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Acute fasting modulates autonomic nervous system function and ambulatory cardiac interoception

BIOLOGICAL PSYCHOLOGY 2024年 186卷 108760页

作者： Schwerdtfeger, Andreas R. Rominger, Christian Karl Franzens Univ Graz Inst Psychol Univ Pl 2-3 Graz Austria

Intermittent fasting has been associated with diverse physical and psychological health benefits. According to previous research, fasting -induced alterations in psychophysiological functioning should facilitate the accurate detection of an internal bodily signal (like the heart), which is referred to as interoceptive accuracy. In two within -subjects studies we aimed to examine whether an intermittent fasting protocol (i) evokes distinct autonomic nervous system changes in the laboratory and (ii) improves (objectifiable) interoceptive accuracy and sensibility (i.e., the subjective belief in perceiving bodily signals) in everyday life. Study 1 (N = 36) found increasing heart rate variability (precisely, the root mean square of successive differences;RMSSD) accompanied by a more vascular than myocardial response following a 16 h fast. Study 2 (N = 40) applied an ecological momentary assessment design including intermittent fasting (8 h normal eating followed by 16 h fasting) and normal eating (24 h normal eating) for three consecutive days each. Findings suggested a tendency toward higher interoceptive accuracy and sensibility during the fasting regimen, which was particularly pronounced in individuals exhibiting lower RMSSD. Together, findings suggest that (short-term) fasting seems to facilitate momentary attention to organismic cues due to alterations in autonomic nervous system function.

关键词： Cardiovascular activity Ecological momentary assessment Fasting Heart rate variability Interoception predictive coding

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Deep Pixel Restoration Loop coding Network [Application Notes] [Application Notes]

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IEEE COMPUTATIONAL INTELLIGENCE MAGAZINE 2023年第2期18卷 94-103页

作者： Wang, Tianxiang Dai, Qun Yuan, Muxuan Nanjing Univ Aeronaut & Astronaut Nanjing Peoples R China

This paper presents a solution to the video prediction problem based on the deep learning paradigm. Predicted frames generated by existing video prediction models are often blurry and have difficulty maintaining accuracy in multi-step prediction. To overcome these limitations, this paper presents a deep learning model, named the deep pixel restoration loop coding network (DPR-LC-Net), which employs the concept of predictive coding and adopts pixels in the real frames. While making a long-term prediction, it can generate clear prediction frames with few errors. The calculation process of DPR-LC-Net is multi-sequential: it completes calculations in the form of an approximate loop from top to bottom and then from left to right. After predicting subsequent steps, DPR-LC-Net both calculates errors and removes them from sequential prediction. Finally, the model includes a unique pixel restoration module that works efficiently on pixels in the preceding real frames to generate predicted frames, thereby improving the clarity of the predicted frames. Extensive experiments using four video datasets illustrate that the prediction performance of DPR-LC-Net is superior to that of the state-of-the-art models.

关键词： Deep learning Video communication Visualization Computational modeling predictive models predictive coding Brain modeling

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Communication- and Control-Aware Optimal Quantizer Selection for Multi-Agent Control

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IEEE CONTROL SYSTEMS LETTERS 2024年 8卷 2385-2390页

作者： Afshari, Mohammad Maity, Dipankar Tsiotras, Panagiotis Georgia Inst Technol D Guggenheim Sch Aerosp Engn Atlanta GA 30332 USA Univ N Carolina Dept Elect & Comp Engn Charlotte NC 28223 USA

We consider a multi-agent linear quadratic optimal control problem. Due to communication constraints, the agents are required to quantize their local state measurements before communicating them to the rest of the team, thus resulting in a decentralized information structure. The optimal controllers are to be synthesized under this decentralized and quantized information structure. The agents are given a set of quantizers with varying quantization resolutions-higher resolution incurs higher communication cost and vice versa. The team must optimally select the quantizer to prioritize agents with 'high-quality' information for optimizing the control performance under communication constraints. We show that there exist a separation between the optimal solution to the control problem and the choice of the optimal quantizer. We show that the optimal controllers are linear and the optimal selection of the quantizers can be determined by solving a linear program.

