Biology subjects

Rajan, K.

Publications and source records attributed to Rajan, K..

12 recordsLinked to original sources

bioRxiv2026

Spontaneous behavior predicts colony membership, individual identity, and rank in naked mole-rat societies

Animal societies are organized across scales, from moment-to-moment actions of individuals to dynamic group structure. A central challenge in sociobiology is to understand how behaviors of individuals shape group structure. Here, using machine vision to map recurrent fine-scale behavioral modules in the eusocial naked mole-rat (Heterocephalus glaber), we show that spontaneous behavior in isolation predicts group-level social organization, including colony membership and individual identity. We determined dominance relationships and found highly linear hierarchies, with ranks stable over months to years. Remarkably, fine-scale behavioral profiles in isolation correctly predicted pairwise dominance relationships, allowing reconstruction of rank order within each colony. Models trained within one colony predicted rank poorly in others, suggesting that naked mole-rats express social position through behavioral patterns specific to each colony. Together, our results connect fine-scale behavior to social structure maintained over years and show that, even when an animal is alone, its behavior carries signatures of its identity, colony, and place within that society.

animal behavior and cognition↗

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

Behavioral fingerprinting of the naked mole-rat uncovers signatures of eusociality and social touch

The East African naked mole-rat (Heterocephalus glaber) lives in cooperative subterranean colonies and displays a capacity to recognize social novelty, rank, and identity. The sensory cues used for social recognition remain poorly understood, especially because many of their senses are either lost or greatly reduced in comparison to other mammals. Here, we found that naked mole-rats actively touch faces 100% of the time they encounter one another in a tunnel test, followed by milliseconds speed determination of the rank of the other animal. Even in an open arena, naked mole-rats engage in face-to-face touch hundreds of times in a 10-minute social pairing and colonies do so tens of thousands of times over a 24-hour period in their home environment. To demonstrate the prominence of face touch at a molecular level, we show that social housing conditions lead to widespread activation of mechanosensory ion channels, including Piezo2, in neurons that innervate the face, but not the body. Lastly, to determine the ethological relevance of face touch, we reduced its capacity with facial whisker trimming and revealed an apparent inability for animals to recognize colony members. Together, these findings uncover face touch as a prominent social behavior in naked mole-rats that is intimately linked to social recognition.

neuroscience↗

bioRxiv2023

Representations of information value in mouse orbitofrontal cortex during information seeking

Animals are motivated to seek information that does not influence reward outcomes, suggesting that information has intrinsic value. We have developed an odor-based information seeking task that reveals that mice choose to receive information even though it does not alter the reward outcome. Moreover, mice are willing to pay for information by sacrificing water reward, suggesting that information is of intrinsic value to a mouse. We used a microendoscope to reveal neural activity in orbitofrontal cortex (OFC) while mice learned the information seeking task. We observed the emergence of distinct populations of neurons responsive to odors predictive of information and odors predictive of water reward. A latent variable model recapitulated these different representations in the low-dimensional dynamics of OFC neuronal population activity. These data suggest that mice have evolved separate pathways to represent the intrinsic value of information and the extrinsic value of water reward. Thus, the desire to acquire knowledge is observed in mice, and the value of this information is represented in the OFC. The mouse now provides a facile experimental system to study the representation of the value of information, a higher cognitive variable.

neuroscience↗

bioRxiv2023

Aversive experience drives offline ensemble reactivation to link memories across days

Memories are encoded in neural ensembles during learning and stabilized by post-learning reactivation. Integrating recent experiences into existing memories ensures that memories contain the most recently available information, but how the brain accomplishes this critical process remains unknown. Here we show that in mice, a strong aversive experience drives the offline ensemble reactivation of not only the recent aversive memory but also a neutral memory formed two days prior, linking the fear from the recent aversive memory to the previous neutral memory. We find that fear specifically links retrospectively, but not prospectively, to neutral memories across days. Consistent with prior studies, we find reactivation of the recent aversive memory ensemble during the offline period following learning. However, a strong aversive experience also increases co-reactivation of the aversive and neutral memory ensembles during the offline period. Finally, the expression of fear in the neutral context is associated with reactivation of the shared ensemble between the aversive and neutral memories. Taken together, these results demonstrate that strong aversive experience can drive retrospective memory-linking through the offline co-reactivation of recent memory ensembles with memory ensembles formed days prior, providing a neural mechanism by which memories can be integrated across days.

