Modeling orientation perception adaptation to altered gravity environments with memory of past sensorimotor states.

IF 3.4 3区 医学 Q2 NEUROSCIENCES Frontiers in Neural Circuits Pub Date : 2023-01-01 DOI:10.3389/fncir.2023.1190582
Aaron R Allred, Victoria G Kravets, Nisar Ahmed, Torin K Clark
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引用次数: 2

Abstract

Transitioning between gravitational environments results in a central reinterpretation of sensory information, producing an adapted sensorimotor state suitable for motor actions and perceptions in the new environment. Critically, this central adaptation is not instantaneous, and complete adaptation may require weeks of prolonged exposure to novel environments. To mitigate risks associated with the lagging time course of adaptation (e.g., spatial orientation misperceptions, alterations in locomotor and postural control, and motion sickness), it is critical that we better understand sensorimotor states during adaptation. Recently, efforts have emerged to model human perception of orientation and self-motion during sensorimotor adaptation to new gravity stimuli. While these nascent computational frameworks are well suited for modeling exposure to novel gravitational stimuli, they have yet to distinguish how the central nervous system (CNS) reinterprets sensory information from familiar environmental stimuli (i.e., readaptation). Here, we present a theoretical framework and resulting computational model of vestibular adaptation to gravity transitions which captures the role of implicit memory. This advancement enables faster readaptation to familiar gravitational stimuli, which has been observed in repeat flyers, by considering vestibular signals dependent on the new gravity environment, through Bayesian inference. The evolution and weighting of hypotheses considered by the CNS is modeled via a Rao-Blackwellized particle filter algorithm. Sensorimotor adaptation learning is facilitated by retaining a memory of past harmonious states, represented by a conditional state transition probability density function, which allows the model to consider previously experienced gravity levels (while also dynamically learning new states) when formulating new alternative hypotheses of gravity. In order to demonstrate our theoretical framework and motivate future experiments, we perform a variety of simulations. These simulations demonstrate the effectiveness of this model and its potential to advance our understanding of transitory states during which central reinterpretation occurs, ultimately mitigating the risks associated with the lagging time course of adaptation to gravitational environments.

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用过去感觉运动状态的记忆来模拟改变重力环境下的定向知觉适应。
在重力环境之间的转换导致感觉信息的中心重新解释,产生适应的感觉运动状态,适合新环境中的运动动作和感知。关键的是,这种中心适应不是瞬间的,完全适应可能需要数周的时间长时间暴露在新的环境中。为了减轻与适应滞后时间过程相关的风险(例如,空间方向误解、运动和姿势控制的改变以及晕动病),我们必须更好地了解适应过程中的感觉运动状态。最近,在新的重力刺激的感觉运动适应过程中,人们开始努力模拟人类对方向和自我运动的感知。虽然这些新生的计算框架非常适合模拟暴露于新的重力刺激,但它们尚未区分中枢神经系统(CNS)如何从熟悉的环境刺激中重新解释感觉信息(即重新适应)。在这里,我们提出了一个理论框架和由此产生的前庭适应重力转换的计算模型,其中捕获了内隐记忆的作用。通过贝叶斯推理,考虑依赖于新重力环境的前庭信号,这一进步使重复飞行者能够更快地重新适应熟悉的重力刺激,这在重复飞行中已经观察到。通过rao - blackwell化粒子滤波算法对CNS所考虑的假设的演化和加权进行建模。感觉运动适应学习是通过保留过去和谐状态的记忆来促进的,由条件状态转移概率密度函数表示,这使得模型在制定新的重力替代假设时考虑到以前经历的重力水平(同时也动态学习新状态)。为了证明我们的理论框架和激励未来的实验,我们进行了各种模拟。这些模拟证明了该模型的有效性,并有可能促进我们对中心重新解释发生的过渡状态的理解,最终减轻与适应重力环境的滞后时间过程相关的风险。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
6.00
自引率
5.70%
发文量
135
审稿时长
4-8 weeks
期刊介绍: Frontiers in Neural Circuits publishes rigorously peer-reviewed research on the emergent properties of neural circuits - the elementary modules of the brain. Specialty Chief Editors Takao K. Hensch and Edward Ruthazer at Harvard University and McGill University respectively, are supported by an outstanding Editorial Board of international experts. This multidisciplinary open-access journal is at the forefront of disseminating and communicating scientific knowledge and impactful discoveries to researchers, academics and the public worldwide. Frontiers in Neural Circuits launched in 2011 with great success and remains a "central watering hole" for research in neural circuits, serving the community worldwide to share data, ideas and inspiration. Articles revealing the anatomy, physiology, development or function of any neural circuitry in any species (from sponges to humans) are welcome. Our common thread seeks the computational strategies used by different circuits to link their structure with function (perceptual, motor, or internal), the general rules by which they operate, and how their particular designs lead to the emergence of complex properties and behaviors. Submissions focused on synaptic, cellular and connectivity principles in neural microcircuits using multidisciplinary approaches, especially newer molecular, developmental and genetic tools, are encouraged. Studies with an evolutionary perspective to better understand how circuit design and capabilities evolved to produce progressively more complex properties and behaviors are especially welcome. The journal is further interested in research revealing how plasticity shapes the structural and functional architecture of neural circuits.
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