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      Neural constraints on learning

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          Abstract

          Motor, sensory, and cognitive learning require networks of neurons to generate new activity patterns. Because some behaviors are easier to learn than others 1, 2 , we wondered if some neural activity patterns are easier to generate than others. We asked whether the existing network constrains the patterns that a subset of its neurons is capable of exhibiting, and if so, what principles define the constraint. We employed a closed-loop intracortical brain-computer interface (BCI) learning paradigm in which Rhesus monkeys controlled a computer cursor by modulating neural activity patterns in primary motor cortex. Using the BCI paradigm, we could specify and alter how neural activity mapped to cursor velocity. At the start of each session, we observed the characteristic activity patterns of the recorded neural population. These patterns comprise a low-dimensional space (termed the intrinsic manifold, or IM) within the high-dimensional neural firing rate space. They presumably reflect constraints imposed by the underlying neural circuitry. We found that the animals could readily learn to proficiently control the cursor using neural activity patterns that were within the IM. However, animals were less able to learn to proficiently control the cursor using activity patterns that were outside of the IM. This result suggests that the existing structure of a network can shape learning. On the timescale of hours, it appears to be difficult to learn to generate neural activity patterns that are not consistent with the existing network structure. These findings offer a network-level explanation for the observation that we are more readily able to learn new skills when they are related to the skills that we already possess 3, 4 .

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          Most cited references 49

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          The information capacity of the human motor system in controlling the amplitude of movement.

           Paul Fitts (1954)
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            Maximum Likelihood from Incomplete Data Via the EM Algorithm

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              Cortical control of a prosthetic arm for self-feeding.

              Arm movement is well represented in populations of neurons recorded from the motor cortex. Cortical activity patterns have been used in the new field of brain-machine interfaces to show how cursors on computer displays can be moved in two- and three-dimensional space. Although the ability to move a cursor can be useful in its own right, this technology could be applied to restore arm and hand function for amputees and paralysed persons. However, the use of cortical signals to control a multi-jointed prosthetic device for direct real-time interaction with the physical environment ('embodiment') has not been demonstrated. Here we describe a system that permits embodied prosthetic control; we show how monkeys (Macaca mulatta) use their motor cortical activity to control a mechanized arm replica in a self-feeding task. In addition to the three dimensions of movement, the subjects' cortical signals also proportionally controlled a gripper on the end of the arm. Owing to the physical interaction between the monkey, the robotic arm and objects in the workspace, this new task presented a higher level of difficulty than previous virtual (cursor-control) experiments. Apart from an example of simple one-dimensional control, previous experiments have lacked physical interaction even in cases where a robotic arm or hand was included in the control loop, because the subjects did not use it to interact with physical objects-an interaction that cannot be fully simulated. This demonstration of multi-degree-of-freedom embodied prosthetic control paves the way towards the development of dexterous prosthetic devices that could ultimately achieve arm and hand function at a near-natural level.
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                Author and article information

                Journal
                0410462
                6011
                Nature
                Nature
                Nature
                0028-0836
                1476-4687
                15 July 2014
                28 August 2014
                11 April 2015
                : 512
                : 7515
                : 423-426
                Affiliations
                [1 ]Department of Bioengineering, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America, 15261
                [2 ]Center for the Neural Basis of Cognition, Pittsburgh, Pennsylvania, United States of America, 15213
                [3 ]Department of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America, 15213
                [4 ]Department of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America, 15213
                [5 ]Department of Electrical Engineering, Stanford University, Stanford, California, United States of America, 94305
                [6 ]Department of Neurosurgery, Palo Alto Medical Foundation, Palo Alto, California, United States of America, 94301
                [7 ]Department of Physical Medicine and Rehabilitation, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America, 15213
                [8 ]Department of Neurological Surgery, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America, 15213
                Author notes
                Correspondence and requests for materials should be addressed to B.M.Y. ( byronyu@ 123456cmu.edu ) or A.P.B. ( apb10@ 123456pitt.edu )
                [*]

                contributed equally

                Article
                NIHMS611861
                10.1038/nature13665
                4393644
                25164754
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