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predictive coding: An account of the mirror neuron system

Cognitive Processing 2007年第3期8卷 159-166页

作者： Kilner, James M. Friston, Karl J. Frith, Chris D. Wellcome Trust Centre for Neuroimaging Institute of Neurology London WC1N 3BG 12 Queen Square United Kingdom

Is it possible to understand the intentions of other people by simply observing their actions? Many believe that this ability is made possible by the brain's mirror neuron system through its direct link between action and observation. However, precisely how intentions can be inferred through action observation has provoked much debate. Here we suggest that the function of the mirror system can be understood within a predictive coding framework that appeals to the statistical approach known as empirical Bayes. Within this scheme the most likely cause of an observed action can be inferred by minimizing the prediction error at all levels of the cortical hierarchy that are engaged during action observation. This account identifies a precise role for the mirror system in our ability to infer intentions from actions and provides the outline of the underlying computational mechanisms. © 2007 Marta Olivetti Belardinelli and Springer-Verlag.

关键词： Action observation Bayesian inference Mirror neurons predictive coding

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predictive coding-based Deep Dynamic Neural Network for Visuomotor Learning 7

Predictive Coding-based Deep Dynamic Neural Network for Visu...

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7th joint IEEE International Conference on Development Learning and Epigenetic Robotic (IEEE ICDL-EpiRob)

作者： Hwang, Jungsik Kim, Jinhyung Ahmadi, Ahmadreza Choi, Minkyu Tani, Jun Korea Adv Inst Sci & Technol Daejeon South Korea Okinawa Inst Sci Technol Kunigami Dist Okinawa Japan

ISBN: (纸本)9781538637159

This study presents a dynamic neural network model based on the predictive coding framework for perceiving and predicting the dynamic visuo-proprioceptive patterns. In our previous study [1], we have shown that the deep dynamic neural network model was able to coordinate visual perception and action generation in a seamless manner. In the current study, we extended the previous model under the predictive coding framework to endow the model with a capability of perceiving and predicting dynamic visuo-proprioceptive patterns as well as a capability of inferring intention behind the perceived visuomotor information through minimizing prediction error. A set of synthetic experiments were conducted in which a robot learned to imitate the gestures of another robot in a simulation environment. The experimental results showed that with given intention states, the model was able to mentally simulate the possible incoming dynamic visuo-proprioceptive patterns in a top-down process without the inputs from the external environment. Moreover, the results highlighted the role of minimizing prediction error in inferring underlying intention of the perceived visuo-proprioceptive patterns, supporting the predictive coding account of the mirror neuron systems. The results also revealed that minimizing prediction error in one modality induced the recall of the corresponding representation of another modality acquired during the consolidative learning of raw-level visuo-proprioceptive patterns.

关键词： Cognitive robotics dynamic neural network predictive coding visuomotor learning

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predictive coding in agency detection

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RELIGION BRAIN & BEHAVIOR 2019年第1期9卷 65-84页

作者： Andersen, Marc Aarhus Univ Dept Culture & Soc Aarhus Denmark Aarhus Univ Relig Cognit & Culture Aarhus Denmark Aarhus Univ Interacting Minds Ctr Aarhus Denmark

Agency detection is a central concept in the cognitive science of religion (CSR). Experimental studies, however, have so far failed to lend support to some of the most common predictions that follow from current theories on agency detection. In this article, I argue that predictive coding, a highly promising new framework for understanding perception and action, may solve pending theoretical inconsistencies in agency detection research, account for the puzzling experimental findings mentioned above, and provide hypotheses for future experimental testing. predictive coding explains how the brain, unbeknownst to consciousness, engages in sophisticated Bayesian statistics in an effort to constantly predict the hidden causes of sensory input. My fundamental argument is that most false positives in agency detection can be seen as the result of top-down interference in a Bayesian system generating high prior probabilities in the face of unreliable stimuli, and that such a system can better account for the experimental evidence than previous accounts of a dedicated agency detection system. Finally, I argue that adopting predictive coding as a theoretical framework has radical implications for the effects of culture on the detection of supernatural agency and a range of other religious and spiritual perceptual phenomena.

关键词： Agency detection HADD predictive coding perception supernatural agents religion epidemiology cognitive science of religion

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predictive coding OF CU QUADTREE STRUCTURE FOR HEVC QUALITY SCALABILITY

PREDICTIVE CODING OF CU QUADTREE STRUCTURE FOR HEVC QUALITY ...

