Cristopher M. Harris


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 conclude 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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Paper Citation

in Harvard Style

Harris C. (2009). DOES FISHER INFORMATION CONSTRAIN HUMAN MOTOR CONTROL? . In Proceedings of the International Joint Conference on Computational Intelligence - Volume 1: ICNC, (IJCCI 2009) ISBN 978-989-674-014-6, pages 414-420. DOI: 10.5220/0002284704140420

in Bibtex Style

author={Cristopher M. Harris},
booktitle={Proceedings of the International Joint Conference on Computational Intelligence - Volume 1: ICNC, (IJCCI 2009)},

in EndNote Style

JO - Proceedings of the International Joint Conference on Computational Intelligence - Volume 1: ICNC, (IJCCI 2009)
SN - 978-989-674-014-6
AU - Harris C.
PY - 2009
SP - 414
EP - 420
DO - 10.5220/0002284704140420