Literature DB >> 35219429

Diverse operant control of different motor cortex populations during learning.

Nuria Vendrell-Llopis1, Ching Fang2, Albert J Qü2, Rui M Costa3, Jose M Carmena4.   

Abstract

During motor learning,1 as well as during neuroprosthetic learning,2-4 animals learn to control motor cortex activity in order to generate behavior. Two different populations of motor cortex neurons, intra-telencephalic (IT) and pyramidal tract (PT) neurons, convey the resulting cortical signals within and outside the telencephalon. Although a large amount of evidence demonstrates contrasting functional organization among both populations,5,6 it is unclear whether the brain can equally learn to control the activity of either class of motor cortex neurons. To answer this question, we used a calcium-imaging-based brain-machine interface (CaBMI)3 and trained different groups of mice to modulate the activity of either IT or PT neurons in order to receive a reward. We found that the animals learned to control PT neuron activity faster and better than IT neuron activity. Moreover, our findings show that the advantage of PT neurons is the result of characteristics inherent to this population as well as their local circuitry and cortical depth location. Taken together, our results suggest that the motor cortex is more efficient at controlling the activity of pyramidal tract neurons, which are embedded deep in the cortex, and relaying motor commands outside the telencephalon.
Copyright © 2022 The Authors. Published by Elsevier Inc. All rights reserved.

Entities:  

Keywords:  brain-machine interfaces; motor cortex; neural circuits; neuroprosthetics; operant control; operant learning; pyramidal tract neurons

Mesh:

Year:  2022        PMID: 35219429      PMCID: PMC9007898          DOI: 10.1016/j.cub.2022.02.006

Source DB:  PubMed          Journal:  Curr Biol        ISSN: 0960-9822            Impact factor:   10.834


  29 in total

1.  Reversible large-scale modification of cortical networks during neuroprosthetic control.

Authors:  Karunesh Ganguly; Dragan F Dimitrov; Jonathan D Wallis; Jose M Carmena
Journal:  Nat Neurosci       Date:  2011-04-17       Impact factor: 24.884

2.  From Local Explanations to Global Understanding with Explainable AI for Trees.

Authors:  Scott M Lundberg; Gabriel Erion; Hugh Chen; Alex DeGrave; Jordan M Prutkin; Bala Nair; Ronit Katz; Jonathan Himmelfarb; Nisha Bansal; Su-In Lee
Journal:  Nat Mach Intell       Date:  2020-01-17

Review 3.  The neocortical circuit: themes and variations.

Authors:  Kenneth D Harris; Gordon M G Shepherd
Journal:  Nat Neurosci       Date:  2015-01-27       Impact factor: 24.884

4.  Evidence for a neural law of effect.

Authors:  Vivek R Athalye; Fernando J Santos; Jose M Carmena; Rui M Costa
Journal:  Science       Date:  2018-03-02       Impact factor: 47.728

5.  CaImAn an open source tool for scalable calcium imaging data analysis.

Authors:  Andrea Giovannucci; Johannes Friedrich; Pat Gunn; Jérémie Kalfon; Brandon L Brown; Sue Ann Koay; Jiannis Taxidis; Farzaneh Najafi; Jeffrey L Gauthier; Pengcheng Zhou; Baljit S Khakh; David W Tank; Dmitri B Chklovskii; Eftychios A Pnevmatikakis
Journal:  Elife       Date:  2019-01-17       Impact factor: 8.140

6.  Projection-specific neuromodulation of medial prefrontal cortex neurons.

Authors:  Nikolai C Dembrow; Raymond A Chitwood; Daniel Johnston
Journal:  J Neurosci       Date:  2010-12-15       Impact factor: 6.167

7.  Active dendritic currents gate descending cortical outputs in perception.

Authors:  Naoya Takahashi; Christian Ebner; Johanna Sigl-Glöckner; Sara Moberg; Svenja Nierwetberg; Matthew E Larkum
Journal:  Nat Neurosci       Date:  2020-08-03       Impact factor: 24.884

8.  Preferential labeling of inhibitory and excitatory cortical neurons by endogenous tropism of adeno-associated virus and lentivirus vectors.

Authors:  J L Nathanson; Y Yanagawa; K Obata; E M Callaway
Journal:  Neuroscience       Date:  2009-03-24       Impact factor: 3.590

9.  Brain-Computer Interface with Inhibitory Neurons Reveals Subtype-Specific Strategies.

Authors:  Akinori Mitani; Mingyuan Dong; Takaki Komiyama
Journal:  Curr Biol       Date:  2017-12-14       Impact factor: 10.834

10.  Corticostriatal plasticity is necessary for learning intentional neuroprosthetic skills.

Authors:  Aaron C Koralek; Xin Jin; John D Long; Rui M Costa; Jose M Carmena
Journal:  Nature       Date:  2012-03-04       Impact factor: 49.962

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