An improved neural-network model for the neural integrator of the oculomotor system: more realistic neuron behavior

Abstract

The discharge rates of premotor, brain-stem neurons that create eye movements modulate in relation to eye velocity yet firing rates of extraocular motoneurons contain both eye-position and eye-velocity signals. The eye-position signal is derived from the eye-velocity command by means of a neural network which functions as a temporal integrator. We have previously proposed a network of lateral-inhibitory neurons that is capable of performing the required integration. That analysis centered on the temporal aspects of the signal processing for a limited class of idealized inputs. All of its cells were identical and carried only the integrated signal. Recordings in the brain stem, however, show that neurons in the region of the neural integrator have a variety of background firing rates, all carry some eye-velocity signal as well as the eye-position signal, and carry the former with different strengths depending on the type of eye movement being made. It was necessary to see if the proposed model could be modified to make its neurons more realistic. By modifying the spatial distribution of afferents to the network, we demonstrate that the same basic model functions properly in spite of afferents with nonuniform background firing rates. To introduce the eye-velocity signal a double-layer network, consisting of inhibitory and excitatory cells, was necessary. By presenting the velocity input to only local regions of this network it was shown that all cells in the network still carried the integrated signal and that its cells could carry different eye-velocity signals for different types of eye movements. Thus, this model stimulates quantitatively and qualitatively, the behavior of neurons seen in the region of the neural integrator.