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Atypical visual-auditory predictive coding in autism spectrum disorder: Electrophysiological evidence from stimulus omissions

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AUTISM 2020年第7期24卷 1849-1859页

作者： van Laarhoven, Thijs Stekelenburg, Jeroen J. Eussen, Mart L. J. M. Vroomen, Jean Tilburg Univ Tilburg Netherlands Yulius Mental Hlth Org Dordrecht Netherlands

Autism spectrum disorder is a pervasive neurodevelopmental disorder that has been linked to a range of perceptual processing alterations, including hypo- and hyperresponsiveness to sensory stimulation. A recently proposed theory that attempts to account for these symptoms, states that autistic individuals have a decreased ability to anticipate upcoming sensory stimulation due to overly precise internal prediction models. Here, we tested this hypothesis by comparing the electrophysiological markers of prediction errors in auditory prediction by vision between a group of autistic individuals and a group of age-matched individuals with typical development. Between-group differences in prediction error signaling were assessed by comparing event-related potentials evoked by unexpected auditory omissions in a sequence of audiovisual recordings of a handclap in which the visual motion reliably predicted the onset and content of the sound. Unexpected auditory omissions induced anincreasedearly negative omission response in the autism spectrum disorder group, indicating that violations of the prediction model produced larger prediction errors in the autism spectrum disorder group compared to the typical development group. The current results show that autistic individuals have alterations in visual-auditory predictive coding, and support the notion of impaired predictive coding as a core deficit underlying atypical sensory perception in autism spectrum disorder. Lay abstract Many autistic individuals experience difficulties in processing sensory information (e.g. increased sensitivity to sound). Here we show that these difficulties may be related to an inability to process unexpected sensory stimulation. In this study, 29 older adolescents and young adults with autism and 29 age-matched individuals with typical development participated in an electroencephalography study. The electroencephalography study measured the participants' brain activity during unexpected silences in

关键词： autism spectrum disorder event-related potentials predictive coding visual-auditory

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Efficient and predictive coding From Compression and Control by Human Brain Networks

Efficient and Predictive Coding From Compression and Control...

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作者： Zhou, Dale University of Pennsylvania

学位级别：Ph.D., Doctor of Philosophy

Most theories of brain function depend on information processing and the manipulation of neural or cognitive representations. This information processing is thought to be efficient and manipulations are thought to update representations that are predictive of future needs. These ideas are formulated by theories of efficient coding and predictive coding. Efficient coding is transmitting maximal information while minimizing the use of limited resources. predictive coding is transmitting maximal information about the future while minimizing the use of limited resources. Although these parsimonious theories have accumulated evidence at the cellular level and in sensory regions, different models and data are needed to test the theories at the macroscale and across the brain network. This dissertation investigates how we can generalize efficient and predictive coding to the brain network by drawing from network science, information theory, and control theory. Using these frameworks, we operationalize compression and control as two key processes underlying efficient and predictive coding. Data compression distills predictive from unpredictive information using limited metabolic resources. Optimal control governs how the brain network should distribute the control signals needed to transition to diverse future states according to feedback from structured representations of the world. We test the compression and control models with hypothesized features of an efficient and predictive code. We find relationships between our models and the dimensionality and timescales of brain activity, metabolic resource expenditure, myelin content, areal expansion, functional specialization, and behavioral speed and accuracy. These findings support the efficient and predictive coding hypotheses across the brain and open new avenues to investigate brain function and mental health.

关键词： predictive coding Brain network Brain function Efficient coding

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An efficient predictive coding of integers with real-domain predictions using distributed source coding techniques

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13th International Multimedia Modeling Conference (MMM 2007)

作者： Ali, Mortuza Murshed, Manzur Monash Univ Gippsland Sch Informat Technol Churchill Vic 3842 Australia

ISBN: (纸本)9783540694212

By exploiting the commonly observed Laplacian probability distribution of audio, image, and video prediction residuals, many researchers proposed low complexity prefix codes to compress integer residual data. All these techniques treated predictions as integers despite being drawn from the real domain in lossless compression. Among these, Golomb coding is widely used for being optimal with non-negative integers that follow geometric distribution, a two-sided extension of which is the discrete analogue of Laplacian distribution. This paper for the first time presents a novel predictive codec which treats real-domain predictions without rounding to the nearest integers and thus avoids any coding loss due to rounding. The proposed codec innovatively uses the concept of distributed source coding by replacing the reminder part of Golomb code with the index of the coset containing the actual value.

