Biology subjects

Webb, B.

Publications and source records attributed to Webb, B..

At least 19 recordsLinked to original sources

bioRxiv2026

RhabdoForge: A Modular, Biophysically-Grounded Rendering Framework for Insect Vision Neuroethology

Insects solve complex behavioural tasks with remarkable efficiency, using minimal neural hardware tuned to the specific requirements of their ecological niches. To truly understand or replicate these behaviours, it is insufficient to model the brain in isolation: one must account for the dynamic, closed-loop interactions between the environment, the physical organisation of the sensory periphery, and internal biophysical dynamics. To address these issues for visually controlled behaviours, we present RhabdoForge, a modular, hardware-agnostic and high-performance rendering framework specifically designed for insect neuroethology and neuromorphic research. Designed for seamless integration into Python-based workflows, RhabdoForge implements both real-time ray-tracing and stochastic path-tracing using hardware-agnostic GPU pipelines. Crucially, the engine moves beyond the static "ommatidium-as-a-pixel" paradigm by introducing a fully parametrisable model where every layer of the compound eye (from the geometric shape and the topological lattice to the internal rhabdomere blueprint) is a discrete, swappable component. The engine is capable of simulating the high-frequency, sub-ommatidial rhabdomere photomechanical actuation, allowing for the investigation of a variety of active sensing phenomena within a real-time closed-loop environment. The framework also includes an automated morphological pipeline that allows transforming 2D anatomical data into faithful 3D sensory models. We validate the engine through two case studies: a closed-loop optic-flow centring response in a virtual tunnel, and the recovery of spatial hyperacuity via rhabdomere microsaccades. By providing a bridge between high-fidelity visual ecology and neuromorphic modelling, RhabdoForge enables researchers to explore how the interplay of sensory optics and neural processing can generate complex behaviour in both biological and artificial agents.

neuroscience↗

bioRxiv2026

Resolving platypus behaviour from accelerometry: frequency-domain features improve detection of rhythmic behaviours in hydrodynamically challenging aquatic environments

Developing robust methods to quantify how animals allocate time across behaviours is essential for understanding energy use, habitat requirements, and responses to environmental change. For cryptic, semi-aquatic mammals such as the platypus, direct observation is difficult, creating a reliance on remote biologging approaches that can reliably infer behaviour in the wild. However, aquatic environments can both smooth acceleration signals through hydrodynamic damping and introduce noise from water movement, turbulence, and drag, potentially obscuring behavioural differences of similar magnitudes. We tested whether progressively incorporating biomechanical and frequency-domain (FFT-derived) predictors improved behavioural classification in hydrodynamically challenging aquatic environments. Tri-axial accelerometers were deployed on four ex situ platypuses, with synchronised video observations used to validate behaviour. From the acceleration data, we derived three predictor classes of increasing complexity: summary statistics describing activity level, engineered biomechanical variables capturing posture and body orientation, and FFT-derived features describing movement rhythm. These predictors were progressively incorporated into Random Forest models to classify five behaviours: burrow resting, surface resting, grooming, travelling/foraging, and diving. Model performance improved with increasing predictor complexity, although gains were behaviour specific. FFT-derived features substantially improved classification of rhythmic behaviours such as diving and foraging, while engineered biomechanical predictors improved grooming detection. In contrast, resting behaviours, particularly surface resting, showed little improvement. Overall accuracy increased from [~]75% to [~]88% when frequency-domain features were included. Misclassification was greatest among behaviours with overlapping or low-amplitude signals, and cross-individual validation revealed reduced model generalisability, indicating that individual variation in movement patterns constrained transferability. Incorporating frequency-domain features substantially improved behavioural classification in platypuses, particularly for rhythmic behaviours such as diving and foraging. This study provides the first validated accelerometry-based behavioural classification framework for the species and highlights the importance of matching predictor selection to behavioural mechanics. More broadly, the approach offers a transferable framework for aquatic and semi-aquatic taxa.

animal behavior and cognition↗

bioRxiv2026

A model of depth-dependent responses from neural superposition in fly compound eyes

