Biology subjects

Nawrot, M. P.

Publications and source records attributed to Nawrot, M. P..

At least 19 recordsLinked to original sources

bioRxiv2026

Context makes the difference: Temporally Resolved Dopaminergic Teaching Signals Shape Associative Memory in Drosophila Larvae

Animals can adapt their behavioral responses to environmental cues by learning from experience. This ability relies on the formation and recall of memories that are shaped by beneficial or detrimental consequences and regulated by the dopaminergic system, which is highly conserved across insect species. In the Drosophila melanogaster larva, eight of total [~]120 dopaminergic neurons (DANs) innervate the mushroom body (MB), a key center for associative memory. This subset of DANs can be anatomically grouped into two clusters of four cells: the primary protocerebral anterior medial (pPAM) cluster, associated with reward signaling, and the dorsolateral 1 (DL1) cluster, associated with punishment. Such a functional dichotomy is observed in larval and adult Drosophila and reflects a fundamental organizational principle of reinforcement learning across invertebrate and even vertebrate species. Aversive reinforcement through high-salt exposure is encoded within the DL1 cluster in a combinatorial and heterogeneous manner, critically involving two neurons, DAN-f1 and DAN-g1. Using temporally precise optogenetic activation and inhibition during olfactory conditioning, we show that these neurons can modulate memory strength and valence. Their effects are most often consistent and predictable, enabling accurate computational modeling of DAN-driven teaching signals. By manipulating the intrinsic physiology of DAN-f1 and DAN-g1 and altering the valence of gustatory input, we are beginning to understand at the single-cell level how dopaminergic activity is systematically adjusted to control memory formation.

neuroscience↗

bioRxiv2026

Deep Representation Learning on Whole-Brain Population Dynamics Uncovers Geometrically Separable Neural Codes

Learning interpretable low-dimensional representations of whole-brain neuronal dynamics remains a major computational challenge in systems neuroscience. We present a wiring-agnostic deep-learning framework that couples a convolutional encoder with a temporal transformer to learn compact representations directly from volumetric calcium imaging of the entire Drosophila melanogaster brain. Trained to classify 16 experimental conditions that factorially combine metabolic state (fed, starved), sensory modality (olfaction, gustation, or combined), and stimulus valence (appetitive, aversive, or conflicting), the model organizes pan-neuronal whole-brain population activity into geometrically distinct, condition-specific clusters. Analysis of the models latent space reveals that state, modality, and valence are encoded along three near-orthogonal axes: a separable structure that emerges from the classification objective without explicit disentanglement constraints. Spatial attribution and regional importance analyses link modality decoding to distinct anatomical circuits, whereas metabolic state and valence related information show weaker regional specificity and broader distribution across the brain. Our approach does not require anatomical annotation, neuronal identification, or connectivity information, and thus provides a scalable foundation for comparative whole-brain imaging and representation learning of brain wide dynamics.

neuroscience↗

bioRxiv2026

Metastable Neural Assemblies on a Wiring-Weight Continuum

Neural population activity typically evolves on low-dimensional manifolds and can be described as trajectories through quasi-stable assembly states. Here we develop a unified definition of clustered neural networks with local excitatory-inhibitory balance in which enhanced within-cluster effective coupling is implemented through connection probability (structural clustering), synaptic efficacy (weight clustering), or any mixture of both. We introduce a single mixing parameter{kappa} [isin] [0, 1] that redistributes a defined cluster contrast between connection probabilities and synaptic efficacies while preserving the mean input of the underlying balanced random network. Mean-field theory and binary-network simulations show that metastable dynamics are supported across the full{kappa} continuum. Varying{kappa} changes higher-order input structure, reshaping multistable regimes, correlation structure, and the balance between single- and multi-cluster episodes. Because real nervous systems jointly organize topology and synaptic strength, our approach provides a biologically interpretable parametrization of clustered assembly models and a basis for future models combining structural and functional plasticity. We further demonstrate metastable switching in spiking leaky integrate-and-fire networks and in a BrainScaleS-2 neuromorphic implementation for the cases of structural, weight, and combined clustering. The{kappa} -framework offers a controlled translation axis for neuromorphic and other constrained substrates, exposing trade-offs between routed synapse count, fan-in, synaptic weight resolution, and calibration when implementing attractor-based computational primitives.

neuroscience↗

bioRxiv2025

A model investigation of short-term synaptic plasticity tuned via Unc13 isoforms.

