Amazing stuff!
"Researchers discovered that bees use flight movements to sharpen brain signals, enabling them to recognize patterns with remarkable accuracy. A digital model of their brain shows that this movement-based perception could revolutionize AI and robotics by emphasizing efficiency over massive computing power. ...
By building a computational model -- or a digital version of a bee's brain -- researchers have discovered how the way bees move their bodies during flight helps shape visual input and generates unique electrical messages in their brains. These movements generate neural signals that allow bees to easily and efficiently identify predictable features of the world around them. This ability means bees demonstrate remarkable accuracy in learning and recognizing complex visual patterns during flight, such as those found in a flower. ...
The model shows that bee neurons become finely tuned to specific directions and movements as their brain networks gradually adapt through repeated exposure to various stimuli, refining their responses without relying on associations or reinforcement. This lets the bee's brain adapt to its environment simply by observing while flying, without requiring instant rewards. This means the brain is incredibly efficient, using only a few active neurons to recognize things, conserving both energy and processing power. ..."
From the editor's evaluation and abstract:
"Editor's evaluation
Inspired by bee's visual behavior, this manuscript develops a model of visual scanning, processing, and pattern recognition learning. The work shows how pre-training with natural images creates spatiotemporal receptive fields in lobula neurons that enhance pattern discrimination through sparse encoding. The authors provide a solid analysis of neural responses, model performance across tasks, and the contributions of components like scanning strategies and lateral inhibition. While the model represents a functional circuit for active vision, its biological plausibility is somewhat limited by intentional simplifications. The systematic evaluation of necessary components and comparisons with bee behavioral data strengthen the findings. This important work offers insights into motion-driven visual processing in compact neural systems.
Abstract
Bees’ remarkable visual learning abilities make them ideal for studying active information acquisition and representation.
Here, we develop a biologically inspired model to examine how flight behaviours during visual scanning shape neural representation in the insect brain, exploring the interplay between scanning behaviour, neural connectivity, and visual encoding efficiency.
Incorporating non-associative learning—adaptive changes without reinforcement—and exposing the model to sequential natural images during scanning, we obtain results that closely match neurobiological observations.
Active scanning and non-associative learning dynamically shape neural activity, optimising information flow and representation.
Lobula neurons, crucial for visual integration, self-organise into orientation-selective cells with sparse, decorrelated responses to orthogonal bar movements. They encode a range of orientations, biased by input speed and contrast, suggesting co-evolution with scanning behaviour to enhance visual representation and support efficient coding.
To assess the significance of this spatiotemporal coding, we extend the model with circuitry analogous to the mushroom body, a region linked to associative learning. The model demonstrates robust performance in pattern recognition, implying a similar encoding mechanism in insects. Integrating behavioural, neurobiological, and computational insights, this study highlights how spatiotemporal coding in the lobula efficiently compresses visual features, offering broader insights into active vision strategies and bio-inspired automation."
A neuromorphic model of active vision shows how spatiotemporal encoding in lobula neurons can aid pattern recognition in bees (open access)
Fig. 1 "Neural network of active vision inspired by neurobiology and flight dynamics of bees."
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