A distributed, dynamic, parallel computational model: the role of noise in velocity storage

Harvard University, Cambridge, Massachusetts, United States
Journal of Neurophysiology (Impact Factor: 3.04). 04/2012; 108(2):390-405. DOI: 10.1152/jn.00883.2011
Source: PubMed

ABSTRACT Networks of neurons perform complex calculations using distributed, parallel computation, including dynamic "real-time" calculations required for motion control. The brain must combine sensory signals to estimate the motion of body parts using imperfect information from noisy neurons. Models and experiments suggest that the brain sometimes optimally minimizes the influence of noise, although it remains unclear when and precisely how neurons perform such optimal computations. To investigate, we created a model of velocity storage based on a relatively new technique--"particle filtering"--that is both distributed and parallel. It extends existing observer and Kalman filter models of vestibular processing by simulating the observer model many times in parallel with noise added. During simulation, the variance of the particles defining the estimator state is used to compute the particle filter gain. We applied our model to estimate one-dimensional angular velocity during yaw rotation, which yielded estimates for the velocity storage time constant, afferent noise, and perceptual noise that matched experimental data. We also found that the velocity storage time constant was Bayesian optimal by comparing the estimate of our particle filter with the estimate of the Kalman filter, which is optimal. The particle filter demonstrated a reduced velocity storage time constant when afferent noise increased, which mimics what is known about aminoglycoside ablation of semicircular canal hair cells. This model helps bridge the gap between parallel distributed neural computation and systems-level behavioral responses like the vestibuloocular response and perception.

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    • "(2) Offline use of forward models: In order to explore the relationship between sensory anticipation and higher vestibular processing, we propose to use a motion discrimination task, for which the vestibular afferent signals are relatively well understood, such as rotation about the earth's vertical axis. Detailed forward models of semi-circular canal afferent signals have been developed (see Karmali and Merfeld, 2012); the fact that this problem can be described at this level of detail would make self-motion an ideal task to build upon. In order to test our claims that mental imagery and cognitive processes, such as spatial perspective taking, are based on the offline usage of forward models, it is necessary to first determine the effect of expectation, for example in a leftward/rightward rotation discrimination task. "
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