Biology subjects

Naude, J.

Publications and source records attributed to Naude, J..

16 recordsLinked to original sources

bioRxiv2026

A promptable foundation model enables automated multi-task dataset construction and real-time pose estimation in mice

Behavioural experiments often hinge on moments when animals crowd together. In these situations, existing pose-estimation methods usually fail because detected crops include neighbouring animals, and their body parts are easily confused between individuals. We solved it with DeepLabSAM, a pipeline that segments and tracks every animal from a single text prompt using the SAM 3 foundation model, followed by a mask-gated pretrained pose estimation network from DeepLabCut. Across recordings of multiple interacting mice, the proposed method substantially reduces misassigned keypoints under heavy occlusion. Furthermore, we used DeepLabSAM to automatically construct the Multi-Task Mouse Behaviour (MTMB) dataset, comprising 14,300 frames spanning nine behavioural assays, with each instance annotated for bounding box, mask, identity, and pose. SuperAnimal pretrained pose models transferred unpredictably across this dataset. Conversely, MouseLite, our RF-DETR pose-estimation model trained on the MTMB dataset, matched their in-distribution accuracy, generalized better to unseen assays, and achieved real-time (faster-than-acquisition) inference. Combined, DeepLabSAM, MTMB dataset, and MouseLite provide a scalable framework for robust, cross-assay behavioural pose tracking of freely interacting mice.

animal behavior and cognition↗

bioRxiv2026

Mice adapt their learning rate to stochasticity and volatility through a simple heuristic

Learning from outcomes requires weighing each surprise by how much it is likely to mean. Two properties of the environment set that weight in opposite directions: volatility, the rate at which contingencies change, and stochasticity, the noise in outcomes around a stable contingency. Whether animals separate the two is unresolved, in part because varying reward probability in a reversal task makes outcomes noisier and the two options harder to tell apart at once. We trained male and female mice on a probabilistic reversal task for intracranial self-stimulation, scaling reward magnitude with reward probability so that the expected reward of each option and the difference between them stayed constant while outcome variance varied, and crossing this with different reversal rates. Simulations show that adapting the learning rate alone and adapting the decision policy alone are near-equivalent solutions across the environments we tested. Mice adopted the first adjustment: their fitted learning rate rose with volatility and fell with stochasticity, in both directions predicted by reward maximization, while their inverse temperature stayed fixed. This joint dependence was reproduced only by a meta-learning rule that reads stochasticity from the size of prediction errors and volatility from how often recent outcomes favored switching

neuroscience↗

bioRxiv2026

Threat imminence and estimates of agency dictate proactive and reactive defensive behaviors in active place avoidance

Defensive survival behaviors are shaped by both danger attributes and internal physiological states. Well-characterized defensive responses depend not only on whether danger is potential or perceived, but also on its spatial and or temporal proximity. The relationship between danger imminence and defensive behaviors is referred to as the threat imminence continuum, which provides a conceptual framework bridging ecology and behavioral neuroscience. Although this theoretical framework is gaining momentum, another much less explored danger attribute is the extent to which the environment may be controllable. Most paradigms assessing defensive behaviors rely on conditioned stimuli as danger warning-signals in stable environments that hamper the study of environment controllability and its impact on defensive behaviors. To address this question, we repurposed the active place avoidance (APA) paradigm combined with supervised machine learning to characterize multivariate exploratory and defensive behaviors in male and female mice. One important benefit of APA is that it allows the study of active avoidance when the platform is rotating as well as deliberative avoidance when the platform is immobile. As such, APA is very well suited to test the impact of environment controllability on the expression of defensive behaviors. Using this unified preparation, we systematically compared quantitative danger attributes (punishment intensity, behavioral agency) across danger states (absence, presence and omission of punishment) and learning experience (early and late learning stages), while considering sex as a biological variable. Our results demonstrate that defensive behaviors depend on punishment intensity in a sex-dependent manner in APA. Defensive responses can be classified as proactive (risk assessment) or reactive (freezing, escape) in APA. Their response probability depends on behavioral agency, is mutually exclusive and depends on danger spatial proximity. Acute treatment with norepinephrine transporter blocker selectively increases reactive defensive behaviors while sparing proactive defensive behavior. Collectively, these results provide experimental evidence that environmental stability calibrate proactive and reactive defensive behaviors whose neural correlates may be divergent.

animal behavior and cognition↗

bioRxiv2025

Orbitofrontal noradrenaline mediates volatility-dependent adjustment of learning rate

