Biology subjects

DePasquale, B.

Publications and source records attributed to DePasquale, B..

15 recordsLinked to original sources

bioRxiv2026

A Neurotensin Brake on Exploratory Drive under Persistent Threat

Avoidance behavior is an adaptive response that delays exploration to promote survival. Avoidance is enhanced by psychological stress and is a hallmark of many neuropsychiatric disorders. Neural circuits that control avoidance by integrating stressful stimuli and modulating exploratory behavior remain underexplored. Elucidating the functional dynamics of this highly conserved phenomenon and the underlying neural mechanism of avoidance is an important open-ended question, with relevance to understanding both innate behaviors and neuropsychiatric disorders. Using predator odor as an innate, chronic stressor to increase avoidance behaviors in mice, we identified a neural population in the lateral septum (LS) that integrates threat information and modulates latency to explore. Calcium recordings in freely exploring mice combined with activity-based transcriptomics revealed that predator-responsive LS neurons are GABAergic and express neurotensin (LSNT). Further, single nuclei RNA-seq analysis revealed that among predator-responsive neurons, NT-enriched inhibitory clusters are predominant. Chronic activation of LSNT neurons induces avoidance behaviors in the absence of predator odor, while synaptic silencing of this population abrogates predator-enhanced avoidance. Using transgenic mouse models to indelibly tag predator-responsive neurons, we defined the downstream circuit that connects the encoding of predator odor information to the lateral hypothalamus. Projection-specific activation of LSNT[->]LHA neurons recapitulate stress-induced avoidance behaviors in mice. Finally, we showed that deletion of neurotensin from LS neurons prevented the effects of predator odor on exploration. Together, these findings offer a genetically-and projection-defined, top-down circuit linking the limbic neurotensinergic system to chronic psychological stress and avoidance behaviors in mice.

neuroscience↗

bioRxiv2026

BiXformer: A Bidirectional Cross Attention Transformer for Disentangling Inter-Regional Neural Dynamics

Advances in high-throughput neural recording technologies enable simultaneous measurement of activity across multiple brain regions in behaving animals, producing datasets of unprecedented scale and richness. Interpreting these data remains challenging due to the bidirectional and temporally offset nature of inter-regional communication, where feedforward and feedback signals are superimposed within neural populations. We introduce BiXformer, a bidirectional cross-attention transformer that disentangles these interactions by decomposing inter-regional communication into causal and acausal streams using directionally masked attention. By enforcing temporal constraints within attention heads, BiXformer recovers low-dimensional, directed latent dynamics and estimates communication delays without relying on linearity or stationarity assumptions. We validate the model on synthetic datasets with known ground-truth delays, demonstrating accurate recovery of both latent structure and inter-regional timing. Applied to simultaneous neural-behavioral recordings and multi-region neural recordings during a movement task, BiXformer reveals interpretable, temporally structured components consistent with the coexistence of sensory feedback and motor-related signals. These results establish BiXformer as a flexible framework for uncovering dynamic, directed communication in complex neural circuits.

neuroscience↗

bioRxiv2026

Diurnal rhythms of choice: a novel state-dependent drift diffusion model uncovers time-dependent changes in rat decision making

2Time-of-day severely impacts human decision-making, with real-world consequences. Studying shifts in decision-making strategy requires controlled, long timescale behavioral measurement and analyses that can extract insight from time-varying behavior. We introduce two complementary advances to address this gap: an autonomous 24-hour training facility for continuous behavioral measurement during decision-making and an interpretable modeling framework that captures non-stationary decision dynamics from reaction times and choices. Rats were trained on a visual evidence accumulation task across months, generating over a half million trials spanning the circadian period. Our model revealed latent behavioral states characterized by distinct evidence accumulation parameters, including differences in drift rate, bias, and decision-commitment time. These states recur across days and align with feeding schedules and the light-dark cycle, producing periodic fluctuations in performance over 24 hours. Together, these results demonstrate how continuous behavioral sampling combined with generative modeling uncovers long-timescale structure in decision-making obscured by stationary analyses. 1 HIGHLIGHTSO_LI24-hour live-in operant system allows autonomous training in cognitive tasks across months C_LIO_LI24-hour measurements reveal that rat performance fluctuates with time of day C_LIO_LINovel DDM-HMM framework identifies reaction time and accuracy shifts across multiple timescales C_LIO_LIDDM-HMM captures serial dependence in decisions that classic models ignore C_LI