Short-term synaptic plasticity (STP) is a fundamental mechanism of neural computation supporting a variety of nervous system functions from sensory adaptation and gain control to working memory and decision making. At the presynaptic release site, an interplay between distinct (M)Unc13 protein isoforms is suggested to orchestrate depressing and facilitating components of STP. In this study, we introduce a modification of the well-established TsodyksMarkram Model (TMM) for STP. We constrain our model by in vivo intracellular recordings in the olfactory system of the fruit fly, where previous work suggested Unc13A to provide a phasic, depressing and Unc13B a tonic, facilitating release component. A combination of a facilitating and a depressing model component indeed allowed for accurate model fits. Differential knock-down experiments of the Unc13A and Unc13B gene variants provide biological model interpretation, linking the protein-specific molecular mechanisms to synaptic function and STP. Our mathematical formulation of protein-dependent STP can be readily and efficiently used to design biologically realistic spiking neural network models that feature different genetically defined synapse types.

neuroscience↗

bioRxiv2025

Larvaworld : A behavioral simulation and analysis platform for Drosophila larva

Behavioral modeling supports theory building and evaluation across disciplines. Leveraging advances in motion-tracking and computational tools, we present a virtual laboratory for Drosophila larvae that integrates agent-based modeling with multiscale neural control and supports analysis of both simulated and experimental data. Virtual larvae are implemented as 2D agents capable of realistic locomotion, guided by multimodal sensory input and constrained by a dynamic energy-budget model that balances exploration and exploitation. Each agent is organized as a hierarchical, behavior-based control system comprising three layers: low-level locomotion, optionally incorporating neuromechanical models; mid-level sensory processing; and high-level behavioral adaptation. Neural control models can range from simple linear transfer models to rate-based or spiking neural network models, e.g. to accomodate associative learning. Simulations operate across sub-millisecond neuronal dynamics, sub-second closed-loop behavior, and circadian-scale metabolic regulation. Users can configure both larval models and virtual environments, including sensory landscapes, nutrient sources, and physical arenas. Real-time visualization is integrated into the simulation and analysis pipeline, which also allows for standardized processing of motion-tracking data from real experiments. Distributed as an open-source Python package, the platform includes tutorial experiments to support accessibility, customization, and use in both research and education. Author summaryLarvaworld was developed to address two key challenges in behavioral neuroscience and computational modeling. First, it responds to the growing call for closer collaboration between experimentalists and modelers by providing a shared platform -a virtual laboratory-where experimental data analysis and behavioral modeling can be seamlessly integrated. By standardizing dataset formats and ensuring identical, unbiased analysis pipelines for experimental and simulated data, Larvaworld facilitates methodological consistency and enables rigorous model evaluation. Second, it aims to bridge a long-standing gap in theory building and computational modeling at the level of the individual behaving organism. Historically, neuroscience has focused on sub-individual processes, while ecology has concentrated on supra-individual dynamics, resulting in discontinuities among the respective modeling approaches. Recent advances, however, have begun to align these fields, with neuroscience incorporating slower homeostatic processes and ecology integrating faster neurally-mediated mechanisms. Larvaworld boosts this convergence by adopting a nested, multi-timescale modeling approach, thus achieving behavioral regulation within the normative homeostatic constraints as these dynamically unfold during larval development. By combining established modeling paradigms from neuroscience and ecology, it provides a novel and flexible platform for studying behavior at the level of the individual organism, promoting cross-disciplinary insights and advancing computational neuroethology [1].

neuroscience↗

bioRxiv2024

Fast and recurrent evolution of temperature preference among drosophilids

Small-bodied ectotherms are acutely vulnerable to temperature changes, but diverse thermotactic behaviours have contributed to their ability to inhabit broad climatic niches. Understanding how - and how quickly - these behaviours evolve are outstanding biological questions that are also relevant to conservation. Among insects, Drosophila melanogaster is a preeminent ectothermic model for temperate sensing and thermotaxis. However, little is known about how its temperature-related behaviours have evolved in comparison to its closely related species. We have thermo-profiled over 2400 larvae from eight closely related species of Drosophila from different thermal habitats. Consistent with local adaptation, we found substantial variation in temperature preference and fine-scale navigational behaviours amongst these species. Agent-based modelling of the larval thermotaxis circuit suggests that it is the balance between cool and warm avoidance circuits, rather than changes in temperature sensitivity, that drive differences in temperature preference. Our findings highlight the recurrent evolution of temperature-related behaviours in an experimentally tractable cross-species system.

