Mud pulse transmission technology is widely used in logging while drilling (LWD), while its low data transmission efficiency has restricted the development of LWD technology for a long time. In order to improve the eq...
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Mud pulse transmission technology is widely used in logging while drilling (LWD), while its low data transmission efficiency has restricted the development of LWD technology for a long time. In order to improve the equivalent data transmission efficiency of LWD system, a data compression method, namely, Frame Prediction Huffman coding (FPHC) with a group of frames, is proposed in this paper. Several factors including the data accuracy, the need for real-time transmission, and the existence of transmission errors are comprehensively considered in the proposed method. Through the probability statistical analysis of LWD data, it is proved that the data have a relatively stable probability distribution after predictive coding. In addition, the Huffman code table generated from predictive coding is universally applicable to other LWD data. Through data compression experiments on logging data, it is verified that the proposed method has higher compression efficiency compared with Huffman coding, Lempel-Ziv-Welch algorithm, adaptive Huffman coding and adaptive arithmetic coding. This lossless compression method will have a good application prospect because of its ability to prevent error diffusion, simple implementation and easy to be embedded in down-hole equipment.
Historically, data from brain imaging and brain stimulation studies have supported the idea that the processing of observed actions recruits - among other areas - a distinct sub-set of brain sites in the sensory and m...
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Historically, data from brain imaging and brain stimulation studies have supported the idea that the processing of observed actions recruits - among other areas - a distinct sub-set of brain sites in the sensory and motor cortices. These empirical findings have initially been linked with the thesis of direct matching as a mechanism of action understanding, i.e., the idea of motor resonance implemented by mirror neurons. In more recent approaches, it has been proposed that the mirror neuron system plays a role in minimizing prediction error when inferring the most likely cause of an observed action. According to these theories, motor resonance is thought to function as predictive coding. Other theoretical accounts suggest that action understanding might result from a hypothesis testing mechanism in which potential goals are continually fed into the system until the correct one is identified. In this review, we will explore the relationship of these theories to specific empirical findings. Finally, we will discuss the implications of these theoretical structures on action observation-based approaches to the optimization of skilled performance in athletes and patients. (C) 2011 Elsevier Ltd. All rights reserved.
An image sequence coding scheme for transmission at 384 kbits/s and 64 kbits/s is presented. In comparison to known schemes, picture quality in still picture areas is improved without impairing the rendition of tempor...
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An image sequence coding scheme for transmission at 384 kbits/s and 64 kbits/s is presented. In comparison to known schemes, picture quality in still picture areas is improved without impairing the rendition of temporally changed areas. Bit-rate reduction is obtained by a conventional hybrid DCT coding technique in changed areas, and a predictive coding technique using a raster interleaving approach to update still picture areas. Preferentially treating the image signal in still areas with a not-block-oriented coder yields in these regions a reconstruction with full spatial resolution and free of any block effects. Experimental results are presented confirming that the quality in still picture areas is improved compared to that of known low bit-rate image sequence coding schemes, while the quality in changed areas is maintained.
When a bistable visual image, such as the Necker cube, is continuously viewed, the percept of the image endogenously alternates between one possible percept and the other. However, perceptual alternation can also be i...
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When a bistable visual image, such as the Necker cube, is continuously viewed, the percept of the image endogenously alternates between one possible percept and the other. However, perceptual alternation can also be induced by an exogenous perturbation. For example, a typical external perturbation is the flashlight, which is expected to pervasively activate many brain regions. Therefore, the neural mechanism related to exogenous perceptual alternation remains to be clarified. As a cue to solving this problem, our recent psychophysiological experiment reported a positive correlation between the enhancement of visual mismatch negativity evoked by breaks in the sequential regularity of the visual stimuli and the proportion of perceptual alternation. To elucidate the mechanism underlying exogenous perceptual alternation induced by visual mismatch negativity, the present study attempted to construct a neural network model for bistable perception of the Necker cube, whose perceptual alternation is facilitated by an increase in visual mismatch negativity. The model consists of both a prediction layer and a prediction error layer, following the predictive coding framework for biologically plausible relationships between the change detection process and the perceptual alternation mechanism. Computer simulations showed that the mean duration of perception decreased as the response increased, which is in concordance with the experimental data. This result suggested that the excitatory feedforward and inhibitory feedback connections play an important role. Additionally, the validity of this model suggests that the visual mismatch signal propagates in the neural systems and affects the visual perceptual mechanism as a prediction error signal.
Magic tricks have enjoyed an increasing interest by scientists. However, most research in magic focused on isolated aspects of it and a conceptual understanding of magic, encompassing its distinct components and varie...
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Magic tricks have enjoyed an increasing interest by scientists. However, most research in magic focused on isolated aspects of it and a conceptual understanding of magic, encompassing its distinct components and varieties, is missing. Here, we present an account of magic within the theory of Bayesian predictive coding. We present the "wow" effect of magic as an increase in surprise evoked by the prediction error between expected and observed data. We take into account prior knowledge of the observer, attention, and (mis-)direction of perception and beliefs by the magician to bias the observer's predictions and present a simple example for the modelling of the evoked surprise. The role of misdirection is described as everything that aims to maximize the surprise a trick evokes by the generation of novel beliefs, the exploitation of background knowledge and attentional control of the incoming information. Understanding magic within Bayesian predictive coding allows unifying all aspects of magic tricks within one framework, making it tractable, comparable and unifiable with other models in psychology and neuroscience.