animal behavior and cognition↗

bioRxiv2026

Dynamic engagement of the motor cortex in controlling movement

Neural circuits do not contribute equally or continuously to behavior. In mice, the motor cortex can be essential or dispensable for movement in different contexts, but how it is dynamically recruited as behavioral demands evolve remains unclear. Here, we demonstrate that motor cortical involvement in movement exhibits rapid, discrete state transitions even during movements that otherwise appear continuous. While robust and reproducible across animals, the timing and presence of these state transitions are highly sensitive to task structure. We find that motor cortical engagement is sustained under conditions of sensorimotor uncertainty and dissipates rapidly when sensorimotor contingencies are resolved and actions and outcomes thus become predictable. These findings reveal a previously unrecognized layer of fast-timescale flexibility in the neural control of movement and offer a conceptual framework for understanding how cortical circuits dynamically govern behavior as demands evolve.

neuroscience↗

bioRxiv2025

Low rank adaptation of chemical foundation models generate effective odorant representations

AO_SCPLOWBSTRACTC_SCPLOWFeaturizing odorants to enable robust prediction of their properties is difficult due to the complex activation patterns that odorants evoke in the olfactory system. Structurally similar odorants can elicit distinct activation patterns in both the sensory periphery (i.e., at the receptor level) and downstream brain circuits (i.e., at a perceptual level). Despite efforts to design odorant features to better predict how they interact with the olfactory system, there is still no universally accepted approach to this problem. We demonstrate that feature-based approaches that rely on pre-trained foundation models to generate odorant representations do not significantly outperform classical hand-designed features on odorant-receptor binding tasks. Instead, we show that it is necessary to fine-tune these features to increase predictive performance. To show this, we introduce a new model that creates olfaction-specific representations: LoRA-based Odorant-Receptor Affinity prediction with CROSS-attention (LORAX). We compare existing chemical foundation model representations to hand-designed physicochemical descriptors using feature-based methods and identify large information overlap between these representations, highlighting the necessity of fine-tuning to generate novel and superior odorant representations. We show that LORAX produces a feature space more closely aligned with olfactory neural representation, enabling it to outperform existing models on predictive tasks.

neuroscience↗

bioRxiv2025

Recurrent connectivity supports carbon dioxide sensitivity in Aedes aegypti mosquitoes

The mosquito Aedes aegyptis human host-seeking behavior depends on the integration of multiple sensory cues. One of these cues, carbon dioxide (CO2), gates odorant and heat pathways and activates host-seeking behavior. The neuronal circuits underlying processing of CO2 information remain unclear. We used automated serial-section transmission electron microscopy (EM) to image and reconstruct the circuitry of the glomeruli that are innervated by the Ae. aegypti maxillary palp, including the glomerulus that responds to CO2. Notably, CO2-sensitive olfactory sensory neurons (OSNs) make high levels of recurrent synaptic connections with one another, while making a low density of feedforward synapses. At some of these contacts between CO2 OSNs, we observe ribbon- like presynaptic structures, which may further enhance recurrent signaling. We compared both feedforward and recurrent connectivity with all olfactory glomeruli in Drosophila melanogaster, and we found more recurrent connections between the Ae. aegypti CO2-responsive OSNs than in any D. melanogaster glomeruli. We developed a computational circuit model that demonstrates recurrent synapses are necessary for robust CO2 detection under normal physiological conditions. Together, elevated levels of recurrent connectivity and ribbon-like structures may amplify sensory information detected by CO2-sensitive OSNs to support mosquito activation and sensitization by CO2, even in the presence of high levels of other odorants in the environment. We propose that this circuit organization supports the salience of CO2 as a mosquito host cue. One Sentence SummaryConnectomic analysis of carbon dioxide circuitry in the disease-vector mosquito Aedes aegypti.

neuroscience↗

bioRxiv2025

A State-Dependent Drift Diffusion Model Reveals Mice Actively Explore A Speed-Accuracy Continuum During Decision-Making

