A one-dimensional spiking neural network model of the midbrain superior colliculus

2015 ◽  
Author(s):  
Bahadir Kasap ◽  
John van Opstal
Keyword(s):  
Neural Network ◽  
Superior Colliculus ◽  
Network Model ◽  
Neural Network Model ◽  
Spiking Neural Network ◽  
One Dimensional ◽  
Spiking Neural Network Model

scholarly journals A spiking neural network model of the Superior Colliculus that is robust to changes in the spatial-temporal input

2021 ◽  
Author(s):  
Arezoo Alizadeh ◽  
John Van Opstal
Keyword(s):  
Neural Network ◽  
Superior Colliculus ◽  
Eye Movement ◽  
Network Model ◽  
Neural Network Model ◽  
Neural Population ◽  
Spiking Neural Network ◽  
Movement Vector ◽  
Electrical Microstimulation ◽  
Spiking Neural Network Model

Previous 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.

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