Theories for autism spectrum disorder (ASD) have been formulated at different levels, ranging from physiological observations to perceptual and behavioral descriptions. Understanding the physiological underpinnings of...
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Theories for autism spectrum disorder (ASD) have been formulated at different levels, ranging from physiological observations to perceptual and behavioral descriptions. Understanding the physiological underpinnings of perceptual traits in ASD remains a significant challenge in the field. Here we show how a recurrent neural circuit model that was optimized to perform sampling-based inference and displays characteristic features of cortical dynamics can help bridge this gap. The model was able to establish a mechanistic link between two descriptive levels for ASD: a physiological level, in terms of inhibitory dysfunction, neural variability, and oscillations, and a perceptual level, in terms of hypopriors in Bayesian computations. We took two parallel paths-inducing hypopriors in the probabilistic model, and an inhibitory dysfunction in the network model-which lead to consistent results in terms of the represented posteriors, providing support for the view that both descriptions might constitute two sides of the same coin. Author Summary Two different views of autism, one regarding altered probabilistic computations, and one regarding inhibitory dysfunction, are brought together by means of a recurrent neural network model trained to perform sampling-based inference in a visual setting. Moreover, the model captures a variety of experimental observations regarding differences in neural variability and oscillations in subjects with autism. By linking neural connectivity, dynamics, and function, this work contributes to the understanding of the physiological underpinnings of perceptual traits in autism spectrum disorder.
The nonparametric Bayesian approach for inference regarding the unknown distribution of a random sample customarily assumes that this distribution is random and arises through Dirichlet-process mixing. Previous work w...
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The nonparametric Bayesian approach for inference regarding the unknown distribution of a random sample customarily assumes that this distribution is random and arises through Dirichlet-process mixing. Previous work within this setting has focused on the mean of the posterior distribution of this random distribution, which is the predictive distribution of a future observation given the sample. Our interest here is in learning about other features of this posterior distribution as well as about posteriors associated with functionals of the distribution of the data. We indicate how to do this in the case of linear functionals. An illustration, with a sample from a Gamma distribution, utilizes Dirichlet-process mixtures of normals to recover this distribution and its features.
We propose a grammar-based approach to active inferencebased on hypothesis-driven rule learning where new hypotheses are generated on the fly. This contrasts with traditional approaches based on fixed hypothesis spac...
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ISBN:
(纸本)9783030937362;9783030937355
We propose a grammar-based approach to active inferencebased on hypothesis-driven rule learning where new hypotheses are generated on the fly. This contrasts with traditional approaches based on fixed hypothesis spaces and Bayesian model reduction. We apply these two contrasting approaches to an established active inference task and show that grammar-based agents' performance benefits from the explicit rule representation underpinning hypothesis generation. Our proposal is a synthesis of the active inference framework with language-of-thought models, which paves the way for computational-level descriptions of false inferencebased on an aberrant hypothesis-generating process.
Use of errors-in-variables models is appropriate in many practical experimental problems. However, inferencebased on such models is by no means straightforward. In previous analyses, simplifying assumptions have been...
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Use of errors-in-variables models is appropriate in many practical experimental problems. However, inferencebased on such models is by no means straightforward. In previous analyses, simplifying assumptions have been made in order to ease this intractability, but assumptions of this nature are unfortunate and restrictive. In this paper, we analyse errors-in-variables models in full generality under a Bayesian formulation. In order to compute the necessary posterior distributions, we utilize various computational techniques. Two specific non-linear errors-in-variables regression examples are considered;the first is a re-analysed Berkson-type model, and the second is a classical errors-in-variables model. Our analyses are compared and contrasted with those presented elsewhere in the literature.
Online change detection of multimode processes is important for process monitoring and control, which aims to timely and accurately detect two types of changes: 1) mode changes and 2) parameter changes. However, the e...
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Online change detection of multimode processes is important for process monitoring and control, which aims to timely and accurately detect two types of changes: 1) mode changes and 2) parameter changes. However, the existing online methods mainly focus on one type of change and, thus, have difficulty capturing the complex change structure. Motivated by this, we propose a novel Bayesian online change detection method for multimode processes (BCD-MMP). Specifically, the complex change structure is characterized by three state variables (the latest change point (LCP), the mode of the current segment, and the indicator variable of parameter changes). When a new data point arrives, we infer the posterior distribution of the state variables. To make the inference process tractable, we develop a sampling-based inference algorithm, and a pruning strategy is also provided to improve the computation efficiency. Simulation and real case studies of wind turbine torque control process indicate that the proposed method can achieve better online change detection performance than the state-of-the-art methods.
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