Distinct types of neural reorganization during long-term learning

Abstract

What are the neural mechanisms of skill acquisition? Many studies find that long-term practice is associated with a functional reorganization of cortical neural activity. However, the link between these changes in neural activity and the behavioral improvements that occur is not well understood, especially for long-term learning that takes place over several weeks. To probe this link in detail, we leveraged a brain-computer interface (BCI) paradigm in which rhesus monkeys learned to master nonintuitive mappings between neural spiking in primary motor cortex and computer cursor movement. Critically, these BCI mappings were designed to disambiguate several different possible types of neural reorganization. We found that during the initial phase of learning, lasting minutes to hours, rapid changes in neural activity common to all neurons led to a fast suppression of motor error. In parallel, local changes to individual neurons gradually accrued over several weeks of training. This slower timescale cortical reorganization persisted long after the movement errors had decreased to asymptote and was associated with more efficient control of movement. We conclude that long-term practice evokes two distinct neural reorganization processes with vastly different timescales, leading to different aspects of improvement in motor behavior. NEW & NOTEWORTHY We leveraged a brain-computer interface learning paradigm to track the neural reorganization occurring throughout the full time course of motor skill learning lasting several weeks. We report on two distinct types of neural reorganization that mirror distinct phases of behavioral improvement: a fast phase, in which global reorganization of neural recruitment leads to a quick suppression of motor error, and a slow phase, in which local changes in individual tuning lead to improvements in movement efficiency.

From thought to action: The brain–machine interface in posterior parietal cortex

“…Similar results, consistent with intrinsic variable learning, were found in monkey primary motor cortex (28), in which only existing patterns of activity could be manipulated by the monkeys. Over the timescale of many days, individual neuron changes have been reported (32)(33)(34)(35) and hypothesized to be the result of motor skill learning (33,35). The above results indicate that the intrinsic structure of cortical areas is maintained on the short term, and even on the long term the type of learning engaged may be restricted to the type of learning a cortical area normally does; for instance, motor skill learning may be apparent only in motor and sensorimotor cortical areas.…”

“…Subsequent studies in monkeys (28)(29)(30), and more recently in humans (31), argue that, in the short term, cortical circuits are limited to processing their intrinsic functions and adapt through cognitive manipulations such as reaiming or imagery. Changes in cortical structure have been reported over longer timescales of many days which may reflect motor skill learning (32)(33)(34)(35). Limitations on plasticity emphasize the importance of selecting brain sites for recording that are well matched to the desired function of the neural prosthesis.…”

Learning, Generalization, and Scalability of Abstract Myoelectric Control

“…Studies of motor learning often depend upon modifying well established mappings through visual rotations [24] or perturbation [31]- [33] because it is difficult to create novel movements in physical space [20]. Human machine interfaces, including myoelectric control, are novel by default and have therefore acted as a catalyst to study how novel motor tasks are learned [12], [16], [23], [34]- [39]. Evidence suggests new mappings can be generated from scratch [20] and they can be arbitrary and non-intuitive [12], [15], although they do not have to be [17], [39], [40].…”

Shared internal models for feedforward and feedback control of arm dynamics in non-human primates

“…Learning novel for arm dynamics during self-initiated reaching Like humans, monkeys can learn a variety of upper-limb motor tasks from reaching in the presence of force environments (Gandolfo et al , 2000;Li et al , 2001;Gribble & Scott, 2002;Arce et al , 2010;Richardson et al , 2012;Cherian et al , 2013;Perich et al , 2018) to visuomotor mapping representations (Paz et al , 2003;Mandelblat-Cerf et al , 2009;Perich et al , 2018;Vyas et al , 2018Vyas et al , , 2020) and brain machine interface (i.e., BMI) control (Sadtler et al , 2014;Golub et al , 2015Golub et al , , 2018Hennig et al , 2018;Vyas et al , 2018;Oby et al , 2019;Zhou et al , 2019) . These types of learning, however, unfold as a function of errors the animals make in the task and are consistent with the monkey updating an internal model of the environment (for review on error-based learning see, Shadmehr et al , 2010) .…”