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20th IEEE International Conference on Image Processing (ICIP)

作者： Won, Kwanghyun Lee, Hoyoung Park, Jeonghoon Jeon, Byeungwoo Sungkyunkwan Univ Dept Elect & Elect Engn Suwon South Korea Samsung Elect Co Ltd Suwon South Korea

ISBN: (纸本)9781479923410

Scalability in video coding is an effective functionality to serve various video contents at different levels of resolution and quality. Based on the High Efficiency Video coding (HEVC) standard which achieves superior compression performance compared to the H.264/AVC, this paper proposes a new scalable coding method with a coding unit (CU) structure prediction technique for HEVC-based quality scalability. The CU structure in enhancement layer (EL) is differentially encoded using that of the basement layer (BL) as a predictor. The binary values describing the CU structure of BL are operated exclusive-OR (XOR) with those in EL at each CU depth and position. Compared to the simulcast coding, simulation without having any residual prediction technique verifies that the proposed method gains in bit-saving by 0.4% on average.

关键词： SVC HEVC coding unit exclusive-OR predictive coding

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predictive coding FOR ANIMATION-BASED VIDEO COMPRESSION 30

PREDICTIVE CODING FOR ANIMATION-BASED VIDEO COMPRESSION

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30th IEEE International Conference on Image Processing (ICIP)

作者： Konuko, Goluck Lathuiliere, Stephane Valenzise, Giuseppe Univ Paris Saclay Cent Supelec Lab Signaux & Syst Paris France Inst Polytech Paris Telecom Paris LTCI Paris France

ISBN: (纸本)9781728198354

We address the problem of efficiently compressing video for conferencing-type applications. We build on recent approaches based on image animation, which can achieve good reconstruction quality at very low bitrate by representing face motions with a compact set of sparse keypoints. However, these methods encode video in a frame-by-frame fashion, i.e., each frame is reconstructed from a reference frame, which limits the reconstruction quality when the bandwidth is larger. Instead, we propose a predictive coding scheme which uses image animation as a predictor, and codes the residual with respect to the actual target frame. The residuals can be in turn coded in a predictive manner, thus removing efficiently temporal dependencies. Our experiments indicate a significant bitrate gain, in excess of 70% compared to the HEVC video standard and over 30% compared to VVC, on a dataset of talking-head videos.

关键词： Video compression image animation generative models video conferencing predictive coding

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predictive coding FOR LOSSLESS DATASET COMPRESSION

PREDICTIVE CODING FOR LOSSLESS DATASET COMPRESSION

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IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

作者： Barowsky, Madeleine Mariona, Alexander Calmon, Flavio P. Harvard Univ Cambridge MA 02138 USA

ISBN: (纸本)9781728176055

Lossless compression of datasets is a problem of significant theoretical and practical interest. It appears naturally in the task of storing, sending, or archiving large collections of information for scientific research. We can greatly improve encoding bitrate if we allow the compression of the original dataset to decompress to a permutation of the data. We prove the equivalence of dataset compression to compressing a permutation-invariant structure of the data and implement such a scheme via predictive coding. We benchmark our compression procedure against state-of-the-art compression utilities on the popular machine-learning datasets MNIST and CIFAR-10 and outperform for multiple parameter sets.

关键词： Compression source coding dataset predictive coding

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On the role of feedback in image recognition under noise and adversarial attacks: A predictive coding perspective

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NEURAL NETWORKS 2023年 157卷 280-287页

作者： Alamia, Andrea Mozafari, Milad Choksi, Bhavin VanRullen, Rufin CNRS CerCo F-31052 Toulouse France Univ Toulouse ANITI F-31062 Toulouse France CNRS IRIT F-31062 Toulouse France

Brain-inspired machine learning is gaining increasing consideration, particularly in computer vision. Several studies investigated the inclusion of top-down feedback connections in convolutional networks;however, it remains unclear how and when these connections are functionally helpful. Here we address this question in the context of object recognition under noisy conditions. We consider deep convolutional networks (CNNs) as models of feed-forward visual processing and implement predictive coding (PC) dynamics through feedback connections (predictive feedback) trained for reconstruction or classification of clean images. First, we show that the accuracy of the network implementing PC dynamics is significantly larger compared to its equivalent forward network. Importantly, to directly assess the computational role of predictive feedback in various experimental situations, we optimize and interpret the hyper-parameters controlling the network's recurrent dynamics. That is, we let the optimization process determine whether top-down connections and predictive coding dynamics are functionally beneficial. Across different model depths and architectures (3-layer CNN, ResNet18, and EfficientNetB0) and against various types of noise (CIFAR100-C), we find that the network increasingly relies on top-down predictions as the noise level increases;in deeper networks, this effect is most prominent at lower layers. All in all, our results provide novel insights relevant to Neuroscience by confirming the computational role of feedback connections in sensory systems, and to Machine Learning by revealing how these can improve the robustness of current vision models. (c) 2022 Elsevier Ltd. All rights reserved.

