Abstract
Certain premotor neurons of the oculomotor system fire at a rate proportional to desired eye velocity. Their output is integrated by a network of neurons to supply an eye positon command to the motoneurons of the extraocular muscles. This network, known as the neural integrator, is calibrated during infancy and then maintained through development and trauma with remarkable precision. We have modeled this system with a self-organizing neural network that learns to integrate vestibular velocity commands to generate appropriate eye movements. It learns by using current eye movement on any given trial to calculate the amount of retinal image slip and this is used as the error signal. The synaptic weights are then changed using a straightforward algorithm that is independent of the network configuration and does not necessitate backwards propagation of information. Minimization of the error in this fashion causes the network to develop multiple positive feedback loops that enable it to integrate a push-pull signal without integrating the background rate on which it rides. The network is also capable of recovering from various lesions and of generating more complicated signals to simulate induced postsaccadic drift and compensation for eye muscle mechanics.
Article PDF
Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.Avoid common mistakes on your manuscript.
References
Baker R, Berthoz A (1975) Is the prepositus hypoglossi nucleus the source of another vestibulo-ocular pathway? Brain Res 86:121–127
Cannon SC, Robinson DA (1985) An improved neural network model for the neural integrator of the oculomotor system: more realistic neuronal behavior. Biol Cybern 53:93–108
Cannon SC, Robinson DA, Shamma S (1983) A proposed neural network for the integrator of the oculomotor system. Biol Cybern 49:127–136
Fetter M, Zee DS (1988) Recovery from unilateral hemilabyrinthectomy in rhesus monkey. J Neurophysiol 59:370–393
Kamath BY, Keller EL (1976) A neurological integrator for the oculomotor system. Math Biosci 30:341–352
Kapoula Z, Optican LM, Robinson DA (1989) Visually induced plasticity of post-saccadic ocular drift in normal humans. J Neurophysiol 61:879–891
Lopez-Barneo J, Darlot C, Berthoz A, Baker R (1982) Neuronal activity in prepositus nucleus correlated with eye movement in the alert cat. J Neurophysiol 47:329–352
Pearlmutter BA (1989) Learning state space trajectories in recurrent neural networks. Neural Comput 1:263–269
Robinson DA (1968) Eye movement control in primates. Science 161:1219–1224
Robinson DA (1989) Integrating with neurons. Ann Rev Neurosci 12:33–45
Rumelhart DE, Hinton GE, Williams RJ (1986) Learning internal representations by error propagation. In: Rumelhart DE, McClelland JL, PDP Research Group (eds) Parallel distributed processing: explorations in the microstructure of cognition, vol 1: Foundations. MIT Press, Cambridge, pp 318–362
Tweed D, Vilis T (1987) Implications of rotational kinematics for the oculomotor system in three dimensions. J Neurophysiol 58:832–849
Weissman BM, DiScenna AO, Leigh RJ (1989) Maturation of the vestibulo-ocular reflex in normal infants during the first two months of life. Neurology 39:534–538
Williams RJ, Zipser D (1989) A learning algorithm for continually running fully recurrent neural networks. Neural Comput 1:270–280
Author information
Authors and Affiliations
Rights and permissions
About this article
Cite this article
Arnold, D.B., Robinson, D.A. A learning network model of the neural integrator of the oculomotor system. Biol. Cybern. 64, 447–454 (1991). https://doi.org/10.1007/BF00202608
Received:
Accepted:
Issue Date:
DOI: https://doi.org/10.1007/BF00202608