Microstimulation in a spiking neural network model of the midbrain superior colliculus.

The midbrain superior colliculus (SC) generates a rapid saccadic eye movement to a sensory stimulus by recruiting a population of cells in its topographically organized motor map. Supra-threshold electrical microstimulation in the SC reveals that the site of stimulation produces a normometric saccad...

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Main Authors: Bahadir Kasap, A John van Opstal
Format: Article
Language:English
Published: Public Library of Science (PLoS) 2019-04-01
Series:PLoS Computational Biology
Online Access:http://europepmc.org/articles/PMC6481873?pdf=render
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spelling doaj-61de75c81a4b4ec1a3dc6f27d1d978062020-11-25T02:43:14ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582019-04-01154e100652210.1371/journal.pcbi.1006522Microstimulation in a spiking neural network model of the midbrain superior colliculus.Bahadir KasapA John van OpstalThe midbrain superior colliculus (SC) generates a rapid saccadic eye movement to a sensory stimulus by recruiting a population of cells in its topographically organized motor map. Supra-threshold electrical microstimulation in the SC reveals that the site of stimulation produces a normometric saccade vector with little effect of the stimulation parameters. Moreover, electrically evoked saccades (E-saccades) have kinematic properties that strongly resemble natural, visual-evoked saccades (V-saccades). These findings support models in which the saccade vector is determined by a center-of-gravity computation of activated neurons, while its trajectory and kinematics arise from downstream feedback circuits in the brainstem. Recent single-unit recordings, however, have indicated that the SC population also specifies instantaneous kinematics. These results support an alternative model, in which the desired saccade trajectory, including its kinematics, follows from instantaneous summation of movement effects of all SC spike trains. But how to reconcile this model with microstimulation results? Although it is thought that microstimulation activates a large population of SC neurons, the mechanism through which it arises is unknown. We developed a spiking neural network model of the SC, in which microstimulation directly activates a relatively small set of neurons around the electrode tip, which subsequently sets up a large population response through lateral synaptic interactions. We show that through this mechanism the population drives an E-saccade with near-normal kinematics that are largely independent of the stimulation parameters. Only at very low stimulus intensities the network recruits a population with low firing rates, resulting in abnormally slow saccades.http://europepmc.org/articles/PMC6481873?pdf=render
collection DOAJ
language English
format Article
sources DOAJ
author Bahadir Kasap
A John van Opstal
spellingShingle Bahadir Kasap
A John van Opstal
Microstimulation in a spiking neural network model of the midbrain superior colliculus.
PLoS Computational Biology
author_facet Bahadir Kasap
A John van Opstal
author_sort Bahadir Kasap
title Microstimulation in a spiking neural network model of the midbrain superior colliculus.
title_short Microstimulation in a spiking neural network model of the midbrain superior colliculus.
title_full Microstimulation in a spiking neural network model of the midbrain superior colliculus.
title_fullStr Microstimulation in a spiking neural network model of the midbrain superior colliculus.
title_full_unstemmed Microstimulation in a spiking neural network model of the midbrain superior colliculus.
title_sort microstimulation in a spiking neural network model of the midbrain superior colliculus.
publisher Public Library of Science (PLoS)
series PLoS Computational Biology
issn 1553-734X
1553-7358
publishDate 2019-04-01
description The midbrain superior colliculus (SC) generates a rapid saccadic eye movement to a sensory stimulus by recruiting a population of cells in its topographically organized motor map. Supra-threshold electrical microstimulation in the SC reveals that the site of stimulation produces a normometric saccade vector with little effect of the stimulation parameters. Moreover, electrically evoked saccades (E-saccades) have kinematic properties that strongly resemble natural, visual-evoked saccades (V-saccades). These findings support models in which the saccade vector is determined by a center-of-gravity computation of activated neurons, while its trajectory and kinematics arise from downstream feedback circuits in the brainstem. Recent single-unit recordings, however, have indicated that the SC population also specifies instantaneous kinematics. These results support an alternative model, in which the desired saccade trajectory, including its kinematics, follows from instantaneous summation of movement effects of all SC spike trains. But how to reconcile this model with microstimulation results? Although it is thought that microstimulation activates a large population of SC neurons, the mechanism through which it arises is unknown. We developed a spiking neural network model of the SC, in which microstimulation directly activates a relatively small set of neurons around the electrode tip, which subsequently sets up a large population response through lateral synaptic interactions. We show that through this mechanism the population drives an E-saccade with near-normal kinematics that are largely independent of the stimulation parameters. Only at very low stimulus intensities the network recruits a population with low firing rates, resulting in abnormally slow saccades.
url http://europepmc.org/articles/PMC6481873?pdf=render
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