Abstract
Naively speaking, the brain could be said to handle and store information. Thus, information theory seems a most natural tool for understanding how the brain works. However, one must not forget that, whenever one uses a certain mathematical framework as an approach to a sufficiently complex biological (or other) phenomenon, one is bound to describe and formulate only certain aspects (which are essentially predetermined by the mathematical framework chosen) of the problem of interest, and it seems, therefore, that the very general question of ‘understanding how the brain works’ is itself rather ill defined. Information theory, e.g., is certainly relevant to the problem of efficient neuronal coding of sensory inputs, but has not much to say, at least at present, about the microscopic dynamics of nerve impulse generation.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Gallager, R.G.: Information Theory and Reliable Communication, Wiley, New York, 1968 (Chapter 2).
Pfaffelhuber, E. and Güttinger, W.: Kybernetik (Informations-, Systemund Nachrichtentheorie) I,II, University of Munich Lecture Notes, 1967/68.
Barlow, H.B.: Trigger features, adaptation and economy of impulses. In: Information Processing in the Nervous System, ed. by K.N. Leibovic. Springer-Verlag, New York, 1969.
Barlow, H.B.: The coding of sensory messages. In: Current Problems in Animal Behavior, ed. by W.H. Thorpe and O.L. Zangwill. Cambridge University Press, Cambridge, 1961.
Barlow, H.B.: Redundancy and perception, this volume.
Pfaffelhuber, E.: Sensory coding and economy of nerve impulses, this volume.
Barlow, H.B.: Detection of form and pattern by retinal neurones, this volume.
Gross, C.G., Rocha-Miranda, C.E. and Bender, D.B.: Visual properties of neurons in inferotemporal cortex of Macaque, J. Neurophysiol. 35, 96 (1972).
Willshaw, D. J., Buneman, O.P. and Longuet-Higgins, H.C.: Non-holographic associative memory, Nature 222, 960 (1969).
Willshaw, D.J. and Longuet-Higgins, H.C.: Associative memory models, Machine Intelligence 5, 351 (1970).
Nilsson, N.J.: Learning Machines, McGraw-Hill, New York, 1965 (Chapter 2).
Marr, D.: A theory of the cerebellar cortex, J. Physiol. 202, 437 (1969).
Albus, J.S.: A theory of cerebellar function, Math. Biosciences 1025 (1971).
Steinbuch, K.: Die Lernmatrix, Kybernetik 1, 36 (1961).
Braitenberg, V.: Functional interpretation of cerebellar histology, Nature 190, 539 (1961).
Braitenberg, V.: Is the cerebellar cortex a biological clock in the millisecond range?, Progress in Brain Research 25, 334 (1967).
Pfaffelhuber, E.: Learning and information theory, Intern. J. Neuroscience 3 ,83 (1972), and in: Physical Principles of Neuronal and Organismic Behavior, ed. by M. Conrad and M. Magar. Gordon and Breach, New York, 1973.
Heim, R.: An algorithmic approach to information theory, this volume.
Pfaffelhuber, E.: A model for learning and imprinting with finite and infinite memory range, Kybernetik 12, 229 (1973).
Pfaffelhuber, E. and Damle, P.S.: Learning and imprinting in stationary and non-stationary environment, Kybernetik 13, 229 (1973).
Pfaffelhuber, E.: Finite and infinite memory range learning processes in stationary and nonstationary environments, Proc. 12th IEEE Symp. Adaptive Processes, San Diego, Paper No. TA5–8 (1973).
Pfaffelhuber, E.: Mathematical learning models and neuronal networks, J. theor. Biol. 40, 63 (1973).
Pfaffelhuber, E. and Damle, P.S.: Mathematical learning models and modifiable synapses, Intern. J. Neuroscience 6, 35 (1973).
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 1974 Springer-Verlag Berlin · Heidelberg
About this paper
Cite this paper
Pfaffelhuber, E. (1974). Information Theory and Learning in Biology Summary of the Workgroup Sessions. In: Conrad, M., Güttinger, W., Dal Cin, M. (eds) Physics and Mathematics of the Nervous System. Lecture Notes in Biomathematics, vol 4. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-80885-2_30
Download citation
DOI: https://doi.org/10.1007/978-3-642-80885-2_30
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-07014-6
Online ISBN: 978-3-642-80885-2
eBook Packages: Springer Book Archive