Estimating Information Processing of Human Fast Continuous Tapping from Trajectories

: Calculating the amount of information about a random function contained in another random function has important uses in communication theory. An expression for the mutual information for continuous time random processes has been given by Gelfand and Yaglom, Chiang, and Perez by generalizing Shannon's result in a natural way. Under a condition of absolute continuity of measures the continuous time expression has the same form as Shannon's result. For two Gaussian processes Gelfand and Yaglom express the mutual information in terms of a mean square estimation error. We generalize this result to diffusion processes and express the solution in a different form which is more naturally related to a corresponding filtering problem. We also use these results to calculate some information rates.

Information entropy analysis of discrete aiming movements

To apply information theory to more areas, the author proposed semantic information G theory, which is a natural generalization of Shannon’s information theory. This theory uses the P-T probability framework so that likelihood functions and truth functions (or membership functions), as well as sampling distributions, can be put into the semantic mutual information formula at the same time. Hence, we can connect statistics and (fuzzy) logic. Rate-distortion function R(D) becomes rate-verisimilitude function R(G) (G is the lower limit of the semantic mutual information) when the distortion function is replaced with the semantic information function. Seven difficult tasks are 1) clarifying the relationship between minimum information and maximum entropy in statistical mechanics, 2) compressing images according to visual discrimination, 3) multilabel learning for obtaining truth functions or membership functions from sampling distributions, 4) feature classifications with maximum mutual information criterion, 5) proving the convergence of the expectation-maximization algorithm for mixture models, 6) interpreting Popper’s verisimilitude and reconciling the contradiction between the content approach and the likeness approach, and 7) providing practical confirmation measures and clarifying the raven paradox. This paper simply introduces the mathematical methods for these tasks and the conclusions. The P-T probability framework and the semantic information G theory should have survived the tests. They should have broader applications. Further studies are needed for combining them with neural networks for machine learning.

ABSTRACTPreviously, we have shown that discrete and continuous rapid aiming tasks are governed by distinct visuomotor control mechanisms by assessing the combined visual illusion effects on the perceived and effective index of difficulty (ID). All participants were perceptually biased by the combined visual illusion before they performed the rapid aiming tasks. In the current study, the authors manipulated the order of performing perceptual and motor tasks to examine whether perceptual or motor experience with the illusory visual target would influence the subsequent perceived and effective ID in discrete and continuous tapping tasks. The results supported our hypothesis showing that perceptual experience with the illusory visual target in the discrete condition reduced the effective ID in the subsequent discrete tapping task, and motor experience with the illusory visual target in the continuous condition reduced the illusion effects on the perceived ID in the subsequent perceptual judgment task. The study demonstrates the coinfluence of perception and action, and suggests that perception and action influence one another with different magnitude depending on the spatial frame of reference used to perform the perceptuomotor task.

Editorial| July 2023 Anesthesia and the Merry-go-round of Information in the Brain Jamie W. Sleigh, M.D.; Jamie W. Sleigh, M.D. 1Department of Anaesthesiology, Waikato Clinical Campus, Faculty of Medical and Health Sciences, University of Auckland, Hamilton, New Zealand. Search for other works by this author on: This Site PubMed Google Scholar Logan Voss, Ph.D. Logan Voss, Ph.D. 2Department of Anaesthesiology, Waikato Clinical Campus, Faculty of Medical and Health Sciences, University of Auckland, Hamilton, New Zealand. Search for other works by this author on: This Site PubMed Google Scholar Author and Article Information This editorial accompanies the article on p. 49. Accepted for publication March 30, 2023. Address correspondence to Dr. Sleigh: jamie.sleigh@waikatodhb.health.nz Anesthesiology July 2023, Vol. 139, 4–5. https://doi.org/10.1097/ALN.0000000000004571 Connected Content Article: Measures of Information Content during Anesthesia and Emergence in the Caenorhabditis elegans Nervous System Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share Facebook Twitter LinkedIn Email Cite Icon Cite Get Permissions Search Site Citation Jamie W. Sleigh, Logan Voss; Anesthesia and the Merry-go-round of Information in the Brain. Anesthesiology 2023; 139:4–5 doi: https://doi.org/10.1097/ALN.0000000000004571 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAll PublicationsAnesthesiology Search Advanced Search Topics: brain It is widely believed that anesthesia is a syndrome caused by drug-induced disruption of coordination within and between neuronal networks. The underlying teleological assumption is that normal function of the central nervous system requires precise electrical and chemical communication between neurons, and communication implies flow of “information.” In this issue of Anesthesiology is an article by Chang et al.1 in which isoflurane was given to a nematode (Caenorhabditis elegans), and the resultant real-time changes in activity of about half of its 302 neurons were visualized using high-resolution calcium fluorescence imaging. In this study, the authors focused on how anesthesia alters interneuronal coupling by measuring the entropy of the calcium transients in the imaged neurons. The meaning of the word “entropy” is intuitively difficult. It is often used as a synonym for disorder, uncertainty, or unpredictability. To take the extreme examples: if a neuron has no activity (a... You do not currently have access to this content.