Fly compound eyes pool signals from photoreceptors that sample the same region of visual space through neural superposition in the lamina. The optical axes of photoreceptors projecting to a single lamina cartridge are not perfectly parallel, but instead converge at a point a few millimeters in front of the eye. At short viewing distances (1--10mm) this leads to distance-dependent differences in receptive field overlap. We explored whether it was possible that flies could sense depth in this "personal space" purely from the geometry of neural superposition. To this end, we combined a computational model of fly eye optics with a disparity-tuned lamina model originally developed for stereoscopic prey capture in praying mantises, and simulated the responses of lamina monopolar cells to moving stimuli at different distances. Across variations in stimulus parameters and lamina models, we found that lamina neuron responses indeed contain a distance-dependent component, which can overall be summarised as an enhanced response due to temporally overlapping receptor responses at a critical distance of 3--4mm, the convergence distance of photoreceptor axes. Depending on the lamina model and stimulus size, either response amplitude or onset gradient, or both, exhibited this peak. We further show that changes in eye size systematically shift this preferred distance, such that larger flies had a peak response at a greater distance. Our results demonstrate that lamina cell responses may contain a robust, geometry-derived component that is specific to object distance and invariant to other stimulus properties. This suggests that neural superposition, beyond improving sensitivity, may function analogously to a light-field camera system that is effectively "focused" on a behaviorally relevant distance.

neuroscience↗

bioRxiv2026

A robot model of compass cue calibration in the insect brain

Dung beetles can use a variety of orientation cues to maintain a consistent bearing during ball-rolling. Where several cues are available, they appear to learn the spatial relationship between them, providing redundancy if some cues are removed. Mounting evidence indicates that such a learning process is implemented in the insect head direction circuit; specifically, in the plastic substrate between sensory input neurons and compass neurons in the central complex. This plasticity appears to be driven by rotational movements, providing a clear link with observed beetle dance behaviour. Here, we extend our functional model of this circuit and use it on a robot platform, to test it in the same behavioural assay as was used for the beetles. The robot was able to replicate the beetles ability to substitute a directional wind cue for a point source light cue in guiding straight-line movement. However, it also revealed significant biasing coupled to dance direction. This biasing appears to be caused by inherent conflict between recurrent and instantaneous inputs to the compass circuit. We predict that the real insect should experience similar issues unless it has evolved a neural mechanism to compensate.

bioinformatics↗

bioRxiv2026

From eye anatomy to navigation: a biologically accurate model of bees polarisation vision

Skylight polarisation patterns provide a critical navigational cue for many insects. Bees perceive these patterns through specialised ommatidia in the dorsal rim area of their compound eyes, enabling them to estimate the suns direction and navigate between food sources and the hive. Although polarisation-based navigation has been extensively studied behaviourally, computational models that link DRA anatomy with navigational performance are lacking. Here, we simulate polarisation vision in honeybees (Apis mellifera) and bumblebees (Bombus terrestris) using real sky polarisation images to capture biologically relevant skylight properties. Our biologically grounded simulation incorporates species-specific DRA anatomy, including ommatidial optical axis directions, photoreceptor receptive fields, and microvillar orientations. We evaluate navigational accuracy and consistency across sun elevations under two distinct, potentially complementary navigational models: the matched filter, which requires scanning across body orientations to identify the solar axis, and the vector-sum model, which generates instantaneous sun azimuth estimates from a single body orientation, making it independent of active scanning. Matched filter errors in estimating solar axis are below 5{degrees} across most sun elevations and in both species. Absolute errors in the vector-sum model are lower for honeybees than bumblebees (median [~]10{degrees} and [~]30{degrees}, respectively), reflecting differences in DRA anatomy, particularly viewing direction and microvillar arrangement. Both models allow stable course control across most sun elevations in both species, yet the matched filter, being limited to solar axis alignment, only enables positive or negative phototaxis. Overall, this work provides a mechanistic and comparative framework based on realistic DRA anatomy to study polarisation-based navigation, generating testable predictions for insect navigation under natural sky conditions. Author SummaryMany insects, including bees, navigate with the help of skylight polarisation patterns which hold information about the suns position even when it is not visible. Bees detect these patterns through the dorsal rim area (DRA) of their complex eyes. How differences in DRA anatomy between bee species translate into differences in navigational ability has remained unclear. Here, we built a biologically realistic simulation of polarisation vision in honeybees and bumblebees. We used real sky images to examine what polarisation information is available to each species. We then tested two models of sun position estimation based on the polarisation pattern: one that requires the bee to actively scan the sky, and one that generates an instantaneous estimate from a single body orientation. In both species, both models show that accurate sun position estimation and stable navigation are possible using just polarisation information under a wide range of sun elevations. Differences in navigational performance between honeybees and bumblebees arise because the two DRAs look at different parts of the sky. Our results provide a robust framework for understanding how DRA anatomy shapes polarisation-based navigation in bees.

