Long-Term Stability of Motor Cortical Activity: Implications for Brain Machine Interfaces and Optimal Feedback Control

Flint, Robert D.; Scheid, Michael R.; Wright, Zachary A.; Solla, Sara A.; Slutzky, Marc W.

doi:10.1523/jneurosci.2339-15.2016

Cited by 88 publications

(88 citation statements)

References 51 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, this interpretation has several limitations or other possible explanations: positional effects could be small within the limited range of positions sampled during the Radial 8 Target Task; position-related modulation could be outside the two neural dimensions that affect the decoder 26,27 ; and/or position-related neural modulation could be poorly described by a linear fit. To more thoroughly investigate cursor positional effects without these limitations, we performed a set of experiments where monkeys performed a Random Target Task in which longer target hold epochs occurred across a larger span of the workspace.…”

Section: Resultsmentioning

confidence: 99%

Brain-machine interface cursor position only weakly affects monkey and human motor cortical activity in the absence of arm movements

Stavisky

Kao

Nuyujukian

et al. 2018

Sci Rep

View full text Add to dashboard Cite

Brain-machine interfaces (BMIs) that decode movement intentions should ignore neural modulation sources distinct from the intended command. However, neurophysiology and control theory suggest that motor cortex reflects the motor effector’s position, which could be a nuisance variable. We investigated motor cortical correlates of BMI cursor position with or without concurrent arm movement. We show in two monkeys that subtracting away estimated neural correlates of position improves online BMI performance only if the animals were allowed to move their arm. To understand why, we compared the neural variance attributable to cursor position when the same task was performed using arm reaching, versus arms-restrained BMI use. Firing rates correlated with both BMI cursor and hand positions, but hand positional effects were greater. To examine whether BMI position influences decoding in people with paralysis, we analyzed data from two intracortical BMI clinical trial participants and performed an online decoder comparison in one participant. We found only small motor cortical correlates, which did not affect performance. These results suggest that arm movement and proprioception are the major contributors to position-related motor cortical correlates. Cursor position visual feedback is therefore unlikely to affect the performance of BMI-driven prosthetic systems being developed for people with paralysis.

show abstract

Section: Resultsmentioning

confidence: 99%

Brain-machine interface cursor position only weakly affects monkey and human motor cortical activity in the absence of arm movements

Stavisky

Kao

Nuyujukian

et al. 2018

Sci Rep

View full text Add to dashboard Cite

show abstract

“…The separation between potent and null spaces was also used by Slutzky and colleagues to investigate the long-term stability of BMIs (Flint et al, 2016). They found that the stability of all recorded neurons was not uniformly necessary to achieve stable BMI control, and showed that neural activity in the potent space was significantly more stable than neural activity in the null space.…”

Section: From Single Neurons To Neural Manifoldsmentioning

confidence: 99%

Neural Manifolds for the Control of Movement

Gallego

Perich

Miller

et al. 2017

Neuron

527

531

View full text Add to dashboard Cite

The analysis of neural dynamics in several brain cortices has consistently uncovered low-dimensional manifolds that capture a significant fraction of neural variability. These neural manifolds are spanned by specific patterns of correlated neural activity, the “neural modes.” We discuss a model for neural control of movement in which the timedependent activation of these neural modes is the generator of motor behavior. This manifold-based view of motor cortex may lead to a better understanding of how the brain controls movement.

show abstract

“…Here, it has direct implications for the design and prospective long-term performance of brain–machine interfaces (BMIs) for the control of motor prosthetics [61,65,71]. The stability of movement representations by individual M1 neurons has therefore been addressed comparatively well, yielding, however, conflicting results.…”

Section: Stability Versus Variability In Motor Cortexmentioning

confidence: 99%

“…Furthermore, the ensemble behaviour–response association during learning of a task stabilized, which led to higher across-session correlations in movement-related single-cell activity with increasing task performance [27,37]. Some of the discrepancies in reported single-cell stability may therefore be owing to different levels of task proficiency (also see [71]) or owing to cell-type biases as a result of different cortical recording depths [16]. …”

Section: Stability Versus Variability In Motor Cortexmentioning

confidence: 99%

Variance and invariance of neuronal long-term representations

Clopath

Bonhoeffer

Hübener

et al. 2017

Phil. Trans. R. Soc. B

133

150

View full text Add to dashboard Cite

The brain extracts behaviourally relevant sensory input to produce appropriate motor output. On the one hand, our constantly changing environment requires this transformation to be plastic. On the other hand, plasticity is thought to be balanced by mechanisms ensuring constancy of neuronal representations in order to achieve stable behavioural performance. Yet, prominent changes in synaptic strength and connectivity also occur during normal sensory experience, indicating a certain degree of constitutive plasticity. This raises the question of how stable neuronal representations are on the population level and also on the single neuron level. Here, we review recent data from longitudinal electrophysiological and optical recordings of single-cell activity that assess the long-term stability of neuronal stimulus selectivities under conditions of constant sensory experience, during learning, and after reversible modification of sensory input. The emerging picture is that neuronal representations are stabilized by behavioural relevance and that the degree of long-term tuning stability and perturbation resistance directly relates to the functional role of the respective neurons, cell types and circuits. Using a ‘toy’ model, we show that stable baseline representations and precise recovery from perturbations in visual cortex could arise from a ‘backbone’ of strong recurrent connectivity between similarly tuned cells together with a small number of ‘anchor’ neurons exempt from plastic changes.This article is part of the themed issue ‘Integrating Hebbian and homeostatic plasticity’.

show abstract

Long-Term Stability of Motor Cortical Activity: Implications for Brain Machine Interfaces and Optimal Feedback Control

Cited by 88 publications

References 51 publications

Brain-machine interface cursor position only weakly affects monkey and human motor cortical activity in the absence of arm movements

Brain-machine interface cursor position only weakly affects monkey and human motor cortical activity in the absence of arm movements

Neural Manifolds for the Control of Movement

Variance and invariance of neuronal long-term representations

Contact Info

Product

Resources

About