Biology subjects

El Hady, A.

Publications and source records attributed to El Hady, A..

18 recordsLinked to original sources

bioRxiv2026

Socio-cognitive models in a patch foraging setting: a case study for model selection and parameter identifiability methods

The mechanisms by which organisms extract relevant information from complex sensory signals and use it for decision-making constitute a fundamental issue in biological and cognitive sciences. In social animal societies, individual decision-making is profoundly shaped by information provided by conspecifics. Collective patch-foraging experiments in the laboratory provide a controlled setting in which social information use can be quantified. Here, we consider a go/no-go task in which groups choose between two patches differing in food reward probability. We use agent-based simulations, underpinned by an augmented collective drift-diffusion model, to investigate alternative mechanisms for representing and integrating social information. We consider two representations, continuous (counting representation) and discrete (pulsatile representation), and two integration mechanisms, biasing the decision threshold (threshold modulation) and modifying the accumulated belief (belief modulation), yielding four distinct social models, in addition to a non-interacting model. We first characterize the collective dynamics generated by these models across cognitive parameters and experimental conditions. The temporal dynamics of group accuracy provide informative signatures of the underlying mechanisms. We then assess model selection and parameter identifiability using Bayesian inference and Wasserstein distance minimization, applied to group-accuracy and departure-time distributions. Bayesian inference using distributions of group accuracy provides the most reliable identification across the conditions considered. Importantly, model selection and parameter identifiability are associated with different aspects of collective behavior: model selection is closely linked to the presence of oscillations in the temporal dynamics, whereas parameter identifiability is more closely related to the global accuracy value. Thus, the information available for distinguishing the underlying cognitive mechanisms is not necessarily the same as that required to recover their parameters. Our results further show that identification depends not only on model structure but also on the experimental conditions. Overall, this study provides practical guidelines for identifying socio-cognitive mechanisms in collective foraging experiments.

animal behavior and cognition↗

bioRxiv2026

Resource depletion accelerates rate learning but not composition learning in patch foraging

Foraging is a universal animal behavior that has increasingly attracted the interest of both experimentalists and theorists. Most prior models assume an animal knows the distribution of resources in its environment, but this structure must be learned as the animal explores its environment. Foraging can thus be regarded as a hierarchical inference problem. We develop a normative Bayesian account of an agent learning a patchy environment while exploiting it, and show that resource depletion shapes the levels of that hierarchy differently. Within a patch, depletion accelerates rate learning, since successive encounters occur at falling rates whose spacing pins down the initial rate. Across patches, composition learning, inferring the fraction of patches that are high yield, is slow, set by the number of patches sampled rather than the time spent in each, and unaffected by depletion once the rates are known. Reward-maximizing and information-seeking strategies therefore diverge, a forager resolving the composition underharvesting rich patches because learning it requires departures, most sharply early in exposure. When a fixed set of patches replenishes between visits, the reward-maximizing policy collapses onto a stable orbit over the high-yield patches, and the replenishment rate sets whether the forager maps the whole environment or locks onto a rich subset. Which departure rule maximizes intake is itself set by how variable the patches are, switching from counting prey to timing the gaps between them once richness varies by more than about a quarter. Learning the environment thus buys significant intake over learning a rule from reward alone, as long as its assumptions about depletion leave room for the truth. Author summaryAnimals entering an unfamiliar landscape must both gather food and learn what kind of environment they face. We study this using a mathematical model of an animal moving among food patches and deciding when to keep feeding or leave. Feeding depletes a patch, usually viewed only as a cost. We show it is also informative. As food is removed items arrive more slowly, and that pattern reveals how rich the patch was to begin with, so an animal learns patch quality faster where supply runs down than where it does not. The benefit does not extend to every feature. Learning what fraction of patches are rich depends on how many patches are visited rather than how long the animal stays in each, so depletion neither helps nor harms it, and an animal seeking that information should leave even rich patches early to sample more locations. Finally, the best rule for when to leave depends on how much patches differ. Where patches are alike an animal should count what it has eaten, and where they differ it should time the gap since its last find, because a long gap then signals a poor patch rather than an emptied one.

animal behavior and cognition↗

bioRxiv2026

Environmental statistics and sensory experience shape patch foraging strategies in Drosophila larvae