Adaptive decision-making in dynamic environments requires flexible adjustment of learning speed to balance stability and flexibility. When outcomes are highly stochastic, learners must avoid over interpreting noise and update more slowly, whereas in volatile environments where contingencies change frequently, learning should accelerate to rapidly incorporate new evidence. Theories propose that internal estimates of uncertainty tune learning rates through neuromodulatory-dependent mechanisms. Here, we investigated how noradrenergic inputs from the locus coeruleus (LC) to the orbitofrontal cortex (OFC) support adaptive learning under uncertainty. We show that rats performing a probabilistic reversal learning task exhibited behavior that was best explained by an adaptive reinforcement-learning model in which learning rates dynamically adjust according to estimated environmental volatility and stochasticity, outperforming standard fixed-rate models. Noradrenaline release in the OFC closely tracked trial-by-trial, model-derived volatility estimates around contingency changes. Disrupting LC[->]OFC noradrenergic inputs reproduced the model-predicted deficit in volatility-dependent adjustments of learning rate. Together, these findings identify OFC noradrenergic signaling as a key circuit mechanism for volatility-dependent modulation of learning rates during adaptive decision-making.

neuroscience↗

bioRxiv2025

Dopaminergic mechanisms of dynamical social specialization in mouse microsocieties

Social organization and division of labor are fundamental to animal societies, but how do these structures emerge from individual interactions, and what role does neuromodulation play in shaping them? Using behavioral tracking in a semi-natural environment, neural recordings, and computational models integrating reinforcement and social learning, we show that groups of three isogenic mice spontaneously develop specialized roles while solving a foraging task requiring individual decisions under social constraints. Moreover, these roles are shaped by dopaminergic activity in the ventral tegmental area. Strikingly, despite minor sex-differences in behavior when mice were tested alone, male triads formed stable worker-scrounger relationships driven by competition, whereas female triads adopted uniform, cooperative strategies. Model analysis revealed how intra- and inter-sex parameter differences in resource exploitation, combined with contingent and dynamic social interactions, drive behavioral specialization and labor division. Most notably, it highlighted how contingency, amplified by competition, magnifies individual differences and shapes social profiles. The plastic, adaptive nature of social organization within triads was confirmed by manipulating dopaminergic cell activity, which reshaped social roles and altered group structure. Our findings support a feedback loop where social context shapes neural states, which in turn reinforce behavioral specialization and stabilize social structures.

neuroscience↗

bioRxiv2024

Direct and indirect striatal projecting neurons exert strategy-dependent effects on decision-making.

The striatum plays a key role in decision-making, with its effects varying with anatomical location and direct and indirect pathway striatal projecting neuron (d- and i-SPN) populations. Using a mouse gambling task with a reinforcement-learning model, we described of individual decision-making profiles as a combination of three archetypal strategies: Optimizers, Risk-averse, Explorers. Such strategies reflected stable differences in the parameters generating decisions (sensitivity to the reward magnitude, to risk or to punishment) derived from a reinforcement-learning model of animal choice. Chemogenetic manipulation showed that dorsomedial striatum (DMS) neurons substantially impact decision-making, while the nucleus accumbens (NAc) and dorsolateral striatum neurons (DLS) have lesser or no effects, respectively. Specifically, DMS dSPNs decrease risk aversion by increasing the perceived value of risky choices, while DMS iSPNs emphasize large gains, affecting decisions depending on decision-making profiles. Hence, we propose that striatal populations from different subregions influence distinct decision-making parameters, leading to profile-dependent choices.

neuroscience↗

bioRxiv2022

Biophysical control of neural trajectories under disordered brain dynamics

Neural representations rely on the ability of neuronal assemblies to display organized spiking patterns, despite being embedded within noisy networks. These structured patterns arise from attractor dynamics due to activity reverberation promoted by learnt assembly connectivity. Yet, attractor dynamics have been assessed either under low-noise conditions or in highly idealized neuronal assemblies. Here, in a spiking recurrent neural network model displaying asynchronous irregular noise, we show that realistic spike timing-dependent plasticity (STDP) imposes either low controllability of attractor recall (when STDP is strong), low stability of attractor maintenance (when STDP is low), or both. Moreover, STDP-built attractors display low independence, i.e., they perturb activity in the surrounding network. These constraints may favor self-generated representation switches essential for cognitive flexibility but dampening cognitive reliability. We reveal, by contrast, that several biophysical mechanisms alleviate these issues through a common dynamical principle, protecting excitatory-driven attractorial dynamics from inhibitory-driven spontaneous fluctuations. Amongst biophysical determinants, intrinsic properties were most efficient to increase controllability, stability and independence of attractor dynamics. Specifically, spike-triggered calcium-activated conductances improved reliability by mitigating reliance on connectivity, even at low conductance levels, i.e., in the absence of cell-autonomous intrinsic bistability. Finally, we show that the mechanisms we identify operate over a large repertoire of static (e.g., Hebbian or ring) and dynamic (e.g., sequences) attractors, with uni- and bidirectional propagation. Altogether, these results pinpoint synaptic and intrinsic synergy as a generic principle to regulate attractor reliability, as a function of the cognitive demand.

