Biology subjects

Nowotny, T.

Publications and source records attributed to Nowotny, T..

13 recordsLinked to original sources

bioRxiv2026

History-dependent ephaptic interactions in paired olfactory receptor neurons

Olfactory sensing begins with the transduction of odors into receptor currents on the dendrites of olfactory receptor neurons (ORNs). In insects and many other arthropods, ORNs are grouped stereotypically in hair-like sensilla on the surface of olfactory organs, enabling mutual inhibition through non-synaptic ephaptic interactions (NSIs). Given the electrical, and therefore virtually instantaneous, nature of NSIs, it has been hypothesized that they contribute to processing fast temporal elements of mixed odor plumes. Here, we present single sensillum recordings and computational modeling that characterize NSIs during short offset dual-odor stimulations in the olfactory sensilla of adult female Drosophila melanogaster. We find in the experiments that the magnitude of inhibition between co-housed ORNs cannot be predicted by their instantaneous activity (firing rate) alone. It is adaptation-dependent, with strong effects only occurring when the inhibited ORN is adapted. This limits the usefulness of NSIs for fast odor processing when ORNs lack time to adapt. We reproduced the observed phenomena in a computational model and use this model to explain how the adaptation-dependence of NSI-mediated inhibition arises from nonlinearities in neural responses. We conclude that NSIs are unlikely to support the encoding of fast temporal dynamics in mixed odor stimuli, instead contributing to slower peripheral processing, supporting roles such as novelty detection. More broadly, we demonstrate how the nonlinear interactions of fairly simple electrical components lead to non-intuitive results, offering insight into the longstanding debate around ephaptic interactions in other systems, such as the mammalian CNS.

neuroscience↗

bioRxiv2025

Understanding the mechanism of facilitation in hoverfly TSDNs

Many animals use visual motion cues to track and pursue small, fast-moving targets, such as prey or conspecifics. In target-pursuing insects, including dragonflies and hoverflies, Small Target Motion Detector (STMD) neurons are found in the optic lobes and are believed to be presynaptic to Target Selective Descending Neurons (TSDNs) that project to motor command centres. While STMDs respond robustly to target motion - even when displayed against moving backgrounds - TSDN target responses are modulated by background motion. Depending on whether the background motion is syn- or contra-directional to the target motion, the response of the TSDNs is either suppressed or facilitated (amplified). This suggests that TSDNs not only receive input from STMDs but also from neurons sensitive to background motion, but this neural circuit is not clearly understood. To explore the underlying neural mechanisms, we developed three candidate TSDN circuit models - which combine input from bio-plausible STMDs and optic flow-sensitive Lobula Plate Tangential Cells (LPTCs) in different ways - and fitted them to published electrophysiology data from hoverfly TSDNs. We then tested the best-fitting models against new electrophysiological data using different background patterns. We found that the overall best model suggests simple inhibition from LPTCs with the same preferred direction as the STMDs feeding into the TSDN. This parsimonious mechanism can explain the facilitation and suppression of TSDN responses to small targets, may inform similar studies in other animals and open new avenues for bio-inspired robotics. Author summaryMany human sports, including tennis, football, and basketball, rely on the ability to visually detect and respond to the motion of a small, rapidly moving object. Indeed, some sports stars seem to have an uncanny ability to predict the future location of a ball. Similarly, many animals also need to detect the motion of small objects, as these may represent an approaching predator, prey, or a conspecific that needs to be avoided or pursued. Interestingly, while the detection of small visual targets is computationally a hard problem, insects, which have small brains with very modest power requirements, appear to solve this effortlessly. Thus, understanding how target detection is performed in insects can open up avenues for bio-inspired robotics. Male hoverflies naturally engage in complex pursuit behavior, and since they have specialised neurons for target tracking, hoverflies are ideal for understanding the underlying neural circuitry. Here, we combine neural recordings from hoverflies with computational modelling to show how neurons that respond selectively to target motion are combined with neurons that respond to widefield optic-flow to generate behaviourally relevant sensorimotor responses. Importantly, our model does not involve learning and hence suggests that these behaviours can be innate.