Methods originally developed for communication systems are widely used in computational neuroscience to understand the information representation and processing performed by neurons and neural circuits in the brain. Information theoretic quantities Entropy and Mutual Information (MI) have been used in neuroscience as a metric to estimate the efficiency of information representation by neurons. These quantities are used here to measure the stimulus discrimination reliability of the cerebellar granule neurons using simulated response trains produced by a multi-compartmental model of Wistar rat neuron. With ∼1011 granule neurons in the cerebellum, understanding spatio-temporal processing in such structures demands efficient, fast algorithms. Since the serial version of the algorithm had multiple estimation loops which increased the process time considerably with the problem size, we re-implemented the MI algorithm in GPGPU hardware as an efficient way of parallelizing the MI computations. Task-level parallelism and GPU optimizations were used to improve the process time. Estimates on GPGPUs showed 15X time efficiency compared to the CPU version of the algorithm. In order to understand learning inside the cerebellar circuit, synaptic plasticity conditions were simulated in the neuron model. We were able to quantify the stimulus discrimination reliability of granule neurons under control, LTP and LTD conditions and the analysis revealed that stimulus discrimination capability of the neuron was increased during high plasticity state.

Both discrete and continuous bimanual coordination patterns are difficult to effectively perform when the two limbs are required to perform different movements patterns, move at different velocities and/or move different amplitudes unless some form of integrated feedback is provided. The purpose of the present experiment was to determine the degree to which a complex bimanual coordination pattern could be performed when integrated feedback and movement template are provided. The complex bimanual coordination pattern involved reciprocal movements of the two limbs under different difficulty requirements. As defined by Fitts' index of difficulty (ID), the left arm (ID = 3, A = 16°, W = 4°) task was of lower difficulty than the right arm task (ID = 5, A = 32°, W = 2°). Note that the left and right limb movements are also different in terms of movement time, movement velocity, accuracy requirements and amplitude as well as one movement was continuous and the other intermittent. Participants were provided 2 blocks of 9 trials in the bimanual condition (30 s/trial). Following the bimanual phase, participants performed two unimanual test trials-one with each limb. The results demonstrated that the performance for each limb in the bimanual condition was similar to the performance for the same limb and conditions in the unimanual control conditions. The similarity was indicated by the same movement speed, movement structure, endpoint variability and hit rates for the bimanual and unimanual conditions. The results support our hypothesis that people can overcome the intrinsic difficulties associated with performing complex bimanual coordination patterns when provided appropriate perceptual information feedback that allows them to detect and correct coordination errors.

Ocean model simulations show variability due to intrinsic chaos and external forcing (air‐sea fluxes, river input, etc.). It is important to estimate their contributions to total variability for attribution. Using variance to estimate variability might be unreliable due to non‐Gaussian higher statistical moments. We show the use of non‐parametric information theory metrics, Shannon entropy and mutual information, for measuring internal and forced variability in ocean models. These metrics are applied to spatially and temporally averaged data. The metrics delineate relative intrinsic to total variability in a wider range of circumstances than previous approaches based on variance ratios. The metrics are applied to (a) a synthetic ensemble of random vectors, (b) ocean component of a global climate (GFDL‐ESM2M) large ensemble, (c) ensemble of a realistic coastal ocean model. The information theory metric qualitatively agrees with the variance‐based metric and possibly identifies regions of nonlinear correlations. In application (2)–the climate ensemble–the information theory metric detects higher temperature intrinsic variability in the Arctic region compared to the variance metric illustrating that the former is robust in a skewed probability distribution (Arctic sea surface temperature) resulting from sharply nonlinear behavior (freezing point). In application (3)–coastal ensemble–variability is dominated by external forcing. Using different selective forcing ensembles, we quantify the sensitivity of the coastal model to different types of external forcing: variations in the river runoff and changes in wind product do not add information (i.e., variability) during summer. Information theory enables ranking how much each forcing type contributes across multiple variables.

A new approach to the problem of multimodality medical image registration is proposed, using a basic concept from information theory, mutual information (MI), or relative entropy, as a new matching criterion. The method presented in this paper applies MI to measure the statistical dependence or information redundancy between the image intensities of corresponding voxels in both images, which is assumed to be maximal if the images are geometrically aligned. Maximization of MI is a very general and powerful criterion, because no assumptions are made regarding the nature of this dependence and no limiting constraints are imposed on the image content of the modalities involved. The accuracy of the MI criterion is validated for rigid body registration of computed tomography (CT), magnetic resonance (MR), and photon emission tomography (PET) images by comparison with the stereotactic registration solution, while robustness is evaluated with respect to implementation issues, such as interpolation and optimization, and image content, including partial overlap and image degradation. Our results demonstrate that subvoxel accuracy with respect to the stereotactic reference solution can be achieved completely automatically and without any prior segmentation, feature extraction, or other preprocessing steps which makes this method very well suited for clinical applications.

