Author:
Christopher M. Harris
Affiliation:
Centre for Theoretical and Computational Neuroscience, Universityv of Plymouth, United Kingdom
Keyword(s):
Human movement, optimal control, minimum variance, minimum jerk, channel capacity, neuromorphic engineering, biomimicry, size principle, saccade, arm reaching.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Computational Intelligence
;
Health Engineering and Technology Applications
;
Human-Computer Interaction
;
Methodologies and Methods
;
Neural Networks
;
Neurocomputing
;
Neurotechnology, Electronics and Informatics
;
Pattern Recognition
;
Physiological Computing Systems
;
Physiological Processes and Bio-Signal Modeling, Non-Linear Dynamics
;
Sensor Networks
;
Signal Processing
;
Soft Computing
;
Theory and Methods
Abstract:
Proportional noise, in which the standard deviation of signal noise is proportional to signal mean, is a fundamental constraint on human motor performance but why it occurs is unknown. We show that for neural networks with binary thresholded units, channel capacity is maximised with a recruitment strategy that produces PN. The size principle also emerges, in agreement with observation. We therefore argue that Fitt’s law, speed-accuracy trade-off, and the minimum variance trajectories (including minimum jerk trajectories for limiting brief movements), which are observed in most human point-to-point movements, have evolved as optimal strategies resulting from maximising channel capacity. We conclude that biomimicry of minimum variance and minimum jerk trajectories in robotics is probably only of aesthetic value when using standard technology. In contrast, biomimicry using neuromorphic technology in which networks are built from stochastic silicon ‘neurons’ with thresholds, is functiona
l biomimetics and optimization of channel capacity will produce behaviours that are human-like.
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