Animals foraging in patchy environments must balance exploiting current resources with exploring for better alternatives to maximize resource intake and to survive. However, the neural and computational mechanisms underlying such adaptive decisions have just recently begun to be understood. Using Drosophila larvae as an experimentally tractable model, we combine long-timescale behavioral tracking in controlled patchy environments with varying statistics, along with quantitative analysis and computational modeling, to dissect foraging decision strategies. We show that larvae flexibly adjust their behavior according to both the quality and valence of available resources, shaped by prior foraging experience. A simple integration model recapitulates larval patch-leaving behavior, with model parameters tuned by environmental statistics and foraging history. Together, these findings establish Drosophila larvae as a powerful system for studying adaptive foraging and for uncovering the neural circuit mechanisms that implement experience-dependent foraging decisions.

systems biology↗

bioRxiv2026

Developmental and genetic modulation of evidence integration dynamics in zebrafish sensorimotor decision-making

Animals integrate information over time and maintain persistent internal representations of cues to guide decision-making. How the underlying behavioral algorithms of individual animals depend on factors such as experience, developmental stage, or genotype remains poorly understood. Drift-diffusion models provide a powerful theoretical framework to describe and predict performance metrics across a wide range of species. The stochastic nature of these models and the typical limited throughput of most experimental designs challenge the automatic inference of latent variables. Here, we combine high-throughput behavioral assays in larval zebrafish with drift-diffusion modeling, revealing that larvae progressively develop more persistent, self-reinforcing integration dynamics during early development. This effect is reduced in fish carrying mutations in genes linked to human epilepsy and schizophrenia. Our results show that behavior-based drift-diffusion modeling can offer a scalable, automated approach to generate experimentally testable hypotheses about the algorithmic implementation of sensorimotor integration in health and disease. LINK TO VIDEOShttps://cloud.uni-konstanz.de/index.php/s/qx9DSFciAey3aD7 TEASEREvidence-integration dynamics in zebrafish mature during development and are selectively altered by disease-associated mutations.

neuroscience↗

bioRxiv2025

Habitat structure and social behavior - the development of social phenotypes in the desert locust

Habitat structure has fundamental implications for behavior, foraging dynamics, and the social organization of animals. Locusts, which exhibit both a sedentary solitarious morph and a gregarious swarm-forming one, provide a powerful model to investigate how environmental factors shape social phenotypes. We conducted long-term continuous monitoring of locusts raised in semi-natural arenas with either clumped or distributed resources. Our findings reveal that clumped overnight roosting led to an increased propensity for coordinated marching the following day, a key precursor of swarm formation. These patterns became clearer during larval development, driving divergence in social affinity and group dynamics between habitat groups. At the molecular level, transcriptomic analysis identified a narrow set of differentially expressed genes, with enrichment in neuromodulatory and metabolic pathways, including candidates linked to monoaminergic signaling and juvenile hormone biology, such as a takeout-like JH-binding gene. These results demonstrate that spatial resource configuration is sufficient for lasting differences in individual and group dynamics and offer insight into the molecular processes mediating spontaneous, habitat-driven, phenotypic changes in a devastating plague insect.

animal behavior and cognition↗

bioRxiv2025

Computational Urban Ecology of New York City Rats

Urban rats are highly adaptable, thriving in the dynamic and often inhospitable conditions of modern cities. Despite substantial mitigation efforts, they remain an enduring presence in urban environments, yet surprisingly little is known about the daily lives and behavioral strategies that underlie their success. Here, we conducted fieldwork on free-ranging rats in New York City, using thermal imaging and ultrasonic audio recordings. We apply cutting-edge artificial intelligence techniques to capture high-resolution movement patterns and generate 3D reconstructions of foraging environments including subways, streets, and parks. We characterize social vocalizations across environmental contexts, and compare the patterns of social communication observed in NYC rats to the distribution of rodent vocalizations reported in the literature. This work provides a foundation for translating techniques and theories of rodent cognition from the lab to urban ecological settings.

