriate memory retrieval. Finally, our model is compatible with previous models of the hippocampus as generating a predictive map. Distinct predictive and indexing functions of the network are achieved via an adjustment of global recurrent gain. Our results suggest how the hippocampus may use barcodes to resolve fundamental tensions between memory specificity (pattern separation) and flexible recall (pattern completion) in general memory systems.

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bioRxiv2023

Barcoding of episodic memories in the hippocampus of a food-caching bird

Episodic memory, or memory of experienced events, is a critical function of the hippocampus1-3. It is therefore important to understand how hippocampal activity represents specific events in an animals life. We addressed this question in chickadees - specialist food-caching birds that hide food at scattered locations and use memory to find their caches later in time4, 5. We performed high-density neural recordings in the hippocampus of chickadees as they cached and retrieved seeds in a laboratory arena. We found that each caching event was represented by a burst of firing in a unique set of hippocampal neurons. These barcode-like patterns of activity were sparse (<10% of neurons active), uncorrelated even for immediately adjacent caches, and different even for separate caches at the same location. The barcode representing a specific caching event was transiently reactivated whenever a bird later interacted with the same cache - for example, to retrieve food. Barcodes co-occurred with conventional place cell activity6, 7, as well as location-independent responses to cached seeds. We propose that barcodes are signatures of episodic memories evoked during memory recall. These patterns assign a unique identifier to each event and may be a mechanism for rapid formation and storage of many non-interfering memories.

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bioRxiv2023

Topography of inputs into the hippocampal formation of a food-caching bird

The mammalian hippocampal formation (HF) is organized into domains associated with different functions. These differences are driven in part by the pattern of input along the hippocampal long axis, such as visual input to the septal hippocampus and amygdalar input to temporal hippocampus. HF is also organized along the transverse axis, with different patterns of neural activity in the hippocampus and the entorhinal cortex. In some birds, a similar organization has been observed along both of these axes. However, it is not known what role inputs play in this organization. We used retrograde tracing to map inputs into HF of a food-caching bird, the black-capped chickadee. We first compared two locations along the transverse axis: the hippocampus and the dorsolateral hippocampal area (DL), which is analogous to the entorhinal cortex. We found that pallial regions predominantly targeted DL, while some subcortical regions like the lateral hypothalamus (LHy) preferentially targeted the hippocampus. We then examined the hippocampal long axis and found that almost all inputs were topographic along this direction. For example, the anterior hippocampus was preferentially innervated by thalamic regions, while posterior hippocampus received more amygdalar input. Some of the topographies we found bear resemblance to those described in the mammalian brain, revealing a remarkable anatomical similarity of phylogenetically distant animals. More generally, our work establishes the pattern of inputs to HF in chickadees. Some of these patterns may be unique to chickadees, laying the groundwork for studying the anatomical basis of these birds exceptional hippocampal memory.

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bioRxiv2023

An entorhinal-like region in food-caching birds

The mammalian entorhinal cortex routes inputs from diverse sources into the hippocampus. This information is mixed and expressed in the activity of many specialized entorhinal cell types, which are considered indispensable for hippocampal function. However, functionally similar hippocampi exist even in non-mammals that lack an obvious entorhinal cortex, or generally any layered cortex. To address this dilemma, we mapped extrinsic hippocampal connections in chickadees, whose hippocampi are used for remembering numerous food caches. We found a well-delineated structure in these birds that is topologically similar to the entorhinal cortex and interfaces between the hippocampus and other pallial regions. Recordings of this structure revealed entorhinal-like activity, including border and multi-field grid-like cells. These cells were localized to the subregion predicted by anatomical mapping to match the dorsomedial entorhinal cortex. Our findings uncover an anatomical and physiological equivalence of vastly different brains, suggesting a fundamental nature of entorhinal-like computations for hippocampal function.

