Abstract
AbstractPrevious studies have indicated that the location of a large neural population in the Superior Colliculus (SC) motor map specifies the amplitude and direction of the saccadic eye-movement vector, while the saccade trajectory and velocity profile are encoded by the population firing rates. We recently proposed a simple spiking neural network model of the SC motor map, based on linear summation of individual spike effects of each recruited neuron, which accounts for many of the observed properties of SC cells in relation to the ensuing eye movement. However, in the model, the cortical input was kept invariant across different saccades. Electrical microstimulation and reversible lesion studies have demonstrated that the saccade properties are quite robust against large changes in supra-threshold SC activation, but that saccade amplitude and peak eye-velocity systematically decrease at low input strengths. These features are not accounted for by the linear spike-vector summation model. Here we show that the model’s input projection strengths and intra-collicular lateral connections can be tuned to generate saccades that follow the experimental results.Author statementThe midbrain SC generates fast saccadic eye movements through a large population of cells within a topographically organized motor map, in which the location, spike count and temporal firing patterns of recruited cells determine saccade metrics and kinematics. According to the dynamic ensemble-coding model, each recruited SC cell contributes to the saccade by linear vector summation of all its spike contributions. Our previous spiking neural network model used invariant cortical inputs to the SC cells for all saccades. We here improved the robustness of the model to large spatial-temporal variations in the input patterns, by tuning its top-down and lateral synaptic connections, to generate saccades with properties observed in electrophysiological experiments.
Publisher
Cold Spring Harbor Laboratory