animal behavior and cognition↗

bioRxiv2025

Social hierarchy shapes foraging decisions

Social foraging is a widespread form of animal foraging in which groups of individuals coordinate their decisions to exploit resources in the environment. Animals show a variety of social structures from egalitarian to hierarchical. In this study, we examine how different forms of social hierarchy shape foraging decisions. We developed a mechanistic analytically tractable model to study the underlying processes of social foraging, tying the microscopic individual to the macroscopic group levels. Based on a stochastic evidence accumulation framework, we developed a model of patch-leaving decisions in a large hierarchical group with leading and following individuals. Across a variety of information sharing mechanisms, we were able to analytically quantify emergent collective dynamics. We found that follower-leader dynamics through observations of leader movements or through counting the number of individuals in a patch confers, for most conditions, a benefit for the following individuals by increasing their accuracy in inferring patch richness. On the other hand, misinformation, through the communication of false beliefs about food rewards or patch quality, shows to be detrimental to following individuals, but paradoxically leads to increased group cohesion. In an era where there is a huge amount of animal foraging data collected, our model provides a systematic way to conceptualize and understand those data by uncovering hidden mechanisms underlying social foraging decisions.

biophysics↗

bioRxiv2024

Social patch foraging theory in an egalitarian group

Foraging is a widespread behavior, and being part of a group may bring several benefits compared to solitary foraging, such as collective pooling of information and reducing environmental uncertainty. Often theoretical models of collective behavior use coarse-grained representations, or are too complex for analytical treatment, and generally do not take into account the noisy decision making process implemented by individual agents. This calls for the development of a mechanistic, analytically tractable, and stochastic framework to study the underlying processes of social foraging, tying the microscopic to the macroscopic levels. Based on an evidence accumulation framework, we developed a model of patch-leaving decisions in a large egalitarian group. Across a variety of environmental statistics and information sharing mechanisms, we were able to analytically derive optimal agent strategies. The environmental statistics considered are either two non-depleting or several successive depleting patches. The social information sharing mechanisms are either through observation of others food rewards or through belief sharing, with continuous sharing, pulsatile observation of others departures or arrivals, or through counting the number of individuals in a patch. Throughout all these conditions, we quantified how cohesive a group is over time, how much time agents spend on average in a patch and what are their group equilibrium dynamics. We found that social coupling strongly modulates these features across a variety of environmental statistics. This general modeling framework is crucial to both designing social foraging experiments and generating hypotheses that can be tested. Moreover, this framework can be extended to groups exhibiting hierarchical relations.

bioinformatics↗

bioRxiv2023

Social structure and individuals' behavior shaped by group composition in long-term tracking of rats

Rodents serve as an important model for examining both individual and collective behavior. Dominance within rodent social structures can determine access to critical resources, such as food and mating opportunities. Yet, many aspects of the intricate interplay between individual behaviors and the resulting group social hierarchy, especially its evolution over time, remain unexplored. In this study, we utilized an automated tracking system that continuously monitored groups of male rats for over 250 days to enable an in-depth analysis of individual behavior and the overarching group dynamic. We describe the evolution of social structures within a group and additionally investigate how past behaviors influence the emergence of new social hierarchies when group composition and experimental area changes. Notably, we find that conventional individual and pairwise tests exhibit a weak correlation with group behavior, highlighting their limited accuracy in predicting behavioral outcomes in a collective context. These results emphasize the context-dependence of social behavior as an emergent property of interactions within a group and highlight the need to measure and quantify social behavior in more naturalistic environments.

animal behavior and cognition↗

bioRxiv2022

Stochastic dynamics of social patch foraging decisions

Animals typically forage in groups. Social foraging can help animals avoid predation and decrease their uncertainty about the richness of food resources. Despite this, theoretical mechanistic models of patch foraging have overwhelmingly focused on the behavior of single foragers. In this study, we develop a mechanistic model that accounts for the behavior of individuals foraging together and departing food patches following an evidence accumulation process. Each individuals belief about patch quality is represented by a stochastically accumulating variable which is coupled to others belief to represent the transfer of information. We consider a cohesive group, and model information sharing by considering both intermittent pulsatile coupling (only communicate decision to leave) and continuous diffusive coupling (communicate throughout the evidence accumulation process). We find that foraging efficiency under pulsatile coupling has a stronger dependence on the coupling strength parameter compared to diffusive. Despite employing minimal information transfer, pulsatile coupling can still provide similar or higher foraging efficiency compared to diffusive coupling. Conversely, since diffusive coupling is more robust to parameter choices, it performs better when individuals have heterogeneous departure criteria and social information weighting. Efficiency is measured by a reward rate function that balances the amount of energy accumulated against the time spent in a patch, computed by solving an ordered first passage time problem for the patch departures of each individual. Using synthetic data we show that we can distinguish between the two modes of communication and identify the model parameters. Our model establishes a social patch foraging framework to parse and identify deliberative decision strategies, to distinguish different forms of social communication, and to allow model fitting to real world animal behavior data.