neuroscience↗

bioRxiv2022

Dopamine builds and reveals reward-associated latent behavioral attractors

Phasic variations in dopamine levels are interpreted as a teaching signal reinforcing rewarded behaviors. However, behavior also depends on the online, neuromodulatory effect of phasic dopamine signaling. Here, we unravel a new neurodynamical principle that reconciles these roles. In a biophysical recurrent network-based decision architecture, we showed that dopamine-mediated synaptic plasticity stabilized neural assemblies representing rewarded locations as latent, local attractors. Dopamine-modulated synaptic excitability activated these attractors online, and they became accessible as internal goals, even from remote animal positions. We experimentally validated these predictions in mice, using optogenetics, by demonstrating that online dopamine signaling specifically attracts animals toward rewarded locations, without off-target motor effects. We therefore propose that online dopamine signaling reveals potential goals by widening and deepening the basin of dopamine-built attractors representing rewards.

neuroscience↗

bioRxiv2022

Self-generation of goal-directed choices in a distributed dopaminergic andprefrontal circuit

Goal-directed choices that are not triggered by external cues arise from internal representations of the outcomes. The use of a stimulus to specify when to act, which option to take, or whether to explore, has led to consider the reward circuit as a feedforward set of modules carrying independent computations. Here, we develop an uncued task in which mice self-determine the initiation, direction, vigor and pace of their actions based on their knowledge of the outcomes. Using electrophysiological recordings, pharmacology and optogenetics, we identify a sequence of oscillations and firing in the ventral tegmental area (VTA), orbitofrontal (OFC) and prefrontal cortices (PFC) that co-encodes and co-determines self-initiation and choices. This sequence appeared with learning as an unguided realignment of spontaneous dynamics. The interactions between the structures depended on the reward context, in particular regarding the uncertainty associated with the different options. We suggest that self-generated choices arise from a distributed circuit based on an OFC-VTA core setting whether to wait or to initiate actions, while the PFC is specifically engaged by reward uncertainty to participate in both the selection and pace of actions. HighlightsO_LISelf-paced actions arise from contextual reorganization of mesocortical dynamics. C_LIO_LIVTA, PFC and OFC complementarily encode predictions and errors about outcomes. C_LIO_LIDistributed firing-then-oscillations dynamics set the goal, initiation and pace of actions. C_LIO_LIVTA and PFC antagonistically promote and inhibit motivation by reward uncertainty. C_LI

neuroscience↗

bioRxiv2021

LACTATE IS A MAJOR ENERGY SUBSTRATE FOR CORTICAL NEURONS AND ENHANCES THEIR FIRING ACTIVITY

Glucose is the mandatory fuel for the brain, yet the relative contribution of glucose and lactate for neuronal energy metabolism is unclear. We found that increased lactate, but not glucose concentration, enhances the spiking activity of neurons of the cerebral cortex. Enhanced spiking was dependent on ATP-sensitive potassium (KATP) channels formed with Kir6.2 and SUR1 subunits, which we show are functionally expressed in most neocortical neuronal types. We also demonstrate the ability of cortical neurons to take-up and metabolize lactate. We further reveal that ATP is produced by cortical neurons largely via oxidative phosphorylation and only modestly by glycolysis. Our data demonstrate that in active neurons, lactate is preferred to glucose as an energy substrate, and that lactate metabolism shapes neuronal activity in the neocortex through KATP channels. Our results highlight the importance of metabolic crosstalk between neurons and astrocytes for brain function. HighlightsO_LIMost cortical neurons subtypes express pancreatic beta-cell like KATP channels. C_LIO_LILactate enhances spiking activity via its uptake and closure of KATP channels. C_LIO_LICortical neurons take up and oxidize lactate. C_LIO_LICortical neurons produce ATP mainly by oxidative phosphorylation. C_LI

neuroscience↗

bioRxiv2021

Chronic nicotine increases midbrain dopamine neuron activity and biases individual strategies towards reduced exploration in a foraging task.

Long-term exposure to nicotine alters brain circuits and induces profound changes in decision-making strategies, affecting behaviors both related and unrelated to drug seeking and consumption. Using an intracranial self-stimulation reward-based foraging task, we investigated the impact of chronic nicotine on the trade-off between exploitation and exploration, and the role of ventral tegmental area (VTA) dopamine (DA) neuron activity in decision-making unrelated to nicotine-seeking. Model-based and archetypal analysis revealed a substantial inter-individual variability in decision-making strategies, with mice passively exposed to chronic nicotine visiting more frequently options associated with higher reward probability and therefore shifting toward a more exploitative profile compared to non-exposed animals. We then mimicked the effect of chronic nicotine on the tonic activity of VTA DA neurons using optogenetics, and found that photo-stimulated mice had a behavioral phenotype very close to that of mice exposed to nicotine, suggesting that the dopaminergic control of the exploration/exploitation balance is altered under nicotine exposure. Our results thus reveal a key role of tonic midbrain DA in the exploration/exploitation trade-off and highlight a potential mechanism by which nicotine affects decision-making.