关键词： Quantization (signal) Costs Vectors Robot sensing systems Bandwidth predictive coding Optimal control Multi-agent systems networked control systems quantization

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Active inference as a theory of sentient behavior

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BIOLOGICAL PSYCHOLOGY 2024年 186卷 108741页

作者： Pezzulo, Giovanni Parr, Thomas Friston, Karl CNR Inst Cognit Sci & Technol Rome Italy Univ Oxford Nuffield Dept Clin Neurosci Oxford England UCL Wellcome Ctr Human Neuroimaging Queen Sq Inst Neurol London England VERSES AI Res Lab Los Angeles CA 90016 USA CNR Inst Cognit Sci & technol Via S Martino della Battaglia 44 I-00185 Rome Italy

This review paper offers an overview of the history and future of active inference-a unifying perspective on action and perception. Active inference is based upon the idea that sentient behavior depends upon our brains' implicit use of internal models to predict, infer, and direct action. Our focus is upon the conceptual roots and development of this theory of (basic) sentience and does not follow a rigid chronological narrative. We trace the evolution from Helmholtzian ideas on unconscious inference, through to a contemporary understanding of action and perception. In doing so, we touch upon related perspectives, the neural underpinnings of active inference, and the opportunities for future development. Key steps in this development include the formulation of predictive coding models and related theories of neuronal message passing, the use of sequential models for planning and policy optimization, and the importance of hierarchical (temporally) deep internal (i.e., generative or world) models. Active inference has been used to account for aspects of anatomy and neurophysiology, to offer theories of psychopathology in terms of aberrant precision control, and to unify extant psychological theories. We anticipate further development in all these areas and note the exciting early work applying active inference beyond neuroscience. This suggests a future not just in biology, but in robotics, machine learning, and artificial intelligence.

关键词： Active inference predictive coding Generative model

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Can computational models help elucidate the link between complex trauma and hallucinations?

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SCHIZOPHRENIA RESEARCH 2024年 265卷 66-73页

作者： Series, Peggy Veerapa, Emilie Jardri, Renaud Univ Edinburgh IANC Informat 10 Crichton St Edinburgh EH8 9AB Scotland Univ Lille Lille Neurosci & Cognit Ctr Plast & Subject Team INSERMU 1172 Lille France CHU Lille Dept Psychiat F-59000 Lille France CHU Lille Fontan Hosp CURE Platform Psychiat Clin Invest Ctr Lille France PSL Res Univ Lab Neurosci Cognit & Computat LNC 2 INSERMU 960 ENS Paris France Univ Edinburgh 10 Crichton St Edinburgh EH8 9AB Scotland

Recently, a number of predictive coding models have been proposed to account for post-traumatic stress disorder (PTSD)'s symptomatology, including intrusions, flashbacks and hallucinations. These models were usually developed to account for traditional/type-1 PTSD. We here discuss whether these models also apply or can be translated to the case of complex/type-2 PTSD and childhood trauma (cPTSD). The distinction between PTSD and cPTSD is important because the disorders differ in terms of symptomatology and potential mechanisms, how they relate to developmental stages, but also in terms of illness trajectory and treatment. Models of complex trauma could give us insights on hallucinations in physiological/pathological conditions or more generally on the development of intrusive experiences across diagnostic classes.

关键词： Trauma Voice hearing Belief Inference predictive coding Bayesian models

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Insights From Generative Modeling for Neural Video Compression

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IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2023年第8期45卷 9908-9921页

作者： Yang, Ruihan Yang, Yibo Marino, Joseph Mandt, Stephan Univ Calif Irvine Dept Comp Sci Irvine CA 92697 USA CALTECH DeepMind Pasadena CA 91125 USA