animal behavior and cognition↗

bioRxiv2022

Chronic brain functional ultrasound imaging in freely moving rodents performing cognitive tasks

Functional ultrasound imaging (fUS) is an emerging imaging technique that indirectly measures neural activity via changes in blood volume. To date it has not been used to image chronically during cognitive tasks in freely moving animals. Performing those experiments faces a number of exceptional challenges: performing large durable craniotomies with chronic implants, designing behavioural experiments matching the hemodynamic timescale, stabilizing the ultrasound probe during freely moving behavior, accurately assessing motion artifacts and validating that the animal can perform cognitive tasks at high performance while tethered. In this study, we provide validated solutions for those technical challenges. In addition, we present standardized step-by-step repro-ducible protocols, procedures and data processing pipelines that open up the opportunity to perform fUS in freely moving rodents performing complex cognitive tasks. Moreover, we present proof-of-concept analysis of brain dynamics during a decision making task. We obtain stable recordings from which we can robustly decode task variables from fUS data over multiple months. Moreover, we find that brain wide imaging through hemodynamic response is nonlinearly related to cognitive variables, such as task difficulty, as compared to sensory responses previously explored.

neuroscience↗

bioRxiv2021

Active neural coordination of motor behaviors with internal states

The brain continuously coordinates skeletomuscular movements with internal physiological states like arousal, but how is this coordination achieved? One possibility is that brain simply reacts to changes in external and/or internal signals. Another possibility is that it is actively coordinating both external and internal activities. We used functional ultrasound imaging to capture a large medial section of the brain, including multiple cortical and subcortical areas, in marmoset monkeys while monitoring their spontaneous movements and cardiac activity. By analyzing the causal ordering of these different time-series, we found that information flowing from the brain to movements and heart rate fluctuations were significantly greater than in the opposite direction. The brain areas involved in this external versus internal coordination were spatially distinct but also extensively interconnected. Temporally, the brain alternated between network states for this regulation. These findings suggest that the brains dynamics actively and efficiently coordinate motor behavior with internal physiology.

neuroscience↗

bioRxiv2021

Social-vocal brain networks in a non-human primate

During social interactions, individuals influence each other to coordinate their actions. Vocal communication is an exceptionally efficient way to exert such influence. Where and how social interactions are dynamically modulated in the brain is unknown. We used functional ultrasound imaging in marmoset monkeys - a highly vocal species - to investigate the dynamics of medial social brain areas in vocal perception, production, and audio-vocal interaction. We found that the activity of a distributed network of subcortical and cortical regions distinguishes calls associated with different social contexts. This same brain network showed different dynamics during externally and internally driven vocalizations. These findings suggest the existence of a social-vocal brain network in medial cortical and subcortical areas that is fundamental in social communication. One Sentence SummaryA network of medial subcortical and cortical brain areas gate social communication in primates.

neuroscience↗

bioRxiv2021

What is behavior? No seriously, what is it?

Studying behavior lies at the heart of many disciplines. Nevertheless, academics rarely provide an explicit definition of what behavior actually is. What range of definitions do people use, and how does that vary across disciplines? To answer these questions we have developed a survey to probe what constitutes behavior. We find that academics adopt different definitions of behavior according to their academic discipline, animal model that they work with, and level of academic seniority. Using hierarchical clustering, we identify at least six distinct types of behavior which are used in seven distinct operational archetypes of behavior. Individual respondents have clear consistent definitions of behavior, but these definitions are not consistent across the population. Our study is a call for academics to clarify what they mean by behavior wherever they study it, with the hope that this will foster interdisciplinary studies that will improve our understanding of behavioral phenomena.