Understanding how animals shift between different decision-making strategies is critical for bridging normative models with naturalistic behavior. While drift diffusion models (DDMs) provide a powerful framework for describing evidence accumulation in two-alternative forced choice (TAFC) tasks, they assume fixed parameters across trials--an assumption often violated in practice. Here, we intro- duce a state-dependent DDM framework in which discrete latent states modulate decision parameters from trial to trial. This approach reveals that mice dynamically switch between impulsive and deliberative decision states that differ in accuracy and response latency, suggesting active exploration of the speed-accuracy trade-off. We uncover rare high-bound states in which mice exhibit deliberation times and ac- curacies approaching those observed in humans. These results raise new questions about the cognitive flexibility of rodent decision-making and offer a foundation for studying how internal states and external variables--such as reward history or uncertainty--influence strategy selection. Our method provides a natural interface for integration with neural recordings and dynamical systems models, offering a path toward identifying the circuit-level mechanisms underlying adaptive decision behavior.

animal behavior and cognition↗

bioRxiv2024

Spatially organized striatal neuromodulator release encodes trajectory errors

Goal-directed navigation requires animals to continuously evaluate their current direction and speed of travel relative to landmarks to discern whether they are approaching or deviating from their goal. Striatal dopamine and acetylcholine are powerful modulators of goal-directed behavior, but it is unclear whether and how neuromodulator dynamics at landmarks incorporate relative motion for effective behavioral guidance. Using optical measurements in mice, we demonstrate that cue-evoked striatal dopamine release encodes bi-directional trajectory errors reflecting relationships between ongoing speed and direction of locomotion and visual flow relative to optimal goal trajectories. Striatum-wide micro-fiber array recordings resolved an anatomical gradient of trajectory error signaling across the anterior-posterior axis, distinct from trajectory error independent cue signals. Dynamic regression modeling revealed that positive and negative trajectory error encoding emerges early and late respectively during learning and over different time courses in the medial and lateral striatum, enabling region specific contributions to learning. Striatal acetylcholine release also encodes trajectory errors, but encoding is more spatially restricted, opposite polarity, and delayed relative to dopamine, supporting distinct roles in modulating striatal output and behavior. Dopamine trajectory error signaling and task performance were reproduced in a reinforcement learning model incorporating a conjunctive state space representation, suggesting a potential neural substrate for trajectory error generation. Our results establish region specific neuromodulator signals positioned to guide the speed and direction of locomotion to reach goals based on environmental landmarks during navigation.

neuroscience↗

bioRxiv2023

Non-canonical attractor dynamics underlie perceptual decision-making

Perceptual decision-making is the process by which an animal uses sensory stimuli to choose an action or mental proposition. This process is thought to be mediated by neurons organized as attractor networks1,2. However, whether attractor dynamics underlie decision behavior and the complex neuronal responses remains unclear. Here we use simultaneous recordings from hundreds of neurons, together with an unsupervised, deep learning-based method, to discover decision-related neural dynamics in frontal cortex and striatum of rats while the subjects accumulate pulsatile auditory evidence. We found that trajectories evolved along two sequential regimes, the first dominated by sensory inputs, and the second dominated by the autonomous dynamics, with flow in a direction (i.e., "neural mode") largely orthogonal to that in the first regime. We propose that the transition to the second regime corresponds to the moment of decision commitment. We developed a simplified model that approximates the coupled transition in dynamics and neural mode and allows precise inference, from each trials large-scale neural population activity, of a putative neurally-inferred time of commitment ("nTc") on that trial. The simplified model captures diverse and complex single-neuron temporal profiles, such as ramping and stepping3-5, as well as trial-averaged curved trajectories6-8, and reveals distinctions between brain regions. The estimated nTc times were not time-locked to stimulus onset or offset, or to response onset, but were instead broadly distributed across trials. If nTc marks the moment of decision commitment, sensory evidence before nTc should affect the decision, while evidence afterward should not. Behavioral analysis of trials aligned to their estimated nTc times confirmed this prediction. Our results show that the formation of a perceptual choice involves a rapid, coordinated transition in both the dynamical regime and the neural mode of the decision process that corresponds to commitment to a decision, and suggest this moment as a useful entry point for dissecting mechanisms underlying rapid changes in internal state.

