scholarly journals Optimal anticipatory control as a theory of motor preparation: A thalamo-cortical circuit model

Neuron ◽  
2021 ◽  
Author(s):  
Ta-Chu Kao ◽  
Mahdieh S. Sadabadi ◽  
Guillaume Hennequin
Keyword(s):  
Motor Preparation ◽  
Circuit Model ◽  
Anticipatory Control ◽  
Cortical Circuit

scholarly journals Optimal anticipatory control as a theory of motor preparation: a thalamo-cortical circuit model

2020 ◽  
Author(s):  
Ta-Chu Kao ◽  
Mahdieh S. Sadabadi ◽  
Guillaume Hennequin
Keyword(s):  
Internal Control ◽  
Initial Conditions ◽  
Motor Preparation ◽  
Anticipatory Control ◽  
Subsequent Evolution ◽  
Population Activity ◽  
Prospective Control ◽  
Cortical Circuit ◽  
Low Dimensional ◽  
Low Dimensional Dynamics

SummaryAcross a range of motor and cognitive tasks, cortical activity can be accurately described by low-dimensional dynamics unfolding from specific initial conditions on every trial. These “preparatory states” largely determine the subsequent evolution of both neural activity and behaviour, and their importance raises questions regarding how they are — or ought to be — set. Here, we formulate motor preparation as optimal prospective control of future movements. The solution is a form of internal control of cortical circuit dynamics, which can be implemented as a thalamo-cortical loop gated by the basal ganglia. Critically, optimal control predicts selective quenching of variability in components of preparatory population activity that have future motor consequences, but not in others. This is consistent with recent perturbation experiments performed in mice, and with our novel analysis of monkey motor cortex activity during reaching. Together, these results suggest optimal anticipatory control of movement.

scholarly journals Reconstruction and visualization of large-scale volumetric models of neocortical circuits for physically-plausible in silico optical studies

2017 ◽  
Author(s):  
Marwan Abdellah ◽  
Juan Hernando ◽  
Nicolas Antille ◽  
Stefan Eilemann ◽  
Henry Markram ◽  
...  
Keyword(s):  
In Silico ◽  
Large Scale ◽  
Circuit Model ◽  
Optical Studies ◽  
Domain Experts ◽  
Multi Stage ◽  
Volumetric Models ◽  
Imaging Experiment ◽  
Cortical Circuit ◽  
Tissue Models

AbstractBackground We present a software workflow capable of building large scale, highly detailed and realistic volumetric models of neocortical circuits from the morphological skeletons of their digitally reconstructed neurons. The limitations of the existing approaches for creating those models are explained, and then, a multi-stage pipeline is discussed to overcome those limitations. Starting from the neuronal morphologies, we create smooth piecewise watertight polygonal models that can be efficiently utilized to synthesize continuous and plausible volumetric models of the neurons with solid voxelization. The somata of the neurons are reconstructed on a physically-plausible basis relying on the physics engine in Blender.Results Our pipeline is applied to create 55 exemplar neurons representing the various morphological types that are reconstructed from the somatsensory cortex of a juvenile rat. The pipeline is then used to reconstruct a volumetric slice of a cortical circuit model that contains ∼210,000 neurons. The applicability of our pipeline to create highly realistic volumetric models of neocortical circuits is demonstrated with an in silico imaging experiment that simulates tissue visualization with brightfield microscopy. The results were evaluated with a group of domain experts to address their demands and also to extend the workflow based on their feedback.Conclusion A systematic workflow is presented to create large scale synthetic tissue models of the neocortical circuitry. This workflow is fundamental to enlarge the scale of in silico neuroscientific optical experiments from several tens of cubic micrometers to a few cubic millimeters.

scholarly journals "Hyperglutamatergic cortico-striato-thalamo-cortical circuit" breaker drugs alleviate tics in a transgenic circuit model of Tourette׳s syndrome

2015 ◽  
Vol 1629 ◽  
pp. 38-53 ◽  
Author(s):  
Eric J. Nordstrom ◽  
Katie C. Bittner ◽  
Michael J. McGrath ◽  
Clinton R. Parks ◽  
Frank H. Burton
Keyword(s):  
Circuit Breaker ◽  
Circuit Model ◽  
Cortical Circuit

scholarly journals Simulating Cortical Feedback Modulation as Changes in Excitation and Inhibition in a Cortical Circuit Model

eNeuro ◽  
2016 ◽  
Vol 3 (4) ◽  
pp. ENEURO.0208-16.2016 ◽  
Author(s):  
Edward Zagha ◽  
John D. Murray ◽  
David A. McCormick
Keyword(s):  
Circuit Model ◽  
Cortical Feedback ◽  
Cortical Circuit

scholarly journals Cortical Microcircuits from a Generative Vision Model

2018 ◽  
Author(s):  
Dileep George ◽  
Alexander Lavin ◽  
J. Swaroop Guntupalli ◽  
David Mely ◽  
Nick Hay ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Information Processing ◽  
Bayesian Inference ◽  
Real World ◽  
Circuit Model ◽  
Cortical Circuits ◽  
Functional Roles ◽  
Outstanding Problem ◽  
Cortical Circuit ◽  
Anatomical Constraints

AbstractUnderstanding the information processing roles of cortical circuits is an outstanding problem in neuroscience and artificial intelligence. The theoretical setting of Bayesian inference has been suggested as a framework for understanding cortical computation. Based on a recently published generative model for visual inference (George et al., 2017), we derive a family of anatomically instantiated and functional cortical circuit models. In contrast to simplistic models of Bayesian inference, the underlying generative model’s representational choices are validated with real-world tasks that required efficient inference and strong generalization. The cortical circuit model is derived by systematically comparing the computational requirements of this model with known anatomical constraints. The derived model suggests precise functional roles for the feedforward, feedback and lateral connections observed in different laminae and columns, and assigns a computational role for the path through the thalamus.

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