[PDF] Robots with insect brains | Semantic Scholar (2024)

Robotics and neuroscience are sister disciplines that both aim to understand how agile, efficient, and robust locomotion can be achieved in autonomous agents, forging an integrative science of autonomous behavioral control with many exciting future opportunities.

It is argued that inspiration from insect intelligence is a promising alternative to classic methods in robotics for the artificial intelligence needed for the autonomy of small, mobile robots and even for neuromorphic processors, one should not simply apply existing AI algorithms but exploit insights from natural insect intelligence to get maximally efficient AI for robot autonomy.

An artificial neural network for automatic fish tracking is presented and the utility of the framework as a research tool to form biological hypotheses on how fish navigate in complex environments and design better robot controllers on the basis of biological findings is highlighted.

It is suggested that the roles of CPGs, feedback loops and descending modulation vary among animals depending on body size, intrinsic mechanical stability, time required to reach locomotor maturity and speed effects.

A modular architecture for neuromorphic closed-loop control based on bistable relaxation oscillator modules consisting of three spiking neurons each, which can construct a neural state machine capable of generating the cyclic or repetitive behaviors necessary for legged locomotion.

Overall, this study integrates behavioural experiments, neurobiological findings, and computational models to reveal how complex visual features can be condensed through spatiotemporal encoding in the lobula neurons, facilitating efficient sampling of visual cues for identifying rewarding foraging resources.

A neuromorphic implementation of an insect-inspired circuit enables recognition of visual routes by encoding memory in a spiking neural network running on a neuromorphic computer and can evaluate visual familiarity in real time from event camera footage.

This work suggests that male Lucilia control both horizontal and vertical steerings by employing proportional controllers to the error angles, and operates at time delays as small as 10 ms, the fastest steering response observed in any flying animal, so far.

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The results provide insight into the neuronal mechanisms that underlie biological target detection and selection (from a moving platform), as well as highlight the effectiveness of the bio-inspired algorithm in an artificial visual system.

Insects are the largest taxon of arthropods, characterized by a segmented body plan, and have become models for studies of the neuronal basis underlying specific behaviors.

It is argued that developing emulations of insect brains will galvanize the global community of scientists, businesspeople, and policymakers toward pursuing the loftier goal of emulating the human brain, and this could be realistically achievable within the next 20 years.

The main goal was to prove that the same model can mimic a variety of walking‐related behavioral modes, as well as the most common coordination patterns of walking just by changing the values of a few input or internal variables.

Two-photon calcium imaging is used in head-fixed Drosophila melanogaster walking on a ball in a virtual reality arena to demonstrate that landmark-based orientation and angular path integration are combined in the population responses of neurons whose dendrites tile the ellipsoid body, a toroidal structure in the centre of the fly brain.

It is shown that minor, hypothetical but neurally plausible, extensions of this model can additionally explain how insects could store and recall PI vectors to follow food-ward paths, take shortcuts, search at the feeder and re-calibrate their vector-memories with experience.

The resulting FlyNet+CANN network incorporates the compact pattern recognition capabilities of the FlyNet model with the powerful temporal filtering capabilities of an equally compact CANN, replicating entirely in a hybrid neural implementation the functionality that yields high performance in algorithmic localization approaches like SeqSLAM.

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