neuroscience↗

bioRxiv2024

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

The central complex of insects contains cells, organised as a ring attractor, that encode head direction. The bump of activity in the ring can be updated by idiothetic cues and external sensory information. Plasticity at the synapses between these cells and the ring neurons, that are responsible for bringing sensory information into the central complex, has been proposed to form a mapping between visual cues and the heading estimate which allows for more accurate tracking of the current heading, than if only idiothetic information were used. In Drosophila, ring neurons have well characterised non-linear receptive fields. In this work we produce synthetic versions of these visual receptive fields using a combination of excitatory inputs and mutual inhibition between ring neurons. We use these receptive fields to bring visual information into a spiking neural network model of the insect central complex based on the recently published Drosophila connectome. Previous modelling work has focused on how this circuit functions as a ring attractor using the same type of simple visual cues commonly used experimentally. While we initially test the model on these simple stimuli, we then go on to apply the model to complex natural scenes containing multiple conflicting cues. We show that this simple visual filtering provided by the ring neurons is sufficient to form a mapping between heading and visual features and maintain the heading estimate in the absence of angular velocity input. The network is successful at tracking heading even when presented with videos of natural scenes containing conflicting information from environmental changes and translation of the camera. Author summaryTo navigate through the world animals require knowledge of the direction they are facing. Insects keep track of this "head direction" with a population of compass neurons. These cells can use internal measures of angular velocity to maintain the heading estimate but this becomes inaccurate over time and needs to be stabilised by environmental cues. In this work we produce a spiking neural network model replicating the connectivity between regions of the insect brain known to be involved in keeping track of heading and the neurons which are responsible for bringing sensory information into the circuit. We show that the model replicates the dynamics of visual learning from experiments where flies learn simple visual stimuli. Then using panoramic videos of complex natural environments, we show that the learned mapping between the current estimate of heading in the compass neurons and the features of the visual scene can maintain and enforce the correct heading estimate.

neuroscience↗

bioRxiv2023

Familiarity-taxis: A bilateral approach to view-based navigation.

Many insects use view-based navigation, or snapshot matching, to return to familiar locations, or navigate routes. This relies on egocentric memories being matched to current views of the world. Previous snapshot navigation algorithms have used full panoramic vision for the comparison of memorised images with query images to establish a measure of familiarity, which leads to a recovery of the original heading direction from when the snapshot was taken. Many aspects of insect sensory systems are lateralised with steering being derived from the comparison of left and right signals like a classic Braitenberg vehicle. Here we investigate whether view-based route navigation can be implemented using bilateral visual familiarity comparisons. We found that the difference in familiarity between estimates from left and right fields of view can be used as a steering signal to recover the original heading direction. This finding extends across many different sizes of field of view and visual resolutions. In insects, steering computations are implemented in a brain region called the Lateral Accessory Lobe, within the Central Complex. In a simple simulation we show with a SNN model of the LAL an existence proof of how bilateral visual familiarity could drive a search for a visually defined goal.

animal behavior and cognition↗

bioRxiv2023

Neural responses to reconstructed target pursuits

Many animals use motion vision information to control dynamic behaviors. Predatory animals, for example, show an exquisite ability to detect rapidly moving prey followed by pursuit and capture. Such target detection is not only used by predators but can also play an important role in conspecific interactions. Male hoverflies (Eristalis tenax), for example, vigorously defend their territories against conspecific intruders. Visual target detection is believed to be subserved by specialized target tuned neurons that are found in a range of species, including cats, zebrafish, and insects. However, how these target-tuned neurons respond to actual pursuit trajectories is currently not well understood. To redress his, we recorded extracellularly from target selective descending neurons (TSDNs) in male Eristalis tenax hoverflies. We show that the neurons have dorso-frontal receptive fields, with a preferred direction up and away from the visual midline. We next reconstructed visual flow-fields as experienced during pursuits of artificial targets (black beads). We recorded TSDN responses to six reconstructed pursuits and found that each neuron responded consistently at remarkably specific time points, but that these time points differed between neurons. We compared the observed spike probability with the spike probability predicted from each neurons receptive field and size tuning, and found a correlation coefficient of 0.35. Interestingly, however, the overall response rate was low, with individual neurons responding to only a small part of each reconstructed pursuit. In contrast, the TSDN population responded to a substantially larger proportion of the pursuits (up to a median of 23%). This large variation between neurons could be useful if different neurons control different parts of the behavioral output. Significance statementDescending neurons constitute less than 1% of the nervous system, yet have to convey all requisite information from the brain to the body. They are therefore a crucial bottleneck in sensorimotor transformation. Descending target tuned neurons in male hoverflies (Eristalis tenax), for example, are believed to play a key role in territory defense and pursuit of conspecifics. However, this has not been tested using visual stimuli resembling reconstructed target pursuits. We here found that the observed neural responses to reconstructed pursuits are stronger than those predicted from responses to simpler stimuli. In addition, while the responses to simple stimuli suggested a homogenous population of neurons, the reconstructed pursuits showed important differences between individual neurons. Our data thus highlight the need for using more naturalistic stimuli when deciphering neural function.

neuroscience↗

bioRxiv2022

Production of adaptive movement patterns via an insect inspired Spiking Neural Network Central Pattern Generator.