evolutionary biology↗

bioRxiv2023

Minimal circuit motifs for second-order conditioning in the insect mushroom body

In well-established first-order conditioning experiments, the concurrence of a sensory cue with reinforcement forms an association, allowing the cue to predict future reinforcement. Once a sensory cue is established as a predictor, it can also serve as indirect reinforcement, a phenomenon referred to as second-order conditioning. In the insect mushroom body, such associations are encoded in the plasticity of the synapses between the intrinsic and output neurons of the mushroom body, a process mediated by the activity of dopaminergic neurons that encode reinforcement signals. In second-order conditioning, a new sensory cue is paired with an already established one that presumably activates dopaminergic neurons due to its predictive power of the reinforcement. We explore minimal circuit motifs in the mushroom body for their ability to support second-order conditioning. We found that dopaminergic neurons can either be activated directly by the mushroom bodys intrinsic neurons or via feedback from the output neurons via several pathways. We demonstrate that the circuit motifs differ in their computational efficiency and robustness and suggest a particular motif that relies on feedforward input of the mushroom body intrinsic neurons to dopaminergic neurons as a promising additional candidate for experimental evaluation. It differentiates well between trained and novel stimuli, demonstrating robust performance across a range of model parameters.

neuroscience↗

bioRxiv2022

Prediction error drives associative olfactory learning and conditioned behavior in a spiking model of Drosophila larva

Predicting reinforcement from the presence of environmental clues is an essential component of guiding goal-directed behavior. In insect brains, the mushroom body is central to learning the necessary associations between sensory signals and reinforcement. We propose a biologically realistic spiking network model of the Drosophila larva olfactory pathway for the association of odors and reinforcement to bias behavior towards approach or avoidance. We demonstrate that prediction error coding through the integration of currently present and expected reinforcement in dopaminergic neurons can serve as a driving force in learning that can, combined with a synaptic homeostasis mechanism, account for experimentally observed features of acquisition and loss of associations in the larva that depend on the intensity of odor and reinforcement and temporal features of their pairing. To allow direct comparisons of our simulations with behavioral data [1], we model learning-induced plasticity over the complete time course of behavioral experiments and simulate the locomotion of individual larvae towards or away from odor sources in a virtual environment.

neuroscience↗

bioRxiv2022

The mushroom body output encodes behavioral decision during sensory-motor transformation

Animal behavioral decisions are dynamically formed by evaluating momentary sensory evidence on the background of individual experience and the acute motivational state. In insects, the mushroom body (MB) has been implicated in forming associative memories and in assessing the appetitive or aversive valence of sensory stimuli to bias approach versus avoidance behavior. To study the MB involvement in innate feeding behavior we performed extracellular single-unit recordings from MB output neurons (MBONs) while simultaneously monitoring a defined feeding behavior in response to timed odor stimulation in naive cockroaches. All animals expressed the feeding behavior almost exclusively in response to food odors. Likewise, MBON responses were invariably and strongly tuned to the same odors. Importantly, MBON responses were restricted to behaviorally responded trials, which allowed the accurate prediction of the occurrence versus non-occurrence of the feeding behavior in individual trials from the neuronal population activity. During responded trials the neuronal activity generally preceded the onset of the feeding behavior, indicating a causal relation. Our results contest the predominant view that MBONs encode stimulus valence. Rather, we conclude that the MB output dynamically encodes the behavioral decision to inform downstream motor networks.

neuroscience↗

bioRxiv2022

Efficient parameter calibration and real-time simulation of large scale spiking neural networks with GeNN and NEST

Spiking neural networks (SNN) represent the state-of-the-art approach to the biologically realistic modeling of nervous system function. The systematic calibration for multiple free model parameters is necessary to achieve robust network function and demands high computing power and large memory resources. Special requirements arise from closed-loop model simulation in virtual environments, and from real-time simulation in robotic application. Here, we compare two complementary approaches to efficient large scale and realtime SNN simulation. The widely used NEural Simulation Tool (NEST) parallelizes simulation across multiple CPU cores. The GPU-enhanced Neural Network (GeNN) simulator uses the highly parallel GPU-based architecture to gain simulation speed. We quantify fixed and variable simulation costs on single machines with different hardware configurations. As benchmark model we use a spiking cortical attractor network with a topology of densely connected excitatory and inhibitory neuron clusters with homogeneous or distributed synaptic time constants and in comparison to the random balanced network. We show that simulation time scales linearly with the simulated biological model time and, for large networks, approximately linearly with the model size as dominated by the number of synaptic connections. Additional fixed costs with GeNN are almost independent of model size, while fixed costs with NEST increase linearly with model size. We demonstrate how GeNN can be used for simulating networks with up to 3.5 {middle dot} 106 neurons (> 3 {middle dot} 1012 synapses) on a high-end GPU, and up to 250, 000 neurons (25 {middle dot}109 synapses) on a low-cost GPU. Real-time simulation was achieved for networks with 100, 000 neurons. Network calibration and parameter grid search can be efficiently achieved using batch processing. We discuss the advantages and disadvantages of both approaches for different use cases.

neuroscience↗

bioRxiv2021

A realistic locomotory model of Drosophila larva for behavioral simulations.

The Drosophila larva is extensively used as model organism in neuroethological studies where precise behavioral tracking enables the statistical analysis of individual and population-level behavioral metrics that can inform mathematical models of larval behavior. Here, we propose a hierarchical model architecture comprising three layers to facilitate modular model construction, closed-loop simulations, and direct comparisons between empirical and simulated data. At the motor layer, the autonomous locomotory model is capable of performing exploration. Based on novel kinematic analyses our model features intermittent forward crawling that is phasically coupled to lateral bending. At the second layer, navigation is achieved via active sensing in a simulated environment and top-down modulation of locomotion. At the top layer, behavioral adaptation entails associative learning. We evaluate virtual larval behavior across agent-based simulations of autonomous free exploration, chemotaxis, and odor preference testing. Our behavioral architecture is ideally suited for the modular combination of neuromechanical, neural or mere statistical model components, facilitating their evaluation, comparison, extension and integration into multifunctional control architectures.

neuroscience↗

bioRxiv2021

A neuromorphic model of olfactory processing and sparse coding in the Drosophila larva brain

Animal nervous systems are highly efficient in processing sensory input. The neuromorphic computing paradigm aims at the hardware implementation of neural network computations to support novel solutions for building brain-inspired computing systems. Here, we take inspiration from sensory processing in the nervous system of the fruit fly larva. With its strongly limited computational resources of <200 neurons and <1.000 synapses the larval olfactory pathway employs fundamental computations to transform broadly tuned receptor input at the periphery into an energy efficient sparse code in the central brain. We show how this approach allows us to achieve sparse coding and increased separability of stimulus patterns in a spiking neural network, validated with both software simulation and hardware emulation on mixed-signal real-time neuromorphic hardware. We verify that feedback inhibition is the central motif to support sparseness in the spatial domain, across the neuron population, while the combination of spike frequency adaptation and feedback inhibition determines sparseness in the temporal domain. Our experiments demonstrate that such small-sized, biologically realistic neural networks, efficiently implemented on neuromorphic hardware, can achieve parallel processing and efficient encoding of sensory input at full temporal resolution.

neuroscience↗

bioRxiv2021

Single neuron activity predicts behavioral performance of individual animals during memory retention

In 1972 Rescorla and Wagner formulated their model of classical Pavlovian conditioning postulating that the associative strength of a stimulus is expressed directly in the behavior it elicits1. Many biologists and psychologists were inspired by this model, and numerous experiments thereafter were interpreted assuming that the magnitude of the conditioned response (CR) reflects an associative effect at the physiological level. However, a correlation between neural activity and the expression of the CR in individual animals has not yet been reported. Here we show that, following differential odor conditioning, the change in activity of single mushroom body output neurons (MBON) of the honeybee predicts the behavioral performance of the individual during memory retention. The encoding of the stimulus-reward association at the mushroom body output occurs about 600 ms before the initiation of the CR. We conclude that the MB provides a stable representation of the stimulus-reward associative strength, and that this representation is required for behavioral decision-making during memory retention.

neuroscience↗

bioRxiv2020

A mechanistic model for reward prediction and extinction learning in the fruit fly

Extinction learning, the ability to update previously learned information by integrating novel contradictory information, is a key mechanism for adapting our behavior and of high clinical relevance for therapeutic approaches to the modulation of maladaptive memories. Insect models have been instrumental in uncovering fundamental processes of memory formation and memory update. Recent experimental results in Drosophila melanogaster suggest that, after the behavioral extinction of a memory, two parallel but opposing memory traces coexist, residing at different sites within the mushroom body. Here we propose a minimalistic circuit model of the Drosophila mushroom body that supports classical appetitive and aversive conditioning and memory extinction. The model is tailored to the existing anatomical data and involves two circuit motives of central functional importance. It employs plastic synaptic connections between Kenyon cells and mushroom body output neurons (MBONs) in separate and mutually inhibiting appetitive and aversive learning pathways. Recurrent modulation of plasticity through projections from MBONs to reinforcement-mediating dopaminergic neurons implements a simple reward prediction mechanism. A distinct set of four MBONs encodes odor valence and predicts behavioral model output. Subjecting our model to learning and extinction protocols reproduced experimental results from recent behavioral and imaging studies. Simulating the experimental blocking of synaptic output of individual neurons or neuron groups in the model circuit confirmed experimental results and allowed formulation of testable predictions. In the temporal domain, our model achieves rapid learning with a step-like increase in the encoded odor value after a single pairing of the conditioned stimulus with a reward or punishment, facilitating single-trial learning.

neuroscience↗

bioRxiv2020

A plausible mechanism for Drosophila larva intermittent behavior.

