Author:
Cristopher M. Harris
Affiliation:
University of Plymouth, United Kingdom
Keyword(s):
Fisher information, Cramer-Rao bound, Fisher metric, Movement control, Minimum variance model, Proportional noise, Signal dependent noise.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Computational Intelligence
;
Computational Neuroscience
;
Health Engineering and Technology Applications
;
Human-Computer Interaction
;
Methodologies and Methods
;
Neural Networks
;
Neurocomputing
;
Neurotechnology, Electronics and Informatics
;
Pattern Recognition
;
Physiological Computing Systems
;
Sensor Networks
;
Signal Processing
;
Soft Computing
;
Theory and Methods
Abstract:
Fisher information places a bound on the error (variance) in estimating a parameter. The nervous system, however, often has to estimate the value of a variable on different occasions over a range of parameter values (such as light intensities or motor forces). We explore the optimal way to distribute Fisher information across a range of forces. We consider the simple integral of Fisher information, and the integral of the square root of Fisher information because this functional is independent of re-parameterization of force. We show that the square root functional is optimised by signal-dependent noise in which the standard deviation of force noise is approximately proportional to the mean force up to about 50% maximum force, which is in good agreement with empirical observation. The simple integral does not fit observations. We also note that the usual Cramer-Rao bound is ‘extended’ with signal-dependent noise, but that this may not be exploited by the biological motor system. We c
onclude that maximising the integral of the square root of Fisher information can capture the signal dependent noise observed in natural point-to-point movements for forces below about 50% of maximum voluntary contraction.
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