Abstract
The head direction (HD) system in mammals contains neurons that fire to represent the direction the animal is facing in its environment. The ability of these cells to reliably track head direction even after the removal of external sensory cues implies that the HD system is calibrated to function effectively using just internal (proprioceptive and vestibular) inputs. Rat pups and other infant mammals display stereotypical warm-up movements prior to locomotion in novel environments, and similar warm-up movements are seen in adult mammals with certain brain lesion-induced motor impairments. In this study we propose that synaptic learning mechanisms, in conjunction with appropriate movement strategies based on warm-up movements, can calibrate the HD system so that it functions effectively even in darkness. To examine the link between physical embodiment and neural control, and to determine that the system is robust to real-world phenomena, we implemented the synaptic mechanisms in a spiking neural network and tested it on a mobile robot platform. Results show that the combination of the synaptic learning mechanisms and warm-up movements are able to reliably calibrate the HD system so that it accurately tracks real-world head direction, and that calibration breaks down in systematic ways if certain movements are omitted. This work confirms that targeted, embodied behaviour can be used to calibrate neural systems, demonstrates that ‘grounding’ of modelled biological processes in the real world can reveal underlying functional principles (supporting the importance of robotics to biology), and proposes a functional role for stereotypical behaviours seen in infant mammals and those animals with certain motor deficits. We conjecture that these calibration principles may extend to the calibration of other neural systems involved in motion tracking and the representation of space, such as grid cells in entorhinal cortex.
Highlights
Overview Calibration is a major issue for all real-world systems, animal and robotic
Results of the head direction adaptive attractor network (HDAAN) trained on simulated head direction movements are presented first, followed by training based on visual flow from a mobile robot platform
Simulation – 360u turn gain calibration only To demonstrate the functioning of 360u turn gain calibration for stability condition 2 in isolation, results are first presented for an HDAAN with no bias in the connection weights so that tuning for stability condition 1 is not required
Summary
Overview Calibration is a major issue for all real-world systems, animal and robotic. Of particular interest in this paper is how movement strategies of an animal or robot may combine with learning rules to calibrate a neural system. In this paper we are interested in the head direction system, due to its foundational role in navigating systems, both mammalian and robotic. In a population of HD neurons, all directions are represented approximately giving a unique activity pattern called the bump or hill of activity for any given direction the animal faces [3,4,5,6,7,8]. The bump translates in a systematic way through the HD neuron population such that the peak continues to represent the current head direction
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