neuroscience↗

bioRxiv2023

Trial-history biases in evidence accumulation can give rise to apparent lapses

Trial history biases and lapses are two of the most common suboptimalities observed during perceptual decision-making. These suboptimalities are routinely assumed to arise from distinct processes. However, several hints in the literature suggest that they covary in their prevalence and that their proposed neural substrates overlap - what could underlie these links? Here we demonstrate that history biases and apparent lapses can both arise from a common cognitive process that is normative under misbeliefs about non-stationarity in the world. This corresponds to an accumulation- to-bound model with history-dependent updates to the initial state of the accumulator. We test our models predictions about the relative prevalence of history biases and lapses, and show that they are robustly borne out in two distinct rat decision-making datasets, including data from a novel reaction time task. Our model improves the ability to precisely predict decision-making dynamics within and across trials, by positing a process through which agents can generate quasi-stochastic choices.

animal behavior and cognition↗

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

Neural population dynamics underlying evidence accumulation in multiple rat brain regions

Accumulating evidence to make decisions is a core cognitive function. Previous studies have tended to estimate accumulation using either neural or behavioral data alone. Here we develop a unified framework for modeling stimulus-driven behavior and multi-neuron activity simultaneously. We applied our method to choices and neural recordings from three rat brain regions -- the posterior parietal cortex (PPC), the frontal orienting fields (FOF), and the anterior-dorsal striatum (ADS) -- while subjects performed a pulse-based accumulation task. Each region was best described by a distinct accumulation model, which all differed from the model that best described the animals choices. FOF activity was consistent with an accumulator where early evidence was favored while the ADS reflected near perfect accumulation. Neural responses within an accumulation framework unveiled a distinct association between each brain region and choice. Choices were better predicted from all regions using a comprehensive, accumulation-based framework and different brain regions were found to differentially reflect choice-related accumulation signals: FOF and ADS both reflected choice but ADS showed more instances of decision vacillation. Previous studies relating neural data to behaviorally-inferred accumulation dynamics have implicitly assumed that individual brain regions reflect the whole-animal level accumulator. Our results suggest that different brain regions represent accumulated evidence in dramatically different ways and that accumulation at the whole-animal level may be constructed from a variety of neural-level accumulators. Impact StatementA computational framework for combining neural and behavioral data to infer latent dynamics underlying decision-making reveals distinct accumulation dynamics in different brain regions in the rat.

neuroscience↗

bioRxiv2020

Recurrent dynamics of prefrontal cortex during context-dependent decision-making

A key problem in systems neuroscience is to understand how neural populations integrate relevant sensory inputs during decision-making. Here, we address this problem by training a structured recurrent neural network to reproduce both psychophysical behavior and neural responses recorded from monkey prefrontal cortex during a context-dependent per-ceptual decision-making task. Our approach yields a one-to-one mapping of model neurons to recorded neurons, and explicitly incorporates sensory noise governing the animals performance as a function of stimulus strength. We then analyze the dynamics of the resulting model in order to understand how the network computes context-dependent decisions. We find that network dynamics preserve both relevant and irrelevant stimulus information, and exhibit a grid of fixed points for different stimulus conditions as opposed to a one-dimensional line attractor. Our work provides new insights into context-dependent decision-making and offers a powerful framework for linking cognitive function with neural activity within an artificial model.

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↗

bioRxiv2018

Error-correcting dynamics in visual working memory

Working memory is critical to cognition, decoupling behavior from the immediate world. Yet, it is imperfect; internal noise introduces errors into memory representations (1, 2). Such errors accumulate over time (3-5) and increase with the number of items simultaneously held in working memory (6-10). Here, we show that error-correcting attractor dynamics mitigate the impact of noise on working memory. These dynamics pull memories towards a few stable representations in mnemonic space, inducing a bias in memory representations but reducing the effect of noise. Model-based and model-free analyses show attractor dynamics account for the frequency, bias, and precision of working memory reports in both humans and monkeys. Furthermore, attractor dynamics were optimized to the context; they adapted to the statistics of the environment, such that memories drifted towards contextually-predicted values. Our results suggest attractor dynamics mediate errors in working memory by counteracting noise and integrating contextual information into memories.

neuroscience↗