Use this URL to cite or link to this record in EThOS:
Title: A fly-robot interface to investigate the dynamics of closed-loop visuo-motor control in the blowfly
Author: Ejaz, Naveed
Awarding Body: Imperial College London
Current Institution: Imperial College London
Date of Award: 2013
Availability of Full Text:
Access from EThOS:
Access from Institution:
The blowfly Calliphora is one of the most sophisticated fliers in the animal kingdom. It displays a broad repertoire of visually guided behaviours that can readily be quantified, including gaze and flight stabilization reflexes, male chasing flights, collision avoidance and landing responses. The fly achieves such robust visuo-motor control tasks based on a comparatively simple nervous system that is highly accessible for electrophysiological recordings. The ability to investigate the fly’s performance at the behavioural and electrophysiology levels makes this animal an ideal model system to study closed-loop visual motor control. The aim of this thesis was to develop and characterize the dynamics of a fly-robot interface (FRI) while a fly performs a closed-loop visual stabilization task. A novel experimental setup involving a FRI was developed which allowed for simultaneous measurements of neural activity from the fly and the behavioural performance of the robot. In the setup, the neural activity of an identified visual interneuron, the H1 cell, was recorded and its action potentials were used to control the motion of a mobile robot that was free to rotate along its vertical axis. External visual perturbations were introduced into the closed-loop system through a rotating turn-table with the robot using the neural activity to counter-rotate and to minimize the observed visual motion. The closed-loop control delay of the FRI was 50 ms which is well within the range of visual response delays observed in fly behaviour. With the FRI, the closed-loop dynamics of a static-gain proportional controller were characterized. The results explain significant oscillations in the closed-loop responses as a possible consequence of a high controller gain which were also observed but never fully interpreted in previous behavioural studies. Varying the controller gain also offers competing control benefits to the fly, with different gains maximizing performance for different input frequency ranges and thus different behavioural tasks. Results with the proportional controller indicate the dependence of the FRI frequency response on the angular acceleration of visual motion. An adaptive controller designed to dynamically scale the feedback gain was found to increase the bandwidth of the frequency response when compared with the static-gain proportional controller. The image velocities observed under closed-loop conditions using the proportional and the adaptive controllers were correlated with the spiking activity of the H1-cell. A remarkable qualitative similarity was found between the response dynamics of the cell under closed-loop conditions with those obtained in previous open-loop experiments. Specifically, (i) the peak spike rate decreased when the mean image velocity was increased, (ii) the relationship between spike rate and image velocity was dependant on the standard deviation of the image velocities suggesting adaptive scaling of the cell’s signalling range, and (iii) the cell’s gain decreased linearly with increasing image accelerations. Despite the fact that several sensory modalities - including the motion vision pathway - process information in a non-linear fashion signal integration at stages one to two synapses away from the motor systems and the behavioural output itself have been shown to be linear. Quantifying the closed-loop dynamics of visuo-motor control at both the behavioural and neuronal level, may provide a starting point to discover the neural mechanisms underlying an appropriate combination of complementary non-linear processes which ultimately result in a linear performance of the overall system.
Supervisor: Krapp, Holger Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral