publication . Article . Other literature type . 2018

Constraints on neural redundancy

Jay A Hennig; Matthew D Golub; Peter J Lund; Patrick T Sadtler; Emily R Oby; Kristin M Quick; Stephen I Ryu; Elizabeth C Tyler-Kabara; Aaron P Batista; Byron M Yu; ...
Open Access English
  • Published: 15 Aug 2018 Journal: eLife, volume 7 (eissn: 2050-084X, Copyright policy)
  • Publisher: eLife Sciences Publications, Ltd
Abstract
Millions of neurons drive the activity of hundreds of muscles, meaning many different neural population activity patterns could generate the same movement. Studies have suggested that these redundant (i.e. behaviorally equivalent) activity patterns may be beneficial for neural computation. However, it is unknown what constraints may limit the selection of different redundant activity patterns. We leveraged a brain-computer interface, allowing us to define precisely which neural activity patterns were redundant. Rhesus monkeys made cursor movements by modulating neural activity in primary motor cortex. We attempted to predict the observed distribution of redundan...
Subjects
free text keywords: Research Article, Neuroscience, neural redundancy, motor control, brain-computer interface, neural computation, Rhesus macaque
Funded by
NSF| NCS-FO: The Structure of Neural Variability During Motor Learning
Project
  • Funder: National Science Foundation (NSF)
  • Project Code: 1533672
,
NIH| CRCNS:Dissecting brain-computer interfaces:a manifold & feedback-control approach
Project
  • Funder: National Institutes of Health (NIH)
  • Project Code: 5R01HD071686-03
  • Funding stream: EUNICE KENNEDY SHRIVER NATIONAL INSTITUTE OF CHILD HEALTH & HUMAN DEVELOPMENT
,
NIH| CRCNS: Dynamical Constraints on Neural Population Activity
Project
  • Funder: National Institutes of Health (NIH)
  • Project Code: 5R01NS105318-02
  • Funding stream: NATIONAL INSTITUTE OF NEUROLOGICAL DISORDERS AND STROKE
70 references, page 1 of 5

Ajemian, R, D'Ausilio, A, Moorman, H, Bizzi, E. A theory for how sensorimotor skills are learned and retained in noisy and nonstationary neural circuits. PNAS. 2013; 110: E5078-E5087 [OpenAIRE] [PubMed] [DOI]

Athalye, VR, Ganguly, K, Costa, RM, Carmena, JM. Emergence of coordinated neural dynamics underlies neuroprosthetic learning and skillful control. Neuron. 2017; 93: 955-970 [OpenAIRE] [PubMed] [DOI]

Averbeck, BB, Latham, PE, Pouget, A. Neural correlations, population coding and computation. Nature Reviews Neuroscience. 2006; 7: 358-366 [OpenAIRE] [PubMed] [DOI]

Barlow, H. Information Processing in the Nervous System. 1969: 209-230

Bernstein, N. The Coordination and Regulation of Movements. 1967: 15-59

Bjorck, A, Golub, GH. Numerical methods for computing angles between linear subspaces. Mathematics of Computation. 1973; 27: 579-594 [OpenAIRE] [DOI]

Carmena, JM, Lebedev, MA, Crist, RE, O'Doherty, JE, Santucci, DM, Dimitrov, DF, Patil, PG, Henriquez, CS, Nicolelis, MA. Learning to control a brain-machine interface for reaching and grasping by primates. PLoS Biology. 2003; 1 [OpenAIRE] [PubMed] [DOI]

Churchland, MM, Yu, BM, Cunningham, JP, Sugrue, LP, Cohen, MR, Corrado, GS, Newsome, WT, Clark, AM, Hosseini, P, Scott, BB, Bradley, DC, Smith, MA, Kohn, A, Movshon, JA, Armstrong, KM, Moore, T, Chang, SW, Snyder, LH, Lisberger, SG, Priebe, NJ, Finn, IM, Ferster, D, Ryu, SI, Santhanam, G, Sahani, M, Shenoy, KV. Stimulus onset quenches neural variability: a widespread cortical phenomenon. Nature Neuroscience. 2010; 13: 369-378 [OpenAIRE] [PubMed] [DOI]

de Rugy, A, Loeb, GE, Carroll, TJ. Muscle coordination is habitual rather than optimal. Journal of Neuroscience. 2012; 32: 7384-7391 [OpenAIRE] [PubMed] [DOI]

Diedrichsen, J, Shadmehr, R, Ivry, RB. The coordination of movement: optimal feedback control and beyond. Trends in Cognitive Sciences. 2010; 14: 31-39 [OpenAIRE] [PubMed] [DOI]

Driscoll, LN, Pettit, NL, Minderer, M, Chettih, SN, Harvey, CD. Dynamic reorganization of neuronal activity patterns in parietal cortex. Cell. 2017; 170: 986-999 [OpenAIRE] [PubMed] [DOI]

Druckmann, S, Chklovskii, DB. Neuronal circuits underlying persistent representations despite time varying activity. Current Biology. 2012; 22: 2095-2103 [OpenAIRE] [PubMed] [DOI]

Dryden, IL, Koloydenko, A, Zhou, D. Non-Euclidean statistics for covariance matrices, with applications to diffusion tensor imaging. The Annals of Applied Statistics. 2009; 3: 1102-1123 [OpenAIRE] [DOI]

Elsayed, GF, Lara, AH, Kaufman, MT, Churchland, MM, Cunningham, JP. Reorganization between preparatory and movement population responses in motor cortex. Nature Communications. 2016; 7 [OpenAIRE] [PubMed] [DOI]