关键词： predictive coding prediction residual lossless coding Laplacian distribution distributed source coding and coset

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Pel adaptive lossless predictive coding based on image segmentation

Pel adaptive lossless predictive coding based on image segme...

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Meeting on Visual Communications and Image Processing

作者： Nakachi, T Fujii, T Suzuki, J NTT Opt Network Syst Labs Yokosuka Kanagawa 2390847 Japan

ISBN: (纸本)0819431249

Lossless image coding that can recover original image from its compressed signal is required in the fields of medical imaging, fine arts, printing, and any applications demanding high image fidelity. MAR (Multiplicative Autoregressive) predictive coding is an efficient lossless compression scheme. In this method, prediction coefficients are fixed within the subdivided block-by-block image and cannot to be adopted to local statistics efficiently. Furthermore, side-information such as prediction coefficients must be transmitted to the decoder at each block. In this paper, we propose an improved MAR coding method based on image segmentation. The proposed MAR predictor can be adapted to local statistics of image efficiently. This coding method does not need transmit side-information to the decoder at each pixel. The effectiveness of the proposed model is shown through experiments using SHD images.

关键词： lossless coding predictive coding super high definition images image segmentation

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predictive coding is a consequence of energy efficiency in recurrent neural networks

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PATTERNS 2022年第12期3卷

作者： Ali, Abdullahi Ahmad, Nasir de Groot, Elgar van Gerven, Marcel Antonius Johannes Kietzmann, Tim Christian Radboud Univ Nijmegen Donders Inst Brain Cognit & Behav Nijmegen Netherlands Univ Osnabruck Inst Cognit Sci Osnabruck Germany Univ Utrecht Dept Expt Psychol Utrecht Netherlands

predictive coding is a promising framework for understanding brain function. It postulates that the brain continuously inhibits predictable sensory input, ensuring preferential processing of surprising elements. A central aspect of this view is its hierarchical connectivity, involving recurrent message passing between excitatory bottom-up signals and inhibitory top-down feedback. Here we use computational modeling to demonstrate that such architectural hardwiring is not necessary. Rather, predictive coding is shown to emerge as a consequence of energy efficiency. When training recurrent neural networks to minimize their energy consumption while operating in predictive environments, the networks self-organize into prediction and error units with appropriate inhibitory and excitatory interconnections and learn to inhibit predictable sensory input. Moving beyond the view of purely top-down-driven predictions, we demonstrate, via virtual lesioning experiments, that networks perform predictions on two timescales: fast lateral predictions among sensory units and slower prediction cycles that integrate evidence over time.

关键词： predictive coding recurrent neural networks energy efficiency brain-inspired machine learning

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Exploring Approaches to Optimize the Performance of predictive coding on Multilanguage Data Sets

Exploring Approaches to Optimize the Performance of Predicti...

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2023 IEEE International Conference on Big Data, BigData 2023

作者： Mahoney, Christian J. Huber-Fliflet, Nathaniel Gronvall, Peter Clark, Chris Zhang, Jianping Wei, Fusheng Mao, Qiang Gottlieb Steen & Hamilton LLP. E-Discovery Cleary WashingtonDC United States Ankura Consulting Group Llc Data & Technology WashingtonDC United States

ISBN: (纸本)9798350324457

predictive modeling - known in the legal industry as 'predictive coding' or 'Technology Assisted Review (TAR)' - is a popular tool used by legal professionals to augment a historically manual document review and classification process in responding to data requests for various legal proceedings. It has become more commonplace over the past decade due to its proven ability to minimize manual document classification, thus reducing the time and cost associated with this aspect of legal proceedings. There is significant research supporting the effectiveness of this technology that includes topics, such as identifying the most performant machine learning algorithms (e.g., logistic regression) or establishing the best methodologies for selecting representative training and testing data. Primarily, this research has been performed without a focus on multilanguage data sets because many legal proceedings typically involve one primary, dominant language. As acceptance of predictive modeling technology grows, legal practitioners have developed independent preferences for handling multiple languages in data sets. These preferences have been primarily based on anecdotal experience rather than empirical assessments and have resulted in two modeling approaches. The first approach uses a single model, which is less complex, and more cost effective than the next approach. The second develops multiple language-specific models, which can create workflow complexity that may impact the overall cost savings that predictive coding seeks to achieve. Proponents of the second approach believe that creating models per language results in better performance over a single model approach. In this study, we empirically explore the performance differences between the two approaches - single model and multiple language-specific models. We hypothesize that a single model approach performs similarly to a language-specific modeling approach when classifying documents for relevance. We evaluate both a