While recent machine learning research has revealed connections between deep generative models such as VAEs and rate-distortion losses used in learned compression, most of this work has focused on images. In a similar spirit, we view recently proposed neural video coding algorithms through the lens of deep autoregressive and latent variable modeling. We present these codecs as instances of a generalized stochastic temporal autoregressive transform, and propose new avenues for further improvements inspired by normalizing flows and structured priors. We propose several architectures that yield state-of-the-art video compression performance on high-resolution video and discuss their tradeoffs and ablations. In particular, we propose (i) improved temporal autoregressive transforms, (ii) improved entropy models with structured and temporal dependencies, and (iii) variable bitrate versions of our algorithms. Since our improvements are compatible with a large class of existing models, we provide further evidence that the generative modeling viewpoint can advance the neural video coding field.

关键词： Transforms Video compression Data models Image coding predictive coding Streaming media Rate-distortion Autoregressive models generative models normalizing flow variational inference video compression

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From neural noise to co-adaptability: Rethinking the multifaceted architecture of motor variability

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PHYSICS OF LIFE REVIEWS 2023年 47卷 245-263页

作者： Casartelli, Luca Maronati, Camilla Cavallo, Andrea Sci Inst IRCCS E MEDEA Theoret & Cognit Neurosci Unit Rome Italy Univ Torino Dept Psychol Move N Brains Lab Turin Italy Fdn Ist Italiano Tecnol CMoN Unit Genoa Italy

In the last decade, the source and the functional meaning of motor variability have attracted considerable attention in behavioral and brain sciences. This construct classically combined different levels of description, variable internal robustness or coherence, and multifaceted operational meanings. We provide here a comprehensive review of the literature with the primary aim of building a precise lexicon that goes beyond the generic and monolithic use of motor variability. In the pars destruens of the work, we model three domains of motor variability related to peculiar computational elements that influence fluctuations in motor outputs. Each domain is in turn characterized by multiple sub-domains. We begin with the domains of noise and differ-entiation. However, the main contribution of our model concerns the domain of adaptability, which refers to variation within the same exact motor representation. In particular, we use the terms learning and (social)fitting to specify the portions of motor variability that depend on our propensity to learn and on our largely constitutive propensity to be influenced by external factors. A particular focus is on motor variability in the context of the sub-domain named co-adaptability. Further groundbreaking challenges arise in the modeling of motor variability. Therefore, in a separate pars construens, we attempt to characterize these challenges, addressing both theoretical and experimental aspects as well as potential clinical implications for neurorehabilitation. All in all, our work suggests that motor variability is neither simply detrimental nor beneficial, and that studying its fluctuations can provide meaningful insights for future research.

关键词： Co-adaptability Learning Joint action Movement kinematics Motor neuroscience Motor variability neural variability Plasticity predictive coding Rehabilitation Theoretical modeling

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From Ensemble Clustering to Subspace Clustering: Cluster Structure Encoding

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2023年第5期34卷 2670-2681页

作者： Tao, Zhiqiang Li, Jun Fu, Huazhu Kong, Yu Fu, Yun Santa Clara Univ Dept Comp Sci & Engn Santa Clara CA 95053 USA MIT Inst Med Engn & Sci 77 Massachusetts Ave Cambridge MA 02139 USA ASTAR Inst High Performance Comp IHPC Singapore 138632 Singapore Rochester Inst Technol B Thomas Golisano Coll Comp & Informat Sci Rochester NY 14623 USA Northeastern Univ Dept Elect & Comp Engn Boston MA 02115 USA Northeastern Univ Khoury Coll Comp & Informat Sci Boston MA 02115 USA

In this study, we propose a novel algorithm to encode the cluster structure by incorporating ensemble clustering (EC) into subspace clustering (SC). First, the low-rank representation (LRR) is learned from a higher order data relationship induced by ensemble K-means coding, which exploits the cluster structure in a co-association matrix of basic partitions (i.e., clustering results). Second, to provide a fast predictive coding mechanism, an encoding function parameterized by neural networks is introduced to predict the LRR derived from partitions. These two steps are jointly proceeded to seamlessly integrate partition information and original features and thus deliver better representations than the ones obtained from each single source. Moreover, an alternating optimization framework is developed to learn the LRR, train the encoding function, and fine-tune the higher order relationship. Extensive experiments on eight benchmark datasets validate the effectiveness of the proposed algorithm on several clustering tasks compared with state-of-the-art EC and SC methods.

