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.

scholarly journals Exploring the limits of learning: segregation of information integration and response selection is required for learning a serial reversal task

2017 ◽  
Author(s):  
Camilo J. Mininni ◽  
B. Silvano Zanutto
Keyword(s):  
Neural Network ◽  
Synaptic Plasticity ◽  
Network Model ◽  
Neural Network Model ◽  
Response Selection ◽  
Neural Population ◽  
Spiking Neural Network ◽  
Reversal Task ◽  
Spiking Neural Network Model ◽  
Serial Reversal

AbstractAnimals are proposed to learn the latent rules governing their environment in order to maximize their chances of survival. However, rules may change without notice, forcing animals to keep a memory of which one is currently at work. Rule switching can lead to situations in which the same stimulus/response pairing is positively and negatively rewarded in the long run, depending on variables that are not accessible to the animal. This fact rises questions on how neural systems are capable of reinforcement learning in environments where the reinforcement is inconsistent. Here we address this issue by asking about which aspects of connectivity, neural excitability and synaptic plasticity are key for a very general, stochastic spiking neural network model to solve a task in which rules change without being cued, taking the serial reversal task (SRT) as paradigm. Contrary to what could be expected, we found strong limitations for biologically plausible networks to solve the SRT. Especially, we proved that no network of neurons can learn a SRT if it is a single neural population that integrates stimuli information and at the same time is responsible of choosing the behavioural response. This limitation is independent of the number of neurons, neuronal dynamics or plasticity rules, and arises from the fact that plasticity is locally computed at each synapse, and that synaptic changes and neuronal activity are mutually dependent processes. We propose and characterize a spiking neural network model that solves the SRT, which relies on separating the functions of stimuli integration and response selection. The model suggests that experimental efforts to understand neural function should focus on the characterization of neural circuits according to their connectivity, neural dynamics, and the degree of modulation of synaptic plasticity with reward.

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

2018 ◽  
Author(s):  
Bahadir Kasap ◽  
A. John van Opstal
Keyword(s):  
Neural Network ◽  
Superior Colliculus ◽  
Eye Movement ◽  
Large Population ◽  
Spiking Neural Network ◽  
Firing Rates ◽  
Electrical Microstimulation ◽  
Stimulation Parameters ◽  
Spiking Neural Network Model ◽  
Site Of Stimulation

AbstractThe 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.Author SummaryThe midbrain Superior Colliculus (SC) contains a topographically organized map for rapid goal-directed gaze shifts, in which the location of the active population determines size and direction of the eye-movement vector, and the neural firing rates specify the eye-movement kinematics. Electrical microstimulation in this map produces eye movements that correspond to the site of stimulation with normal kinematics. We here explain how intrinsic lateral interactions within the SC network of spiking neurons sets up the population activity profile in response to local microstimulation to explain these results.

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