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Clinical Epidemiology - a basic science for clinical medicine
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Epidemiologic ResearchMotor ControlMovement GenerationSocial SciencesAdaptive SwitchingNeural MechanismNeurodynamicsClinical EpidemiologyMotor NeuroscienceEpidemiological PrincipleGeneral EpidemiologyHealth SciencesInfectious Disease EpidemiologyCognitive ScienceEpidemiological OutcomeNervous SystemEpidemiologyInsect BrainSystems NeuroscienceAdaptive Movement PatternsComputational NeuroscienceSensorimotor TransformationPatient SafetyNeuronal NetworkNeuroscienceClinical Sciences
Navigation in changing environments relies on adaptive motor behaviours, and insects use the Lateral Accessory Lobe to switch between search and steering movements depending on stimulus reliability. The study aims to develop spiking neural network models that emulate insect‑inspired architectures to generate adaptive movements in response to varying sensory inputs. The authors constructed spiking neural network models that map sensory input reliability to motor pattern selection, enabling the network to switch between search and steering behaviours. The models produce diverse adaptive patterns, predominantly zig‑zagging, are robust to noise, and across a wide parameter range generate zig‑zagging dynamics, indicating the architecture is inherently suited for adaptive movement.
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.