关键词： Encoding Codes Clustering algorithms predictive coding Optimization Training Sparse matrices Encoder network ensemble clustering (EC) higher order relationship subspace clustering (SC)

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Attention in flux

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NEURON 2023年第7期111卷 971-986页

作者： Nobre, Anna C. van Ede, Freek Univ Oxford Dept Expt Psychol Oxford OX2 6GG England Univ Oxford Oxford Ctr Human Brain Act Wellcome Ctr Integrat Neuroimaging Dept Psychiat Oxford OX3 7JX England Vrije Univ Amsterdam Inst Brain & Behav Amsterdam Dept Expt & Appl Psychol NL-1081 BT Amsterdam Netherlands

Selective attention comprises essential infrastructural functions supporting cognition-anticipating, prioritizing, selecting, routing, integrating, and preparing signals to guide adaptive behavior. Most studies have examined its consequences, systems, and mechanisms in a static way, but attention is at the confluence of multiple sources of flux. The world advances, we operate within it, our minds change, and all resulting signals progress through multiple pathways within the dynamic networks of our brains. Our aim in this review is to raise awareness of and interest in three important facets of how timing impacts our understanding of attention. These include the challenges posed to attention by the timing of neural processing and psychological functions, the opportunities conferred to attention by various temporal structures in the environment, and how tracking the time courses of neural and behavioral modulations with continuous measures yields surprising insights into the workings and principles of attention.

关键词： temporal anticipation orienting expectation timing dynamics vision working memory action predictive coding

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Neural Signatures of predictive Strategies Track Individuals Along the Autism-Schizophrenia Continuum

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SCHIZOPHRENIA BULLETIN 2023年第5期49卷 1294-1304页

作者： Tarasi, Luca Martelli, Maria Eugenia Bortoletto, Marta di Pellegrino, Giuseppe Romei, Vincenzo Univ Bologna Ctr Studi & Ric Neurosci Cognit Dipartimento Psicol Alma Mater Studiorum Campus Cesenavia Rasi & Spinelli 176 I-47521 Cesena Italy IRCCS Ist Ctr San Giovanni Dio Fatebenefratelli Lab Neurofisiol Via pilastroni 4 I-25125 Brescia Italy Univ Antonio Nebrija Fac Lenguas & Educ Madrid 28015 Spain

Background and Hypothesis Humans develop a constellation of different representations of the external environment, even in the face of the same sensory exposure. According to the Bayesian framework, these differentiations could be grounded in a different weight assigned to prior knowledge vs. new external inputs in predictive inference. Since recent advances in computational psychiatry suggest that autism (ASD) and schizophrenia (SSD) lie on the two diametric poles of the same predictive continuum, the adoption of a specific inferential style could be routed by dispositional factors related to autistic and schizotypal traits. However, no studies have directly investigated the role of ASD-SSD dimension in shaping the neuro-behavioral markers underlying perceptual inference. Study Design We used a probabilistic detection task while simultaneously recording EEG to investigate whether neurobehavioral signatures related to prior processing were diametrically shaped by ASD and SSD traits in the general population (n = 80). Results We found that the position along the ASD-SSD continuum directed the predictive strategies adopted by the individuals in decision-making. While proximity to the positive schizotypy pole was associated with the adoption of the predictive approach associated to the hyper-weighting of prior knowledge, proximity to ASD pole was related to strategies that favored sensory evidence in decision-making. Conclusions These findings revealed that the weight assigned to prior knowledge is a marker of the ASD-SSD continuum, potentially useful for identifying individuals at-risk of developing mental disorders and for understanding the mechanisms contributing to the onset of symptoms observed in ASD and SSD clinical forms.

关键词： alpha oscillations perceptual decision-making predictive coding computational psychiatry

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