关键词： predictive coding Deep learning Feedback Corruption robustness

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Learning a Deep predictive coding Network for a Semi-Supervised 3D-Hand Pose Estimation

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IEEE/CAA Journal of Automatica Sinica 2020年第5期7卷 1371-1379页

作者： Jamal Banzi Isack Bulugu Zhongfu Ye Sokoine University Morogoro 3000Tanzania University of Dar-es-Salaam Dar-es-Salaam 35091Tanzania Department of Electronic Engineering and Information Science University of Science and Technology of ChinaHefei 230026China IEEE

In this paper we present a CNN based approach for a real time 3 D-hand pose estimation from the depth *** discriminative approaches have achieved remarkable success but are facing two main challenges:Firstly,the methods are fully supervised hence require large numbers of annotated training data to extract the dynamic information from a hand ***,unreliable hand detectors based on strong assumptions or a weak detector which often fail in several situations like complex environment and multiple *** contrast to these methods,this paper presents an approach that can be considered as semi-supervised by performing predictive coding of image sequences of hand poses in order to capture latent features underlying a given image without *** hand is modelled using a novel latent tree dependency model(LDTM)which transforms internal joint location to an explicit *** the modeled hand topology is integrated with the pose estimator using data dependent method to jointly learn latent variables of the posterior pose appearance and the pose configuration ***,an unsupervised error term which is a part of the recurrent architecture ensures smooth estimations of the final *** on three challenging public datasets,ICVL,MSRA,and NYU demonstrate the significant performance of the proposed method which is comparable or better than state-of-the-art approaches.

关键词： Convolutional neural networks deep learning hand pose estimation human-machine interaction predictive coding recurrent neural networks unsupervised learning

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predictive coding with Topographic Variational Autoencoders 18

Predictive Coding with Topographic Variational Autoencoders

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18th IEEE/CVF International Conference on Computer Vision (ICCV)

作者： Keller, T. Anderson Welling, Max Univ Amsterdam UvA Bosch Delta Lab Amsterdam Netherlands

ISBN: (纸本)9781665401913

predictive coding is a model of visual processing which suggests that the brain is a generative model of input, with prediction error serving as a signal for both learning and attention. In this work, we show how the equivariant capsules learned by a Topographic Variational Autoencoder can be extended to fit within the predictive coding framework by treating the slow rolling of capsule activations as the forward prediction operator. We demonstrate quantitatively that such an extension leads to improved sequence modeling compared with both topographic and non-topographic baselines, and that the resulting forward predictions are qualitatively more coherent with the provided partial input transformations.

关键词： Visualization Computer vision Conferences Computational modeling predictive models predictive coding Brain modeling

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Energy-information trade-off induces continuous and discontinuous phase transitions in lateral predictive coding

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Science China(Physics,Mechanics & Astronomy) 2024年第6期67卷 79-85页

作者： Zhen-Ye Huang Ruyi Zhou Miao Huang Hai-Jun Zhou Key Laboratory for Theoretical Physics Institute of Theoretical PhysicsChinese Academy of SciencesBeijing 100190China School of Physical Sciences University of Chinese Academy of SciencesBeijing 100049China School of Optics and Photonics Beijing Institute of TechnologyBeijing 100081China Minjiang Collaborative Center for Theoretical Physics Minjiang UniversityFuzhou 350108China

Lateral predictive coding is a recurrent neural network that creates energy-efficient internal representations by exploiting statistical regularity in sensory ***,we analytically investigate the trade-off between information robustness and energy in a linear model of lateral predictive coding and numerically minimize a free energy *** observed several phase transitions in the synaptic weight matrix,particularly a continuous transition that breaks reciprocity and permutation symmetry and builds cyclic dominance and a discontinuous transition with the associated sudden emergence of tight balance between excitatory and inhibitory *** optimal network follows an ideal gas law over an extended temperature range and saturates the efficiency upper bound of energy *** results provide theoretical insights into the emergence and evolution of complex internal models in predictive processing systems.

关键词： predictive coding recurrent neural network phase transition internal model free energy

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