Biology subjects

Philippides, A.

Publications and source records attributed to Philippides, A..

10 recordsLinked to original sources

bioRxiv2026

Emergent control of ant learning walks using the mismatch between path-integration and visual cues

Despite their small brains, desert ants can safely navigate back to their nest after travelling hundreds of metres to find food. This ability relies on visual memories, gathered on initial Learning Walks (LWs), during which ants slowly explore the surroundings of the nest. LWs follow a clear progression: early walks are short, spiraling, and interspersed with pirouettes: brief stops during which the ant perform a visual scan. Successive LWs become straighter and extend farther from the nest, until they transition into foraging trips. However, when the visual panorama changes, LWs reappear. Existing models explain how stored panoramic views can guide homing, but not when visual memories are collected or how LW dynamics are controlled. Here, we propose that a single error signal - generated from the mismatch between the estimated nest direction provided by path integration and visual predictions - scaffolds visual learning and drives LW dynamics. Using 3D reconstructions of desert ant experiments, we show that this mechanism reproduces the main features of LWs, including the transition to foraging and reoccurrence of LWs after environmental change. Our model is biologically plausible and consistent with known insect navigation circuits. Author summaryA tiny nest entrance in the desert is easy to miss, but doing so may have lethal consequences for a foraging desert ant, returning home after a long scavenging trip. To solve this problem, desert ants spend several days performing Learning walks (LWs) to learn the visual landmarks around their nest before they begin searching for food. LWs are structured active learning procedures and clear patterns can be identified across ant species. LWs take the form of nest-centred spirals, interspersed with frequent visual scans. Over two to four days, LWs become longer, straighter and walking speed increases, before the ant finally transitions to foraging. LWs may reoccur even in experienced foragers, when a new landmark is introduced near the nest. No previous model has provided a mechanism for how LWs are controlled. Here, we propose that the transition from LWs to foraging is driven by visual familiarity. In our model, unfamiliar surroundings promote learning and exploration; as the visual panorama becomes familiar, behaviour shifts to foraging. Using 3D simulations of experimental layouts, we demonstrate that LWs can emerge from simple rules, and that our simulated agents reproduce the main features of LWs observed in desert ants.

animal behavior and cognition↗

bioRxiv2025

Ant visual route navigation: How the fine details of behaviour promote successful route performance and convergence

Individually foraging ants use egocentric views as a dominant navigation strategy for learning and retracing routes. Evidence suggests that route retracing can be achieved by algorithms which use views as visual compasses, where individuals choose the heading that leads to the most familiar visual scene when compared to route memories. However, such a mechanism does not naturally lead to route approach, and alternative strategies are required to enable convergence when off-route and for correcting on-route divergence. In this work we investigate how behavior incorporated into visual compass like route learning and recapitulation strategies might enable convergence to a learned route and its destination. The most successful recapitulation method comes from a cast and surge approach, a mechanism seen across arthropods for olfactory navigation. In this strategy casts form a zig-zagged or oscillatory search in space for familiar views, and surges exploit visual familiarity gradients. We also find that performance improves if the learned route consists of an oscillatory motor mechanism with learning gated to occur when the agent approaches the central axis of the oscillation. Furthermore, such oscillations combined with the cast and surge method additively enhance performance, showing that it benefits to incorporate oscillatory behavior in both learning and recapitulation. As destination reaching is the primary goal of navigation, we show that a suitably sized goal-orientated learning walk might suffice, but that the scale of this is dependent on route length and the route learning and recapitulation strategies enabled. Finally we show that view familiarity can modulate on-the-spot scans performed by an agent, providing a better reflection of ant behavior. Overall, our results show that the visual compass can provide a basis for robust visual navigation, so long as it is considered holistically with the details of basic motor and sensory-motor patterns of ants undertaking route learning and recapitulation. Author summaryAnts are capable of learning long visually guided routes. Given their small brains, it is assumed that they must have a computationally efficient strategy. The current dominant explanation for visual route guidance is that ants choose a direction to move in by comparing the current view (at different orientations) with views stored during learning, a so-called visual compass. These algorithms result in routes which typically run parallel to the training route, and do not explain how insects converge back onto a route when displaced. Using extensive simulations of ant route learning, we explore how innate behaviours and sensory motor strategies during learning and recapitulation can improve performance. We find that a mechanism which integrates a search strategy to sample a wider space by casting, interrupted by surging if the visual conditions are favourable, enhances convergence and goal reaching. We also show that altering the basic structure of learning routes to be oscillatory can significantly improve recapitulation. As well as emphasizing the importance of considering behaviour across both learning and recapitulation, this work gives a plausible account of robust route performance that maintains the elegant efficiency expected of insect navigation strategies.

animal behavior and cognition↗

bioRxiv2024

Lateralised memory networks explain the use of higher-order visual features in navigating insects

