Dataset Information

Optimal feedback control successfully explains changes in neural modulations during experiments with brain-machine interfaces.

ABSTRACT: Recent experiments with brain-machine-interfaces (BMIs) indicate that the extent of neural modulations increased abruptly upon starting to operate the interface, and especially after the monkey stopped moving its hand. In contrast, neural modulations that are correlated with the kinematics of the movement remained relatively unchanged. Here we demonstrate that similar changes are produced by simulated neurons that encode the relevant signals generated by an optimal feedback controller during simulated BMI experiments. The optimal feedback controller relies on state estimation that integrates both visual and proprioceptive feedback with prior estimations from an internal model. The processing required for optimal state estimation and control were conducted in the state-space, and neural recording was simulated by modeling two populations of neurons that encode either only the estimated state or also the control signal. Spike counts were generated as realizations of doubly stochastic Poisson processes with linear tuning curves. The model successfully reconstructs the main features of the kinematics and neural activity during regular reaching movements. Most importantly, the activity of the simulated neurons successfully reproduces the observed changes in neural modulations upon switching to brain control. Further theoretical analysis and simulations indicate that increasing the process noise during normal reaching movement results in similar changes in neural modulations. Thus, we conclude that the observed changes in neural modulations during BMI experiments can be attributed to increasing process noise associated with the imperfect BMI filter, and, more directly, to the resulting increase in the variance of the encoded signals associated with state estimation and the required control signal.

SUBMITTER: Benyamini M

PROVIDER: S-EPMC4436824 | biostudies-other | 2015

REPOSITORIES: biostudies-other

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Optimal feedback control successfully explains changes in neural modulations during experiments with brain-machine interfaces.

Benyamini Miri M Zacksenhouse Miriam M

Frontiers in systems neuroscience 20150519

Recent experiments with brain-machine-interfaces (BMIs) indicate that the extent of neural modulations increased abruptly upon starting to operate the interface, and especially after the monkey stopped moving its hand. In contrast, neural modulations that are correlated with the kinematics of the movement remained relatively unchanged. Here we demonstrate that similar changes are produced by simulated neurons that encode the relevant signals generated by an optimal feedback controller during sim ...[more]

PMID: 26042002

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Project description:The brain uses information from different sensory systems to guide motor behavior, and aging is associated with simultaneous decline in the quality of sensory information provided to the brain and deterioration in motor control. Correlations between age-dependent decline in sensory anatomical structures and behavior have been demonstrated in many sensorimotor systems, and it has recently been suggested that a Bayesian framework could explain these relationships. Here we show that age-dependent changes in a human sensorimotor reflex, the vestibuloocular reflex, are explained by a Bayesian optimal adaptation in the brain occurring in response to death of motion-sensing hair cells. Specifically, we found that the temporal dynamics of the reflex as a function of age emerge from ( r = 0.93, P < 0.001) a Kalman filter model that determines the optimal behavioral output when the sensory signal-to-noise characteristics are degraded by death of the transducers. These findings demonstrate that the aging brain is capable of generating the ideal and statistically optimal behavioral response when provided with deteriorating sensory information. While the Bayesian framework has been shown to be a general neural principle for multimodal sensory integration and dynamic sensory estimation, these findings provide evidence of longitudinal Bayesian processing over the human life span. These results illuminate how the aging brain strives to optimize motor behavior when faced with deterioration in the peripheral and central nervous systems and have implications in the field of vestibular and balance disorders, as they will likely provide guidance for physical therapy and for prosthetic aids that aim to reduce falls in the elderly. NEW & NOTEWORTHY We showed that age-dependent changes in the vestibuloocular reflex are explained by a Bayesian optimal adaptation in the brain that occurs in response to age-dependent sensory anatomical changes. This demonstrates that the brain can longitudinally respond to age-related sensory loss in an ideal and statistically optimal way. This has implications for understanding and treating vestibular disorders caused by aging and provides insight into the structure-function relationship during aging.

Dataset Information

Optimal feedback control successfully explains changes in neural modulations during experiments with brain-machine interfaces.

Publications

Optimal feedback control successfully explains changes in neural modulations during experiments with brain-machine interfaces.

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