Intrinsic Variable Learning for Brain-Machine Interface Control by Human Anterior Intraparietal Cortex

Sakellaridi, Sofia; Christopoulos, Vassilios; Aflalo, Tyson; Pejsa, Kelsie; Rosario, Emily R.; Ouellette, Debra; Pouratian, Nader; Andersen, Richard A.

doi:10.1016/j.neuron.2019.02.012

Cited by 41 publications

(36 citation statements)

References 50 publications

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“…We posit that the formation of new neural activity patterns during long-term BCI learning may provide a parsimonious explanation for the tuning curve changes reported in earlier studies. In particular, our results combined with earlier BCI studies (6–9, 15, 16, 18, 22–26) and motor learning studies (27, 28) suggest that fast and slow learning are driven by different neural mechanisms. Fast learning can be accomplished by reassociating preexisting patterns of neural activity with new behaviors (9).…”

Section: Discussionsupporting

confidence: 79%

“…By construction, forming new patterns of neural activity is the optimal neural strategy for learning to control the cursor under an OMP mapping because this would lead to the fastest cursor speeds. However, it is possible that the brain is unable to form new patterns because constraints exist on the patterns of neural activity that a population of neurons can exhibit (8, 9, 15–18). If this is the case, the monkey could still show some limited behavioral improvements by learning to reassociate preexisting patterns of neural activity with different intended movements (9).…”

Section: Resultsmentioning

confidence: 99%

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New neural activity patterns emerge with long-term learning

Oby

Golub

Hennig

et al. 2019

Proc. Natl. Acad. Sci. U.S.A.

188

198

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Learning has been associated with changes in the brain at every level of organization. However, it remains difficult to establish a causal link between specific changes in the brain and new behavioral abilities. We establish that new neural activity patterns emerge with learning. We demonstrate that these new neural activity patterns cause the new behavior. Thus, the formation of new patterns of neural population activity can underlie the learning of new skills.

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Section: Discussionsupporting

confidence: 79%

Section: Resultsmentioning

confidence: 99%

New neural activity patterns emerge with long-term learning

Oby

Golub

Hennig

et al. 2019

Proc. Natl. Acad. Sci. U.S.A.

188

198

View full text Add to dashboard Cite

show abstract

“…The occurrence of uniform shifts provides evidence for formation of new activity patterns during short-term motor learning. Conventionally, the circuit structure or connectivity of an existing network has been thought to constrain the patterns that its neurons are capable of exhibiting, which may limit its capacity for short-term learning [41][42][43] . Here our results suggest that the motor system may be more flexible than previously thought, and can generate novel activity patterns during short-term learning in order to quickly adapt to a changing environment 38 .…”

Section: Discussionmentioning

confidence: 99%

Skill-specific changes in cortical preparatory activity during motor learning

Sun

Golub

et al. 2020

Preprint

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Animals have a remarkable capacity to learn new motor skills, but it remains an open question as to how learning changes neural population dynamics underlying movement 1 . Specifically, we asked whether changes in neural population dynamics relate purely to newly learned movements or if additional patterns are generated that facilitate learning without matching motor output. We trained rhesus monkeys to learn a curl force field 2 task that elicited new arm-movement kinetics for some but not all reach directions 3,4 . We found that along certain neural dimensions, preparatory activity in motor cortex reassociated existing activity patterns with new movements. These systematic changes were observed only for learning-altered reaches. Surprisingly, we also found prominent shifts of preparatory activity along a nearly orthogonal neural dimension. These changes in preparatory activity were observed uniformly for all reaches including those unaltered by learning. This uniform shift during learning implies formation of new neural activity patterns, which was not observed in other short-term learning contexts 5-8 . During a washout period when the curl field was removed, movement kinetics gradually reverted, but the learning-induced uniform shift of preparatory activity persisted and a second, orthogonal uniform shift occurred. This persistent shift may retain a motor memory of the learned field 9-11 , consistent with faster relearning of the same curl field observed behaviorally and neurally. When multiple different curl fields were learned sequentially, we found distinct uniform shifts, each reflecting the identity of the field applied and potentially separating the associated motor memories 12,13 . The neural geometry of these shifts in preparatory activity could serve to organize skill-specific changes in movement production, facilitating the acquisition and retention of a broad motor repertoire. Motor learning encompasses a wide range of phenomena, from relatively low-level mechanisms for calibrating movement parameters, to making high-level cognitive decisions about how to act in a novel environment 1 . Motor adaptation has been a long-standing and widely used paradigm for studying motor learning. Decades of behavioral studies have demonstrated many intriguing phenomena during motor adaptation, such as the error-driven calibration of movements, generalization of learned skills to a new context, savings (faster relearning) or memory retention, and interference between learning multiple skills 3,4,12,[14][15][16][17][18] . Yet their neural mechanisms, in particular the underlying neural population dynamics, remain largely unknown.In the field of motor control, neural population dynamics have provided foundational insight into activity patterns and computational principles not readily apparent at single-neuron resolution 19,20 . Recently, a dynamical system framework has started to help elucidate the neural basis of motor learning 5,8,21-23 . Collectively, these experiments have observed changes in neural population st...

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“…In a unique opportunity, we investigated touch processing at the level of single neurons in a tetraplegic human subject recorded with an electrode array implanted in the left PPC for an ongoing brain machine interface (BMI) clinical trial. In previous work, we have referred to the implant area as the anterior intraparietal cortex, a region functionally defined in NHPs (3)(4)(5)(6)26). Here we will refer to the recording site as the postcentral-intraparietal area (PC-IP), acknowledging that further work is necessary to definitively characterize homologies between human and NHP anatomy.…”

Section: Introductionmentioning

confidence: 99%

Neural Encoding of Felt and Imagined Touch Within Human Posterior Parietal Cortex

Chivukula

Zhang

Aflalo

et al. 2020

Preprint

Self Cite

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In the human posterior parietal cortex (PPC), single units encode high-dimensional information with partially mixed representations that enable small populations of neurons to encode many variables relevant to movement planning, execution, cognition, and perception. Here we test whether a PPC neuronal population previously demonstrated to encode visual and motor information is similarly selective in the somatosensory domain. We recorded from 1423 neurons within the PPC of a human clinical trial participant during objective touch presentation and during tactile imagery. Neurons encoded experienced touch with bilateral receptive fields, organized by body part, and covered all tested regions. Tactile imagery evoked body part specific responses that shared a neural substrate with experienced touch. Our results are the first neuron level evidence of touch encoding in human PPC and its cognitive engagement during tactile imagery which may reflect semantic processing, sensory anticipation, and imagined touch.

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Intrinsic Variable Learning for Brain-Machine Interface Control by Human Anterior Intraparietal Cortex

Cited by 41 publications

References 50 publications

New neural activity patterns emerge with long-term learning

New neural activity patterns emerge with long-term learning

Skill-specific changes in cortical preparatory activity during motor learning

Neural Encoding of Felt and Imagined Touch Within Human Posterior Parietal Cortex

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