Navigation in ever-changing environments requires effective motor behaviours. Many insects have developed adaptive movement patterns which increase their success in achieving navigational goals. A conserved brain area in the insect brain, the Lateral Accessory Lobe, is involved in generating small scale search movements which increase the efficacy of sensory sampling. When the reliability of an essential navigational stimulus is low, searching movements are initiated whereas if the stimulus reliability is high, a targeted steering response is elicited. Thus the network mediates an adaptive switching between motor patterns. We developed Spiking Neural Network models to explore how an insect inspired architecture could generate adaptive movements in relation to changing sensory inputs. The models are able to generate a variety of adaptive movement patterns, the majority of which are of the zig-zagging kind, as seen in a variety of insects. Furthermore, these networks are robust to noise. Because a large spread of network parameters lead to the zig-zagging movement dynamics, we conclude that the investigated network architecture is inherently well suited to generating adaptive movement patterns.

animal behavior and cognition↗

bioRxiv2021

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

Geosmin is an odorant produced by bacteria in moist soil. It has been found to be extraordinarily relevant to some insects, but the reasons for this are not yet fully understood. Here we report the first tests of the effect of geosmin on honey bees. A stinging assay showed that the defensive behaviour elicited by the bees alarm pheromone is strongly suppressed by geosmin. Surprisingly, the suppression is, however, only present at very low geosmin concentrations, and completely disappears at higher concentrations. We investigated the underlying mechanisms of the behavioural change at the level of the olfactory receptor neurons by means of electroantennography and at the level of the antennal lobe output via calcium imaging. Unusual effects were observed at both levels. The responses of the olfactory receptor neurons to mixtures of geosmin and the alarm pheromone component isoamyl acetate (IAA) were lower than to pure IAA, suggesting an interaction of both compounds at the olfactory receptor level. In the antennal lobe, the neuronal representation of geosmin showed a glomerular activation that decreased with increasing concentration, correlating well with the concentration dependence of the behaviour. Computational modelling of odour transduction and odour coding in the antennal lobe suggests that a broader than usual activation of different olfactory receptor types by geosmin in combination with lateral inhibition in the antennal lobe could lead to the observed non-monotonic increasing-decreasing responses to geosmin and thus underlie the specificity of the behavioural response to low geosmin concentrations.

neuroscience↗

bioRxiv2020

Non-synaptic interactions between olfactory receptor neurons, a possible key-feature in insect odor inspection

When flies explore their environment, they encounter odors in complex, highly intermittent plumes. To navigate a plume and, for example, find food, they must solve several challenges, including reliably identifying mixtures of odorants and their intensities, and discriminating odorant mixtures emanating from a single source from odorants emitted from separate sources and just mixing in the air. Lateral inhibition in the antennal lobe is commonly understood to help solving these challenges. With a computational model of the Drosophila olfactory system, we analyze the utility of an alternative mechanism for solving them: Non-synaptic ("ephaptic") interactions (NSIs) between olfactory receptor neurons that are stereotypically co-housed in the same sensilla. We found that NSIs improve mixture ratio detection and plume structure sensing and they do so more efficiently than the traditionally considered mechanism of lateral inhibition in the antennal lobe. However, we also found that NSIs decrease the dynamic range of co-housed ORNs, especially when they have similar sensitivity to an odorant. These results shed light, from a functional perspective, on the role of NSIs, which are normally avoided between neurons, for instance by myelination. Author summaryMyelin is important to isolate neurons and avoid disruptive electrical interference between them; it can be found in almost any neural assembly. However, there are a few exceptions to this rule and it remains unclear why. One particularly interesting case is the electrical interaction between olfactory sensory neurons co-housed in the sensilla of insects. Here, we created a computational model of the early stages of the Drosophila olfactory system and observed that the electrical interference between olfactory receptor neurons can be a useful trait that can help flies, and other insects, to navigate the complex plumes of odorants in their natural environment. With the model we were able to shed new light on the trade-off of adopting this mechanism: We found that the non-synaptic interactions (NSIs) improve both the identification of the concentration ratio in mixtures of odorants and the discrimination of odorant mixtures emanating from a single source from odorants emitted from separate sources - both highly advantageous. However, they also decrease the dynamic range of the olfactory sensory neurons - a clear disadvantage.

neuroscience↗

bioRxiv2020

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

Neural competition plays an essential role in active selection processes of noisy and ambiguous input signals and it is assumed to underlie emergent properties of brain functioning such as perceptual organization and decision making. Despite ample theoretical research on neural competition, experimental tools to allow neurophysiological investigation of competing neurons have not been available. We developed a "hybrid" system where real-life neurons and a computer-simulated neural circuit interacted. It enabled us to construct a mutual inhibition circuit between two real life pyramidal neurons. We then asked what dynamics this minimal unit of neural competition exhibits and compared them to the known behavioral-level dynamics of neural competition. We found that the pair of neurons shows bi-stability when activated simultaneously by current injections. The addition of modelled noise and changes in the activation strength showed that the dynamics of the circuit are strikingly similar to the known properties of bi-stable visual perception.