neuroscience↗

bioRxiv2022

CRF Neurons Establish Resilience via Stress-History-Dependent BNST Modulation

IntroductionCumulative stress is a major risk factor for developing major depressive disorder (MDD), yet not everyone experiencing chronic stress develops MDD. In those who do not, it is unclear at what point, or by what mechanism, a trajectory of stable resiliency emerges. MethodsUtilizing a 10-day repeated social defeat stress model (RSDS) for MDD, we observed that a critical period between 7 and 10 daily defeats marks the phenotypical divergence of resilient from susceptible mice. Using cell-type selective electrophysiology, chemogenetics, optogenetics, fiber photometry and RNA quantification was employed to investigate the nature of stress effects on neuroadaptation in the oval nucleus of the bed nucleus of the stria terminalis (BNSTov) required to determine resilience. ResultsIn response to ongoing stress, corticotropin-releasing factor (CRF+, but not CRF-) neurons of the (BNSTov) displayed a sustained increased firing rate in resilient, but not susceptible mice. This neurophysiological adaptation was self-sustaining, but only after 7 critical stress exposures, indicating that the process of developing resilience is dependent on stress history. ConclusionOur study reveals a novel process by which individuals might persist in the face of adversity by way of stress-provoked activation, not inhibition of a key CRF limbic region that establishes a pathway to resilience.

neuroscience↗

bioRxiv2022

Contributions and synaptic basis of diverse cortical neuron responses to task performance

Neuronal responses during behavior are diverse, ranging from highly reliable classical responses to irregular or seemingly-random non-classically responsive firing. While a continuum of response properties is frequently observed across neural systems, little is known about the synaptic origins and contributions of diverse response profiles to network function, perception, and behavior. Here we use a task-performing, spiking recurrent neural network model incorporating spike-timing-dependent plasticity that captures heterogeneous responses measured from auditory cortex of behaving rodents. Classically responsive and non-classically responsive model units contributed to task performance via output and recurrent connections, respectively. Excitatory and inhibitory plasticity independently shaped spiking responses and task performance. Local patterns of synaptic inputs predicted spiking response properties of network units as well as the responses of auditory cortical neurons from in vivo whole-cell recordings during behavior. Thus a diversity of neural response profiles emerges from synaptic plasticity rules with distinctly important functions for network performance.

neuroscience↗

bioRxiv2021

TRAKR - A reservoir-based tool for fast and accurate classification of neural time-series patterns

Distinguishing between complex nonlinear neural time-series patterns is a challenging problem in neuroscience. Accurately classifying different patterns could be useful for a wide variety of applications, e.g. detecting seizures in epilepsy and optimizing control spaces for brain-machine interfaces. It remains challenging to correctly distinguish nonlinear time-series patterns because of the high intrinsic dimensionality of such data, making accurate inference of state changes (for intervention or control) difficult. On the one hand, simple distance metrics, which can be computed quickly, often do not yield accurate classifications; on the other hand, ensembles or deep supervised approaches offer high accuracy but are training data intensive. We introduce a reservoir-based tool, state tracker (TRAKR), which provides the high accuracy of ensembles or deep supervised methods while preserving the benefits of simple distance metrics in being applicable to single examples of training data (one-shot classification). We show that TRAKR instantaneously detects deviations in dynamics as they occur through time, and can distinguish between up to 40 patterns from different chaotic data recurrent neural networks (RNNs) with above-chance accuracy. We apply TRAKR to a benchmark time-series dataset - permuted sequential MNIST - and show that it achieves high accuracy, performing on par with deep supervised networks and outperforming other distance-metric based approaches. We also apply TRAKR to electrocorticography (ECoG) data from the macaque orbitofrontal cortex (OFC) and, similarly, find that TRAKR performs on par with deep supervised networks, and more accurately than commonly used approaches such as Dynamic Time Warping (DTW). Altogether, TRAKR allows for high accuracy classification of time-series patterns from a range of different biological and non-biological datasets based on single training examples. These results demonstrate that TRAKR could be a viable alternative in the analysis of time-series data, offering the potential to generate new insights into the information encoded in neural circuits from single-trial data.

