Thomas Nowotny

History-dependent ephaptic interactions in paired olfactory receptor neurons

This study shows that ephaptic interactions between olfactory receptor neurons in Drosophila are adaptation-dependent and not useful for fast odor processing, instead supporting slower peripheral processing tasks.

Understanding the mechanism of facilitation in hoverfly TSDNs

The paper develops and tests computational models of TSDN circuits in hoverflies to explain how background motion modulates target responses, finding that simple inhibition from LPTCs explains facilitation and suppression.

Estimating orientation in Natural scenes: A Spiking Neural Network Model of the Insect Central Complex

This paper develops a spiking neural network model of the insect central complex to estimate heading direction using visual cues and demonstrates its ability to track heading in natural scenes.

Neural responses to reconstructed target pursuits

The paper studies how target-tuned neurons in male hoverflies respond to reconstructed pursuit trajectories, finding that individual neurons respond to specific parts of the pursuit while the population covers a larger proportion.

Geosmin suppresses defensive behaviour and elicits unusual neural responses in honey bees.

This study investigates how geosmin affects honey bee defensive behavior and neural responses, finding concentration-dependent suppression of alarm pheromone-induced aggression.

Non-synaptic interactions between olfactory receptor neurons, a possible key feature of odor processing in flies

This paper uses a computational model to show that non-synaptic interactions between olfactory receptor neurons in flies improve odor mixture detection and plume structure sensing but reduce dynamic range.

Larger GPU-accelerated brain simulations with procedural connectivity

This paper introduces GPU-accelerated spiking neural network simulations that procedurally generate connectivity in real-time, enabling large-scale brain modeling on single GPUs.

Dynamics of mutual inhibition between two visual cortical neurons compared to human perceptual competition

The paper compares the dynamics of mutual inhibition between two real neurons with human perceptual competition, finding similar bi-stable behavior under noise and activation changes.

Learning with reward prediction errors in a model of the Drosophila mushroom body

The paper proposes a model of Drosophila mushroom body learning where dopamine neurons signal reward prediction errors, enabling MBONs to learn accurate reward predictions through plasticity rules.

Brian2GeNN: a system for accelerating alarge variety of spiking neural networkswith graphics hardware

Brian2GeNN accelerates spiking neural network simulations by translating Brian scripts into GPU-accelerated C++ code via GeNN, improving performance by tens to hundreds of times.

Mixtures are more salient stimuli in olfaction

The paper builds a model of honeybee olfactory system to show that mixtures of odors elicit different responses than single components, with reduced latency and less variability in response patterns.