Many insects use memories of their visual environment to adaptively drive spatial behaviours. In ants, visual memories are fundamental for navigation, whereby foragers follow long visually guided routes to foraging sites and return to the location of their nest. Whilst we understand the basic visual pathway to the memory centres (Optic Lobes to Mushroom Bodies) involved in the storage of visual information, it is still largely unknown what type of representation of visual scenes underpins view-based navigation in ants. Several experimental studies have shown ants using "higher-order" visual information - that is features extracted across the whole extent of a visual scene - which raises the question as to where these features are computed. One such experimental study showed that ants can use the proportion of a shape experienced left of their visual centre to learn and recapitulate a route, a feature referred to as "fractional position of mass" (FPM). In this work, we use a simple model constrained by the known neuroanatomy and information processing properties of the Mushroom Bodies to explore whether the use of the FPM could be a resulting factor of the bilateral organisation of the insect brain, all the whilst assuming a "retinotopic" view representation. We demonstrate that such bilaterally organised memory models can implicitly encode the FPM learned during training. We find that balancing the "quality" of the memory match across left and right hemispheres allows a trained model to retrieve the FPM defined direction, even when the model is tested with other shapes, as demonstrated by ants. The result is shown to be largely independent of model parameter values, therefore suggesting that some aspects of higher-order processing of a visual scene may be emergent from the structure of the neural circuits, rather than computed in discrete processing modules. Author summaryMany insects are excellent visual navigators, often relying on visual memories to follow long foraging routes and return safely to their nest location. We have a good understanding of the neural substrates supporting the storage of visual memories in ants. However, it is still largely unknown what type of representation of visual scenes underpins the functions of visual navigation. Experimental studies have shown ants using "higher-order" features as part of navigation, that is features that are extracted across the whole extent of a visual scene. Using an anatomically constrained model of the insect memory centers, we address the question of whether the use of higher-order visual features may be emergent from the overall architecture of the vision-to-memory pathways. We find that balancing the quality of left and right visual memory matches provides an explanation for some higher-order visual processing and visual cognition shown in experiments with ants. Overall, this constitutes a contribution to our understanding of visual cognition and the processing of visual scenes used in navigational tasks. We additionally postulate a novel mechanism ants may use to navigate, which is supported by the bilateral structure of the insect brain.

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↗

bioRxiv2022

How bumblebees coordinate path integration and body orientation at the start of their first learning flight

The start of a bumblebees first learning flight from its nest provides an opportunity to examine the bees learning behaviour on its initial view of the nests unfamiliar surroundings. Bumblebees like many other ants, bees and wasps learn views of their nest surroundings while facing their nest. A bumblebees first fixation of the nest is a coordinated manoeuvre in which the insect faces the nest with its body oriented towards a particular visual feature within its surroundings. The manoeuvres utility is that during return flights after foraging bees, when close to the nest, adopt the same preferred body-orientation (Hempel de Ibarra et al., 2009; Robert et al., 2018). A translational scan oriented orthogonally to the bees body-orientation helps the bee reach the preferred conjunction of nest-fixation and body-orientation. How does a bee, unacquainted with its surroundings, know when it is facing its nest? The details of nest-fixation argue that, like desert ants (Fleischmann et al., 2018), the bee relies on path integration. Path integration gives bees continuously updated information about the current direction of their nest and enables them to fixate the nest when the body points in the appropriate direction. We relate the three components of the coordinated manoeuvre to events in the central complex, noting that nest fixation is in egocentric coordinates, whereas body orientation and flight direction within the visual surroundings of the nest are in geocentric coordinates.

animal behavior and cognition↗

bioRxiv2022

Dual role of brain endothelial Gpr126 in blood-brain barrier development and ischemic stroke

The blood-brain barrier (BBB) acquires unique properties for regulation of the neuronal function during development. The genesis of the BBB coupled with angiogenesis is orchestrated by the Wnt/{beta}-catenin signaling pathway. Aside from the importance of Wnt/{beta}-catenin signaling, the molecular mechanisms that regulate these processes are poorly understood. Here, we identify the brain endothelial adhesion G-protein-coupled receptor Gpr126 as a novel target gene of Wnt/{beta}-catenin signaling that is required for postnatal BBB development, and its expression is detrimental for ischemic stroke in adults. We show that Gpr126 expression is high in mouse brain endothelium during BBB formation, but decreases in the adult. Inactivation of Gpr126 in postnatal endothelial cells results in vessel enlargement and impairs acquisition of the BBB characteristics, such as increased neurovascular permeability, and reduced basement membrane protein deposition and pericyte coverage. Mechanistically, Gpr126 is required during developmental angiogenesis to promote endothelial cell migration, acting via an interaction between Lrp1 and 3{beta}1-integrin, which couples vessel morphogenesis to BBB formation. Interestingly, in adult mice with an established BBB, the lack of Gpr126 expression in acute ischemic stroke is protective and coupled with reduced microglia activation, which contributes to an improved neurological outcome. These data identify Gpr126 as a promising therapeutic target to treat ischemic stroke.

cell biology↗

bioRxiv2022

Spatial encoding of forelimb proprioception in the mouse somatosensory cortex

Conscious perception of limb movements depends on proprioceptive neural responses in the somatosensory cortex. In contrast to tactile sensations, proprioceptive cortical coding is barely studied in the mammalian brain and practically non-existent in rodent research. To understand the cortical representation of this important sensory modality we developed a passive forelimb displacement paradigm in behaving mice and also trained them to perceptually discriminate where their limb is moved in space. We delineated, for the first time, the rodent proprioceptive cortex with wide-field calcium imaging and optogenetic silencing experiments during awake behavior. Our results reveal that proprioception is represented in both sensory and motor cortical areas. In addition, behavioral measurements and responses of layer 2/3 neurons imaged with two-photon microscopy reveal that passive limb movements are both perceived and encoded in the mouse cortex as a spatial direction vector that interfaces the limb with the bodys peripersonal space.

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↗

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↗