The cerebral cortex performs computations via numerous six-layer modules. The operational dynamics of these modules were studied primarily in early sensory cortices using bottom-up computation for response selectivity...
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The cerebral cortex performs computations via numerous six-layer modules. The operational dynamics of these modules were studied primarily in early sensory cortices using bottom-up computation for response selectivity as a model, which has been recently revolutionized by genetic approaches in mice. However, cognitive processes such as recall and imagery require top-down generative computation. The question of whether the layered module operates similarly in top-down generative processing as in bottom-up sensory processing has become testable by advances in the layer identification of recorded neurons in behaving monkeys. This review examines recent advances in laminar signaling in these two computations, using predictive coding computation as a common reference, and shows that each of these computations recruits distinct laminar circuits, particularly in layer 5, depending on the cognitive demands. These findings highlight many open questions, including how different interareal feedback pathways, originating from and terminating at different layers, convey distinct functional signals.
predictive coding (PC) theory posits that our brain employs a predictive model of the environment to infer the causes of its sensory inputs. A fundamental but untested prediction of this theory is that the same stimul...
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predictive coding (PC) theory posits that our brain employs a predictive model of the environment to infer the causes of its sensory inputs. A fundamental but untested prediction of this theory is that the same stimulus should elicit distinct precision weighted prediction errors (pwPEs) when different (feature-specific) predictions are violated, even in the absence of attention. Here, we tested this hypothesis using functional magnetic resonance imaging (fMRI) and a multi-feature roving visual mismatch paradigm where rare changes in either color (red, green), or emotional expression (happy, fearful) of faces elicited pwPE responses in human participants. Using a computational model of learning and inference, we simulated pwPE and prediction trajectories of a Bayes-optimal observer and used these to analyze changes in blood oxygen level dependent (BOLD) responses to changes in color and emotional expression of faces while participants engaged in a distractor task. Controlling for visual attention by eye-tracking, we found pwPE responses to unexpected color changes in the fusiform gyrus. Conversely, unexpected changes of facial emotions elicited pwPE responses in cortico-thalamo-cerebellar structures associated with emotion and theory of mind processing. Predictions pertaining to emotions activated fusiform, occipital and temporal areas. Our results are consistent with a general role of PC across perception, from low-level to complex and socially relevant object features, and suggest that monitoring of the social environment occurs continuously and automatically, even in the absence of attention.
Accurate perception of the environment is a constructive process that requires inte-gration of external bottom-up sensory signals with internally generated top-down information. Decades of work have elucidated how sen...
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Accurate perception of the environment is a constructive process that requires inte-gration of external bottom-up sensory signals with internally generated top-down information. Decades of work have elucidated how sensory neocortex processes physical stimulus features. By contrast, examining how top-down information is encoded and integrated with bottom-up signals has been challenging using tradi-tional neuroscience methods. Recent technological advances in functional imaging of brain-wide afferents in behaving mice have enabled the direct measurement of top-down information. Here, we review the emerging literature on encoding of these internally generated signals by different projection systems enriched in neo-cortical layer 1 during defined brain functions, including memory, attention, and pre-dictive coding. Moreover, we identify gaps in current knowledge and highlight future directions for this rapidly advancing field.
Optimal perceptual decisions require sensory signals to be combined with prior information about stimulus probability. Although several theories propose that probabilistic information about stimulus occurrence is enco...
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Optimal perceptual decisions require sensory signals to be combined with prior information about stimulus probability. Although several theories propose that probabilistic information about stimulus occurrence is encoded in sensory cortex, evidence from neuronal recordings has not yet fully supported this view. We recorded activity from single neurons in inferior temporal cortex (IT) while monkeys performed a task that involved discriminating degraded images of faces and fruit. The relative probability of the cue being a face versus a fruit was manipulated by a latent variable that was not revealed to the monkeys and that changed unpredictably over the course of each recording session. In addition to responding to stimulus identity (face or fruit), population responses in IT encoded the long-term stimulus probability of whether a face or a fruit stimulus was more likely to occur. Face-responsive neurons showed reduced firing rates to expected faces, an effect consistent with "expectation suppression," but expected stimuli were decoded from multivariate population signals with greater accuracy. These findings support "predictive coding" theories, whereby neural signals in the mammalian visual system actively encode and update predictions about the local sensory environment.
Despite similar behavioral effects, attention and expectation influence evoked responses differently: Attention typically enhances event-related responses, whereas expectation reduces them. This dissociation has been ...
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Despite similar behavioral effects, attention and expectation influence evoked responses differently: Attention typically enhances event-related responses, whereas expectation reduces them. This dissociation has been reconciled under predictive coding, where prediction errors are weighted by precision associated with attentional modulation. Here, we tested the predictive coding account of attention and expectation using magnetoencephalography and modeling. Temporal attention and sensory expectation were orthogonally manipulated in an auditory mismatch paradigm, revealing opposing effects on evoked response amplitude. Mismatch negativity (MMN) was enhanced by attention, speaking against its supposedly pre-attentive nature. This interaction effect was modeled in a canonical microcircuit using dynamic causal modeling, comparing models with modulation of extrinsic and intrinsic connectivity at different levels of the auditory hierarchy. While MMN was explained by recursive interplay of sensory predictions and prediction errors, attention was linked to the gain of inhibitory interneurons, consistent with its modulation of sensory precision.
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