Adaptive neural acoustic-based navigation
The adaptive navigation system employs only two microphones with a neural mechanism coupled with a model of the peripheral auditory system of lizards (i.e., Lizard ear model). The peripheral auditory model provides sound direction information which the neural mechanism uses to learn the target’s velocity via fast correlation-based unsupervised learning. For more details, see Shaikh and Manoonpong, Front. Neurorobot., 2017.
Neural path integration*
The neural path integration mechanism is for homing behavior and associative goal learning in autonomous robots. The mechanism is fed by inputs from an allothetic compass and an odometer. The home vector is computed and represented in circular arrays of neurons where heading angles are population-coded and linear displacements are rate-coded. Incoming signals are sustained through leaky neural integrator circuits and compute the home vector by local excitation-lateral inhibition interactions. This neural mechanism has been tested on a simulated hexapod robot allowing it to navigate to multiple goals and autonomously return home. The mechanism reproduces various aspects of insect navigation. For more details, see Goldschmidt et al., Front. Neurorobot., 2017.
*This research was initially performed at the Emmy Noether Research group for “Neural Control, Memory, and Learning for Complex Behaviors in Multi Sensori-Motor Robotic Systems” at Bernstein Center for Computational Neuroscience, Department for Computational Neuroscience, Third Institute of Physics-Biophysics, the University of Goettingen. The group was headed by Dr. Poramate Manoonpong.