The behavior of many living organisms is not continuous. Rather, activity emerges in bouts that are separated by epochs of rest, a phenomenon known as intermittent behavior. Although intermittency is ubiquitous across phyla, empirical studies are scarce and the underlying neural mechanisms remain unknown. Here we present the first empirical evidence of intermittency during Drosophila larva free exploration. We report power-law distributed rest-bout and log-normal distributed activity-bout durations. We show that a stochastic network model can transition between power-law and non-power-law distributed states and we suggest a plausible neural mechanism for the alternating rest and activity in the larva. Finally, we discuss possible implementations in behavioral simulations extending spatial Levy-walk or coupled-oscillator models with temporal intermittency.

animal behavior and cognition↗

bioRxiv2020

A spiking neural program for sensory-motor control during foraging in flying insects.

Foraging is a vital behavioral task for living organisms. Behavioral strategies and abstract mathematical models thereof have been described in detail for various species. To explore the link between underlying neural circuits and computational principles we present how a biologically detailed neural circuit model of the insect mushroom body implements sensory processing, learning and motor control. We focus on cast & surge strategies employed by flying insects when foraging within turbulent odor plumes. Using a spike-based plasticity rule the model rapidly learns to associate individual olfactory sensory cues paired with food in a classical conditioning paradigm. We show that, without retraining, the system dynamically recalls memories to detect relevant cues in complex sensory scenes. Accumulation of this sensory evidence on short time scales generates cast & surge motor commands. Our generic systems approach predicts that population sparseness facilitates learning, while temporal sparseness is required for dynamic memory recall and precise behavioral control. Our work successfully combines biological computational principles with spike-based machine learning. It shows how knowledge transfer from static to arbitrary complex dynamic conditions can be achieved by foraging insects and may serve as inspiration for agent-based machine learning.

neuroscience↗

bioRxiv2020

Neural computation underlying rapid learning and dynamic memory recall for sensori-motor control in insects.

Foraging is a vital behavioral task for living organisms. Behavioral strategies and abstract mathematical models thereof have been described in detail for various species. To explore the link between underlying nervous systems and abstract computational principles we present how a biologically detailed neural circuit model of the insect mushroom body implements sensory processing, learning and motor control. We focus on cast & surge strategies employed by flying insects when foraging within turbulent odor plumes. Using a synaptic plasticity rule the model rapidly learns to associate individual olfactory sensory cues paired with food in a classical conditioning paradigm. Without retraining, the system dynamically recalls memories to detect relevant cues in complex sensory scenes. Accumulation of this sensory evidence on short timescales generates cast & surge motor commands. Our systems approach is generic and predicts that population sparseness facilitates learning, while temporal sparseness is required for dynamic memory recall and precise behavioral control.

neuroscience↗

bioRxiv2019

Cockroaches show individuality in learning and memory during classical and operant conditioning

Animal personality and individuality are intensively researched in vertebrates and both concepts are increasingly applied to behavioral science in insects. However, only few studies have looked into individuality with respect to performance in learning and memory tasks. In vertebrates individual learning capabilities vary considerably with respect to learning speed and learning rate. Likewise, honeybees express individual learning abilities in a wide range of classical conditioning protocols. Here, we study individuality in the learning and memory performance of cockroaches, both in classical and operant conditioning tasks. We implemented a novel classical (olfactory) conditioning paradigm where the conditioned response is established in the maxilla-labia response (MLR). Operant spatial learning was investigated in a forced two-choice task using a T-maze. Our results confirm individual learning abilities in classical conditioning of cockroaches that was reported for honeybees and vertebrates but contrast long-standing reports on stochastic learning behavior in fruit flies. In our experiments, most learners expressed a correct behavior after only a single learning trial showing a consistent high performance during training and test. We can further show that individual learning differences in insects are not limited to classical conditioning but equally appear in operant conditioning of the cockroach.

animal behavior and cognition↗