Biology subjects

Stentiford, R.

Publications and source records attributed to Stentiford, R..

3 recordsLinked to original sources

bioRxiv2025

Precise, individualized foraging flights in honey bees revealed by multicopter drone-based tracking

Honey bees routinely fly hundreds or thousands of meters between their hive and established foraging locations [1-3]. To navigate these long distances, they are known to combine both landmark use [4-7] and path integration [8-11] and have been hypothesized to build a cognitive map [12-14]. Due to technical challenges inherent in tracking these small insects, obtaining three-dimensional, high-resolution measurements of individual navigational precision, and thus a detailed understanding of their strategies, has been difficult. Here, we utilize a novel multicopter drone-based tracking system [15] to measure the individual flight paths of honey bees in a structured agricultural landscape at unprecedented spatial and temporal resolution. Although bees could choose from multiple routes, we discovered that individual bees follow idiosyncratic paths with striking and repeatable precision. Flight path variability was highest over visually sparse regions and lowest near prominent proximal landmarks. Furthermore, individual strategies differed: some bees flew directly toward the hive before maneuvering around a specific tree, while others flew directly to a gap between a hedgerow and the tree. Thus, each animal varies in how it uses visual information and selects between behavioral strategies. The level of precision exhibited by their flight paths exceeds that reported in the waggle dance, implying that dance variability does not reflect a limit in the bees underlying spatial representation. Our results demonstrate the remarkable precision of individual bee navigation and illustrate the potential of this new drone-based tracking method to illuminate fine-scale behavioral mechanisms across a spectrum of honey bee ecology and cognition. HighlightsO_LIIndividual honey bees follow strikingly precise, idiosyncratic flight paths, revealing personalized strategies for navigating complex, obstacle-rich landscapes. C_LIO_LIVariability in individual bee flight paths was lower near conspicuous landmarks, but greater when further from such features, as a consequence variability in bee flights was not uniform along a route. C_LIO_LINavigational precision far exceeds waggle dance variability, showing that dance imprecision is not constrained by limits in spatial representation. C_LIO_LIMulticopter drone-based tracking method enables new investigations of 3D flight control and visual navigation in structurally complex realistic environments. C_LI

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↗

bioRxiv2022

A Spiking Neural Network Model of Rodent Head Direction calibrated with Landmark Free Learning.

Maintaining a stable estimate of head direction requires both self motion (ideothetic) information and environmental (allothetic) anchoring. In unfamiliar or dark environments ideothetic drive can maintain a rough estimate of heading but is subject to inaccuracy, visual information is required to stabilise the head direction estimate. When learning to associate visual scenes with head angle, animals do not have access to the ground truth of their head direction, and must use egocentrically derived imprecise head direction estimates. We use both discriminative and generative methods of visual processing to learn these associations without extracting explicit landmarks from a natural visual scene, finding all are sufficiently capable at providing corrective signal. Further, we present a spiking continuous attractor model of head direction (SNN), which when driven by ideothetic input is subject to drift. We show that head direction predictions made by the chosen model-free visual learning algorithms can correct for drift, even when trained on a small training set of estimated head angles self-generated by the SNN. We validate this model against experimental work by reproducing cue rotation experiments which demonstrate visual control of the head direction signal.

neuroscience↗