neuroscience↗

bioRxiv2026

High-Speed AFM Reveals Ligand-Dependent Supramolecular Switching of Human Phosphofructokinase-1

Phosphofructokinase-1 (PFK-1) catalyzes the ATP-dependent conversion of fructose-6-phosphate (F6P) to fructose-1,6-bisphosphate (F1,6BP), the first committed step of glycolysis. Beyond classical allostery, the liver isoform PFKL forms higher-order assemblies, but how ligand binding redirects intermolecular interactions remains unclear. Here we use high-speed atomic force microscopy (HS-AFM), topology-based AFM image simulations, and molecular dynamics (MD) simulations to define ligand-dependent assembly switching of human PFKL. Wild-type PFKL (PFKL WT) forms lattice-like assemblies under APO and ATP conditions, whereas coordinated ATP and F6P loading redirects assembly toward filaments in a ligand-order-dependent manner. APO-PFKL WT also forms lattice-like assemblies on or near membrane-supported surfaces, suggesting that interfacial environments influence where assembly initiates. The filament-defective N702T mutant forms ordered double-layer lattices and preserves this geometry under APO, ATP, and ATP+F6P conditions. MD simulations suggest that lattice stabilization arises from distributed inter-tetramer contacts. These findings define a structural framework for ligand-dependent supramolecular regulation of glycolysis.

biophysics↗

bioRxiv2025

A framework for constructing insect steering circuits

Insects perform a variety of goal-directed navigation behaviours, each of which requires a comparison between their current and desired heading direction. Recent work has uncovered such a steering circuit in the fruit fly Drosophila melanogaster. We used the available neuroanatomical and physiological descriptions to derive six general rules which can be used to construct a class of steering circuits which operate in the same way. These rules are surprisingly permissive, suggesting that across insect species, steering circuits may have differing wiring while remaining functionally identical. We simulate an extreme example and demonstrate that it is functionally identical to the reported fruit fly circuit. Further, we argue that the principled approach we apply here could be applied more generally in performing comparative analyses across different insect species.

bioinformatics↗

bioRxiv2024

A computational model for angular velocity integration in a locust heading circuit

Accurate navigation often requires the maintenance of a robust internal estimate of heading relative to external surroundings. We propose a novel model for angular velocity integration to update the representation of heading in the central complex of the desert locust. In contrast to similar models proposed for the fruit fly, this circuit model uses a single 360{degrees} heading direction representation and is updated by neuromodulatory angular velocity inputs. Our computational model was implemented using steady-state firing rate neurons with dynamical synapses. The circuit connectivity was constrained by biological data and remaining degrees of freedom were optimised with a machine learning approach to yield physiologically plausible neuron activities. We demonstrate that the integration of heading and angular velocity in this circuit is robust to noise. The heading signal can be effectively used as input to an existing insect goal-directed steering circuit, adapted for outbound locomotion in a steady direction that resembles locust migration. Our study supports the possibility that similar computations for orientation may be implemented differently in the neural hardware of the fruit fly and the locust. Author summaryIn both fruit flies and locusts, a specific brain region has been observed to have an activity pattern that resembles a compass, with an activity peak moving across an array of neurons as the animal rotates through 360 degrees. However, some apparent differences in the properties of this pattern between the two species suggest there may be differences in how this internal compass is implemented. Here we focus on the locust brain, building a computational model that is based on observed neural connections and using machine learning to tune the system. Turning by the simulated locust provides modulatory input to the neural circuit that keeps activity in the array aligned to its heading direction. We simulate a migrating locust that tries to keep the same heading despite perturbances and show this circuit can steer it back on course. Our model differs from existing models of the fruit fly compass, showing how similar computations could have different implementations in different species. O_TBL View this table: org.highwire.dtl.DTLVardef@109a8b3org.highwire.dtl.DTLVardef@12298eborg.highwire.dtl.DTLVardef@65a654org.highwire.dtl.DTLVardef@18ae048org.highwire.dtl.DTLVardef@8ace20_HPS_FORMAT_FIGEXP M_TBL O_FLOATNOTable 1.C_FLOATNO O_TABLECAPTIONAbbreviations for neuron types and brain regions in the desert locust (Schistocerca gregaria) and homologues in the fruit fly (Drosophila melanogaster). C_TABLECAPTION C_TBL

neuroscience↗

bioRxiv2022

Isocitrate dehydrogenase 3b is required for spermiogenesis but dispensable for retinal degeneration