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bioRxiv2022

Neural learning rules for generating flexible predictions and computing the successor representation

The predictive nature of the hippocampus is thought to be useful for memory-guided cognitive behaviors. Inspired by the reinforcement learning literature, this notion has been formalized as a predictive map called the successor representation (SR). The SR captures a number of observations about hippocampal activity. However, the algorithm does not provide a neural mechanism for how such representations arise. Here, we show the dynamics of a recurrent neural network naturally calculate the SR when the synaptic weights match the transition probability matrix. Interestingly, the predictive horizon can be flexibly modulated simply by changing the network gain. We derive simple, biologically plausible learning rules to learn the SR in a recurrent network. We test our model with realistic inputs and match hippocampal data recorded during random foraging. Taken together, our results suggest that the SR is more accessible in neural circuits than previously thought and can support a broad range of cognitive functions.

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bioRxiv2021

A high-fat diet changes astrocytic metabolism to enhance synaptic plasticity and promote exploratory behavior

A high-fat diet (HFD) is generally considered to negatively influence the body, the brain, and cognitive abilities. On the other hand, fat and fatty acids are essential for nourishing and constructing brain tissue. Astrocytes are central for lipolysis and fatty acids metabolism. Here we show that exposure of young mice to one month of HFD elevates lipid content and increases the relative amount of reduced cytochromes in astrocytes but not in neurons. Metabolic changes were paralleled with an enlargement of astrocytic territorial domains due to an increased outgrowth of branches and leaflets. Astrocyte remodeling was associated with an increase in expression of ezrin and with no changes in glial fibrillary acidic protein (GFAP), glutamate transporter-1 (GLT-1), and glutamine synthetase (GS). Such physiological (non-reactive) enlargement of astrocytes in the brain active milieu promoted glutamate clearance and long-term potentiation. These changes translated into improved exploratory behavior. Thus, dietary fat intake is not invariably harmful and might exert beneficial effects depending on the biological context. In BriefA high-fat diet stimulates the metabolism and growth of astrocytes, which improves glutamate clearance, synaptic plasticity, and exploratory behavior in young mice. Thus, dietary fat arguably is an essential component of the diet for children and young adults, supporting the optimal development of the brain. HighlightsO_LIExposure of young mice to a high-fat diet elevated lipid content and increased amount of reduced cytochromes in astrocytes but not in neurons. C_LIO_LIMetabolic changes were paralleled with an enlargement of astrocytic territorial domains due to an increased outgrowth of branches and leaflets. C_LIO_LIAstrocytic enlargement was associated with increased expression of ezrin but not GFAP, hence was not reactive but physiological C_LIO_LIExpansion of astrocytes in the brain active milieu improved glutamate clearance and long-term potentiation. C_LIO_LIThe high-fat diet improved exploratory behavior in young mice. C_LI

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bioRxiv2021

Flexible use of memory by food-caching birds in a laboratory behavioral paradigm

Memory is used by animals to influence navigational decisions, and neural activity in memory-related brain regions correlates to spatial variables. However, navigation is a rich behavior that contains a mix of memory-guided and memory-independent strategies. Disentangling the contribution of these strategies to navigation is therefore critical for understanding how memory influences behavioral output. To address this issue, we studied the natural spatial behavior of the chickadee, a food-caching bird. These birds hide food items at concealed, scattered locations and retrieve their caches later in time. We designed an apparatus that allows chickadees to cache and retrieve food while navigating in a laboratory arena. This apparatus enabled detailed, automated, and high-throughput tracking of key behavioral variables - including caches, retrievals, and investigations of cache sites. We built probabilistic models to fit these behavioral data using a combination of various mnemonic and non-mnemonic factors. We find that chickadees use some navigational strategies that are independent of cache memories, including opportunistic foraging and spatial biases. They combine these strategies with spatially precise and long-lasting memories of which sites contain caches and which sites they have previously checked and found to be empty. These memories are used in a context-dependent manner. During caching, chickadees avoid sites that already contain food. During retrieval, they are instead attracted to such occupied sites. These results show that a single memory can be used flexibly by a chickadee to achieve at least two unrelated behavioral goals. Our apparatus enables studying this flexibility in a tractable spatial paradigm.