neuroscience↗

bioRxiv2020

Larger GPU-accelerated brain simulations with procedural connectivity

Large-scale simulations of spiking neural network models are an important tool for improving our understanding of the dynamics and ultimately the function of brains. However, even small mammals such as mice have on the order of 1 x 1012 synaptic connections which, in simulations, are each typically charaterized by at least one floating-point value. This amounts to several terabytes of data - an unrealistic memory requirement for a single desktop machine. Large models are therefore typically simulated on distributed supercomputers which is costly and limits large-scale modelling to a few privileged research groups. In this work, we describe extensions to GeNN - our Graphical Processing Unit (GPU) accelerated spiking neural network simulator - that enable it to procedurally generate connectivity and synaptic weights on the go as spikes are triggered, instead of storing and retrieving them from memory. We find that GPUs are well-suited to this approach because of their raw computational power which, due to memory bandwidth limitations, is often under-utilised when simulating spiking neural networks. We demonstrate the value of our approach with a recent model of the Macaque visual cortex consisting of 4.13 x 106 neurons and 24.2 x 109 synapses. Using our new method, it can be simulated on a single GPU - a significant step forward in making large-scale brain modelling accessible to many more researchers. Our results match those obtained on a supercomputer and the simulation runs up to 35 % faster on a single high-end GPU than previously on over 1000 supercomputer nodes.

neuroscience↗

bioRxiv2019

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

Effective decision making in a changing environment demands that accurate predictions are learned about decision outcomes. In Drosophila, such learning is or-chestrated in part by the mushroom body (MB), where dopamine neurons (DANs) signal reinforcing stimuli to modulate plasticity presynaptic to MB output neurons (MBONs). Here, we extend previous MB models, in which DANs signal absolute rewards, proposing instead that DANs signal reward prediction errors (RPEs) by utilising feedback reward predictions from MBONs. We formulate plasticity rules that minimise RPEs, and use simulations to verify that MBONs learn accurate reward predictions. We postulate as yet unobserved connectivity, which not only overcomes limitations in the experimentally constrained model, but also explains additional experimental observations that connect MB physiology to learning. The original, experimentally constrained model and the augmented model capture a broad range of established fly behaviours, and together make five predictions that can be tested using established experimental methods.

neuroscience↗

bioRxiv2018

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

\"Brian\" is a popular Python-based simulator for spiking neural networks, commonly used in computational neuroscience. GeNN is a C++-based meta-compiler for accelerating spiking neural network simulations using consumer or high performance grade graphics processing units (GPUs). Here we introduce a new software package, Brian2GeNN, that connects the two systems so that users can make use of GeNN GPU acceleration when developing their models in Brian, without requiring any technical knowledge about GPUs, C++ or GeNN. The new Brian2GeNN software uses a pipeline of code generation to translate Brian scripts into C++ code that can be used as input to GeNN, and subsequently can be run on suitable NVIDIA GPU accelerators. From the users perspective, the entire pipeline is invoked by adding two simple lines to their Brian scripts. We have shown that using Brian2GeNN, typical models can run tens to hundreds of times faster than on CPU.

neuroscience↗

bioRxiv2017

Mixtures are more salient stimuli in olfaction

In their natural environment, animals often encounter complex mixtures of odours. It is an open question whether and how responses to complex mixtures of multi-component odours differ from those to simpler mixtures or single components. To approach this question, we built a full-size model of the early olfactory system of honeybees, which predicts responses to both single odorants and mixtures. The model is designed so that olfactory response patterns conform to the statistics derived from experimental data for a variety of their properties. It also takes into account several biophysical processes at a minimal level, including processes of chemical binding and activation in receptors, and spike generation and transmission in the antennal lobe network. We verify that key findings from other experimental data, not used in building the model, are reproduced in it. In particular, we replicate the strong correlation among receptor neurons and the weaker correlation among projection neurons observed in experimental data and show that this decorrelation is predominantly due to inhibition by interneurons. By simulation and mathematical analysis of our model, we demonstrate that the chemical processes of receptor binding and activation already lead to significant differences between the responses to mixtures and those to single component stimuli. On average, the response latency of olfactory receptor neurons at low stimulus concentrations is reduced and the response patterns become less variable across concentrations as the number of odour components in the stimulus increases. These effects are preserved in the projection neurons. Our results suggest that the early olfactory system of insects may be particularly efficient in processing mixtures, which corresponds well to the observation that chemical signalling in nature predominantly involves mixtures.

neuroscience↗