Isocitrate dehydrogenase 3 (IDH3), a key enzyme in mitochondrial tricarboxylic acid (TCA) cycle, catalyzes the decarboxylation of isocitrate into -ketoglutarate (KG), converting NAD+ into NADH. We have found that IDH3 {beta} subunit (IDH3B) is essential for IDH3 activity in multiple tissues. Loss of Idh3b in mice causes substantial accumulation of the isocitrate and its precursors in the TCA cycle, particularly in the testes, whereas the levels of the downstream metabolites remain unchanged or slightly increased. The Idh3b-knockout (KO) mice have normal visual function without retinal degeneration. However, the male KO mice are infertile. Loss of Idh3b causes energetic deficit and disrupts the biogenesis of acrosome and flagellum, resulting in spermiogenesis arrestment in sperm cells. Together, we demonstrate IDH3B controls its substrate levels in the TCA cycle and it is required for sperm mitochondrial metabolism and spermiogenesis, highlighting the importance of the tissue-specific function of the ubiquitous TCA cycle.

biochemistry↗

bioRxiv2021

Deep learning and cross-species analysis identify the influences of sex and cannabinoidsignaling on cerebellar vermis morphology

The cerebellum is the only folded cortical structure present in both rodents and humans, allowing mechanistic studies of gyrification across species. In mice, the extent of anterior vermis folding and the shapes of white matter exhibit sex-dependent differences. We trained a deep convolutional neural network to predict biological sex from midline cerebellar MRI images, showing that human cerebellar midvermis contains sex-dependent anatomical features. Our comparative cross-species analysis shows that in both species, sex and anterior vermis folding patterns are not enough to predict an individuals behavior because the variability in performance between individuals is far greater than the differences between conditions. Finally, utilizing constitutive cannabinoid receptor 1 (CB1) knockout (KO) mice, we identify developmental cannabinoid signaling as a novel molecular mechanism limiting secondary fissure formation. TeaserMales and females display differences in the architecture of cerebellar folding shaped by cannabinoid signaling during development.

neuroscience↗

bioRxiv2021

The incentive circuit: memory dynamics in the mushroom body of Drosophila melanogaster

Insects adapt their response to stimuli, such as odours, according to their pairing with positive or negative reinforcements, such as sugar or shock. Recent electrophysiological and imaging findings in Drosophila melanogaster allow detailed examination of the neural mechanisms supporting the acquisition, forgetting and assimilation of memories. We propose that this data can be explained by the combination of a dopaminergic plasticity rule that supports a variety of synaptic strength change phenomena, and a circuit structure (derived from neuroanatomy) between dopaminergic and output neurons that creates different roles for specific neurons. Computational modelling shows that this circuit allows for rapid memory acquisition, transfer from short-term to long-term, and exploration/exploitation trade-off. The model can reproduce the observed changes in the activity of each of the identified neurons in conditioning paradigms and can be used for flexible behavioural control.

neuroscience↗

bioRxiv2021

Impact of central complex lesions on visual orientation in ants: Turning behaviour, but not the overall movement direction, is disrupted

Wood ants are excellent navigators using a combination of innate and learnt navigational strategies to travel between their nest and feeding sites. Visual navigation in ants has been studied extensively, however, we only know little about the underlying neural mechanisms. The central complex (CX) is located at the midline of the insect brain. It receives sensory input that allows an insect to keep track of the direction of sensory cues relative to its own orientation and to control movement. We show here direct evidence for the involvement of the central complex in the innate visual orientation response of freely moving wood ants. Lesions in the CX disrupted the control of turning in a lateralised manner, but had no effect on the overall heading direction, walking speed or path straightness.

animal behavior and cognition↗

bioRxiv2021

A unified mechanism to support innate and learned use of visual landmark guidance in insects