neuroscience↗

bioRxiv2021

Minian: An open-source miniscope analysis pipeline

Miniature microscopes have gained considerable traction for in vivo calcium imaging in freely behaving animals. However, extracting calcium signals from raw videos is a computationally complex problem and remains a bottleneck for many researchers utilizing single-photon in vivo calcium imaging. Despite the existence of many powerful analysis packages designed to detect and extract calcium dynamics, most have either key parameters that are hard-coded or insufficient step-by-step guidance and validations to help the users choose the best parameters. This makes it difficult to know whether the output is reliable and meets the assumptions necessary for proper analysis. Moreover, large memory demand is often a constraint for setting up these pipelines since it limits the choice of hardware to specialized computers. Given these difficulties, there is a need for a low memory demand, user-friendly tool offering interactive visualizations of how altering parameters at each step of the analysis affects data output. Our open-source analysis pipeline, Minian (Miniscope Analysis), facilitates the transparency and accessibility of single-photon calcium imaging analysis, permitting users with little computational experience to extract the location of cells and their corresponding calcium traces and deconvolved neural activities. Minian contains interactive visualization tools for every step of the analysis, as well as detailed documentation and tips on parameter exploration. Furthermore, Minian has relatively small memory demands and can be run on a laptop, making it available to labs that do not have access to specialized computational hardware. Minian has been validated to reliably and robustly extract calcium events across different brain regions and from different cell types. In practice, Minian provides an open-source calcium imaging analysis pipeline with user-friendly interactive visualizations to explore parameters and validate results.

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

Temporally-specific sequences of neural activity across interconnected corticolimbic structures during reward anticipation

Functional neuroimaging studies indicate that interconnected parts of the subcallosal anterior cingulate cortex (ACC), striatum, and amygdala play a fundamental role in affect in health and disease. Yet, while the patterns of neural activity engaged in striatum and amygdala during affective processing are well established, especially during reward anticipation, little is known about subcallosal ACC. Here we recorded neural activity in non-human primate subcallosal ACC and compared this to interconnected parts of basolateral amygdala and rostromedial striatum while macaque monkeys performed reward-based tasks. Applying multiple analysis approaches, we found that neurons in subcallosal ACC and rostromedial striatum preferentially signal anticipated reward using short bursts of activity that form temporally specific patterns. By contrast, basolateral amygdala uses a mixture of both temporally specific and more sustained patterns of activity to signal anticipated reward. Thus, dynamic patterns of neural activity across populations of neurons are engaged in affect, especially in subcallosal ACC. HIGHLIGHTSO_LISustained changes in neural activity signal anticipated reward in basolateral amygdala C_LIO_LITemporally specific patterns signal anticipated reward in all areas recorded C_LIO_LINeurons exhibit more punctate encoding when tasks become more complex C_LIO_LITemporally specific patterns of neural activity signal different anticipated rewards in BLA C_LI

neuroscience↗

bioRxiv2018

Nominally non-responsive frontal and sensory cortical cells encode task-relevant variables via ensemble consensus-building

Neurons recorded in behaving animals often do not discernibly respond to sensory input and are not overtly task-modulated. These nominally non-responsive neurons are difficult to interpret and are typically neglected from analysis, confounding attempts to connect neural activity to perception and behavior. Here we describe a trial-by-trial, spike-timing-based algorithm to reveal the hidden coding capacities of these neurons in auditory and frontal cortex of behaving rats. Responsive and nominally non-responsive cells contained significant information about sensory stimuli and behavioral decisions, and network modeling indicated that nominally non-responsive cells are important for task performance. Sensory input was more accurately represented in frontal cortex than auditory cortex, via ensembles of nominally non-responsive cells coordinating the behavioral meaning of spike timings on correct but not error trials. This unbiased approach allows the contribution of all recorded neurons - particularly those without obvious task-modulation - to be assessed for behavioral relevance on single trials.

neuroscience↗