Abstract
The present paper proposes a model which applies formal neural network modeling techniques to construct a theoretical representation of the cerebellar cortex and its performances in motor control. A schema that makes explicit use of propagation delays of neural signals, is introduced to describe the ability to store temporal sequences of patterns in the Golgi-granule cell system. A perceptron association is then performed on these sequences of patterns by the Purkinje cell layer. The model conforms with important biological constraints, such as the known excitatory or inhibitory nature of the various synapses. Also, as suggested by experimental evidence, the synaptic plasticity underlying the learning ability of the model, is confined to the parallel fiber — Purkinje cell synapses, and takes place under the control of the climbing fibers. The result is a neural network model, constructed according to the anatomy of the cerebellar cortex, and capable of learning and retrieval of temporal sequences of patterns. It provides a framework to represent and interpret properties of learning and control of movements by the cerebellum, and to assess the capacity of formal neural network techniques for modeling of real neural systems.
Similar content being viewed by others
References
Albus JS (1971) A theory of cerebellar function. Math Biosci 45:247–293
Amit DJ, Wong KYM, Campbell C (1989) Perceptron learning with sign-constrained weights. J Phys A22:2039–2045
Braitenberg V (1990) Reading the structure of brains. Network 1:1–11
Chapeau-Blondeau F, Gouraud A, Chauvet G (1989) What type of neural network for the cerebellar cortex?; Proceedings of the Second International Conference on Neural Network and their Applications, Nîmes, France, pp 295–304 (in French)
Chauvet G (1986) Habituation rules for a theory of the cerebellar cortex. Biol Cybern 55:201–209
Chauvet G (1988) Learning abilities for a cerebellar Purkinje unit. Neural Networks 1 [Suppl 1]:244
Dehaene S, Changeux JP, Nadal JP (1987) Neural networks that learn temporal sequences by selection. Proc Natl Acad Sci USA 84:2727–2731
Dunin-Barkowski WL, Larionova NP (1985) Computer simulation of a cerebellar cortex compartment. Biol Cybern 51:399–406 and 407–415
Eccles JC, Ito M, Szentagothai J (1967) The cerebellum as a neuronal machine. Springer, Berlin Heidelberg New York
Fujita M (1982) Adaptive filter model of the cerebellum. Biol Cybern 45:195–206
Geiger SR (ed) (1977) Handbook of physiology. American Physiological Society, Bethesda, Md
Hopfield JJ (1982) Neural networks and physical systems with emergent collective computational abilities. Proc Natl Acad Sci USA 79:2554–2558
Ito M (1984) The cerebellum and neural control. Raven Press, New York
Kuhn R, van Hemmen JL, Reidel U (1989) Complex temporal association in neural nets. In: Personnaz L, Dreyfus G (eds) Neural networks from models to applications. IDSET, Paris
Marr D (1969) A theory of cerebellar cortex. J Physiol 202:437–470
Massone L, Bizzi E (1989) A neural network model for limb trajectory formation. Biol Cybern 61:417–425
Minsky M, Papert S (1969) Perceptrons. MIT Press, Cambridge, Mass
Pearlmutter BA (1989) Learning state space trajectories in recurrent neural networks. Neural Comput 1:263–269
Pellionisz A, Llinas R (1982) Space-time representation in the brain. The cerebellum as a predictive space-time metric tensor. Neuroscience 7:2949–2970
Rumelhart DE, McClelland JL (1986) Parallel distributed processing, vol 1. MIT Press, Cambridge, Mass
Shinomoto S (1987) A cognitive and associative memory. Biol Cybern 57:197–206
Somopolinsky H, Kanter I (1986) Temporal association in asymmetric neural networks. Phys Rev Lett 57:2861–2864
Author information
Authors and Affiliations
Rights and permissions
About this article
Cite this article
Chapeau-Blondeau, F., Chauvet, G. A neural network model of the cerebellar cortex performing dynamic associations. Biol. Cybern. 65, 267–279 (1991). https://doi.org/10.1007/BF00206224
Received:
Accepted:
Issue Date:
DOI: https://doi.org/10.1007/BF00206224