Insects can navigate efficiently in both novel and familiar environments, and this requires flexiblity in how they are guided by sensory cues. A prominent landmark, for example, can ellicit strong innate behaviours (attraction or menotaxis) but can also be used, after learning, as a specific directional cue as part of a navigation memory. However, the mechanisms that allow both pathways to co-exist, interact or override each other are largely unknown. Here we propose a model for the behavioural integration of innate and learned guidance based on the neuroanatomy of the central complex (CX), adapted to control landmark guided behaviours. We consider a reward signal provided either by an innate attraction to landmarks or a long-term visual memory in the mushroom bodies (MB) that modulates the formation of a local vector memory in the CX. Using an operant strategy for a simulated agent exploring a simple world containing a single visual cue, we show how the generated short-term memory can support both innate and learned steering behaviour. In addition, we show how this architecture is consistent with the observed effects of unilateral MB lesions in ants that cause a reversion to innate behaviour. We suggest the formation of a directional memory in the CX can be interpreted as transforming rewarding (positive or negative) sensory signals into a mapping of the environment that describes the geometrical attractiveness (or repulsion). We discuss how this scheme might represent an ideal way to combine multisensory information gathered during the exploration of an environment and support optimized cue integration. 1 Author summaryIn this paper, we modeled the neural pathway allowing insects to perform landmark guided behaviours using their internal compass. First, we observed the intrinsic property of the connectome, extracted from drosophila online database, between the internal compass neurons and the steering neurons to support an oriented behaviour towards a single landmark. Then, we proposed and evaluated an adaptation of the bees path integration neural circuit, to sustain flexible landmark guidance behaviours such as attraction or menotaxis. We showed the model ability to form a memory during the exploration of the local environment to support both innate or learned navigation behaviour using a single landmark in the environment. In addtion, we demonstrated the transformation of a simple goodness/badness signal, from innate or long-term memory pathways, into an oriented steering signal that could be applied to other sensory pathways. Furthermore, by reproducing lesion experiments in the mushroom bodies of wood ants we highlight the consistency of the model with biological observation. We then discuss the different emergent properties and the potential outcome that this local, and operant, memory supports.

animal behavior and cognition↗

bioRxiv2020

Spatio-temporal Memory for Navigation in a Mushroom Body Model

Insects, despite relatively small brains, can perform complex navigation tasks such as memorising a visual route. The exact format of visual memory encoded by neural systems during route learning and following is still unclear. Here we propose that interconnections between Kenyon cells in the Mushroom Body (MB) could encode spatio-temporal memory of visual motion experienced when moving along a route. In our implementation, visual motion is sensed using an event-based camera mounted on a robot, and learned by a biologically constrained spiking neural network model, based on simplified MB architecture and using modified leaky integrate-and-fire neurons. In contrast to previous image-matching models where all memories are stored in parallel, the continuous visual flow is inherently sequential. Our results show that the model can distinguish learned from unlearned route segments, with some tolerance to internal and external noise, including small displacements. The neural response can also explain observed behaviour taken to support sequential memory in ant experiments. However, obtaining comparable robustness to insect navigation might require the addition of biomimetic pre-processing of the input stream, and determination of the appropriate motor strategy to exploit the memory output.

animal behavior and cognition↗

bioRxiv2020

A neural model for insect steering applied to olfaction and path integration

Many animal behaviours require orientation and steering with respect to the environment. For insects, a key brain area involved in spatial orientation and navigation is the central complex. Activity in this neural circuit has been shown to track the insects current heading relative to its environment, and has also been proposed to be the substrate of path integration. However, it remains unclear how the output of the central complex is integrated into motor commands. Central complex output neurons project to the lateral accessory lobes (LAL), from which descending neurons project to thoracic motor centres. Here, we present a computational model of a simple neural network that has been described anatomically and physiologically in the LALs of male silkworm moths, in the context of odour-mediated steering. We present and analyze two versions of this network, both implemented in the Nengo framework, one rate-based and one based on spiking neurons. The modelled network consists of an inhibitory local interneuron and a bistable descending neuron ( flip-flop), which both receive input in the LAL. The flip-flop neuron projects onto neck motor neurons to induce steering. We show that this simple computational model not only replicates the basic parameters of male silkworm moth behaviour in a simulated odour plume, but can also take input from a computational model of path integration in the central complex and use it to steer back to a point of origin. Furthermore, we find that increasing the level of detail within the model improves the realism of the models behaviour. Our results suggest that descending neurons originating in the lateral accessory lobes, such as flip-flop neurons, are sufficient to mediate multiple steering behaviours. This study is therefore a first step to close the gap between orientation circuits in the central complex and downstream motor centres. Author summaryTargeted movements and steering within an environment are essential for many behaviours. In insects, the brains center for spatial orientation and navigation is the central complex, which processes information about the configuration of the local environment as well as global orientation cues such as the Sun position. Neural networks in the central complex also compute the insects heading direction, and are thought to be involved in generating steering commands. However, it is unclear how these steering commands are transmitted to downstream motor centers. Output neurons from the central complex project to the lateral accessory lobes, a neuropil which also gives rise to descending pre-motor neurons that are involved in steering in the silkworm moth Bombyx mori. In this study, we provide a computational model of a pre-motor neural network in the lateral accessory lobes. We show that this network can steer an agent towards the source of a simulated odor plume, but that it can also steer efficiently when getting input from an anatomically constrained network model of the central complex. This model is therefore a first step to close the gap between the central complex and thoracic motor circuits.