关键词： e-Discovery eDiscovery electronic discover foreign language machine learning NLP predictive coding technology assisted review text classification

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Decay Makes Supervised predictive coding Generative

Decay Makes Supervised Predictive Coding Generative

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作者： Sun, Wei University of Waterloo

学位级别：硕士

predictive coding is a hierarchical model of neural computation that approximates backpropagation using only local computations and local learning rules. An important aspect of predictive coding is the presence of feedback connections between layers. These feedback connections allow predictive coding networks to potentially be generative as well as discriminative. However, predictive coding networks trained on supervised classification tasks cannot generate accurate input samples close to the training inputs from the class vectors alone. This problem arises from the fact that generating inputs from classes requires solving an underdetermined system, which contains an infinite number of solutions. Generating the correct inputs involves reaching a specific solution in that infinite solution space. But by imposing a minimum-norm constraint on the state nodes and the synaptic weights of a predictive coding network, the solution space collapses to a unique solution that is close to the training inputs. This minimum-norm constraint can be enforced by adding decay to the predictive coding equations. Decay is implemented in the form of weight decay and activity decay. Analyses done on linear predictive coding networks show that applying weight decay during training helps the network learn weights that can generate the correct input samples from the class vectors, while applying activity decay during input generation helps to lower the variance in the network's generated samples. Additionally, weight decay regularizes the values of the network weights, avoiding extreme values, and improves the rate at which the network converges to equilibrium by regularizing the eigenvalues of the Jacobian at the equilibrium. Experiments on the MNIST dataset of handwritten digits provide evidence that decay makes predictive coding networks generative even when the network contains deep layers and uses nonlinear tanh activations. A predictive coding network equipped with weight and activity

关键词： neural network predictive coding weight decay regularization supervised learning generative

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Integer wavelet transformations with predictive coding improves 3-D similar image set compression

Integer wavelet transformations with predictive coding impro...

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8th Annual Conference on Wavelet Applications

作者： Qi, XJ Tyler, JM Pianykh, OS Louisiana State Univ Dept Comp Sci Baton Rouge LA 70803 USA

ISBN: (纸本)0819440868

Lossless compression techniques are essential in archival and communication of large amounts of homogeneous data in radiological image databases. This paper exploits dependencies that exist between the pixel intensities in three dimensions to improve compression for a set of similar medical images, These 3-D dependencies are systematically presented as histograms, plots of wavelet decomposition coefficients, feature vectors of wavelet decomposition coefficients, entropy and correlation. This 3-D dependency is called set redundancy for medical image sets. predictive coding is adapted to set redundancy and combined with integer wavelet transformations to improve compression. This set compression improvement is demonstrated with 3-D sets of magnetic resonance (MR) brain images.

关键词： predictive coding integer wavelet transform entropy coding and set redundancy

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Divide & Concur: a predictive coding Account of the N400 ERP Component

Divide & Concur: a Predictive Coding Account of the N400 ERP...

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作者： Nour Eddine, Samer Tufts University

学位级别：M.S., Master of Science/Master of Surgery

The N400 event-related brain potential has provided core insights into the nature of on-line language comprehension, and its amplitude is modulated by a wide variety of lexical and contextual factors. Across many paradigms, the N400 amplitude appears to be a function of the mismatch between information that is anticipated based on learned regularities and information that is actually encountered. Various theories have been verbally formulated to explain what processes the N400 reflects, but much is left unspecified by these theories, making it difficult to test them experimentally. One way forward is to specify their assumptions explicitly in the form of a computational model. The current work proposes a hierarchical predictive coding model of perceptual inference that explicitly specifies how predictions and prediction errors are computed during on-line word comprehension, accounting for the influence of a range of lexical- and contextual-level factors on the N400.

关键词： Computational Modeling Neuroscience predictive coding predictive processing Psycholinguistics

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Bstj 49: 8. October 1970: Adaptive predictive coding of Speech Signals. (Atal, B.S.;Schroeder, M.R.)

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2016年

Bstj 49: 8. October 1970: Adaptive predictive coding of Speech Signals. (Atal, B.S.; Schroeder, M.R.) by published by

关键词： (atal, adaptive b.s. coding coding system difference signal equation error input input speech m.r.) parameters predictive predictive coding predictor predictor parameters reconstructed reconstructed signal reconstructed speech signal speech speech signal speech signals system technical

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