Abstract
Reliable perception of self-motion and orientation requires the central nervous system (CNS) to adapt to changing environments, stimuli, and sensory organ function. The proposed computations required of neural systems for this adaptation process remain conceptual, limiting our understanding and ability to quantitatively predict adaptation and mitigate any resulting impairment prior to completing adaptation. Here, we have implemented a computational model of the internal calculations involved in the orientation perception system’s adaptation to changes in the magnitude of gravity. In summary, we propose that the CNS considers parallel, alternative hypotheses of the parameter of interest (in this case, the CNS’s internal estimate of the magnitude of gravity) and uses the associated sensory conflict signals (i.e., difference between sensory measurements and the expectation of them) to sequentially update the posterior probability of each hypothesis using Bayes rule. Over time, an updated central estimate of the internal magnitude of gravity emerges from the posterior probability distribution, which is then used to process sensory information and produce perceptions of self-motion and orientation. We have implemented these hypotheses in a computational model and performed various simulations to demonstrate quantitative model predictions of adaptation of the orientation perception system to changes in the magnitude of gravity, similar to those experienced by astronauts during space exploration missions. These model predictions serve as quantitative hypotheses to inspire future experimental assessments.
Highlights
In everyday life, we must reliably perceive self-motion and orientation while being capable of adapting to novel stimuli, changing environments, or changes to peripheral sensory organs
For parameters that relate to multiple sensory conflict signals, such as how accelerations and rotations are required for the CNS to determine an accurate perception of gravity, we propose that the CNS weights and normalizes disparate conflicts by the typical reliability of the sensory signals in order to produce a unidimensional metric of conflict for a given alternative hypothesis
The scaling on time depends upon several meaningful factors that we explore below, and on computational assumptions like the granularity of alternative hypotheses, the time step for numerical integration, and whether the orientation perception model vs. the update to the internal magnitude of gravity happen with the same synchronous time steps
Summary
We must reliably perceive self-motion and orientation while being capable of adapting to novel stimuli, changing environments, or changes to peripheral sensory organs (e.g., from childhood development, aging, or injury). While all astronauts eventually adapt to the microgravity environment (Shelhamer, 2015) these adaptations produce sensorimotor impairment upon return to Earth This includes postural (Wood et al, 2015) and locomotion deficits (Mulavara et al, 2018), misperceptions of spatial orientation (Clement and Wood, 2014), altered eye movements (Clement, 1998), manual control decrements (Merfeld, 1996), motion sickness (Lackner and Dizio, 2006; Reschke et al, 2018), and ataxia (Paloski et al, 1993). While the exact neural mechanisms for sensorimotor adaptation to altered gravity remain difficult to identify experimentally, by implementing a computational model we can explore the types of computations necessary to enable such adaptation, as well as produce novel quantitative hypotheses to motivate future experimental investigation
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