neuroscience↗

bioRxiv2020

A physical theory of larval Drosophila behaviour

All animal behaviour must ultimately be governed by physical laws. As a basis for understanding the physics of behaviour in a simple system, we here develop an effective theory for the motion of the larval form of the fruitfly Drosophila melanogaster, and compare it against a quantitative analysis of the real animals behaviour. We first define a set of fields which quantify stretching, bending, and twisting along the larvas antero- posterior axis, and then perform a search in the space of possible theories that could govern the long-wavelength physics of these fields, using a simplified approach inspired by the renormalisation group. Guided by symmetry considerations and stability requirements, we arrive at a unique, analytically tractable free-field theory with a minimum of free parameters. Unexpectedly, we are able to explain a wide-spectrum of features of Drosophila larval behaviour by applying equilibrium statistical mechanics: our theory closely predicts the animals postural modes (eigenmaggots), as well as distributions and trajectories in the postural mode space across several behaviours, including peristaltic crawling, rolling, self-righting and unbiased substrate exploration. We explain the low-dimensionality of postural dynamics via Boltzmann suppression of high frequency modes, and also propose and experimentally test, novel predictions on the relationships between different forms of body deformation and animal behaviour. We show that crawling and rolling are dominated by similar symmetry properties, leading to identical dynamics/statistics in mode space, while rolling and unbiased exploration have a common dominant timescale. Furthermore, we are able to demonstrate that self-righting behaviour occurs continuously throughout substrate exploration, owing to the decoupling of stretching, bending, and twisting at low energies. Together, our results demonstrate that relatively simple effective physics can be used to explain and predict a wide range of animal behaviours.

biophysics↗

bioRxiv2020

Transfer of orientation memories in untethered wood ants (Formica rufa) from walking in an arena to walking on a motion compensation treadmill

The scale of natural insect navigation during foraging makes it challenging to study, in a controlled way, the navigation processes that an insect brain can support. Virtual Reality and trackball setups have offered experimental control over visual environments while studying tethered insects, but potential limitations and confounds introduced by tethering motivates the development of alternative untethered solutions. In this paper we validate the use of a motion compensator (or treadmill) to study visually-driven behaviour of freely moving wood ants (Formica rufa). We show how this setup allows naturalistic walking behaviour and motivation over long timeframes. Furthermore, we show that ants are able to transfer associative and navigational memories from classical maze and arena contexts to our treadmill. Thus, we demonstrate the possibility to study navigational behaviour over ecologically relevant durations (and virtual distances) in precisely controlled environments, bridging the gap between natural and highly controlled laboratory experiments. 1 Summary statementWe have developed and validated a motion compensating treadmill for wood ants which opens new perspectives to study insect navigation behaviour in a fully controlled manner over ecologically relevant durations.

animal behavior and cognition↗

bioRxiv2020

Mushroom bodies are required for accurate visual navigation in ants

Visual navigation in ants has long been a focus of experimental study [1-3], but only recently have explicit hypotheses about the underlying neural circuitry been proposed [4]. Indirect evidence suggests the mushroom bodies (MB), a known site of olfactory learning [5-10], may also be the substrate for visual memory in navigation tasks [11-14]. Computational modelling shows that MB neural architecture could support this function [15, 16], though there is no direct evidence that ants require MBs for visual navigation. Here we show that lesions of MB calyces impair ants visual navigation to a remembered food location whilst leaving their innate responses to visual cues unaffected. Ants are innately attracted to a large visual cue but we trained them to locate a food source at a specific angle to this visual cue. Subsequent bilateral or unilateral lesioning (through procaine hydrochloride injection) of the MB calyces, caused ants to revert to their innate cue attraction whilst control (saline) injected ants still approached the feeder. The ants path straightness and walking speed were unaffected by lesions. Reversion towards the cue direction occurred irrespective of whether it was ipsi-or contralateral to the lesion site, showing this is not due simply to an induced motor bias. Monocular occlusion did not diminish ants ability to locate the feeder, suggesting the lesion is not merely interrupting visual input to the calyx. The demonstrated dissociation between innate and learnt visual responses provides direct evidence for a specific role of the MB in navigational memory.

animal behavior and cognition↗