animal behavior and cognition↗

bioRxiv2021

Stable choice coding during changes of mind

How do we choose the best action in a constantly-changing environment? Many natural decisions unfold in dynamic environments where newer observations carry better information about the present state of the world. Recent work has shown that rats can learn to optimally discount old evidence, updating their provisional decision when the environmental state changes. Provisional decisions are thought to be represented in the Frontal Orienting Fields (FOF), but this has only been tested in static environments where the provisional and final decisions are not easily dissociated. Here, we characterize the representation of accumulated evidence in rat FOF during decision-making in a dynamic environment. We find that FOF encodes evidence throughout decision formation with a temporal gain modulation that rises until the period when the animal may need to act. Using a behavioral model to predict the timing of changes of mind revealed that FOF neurons respond rapidly to these events, representing the new provisional decisions in their firing rates. Our results suggest that the FOF represents provisional decisions even in dynamic, uncertain environments, allowing for rapid motor execution when it is time to act.

neuroscience↗

bioRxiv2021

Uncertainty drives deviations in normative foraging decision strategies

Nearly all animals forage, as it is essential to acquire energy for survival through efficient search and resource harvesting. Patch exploitation is a canonical foraging behavior, but a systematic treatment of how animals cope with uncertainty is lacking. To address these shortcomings, we develop a normative theory of patch foraging decisions, proposing mechanisms by which foraging behaviors emerge in the face of uncertainty. Our model foragers statistically and sequentially infer patch resource yields using Bayesian updating based on their resource encounter history. A decision to leave a patch is triggered when the certainty of the patch type or the estimated yield of the patch fall below a threshold. The timescale over which uncertainty in resource availability persists strongly impacts behavioral variables like patch residence times and decision rules determining patch departures. When patch depletion is slow, as in habitat selection, departures are characterized by a reduction of uncertainty, suggesting the forager resides in a low-yielding patch. Uncertainty leads patch-exploiting foragers to overharvest (underharvest) patches with initially low (high) resource yields in comparison to predictions of the marginal value theorem. These results extend optimal foraging theory and motivate a variety of behavioral experiments investigating patch foraging behavior.

animal behavior and cognition↗

bioRxiv2020

Normative theory of patch foraging decisions

Foraging is a fundamental behavior as animals search for food is crucial for their survival. Patch leaving is a canonical foraging behavior, but classic theoretical conceptions of patch leaving decisions lack some key naturalistic details. Optimal foraging theory provides general rules for when an animal should leave a patch, but does not provide mechanistic insights about how those rules change with the structure of the environment. Such a mechanistic framework would aid in designing quantitative experiments to unravel behavioral and neural underpinnings of foraging. To address these shortcomings, we develop a normative theory of patch foraging decisions. Using a Bayesian approach, we treat patch leaving behavior as a statistical inference problem. We derive the animals optimal decision strategies in both non-depleting and depleting environments. A majority of these cases can be analyzed explicitly using methods from stochastic processes. Our behavioral predictions are expressed in terms of the optimal patch residence time and the decision rule by which an animal departs a patch. We also extend our theory to a hierarchical model in which the forager learns the environmental food resource distribution. The quantitative framework we develop will therefore help experimenters move from analyzing trial based behavior to continuous behavior without the loss of quantitative rigor. Our theoretical framework both extends optimal foraging theory and motivates a variety of behavioral and neuroscientific experiments investigating patch foraging behavior.

animal behavior and cognition↗

bioRxiv2017

Rats optimally accumulate and discount evidence in a dynamic environment

How choices are made within noisy environments is a central question in the neuroscience of decision making. Previous work has characterized temporal accumulation of evidence for decision-making in static environments. However, real-world decision-making involves environments with statistics that change over time. This requires discounting old evidence that may no longer inform the current state of the world. Here we designed a rat behavioral task with a dynamic environment, to probe whether rodents can optimally discount evidence by adapting the timescale over which they accumulate it. Extending existing results about optimal inference in a dynamic environment, we show that the optimal timescale for evidence discounting depends on both the stimulus statistics and noise in sensory processing. We found that when both of these components were taken into account, rats accumulated and temporally discounted evidence almost optimally. Furthermore, we found that by changing the dynamics of the environment, experimenters could control the rats accumulation timescale, switching them from accumulating over short timescales to accumulating over long timescales and back. The theoretical framework also makes quantitative predictions regarding the timing of changes of mind in the dynamic environment. This study establishes a quantitative behavioral framework to control and investigate neural mechanisms underlying the adaptive nature of evidence accumulation timescales and changes of mind.

neuroscience↗