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bioRxiv2020

Precise spatial representations in the hippocampus of a food-caching bird

The hippocampus is an ancient neural circuit required for the formation of episodic memories. In mammals, this ability is thought to depend on well-documented patterns of neural activity, including place cells and sharp wave ripples. Notably, neither pattern has been found in non-mammals, despite compelling examples of episodic-like memory across a wide range of vertebrates. Does episodic memory nonetheless have a universal implementation across distant neural systems? We addressed this question by recording neural activity in the hippocampus of the tufted titmouse - an intense memory specialist from a food-caching family of birds. These birds cache large numbers of food items at scattered, concealed locations and use hippocampus-dependent memory to retrieve their caches. We found remarkably precise spatial representations akin to classic place cells, as well as sharp wave ripples, in the titmouse hippocampus. These patterns were organized along similar anatomical axes to those found in mammals. In contrast, spatial coding was weaker in a different, non-food-caching bird species. Our findings suggest a striking conservation of hippocampal mechanisms across distant vertebrates, in spite of vastly divergent anatomy and cytoarchitecture. At the same time, these results demonstrate that the exact implementation of such common mechanisms may conform to the unique ethological needs of different species.

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bioRxiv2018

Visual cue-related activity of cells in the medial entorhinal cortex during navigation in virtual reality

During spatial navigation, animals use self-motion to estimate positions through path integration. However, estimation errors accumulate over time and it is unclear how they are corrected. Here we report a new cell class (\"cue cell\") in mouse medial entorhinal cortex (MEC) that encoded visual cue information that could be used to correct errors in path integration. Cue cells accounted for a large fraction of unidentified MEC cells. They exhibited firing fields only near visual cues during virtual navigation and spatially stable activity during navigation in a real arena. Cue cells responses occurred in sequences repeated at each cue and were likely driven by visual inputs. In layers 2/3 of the MEC, cue cells formed clusters. Anatomically adjacent cue cells responded similarly to cues. These cue cell properties demonstrate that the MEC circuits contain a code representing spatial landmarks that could play a significant role in error correction during path integration.

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bioRxiv2018

Probing variability in a cognitive map using manifold inference from neural dynamics

Hippocampal neurons fire selectively in local behavioral contexts such as the position in an environment or phase of a task,1-3 and are thought to form a cognitive map of task-relevant variables.1,4,5 However, their activity varies over repeated behavioral conditions,6 such as different runs through the same position or repeated trials. Although widely observed across the brain,7-10 such variability is not well understood, and could reflect noise or structure, such as the encoding of additional cognitive information.6,11-13 Here, we introduce a conceptual model to explain variability in terms of underlying, population-level structure in single-trial neural activity. To test this model, we developed a novel unsupervised learning algorithm incorporating temporal dynamics, in order to characterize population activity as a trajectory on a nonlinear manifold--a space of possible network states. The manifolds structure captures correlations between neurons and temporal relationships between states, constraints arising from underlying network architecture and inputs. Using measurements of activity over time but no information about exogenous behavioral variables, we recovered hippocampal activity manifolds during spatial and non-spatial cognitive tasks in rats. Manifolds were low-dimensional and smoothly encoded task-related variables, but contained an extra dimension reflecting information beyond the measured behavioral variables. Consistent with our model, neurons fired as a function of overall network state, and fluctuations in their activity across trials corresponded to variation in the underlying trajectory on the manifold. In particular, the extra dimension allowed the system to take different trajectories despite repeated behavioral conditions. Furthermore, the trajectory could temporarily decouple from current behavioral conditions and traverse neighboring manifold points corresponding to past, future, or nearby behavioral states. Our results suggest that trial-to-trial variability in the hippocampus is structured, and may reflect the operation of internal cognitive processes. The manifold structure of population activity is well-suited for organizing information to support memory,1,5,14 planning,12,15,16 and reinforcement learning.17,18 In general, our approach could find broader use in probing the organization and computational role of circuit dynamics in other brain regions.

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