Martin Paul Nawrot

The paper develops a deep-learning framework to learn interpretable representations of whole-brain neural dynamics in Drosophila without neuronal identification, revealing factorized organization along metabolic state, sensory modality, and stimulus valence axes.

The paper shows how specific dopaminergic neurons in Drosophila larvae modulate memory formation through temporally precise signaling during learning.

This paper introduces a hierarchical model architecture for simulating Drosophila larva locomotion and foraging behaviors, enabling comparisons between empirical and simulated data.

This paper introduces a framework for modeling metastable neural assemblies by varying a mixing parameter between structural and synaptic clustering, showing its impact on network dynamics and implementation trade-offs.

The paper shows that feeding state modulates inhibitory neurons in Drosophila, biasing sensorimotor decisions by altering aversive response competition.

This study modifies the TsodyksMarkram Model to incorporate Unc13 isoforms, showing how they regulate short-term synaptic plasticity in fruit flies.

Larvaworld is a virtual laboratory platform for Drosophila larvae that integrates agent-based modeling with neural control and analysis of simulated and experimental data.

The paper investigates temperature preference behavior evolution in Drosophila species by analyzing over 2400 larvae and using agent-based modeling to show that behavior differences arise from circuit balance, not sensitivity changes.

The paper shows that target information modulates neural and behavioral variability in motor cortex through a spiking attractor model, explaining reaction time and variability patterns in monkeys.

The paper identifies minimal circuit motifs in the insect mushroom body that support second-order conditioning by analyzing how dopaminergic neurons are activated through different pathways.

The paper develops a spiking neural network model of Drosophila larva olfactory learning that uses prediction error to drive associative learning and behavior.

This study shows that mushroom body output neurons in cockroaches encode behavioral decisions rather than stimulus valence during feeding behavior.

The paper compares two SNN simulation approaches, NEST and GeNN, showing GeNN's efficiency for large-scale and real-time simulations on GPUs.

This paper develops a neuromorphic model of olfactory processing in Drosophila larvae, showing how sparse coding and efficient encoding can be achieved through feedback inhibition and spike frequency adaptation in a small neural network.

The paper proposes a circuit model of the Drosophila mushroom body that simulates reward prediction and extinction learning, reproducing experimental results and generating testable predictions.

The paper shows that changes in single mushroom body output neuron activity in honeybees predict individual behavioral performance during memory retention.

This paper presents a biologically detailed neural circuit model of the insect mushroom body that learns and executes cast & surge strategies for foraging in turbulent odor plumes using spike-based plasticity.

The paper presents a neural circuit model of the insect mushroom body that learns and recalls sensory-motor strategies for foraging in turbulent odor plumes.

The paper investigates how cellular and circuit mechanisms transform dense to sparse odor representations in the insect olfactory system, identifying spike-frequency adaptation and lateral inhibition as key factors.

This paper demonstrates a single spiking neuron model that can learn to estimate numerosity and perform counting tasks with high precision, outperforming convolutional neural networks in some scenarios.

The paper analyzes trial-to-trial and within-trial variability in monkey motor cortex during movement preparation and execution, finding distinct patterns in spike count variability and spike timing irregularity.