Ettema, GJC, Styles, G, Kippers, V. The moment arms of 23 muscle segments of the upper limb with varying elbow and forearm positions: Implications for motor control. Human Movement Science. 1998; 17: 201-220 [OpenAIRE] [DOI]

70 references, page 1 of 5
Abstract
Millions of neurons drive the activity of hundreds of muscles, meaning many different neural population activity patterns could generate the same movement. Studies have suggested that these redundant (i.e. behaviorally equivalent) activity patterns may be beneficial for neural computation. However, it is unknown what constraints may limit the selection of different redundant activity patterns. We leveraged a brain-computer interface, allowing us to define precisely which neural activity patterns were redundant. Rhesus monkeys made cursor movements by modulating neural activity in primary motor cortex. We attempted to predict the observed distribution of redundan...
Subjects
free text keywords: Research Article, Neuroscience, neural redundancy, motor control, brain-computer interface, neural computation, Rhesus macaque
Funded by
NSF| NCS-FO: The Structure of Neural Variability During Motor Learning
Project
  • Funder: National Science Foundation (NSF)
  • Project Code: 1533672
,
NIH| CRCNS:Dissecting brain-computer interfaces:a manifold & feedback-control approach
Project
  • Funder: National Institutes of Health (NIH)
  • Project Code: 5R01HD071686-03
  • Funding stream: EUNICE KENNEDY SHRIVER NATIONAL INSTITUTE OF CHILD HEALTH & HUMAN DEVELOPMENT
,
NIH| CRCNS: Dynamical Constraints on Neural Population Activity
Project
  • Funder: National Institutes of Health (NIH)
  • Project Code: 5R01NS105318-02
  • Funding stream: NATIONAL INSTITUTE OF NEUROLOGICAL DISORDERS AND STROKE
70 references, page 1 of 5

Ajemian, R, D'Ausilio, A, Moorman, H, Bizzi, E. A theory for how sensorimotor skills are learned and retained in noisy and nonstationary neural circuits. PNAS. 2013; 110: E5078-E5087 [OpenAIRE] [PubMed] [DOI]

Athalye, VR, Ganguly, K, Costa, RM, Carmena, JM. Emergence of coordinated neural dynamics underlies neuroprosthetic learning and skillful control. Neuron. 2017; 93: 955-970 [OpenAIRE] [PubMed] [DOI]

Averbeck, BB, Latham, PE, Pouget, A. Neural correlations, population coding and computation. Nature Reviews Neuroscience. 2006; 7: 358-366 [OpenAIRE] [PubMed] [DOI]

Barlow, H. Information Processing in the Nervous System. 1969: 209-230

Bernstein, N. The Coordination and Regulation of Movements. 1967: 15-59

Bjorck, A, Golub, GH. Numerical methods for computing angles between linear subspaces. Mathematics of Computation. 1973; 27: 579-594 [OpenAIRE] [DOI]

Carmena, JM, Lebedev, MA, Crist, RE, O'Doherty, JE, Santucci, DM, Dimitrov, DF, Patil, PG, Henriquez, CS, Nicolelis, MA. Learning to control a brain-machine interface for reaching and grasping by primates. PLoS Biology. 2003; 1 [OpenAIRE] [PubMed] [DOI]

Churchland, MM, Yu, BM, Cunningham, JP, Sugrue, LP, Cohen, MR, Corrado, GS, Newsome, WT, Clark, AM, Hosseini, P, Scott, BB, Bradley, DC, Smith, MA, Kohn, A, Movshon, JA, Armstrong, KM, Moore, T, Chang, SW, Snyder, LH, Lisberger, SG, Priebe, NJ, Finn, IM, Ferster, D, Ryu, SI, Santhanam, G, Sahani, M, Shenoy, KV. Stimulus onset quenches neural variability: a widespread cortical phenomenon. Nature Neuroscience. 2010; 13: 369-378 [OpenAIRE] [PubMed] [DOI]

de Rugy, A, Loeb, GE, Carroll, TJ. Muscle coordination is habitual rather than optimal. Journal of Neuroscience. 2012; 32: 7384-7391 [OpenAIRE] [PubMed] [DOI]

Diedrichsen, J, Shadmehr, R, Ivry, RB. The coordination of movement: optimal feedback control and beyond. Trends in Cognitive Sciences. 2010; 14: 31-39 [OpenAIRE] [PubMed] [DOI]

Driscoll, LN, Pettit, NL, Minderer, M, Chettih, SN, Harvey, CD. Dynamic reorganization of neuronal activity patterns in parietal cortex. Cell. 2017; 170: 986-999 [OpenAIRE] [PubMed] [DOI]

Druckmann, S, Chklovskii, DB. Neuronal circuits underlying persistent representations despite time varying activity. Current Biology. 2012; 22: 2095-2103 [OpenAIRE] [PubMed] [DOI]

Dryden, IL, Koloydenko, A, Zhou, D. Non-Euclidean statistics for covariance matrices, with applications to diffusion tensor imaging. The Annals of Applied Statistics. 2009; 3: 1102-1123 [OpenAIRE] [DOI]

Elsayed, GF, Lara, AH, Kaufman, MT, Churchland, MM, Cunningham, JP. Reorganization between preparatory and movement population responses in motor cortex. Nature Communications. 2016; 7 [OpenAIRE] [PubMed] [DOI]

Ettema, GJC, Styles, G, Kippers, V. The moment arms of 23 muscle segments of the upper limb with varying elbow and forearm positions: Implications for motor control. Human Movement Science. 1998; 17: 201-220 [OpenAIRE] [DOI]

70 references, page 1 of 5
Powered by OpenAIRE Research Graph
Any information missing or wrong?Report an Issue