Many scholars researched social informatization from different perspectives, including profession, economy and technology. In this thesis, the author will studied on social informatization from the perspective of basic characteristics of information. Firstly, the author will describe other scholars’ social informationization theories. Secondly, the definition of information and basic characteristics of information will be given. Then, the production mode informatization and life-style informatization from the perspective of basic characteristics of information will be studied on in this paper. Lastly, the conclusion will be drawn.

Attribute selection in an information system is one of the important applications of rough set theory. This paper studies attribute selection for heterogeneous data based on information entropy. We first define information entropy in an information system with heterogeneous data and then put forward the notions of joint information entropy, conditional information entropy and mutual information entropy in a decision information system with heterogeneous data. We apply information entropy to perform attribute selection in a decision information system with heterogeneous data. We propose two attribute selection algorithms based on information entropy. Finally, we make experimental analysis and comparisons to illustrate the feasibility and efficiency of the proposed algorithms.

This paper investigates the evolution of information entropy (IE) in Hermite–Gaussian correlated Schell-model (HGcSM) beams propagating through a gradient-index (GRIN) fiber using Shannon information theory. Our results reveal that the IE of such beams evolves periodically, with the beam order significantly influencing its initial distribution. Compared with traditional Gaussian Schell-model beams, HGcSM beams exhibit more complex IE dynamics, characterized by periodically emerging low-entropy regions whose IE decreases with increasing beam order. Furthermore, the fiber’s central refractive index and core radius strongly affect the evolution period and fluctuation amplitude of IE. These findings provide a theoretical basis for optimizing partially coherent beams in optical fiber applications.

An important challenge in the study of functional corticomuscular coupling (FCMC) is an accurate capture of the coupling relationship between the cerebral cortex and the effector muscle. The coherence method is a linear analysis method, which has certain limitations in further revealing the nonlinear coupling between neural signals. Although mutual information (MI) and transfer entropy (TE) based on information theory can capture both linear and nonlinear correlations, the equitability of these algorithms is ignored and the nonlinear components of the correlation cannot be separated. The maximal information coefficient (MIC) is a suitable method to measure the coupling between neurophysiological signals. This study extends the MIC to the time-frequency domain, named time-frequency maximal information coefficient (TFMIC), to explore the FCMC in a specific frequency band. The effectiveness, equitability, and robustness of the algorithm on the simulation data was verified and compared with coherence, TE- and MI- based methods. Simulation results showed that the TFMIC could accurately detect the coupling for different functional relationships at low noise levels. The dorsiflexion experimental results revealed that the beta-band (14-30 Hz) significant coupling was observed at channels Cz, C4, FC4, and FCz. Additionally, the results showed that the coupling was higher in the alpha-band (8-13 Hz) and beta-band (14-30 Hz) than in the gamma-band (31-45 Hz). This might be related to a transition between sensorimotor states. Specifically, the nonlinear component of FCMC was also observed at channels Cz, C4, FC4, and FCz. This study expanded the research on nonlinear coupling components in FCMC.

Calculations of entropy of a signal or mutual information between two variables are valuable analytical tools in the field of neuroscience. They can be applied to all types of data, capture non-linear interactions and are model independent. Yet the limited size and number of recordings one can collect in a series of experiments makes their calculation highly prone to sampling bias. Mathematical methods to overcome this so-called “sampling disaster” exist, but require significant expertise, great time and computational costs. As such, there is a need for a simple, unbiased and computationally efficient tool for estimating the level of entropy and mutual information. In this article, we propose that application of entropy-encoding compression algorithms widely used in text and image compression fulfill these requirements. By simply saving the signal in PNG picture format and measuring the size of the file on the hard drive, we can estimate entropy changes through different conditions. Furthermore, with some simple modifications of the PNG file, we can also estimate the evolution of mutual information between a stimulus and the observed responses through different conditions. We first demonstrate the applicability of this method using white-noise-like signals. Then, while this method can be used in all kind of experimental conditions, we provide examples of its application in patch-clamp recordings, detection of place cells and histological data. Although this method does not give an absolute value of entropy or mutual information, it is mathematically correct, and its simplicity and broad use make it a powerful tool for their estimation through experiments.

With regional socio-economic development, the gap between water use demand and available water resources in arid and humid semi-arid areas becomes increasingly serious. In this study, the size of regional water use in the Guanzhong region and Shiyang river basin in northwest China are analyzed to identify important factors affecting it, with the aim of providing better and optimal development planning for the region. Information entropy is used to measure and characterize the diversity of regional water use. Agricultural development and meteorological factors are found to be the main issues affecting regional water use in both regions. A multiple-linear regression (MLR) model was built by applying correlation coefficient (R) and mutual information (MI) scores in the process. Results show that the low value of information entropy of water use in the Shiyang river basin is due to the high proportion of agriculture water use. Using input factors chosen by MI score was found to be the best model to simulate the change of regional water use in both regions. A method using an MLR model together with MI is shown to be able to quantify the relationship between the influencing factors and water use diversity with limited available data.