neuroscience↗

bioRxiv2020

A VTA GABAergic computational model of dissociated reward prediction error computation in classical conditioning

Classical Conditioning is a fundamental learning mechanism where the Ventral Striatum is generally thought to be the source of inhibition to Ventral Tegmental Area (VTA) Dopamine neurons when a reward is expected. However, recent evidences point to a new candidate in VTA GABA encoding expectation for computing the reward prediction error in the VTA. In this system-level computational model, the VTA GABA signal is hypothesised to be a combination of magnitude and timing computed in the Peduncolopontine and Ventral Striatum respectively. This dissociation enables the model to explain recent results wherein Ventral Striatum lesions affected the temporal expectation of the reward but the magnitude of the reward was intact. This model also exhibits other features in classical conditioning namely, progressively decreasing firing for early rewards closer to the actual reward, twin peaks of VTA dopamine during training and cancellation of US dopamine after training.

neuroscience↗

bioRxiv2019

Mice adaptively generate choice variability in a deterministic task

Can our choices just be driven by chance? To investigate this question, we designed a deterministic setting in which mice reinforce non-repetitive choice sequences, and modeled it using reinforcement learning. Mice progressively increased their choice variability using a memory-free, pseudo-random selection, rather than by learning complex sequences. Our results demonstrate that a decision-making process can self-generate variability and randomness even when the rules governing reward delivery are not stochastic.

neuroscience↗

bioRxiv2018

Somatic nicotinic acetylcholine receptors control the activity of dopamine neurons and reward-related behaviors.

Dopamine (DA) neurons of the ventral tegmental area (VTA) integrate cholinergic inputs to regulate key functions such as motivation and goal-directed behaviors. Yet the temporal dynamic range and mechanism of action of acetylcholine (ACh) on the modulation of VTA circuits and reward-related behaviors are not known. Here we used a chemical-genetic approach for rapid and precise optical manipulation of nicotinic neurotransmission in VTA neurons in vivo. We provide direct evidence that the ACh tone fine-tunes the firing properties of VTA DA neurons through somatic {beta}2-containing ({beta}2*) nicotinic ACh receptors (nAChRs). Furthermore, locally photo-antagonizing these receptors in the VTA was sufficient to reversibly switch nicotine reinforcement on and off. By enabling control of nicotinic transmission in targeted brain circuits, this technology will help unravel the various physiological functions of nAChRs and may assist in the design of novel therapies relevant to neuropsychiatric disorders.

neuroscience↗

bioRxiv2018

Acetylcholine-dependent phasic dopamine activity signals exploratory locomotion and choices

Dopamine neurons from the Ventral Tegmental Area (VTA) switch from tonic to phasic burst firing in response to reward-predictive cues and actions. Bursting is influenced by nicotinic acetylcholine receptors (nAChRs), which are not implicated in reinforcement learning, but rather in exploration and uncertainty-seeking. The leading model assigns these functions to tonic dopamine firing. To investigate this paradox, we recorded the activity of VTA dopamine neurons during a spatial decision-making task. When reward was certain, mice adopted a stereotyped behavior, and dopamine neurons signaled reward. When confronted with uncertain rewards or a novel environment, mice exhibited exploration. Modulation of phasic, but not tonic, dopamine activity predicted uncertainty-seeking and locomotor exploration. Deletion of nAChRs disrupted the influence of uncertainty and novelty on dopamine firing and behavior, sparing reward signaling and learning. Hence, nAChR modulation of dopamine neurons can influence cognitive functions on a short timescale, through the modulation of phasic, synchronous bursting.

neuroscience↗

bioRxiv2017

Social interactions impact on the dopaminergic system and drive individuality

Individuality is a ubiquitous and well-conserved feature among animal species. The behavioral patterns of individual animals affect their respective role in the ecosystem and their prospects for survival. Even though some of the factors shaping individuality have been identified, the mechanisms underlying individuation are poorly understood and are generally considered to be genetics-based. Here we devised a large environment where mice live continuously, and observed that individuality, measured by both social and individual traits, emerged and settled within the group. Midbrain dopamine neurons underwent neurophysiological adaptations that mirrored this phenotypic divergence in individual behaviors. Strikingly, modifying the social environment resulted in a fast re-adaptation of both the animals personality and its dopaminergic signature. These results indicate that individuality can rapidly evolve upon social challenges, and does not just depend on the genetic or epigenetic initial status of the animal.

neuroscience↗