Network Psychometrics

This chapter provides a general introduction of network modeling in psychometrics. The chapter starts with an introduction to the statistical model formulation of pairwise Markov random fields (PMRF), followed by an introduction of the PMRF suitable for binary data: the Ising model. The Ising model is a model used in ferromagnetism to explain phase transitions in a field of particles. Following the description of the Ising model in statistical physics, the chapter continues to show that the Ising model is closely related to models used in psychometrics. The Ising model can be shown to be equivalent to certain kinds of logistic regression models, loglinear models and multi-dimensional item response theory (MIRT) models. The equivalence between the Ising model and the MIRT model puts standard psychometrics in a new light and leads to a strikingly different interpretation of well-known latent variable models. The chapter gives an overview of methods that can be used to estimate the Ising model, and concludes with a discussion on the interpretation of latent variables given the equivalence between the Ising model and MIRT.Comment: In Irwing, P., Hughes, D., and Booth, T. (2018). The Wiley Handbook of Psychometric Testing, 2 Volume Set: A Multidisciplinary Reference on Survey, Scale and Test Development. New York: Wile

Feature-Specific Patterns of Attention and Functional Connectivity in Human Visual Cortex

The ability to successfully allocate attention to a particular space or feature in the visual world is vital for successful day-to-day functioning. Attention refers to a narrowing of focus, with increased processing of an attended attribute at the expense of other non-attended dimensions. This attentional mechanism can modulate activity in the visual cortex and beyond. However, the full range of spatial scales at which attentional effects are evident in the visual cortex as a function of task is still relatively little understood. This thesis aimed to investigate the effects of attentional modulation across the visual cortex at several spatial scales, examining activation at the level of mean activity in individual regions-of-interest (ROIs), comparing patterns of voxel-level activity, and employing connectivity-style approaches to examine communication between multiple visual areas simultaneously

Entropy-based parametric estimation of spike train statistics

We consider the evolution of a network of neurons, focusing on the asymptotic behavior of spikes dynamics instead of membrane potential dynamics. The spike response is not sought as a deterministic response in this context, but as a conditional probability : "Reading out the code" consists of inferring such a probability. This probability is computed from empirical raster plots, by using the framework of thermodynamic formalism in ergodic theory. This gives us a parametric statistical model where the probability has the form of a Gibbs distribution. In this respect, this approach generalizes the seminal and profound work of Schneidman and collaborators. A minimal presentation of the formalism is reviewed here, while a general algorithmic estimation method is proposed yielding fast convergent implementations. It is also made explicit how several spike observables (entropy, rate, synchronizations, correlations) are given in closed-form from the parametric estimation. This paradigm does not only allow us to estimate the spike statistics, given a design choice, but also to compare different models, thus answering comparative questions about the neural code such as : "are correlations (or time synchrony or a given set of spike patterns, ..) significant with respect to rate coding only ?" A numerical validation of the method is proposed and the perspectives regarding spike-train code analysis are also discussed.Comment: 37 pages, 8 figures, submitte

A Geneaology of Correspondence Analysis: Part 2 - The Variants

In 2012, a comprehensive historical and genealogical discussion of correspondence analysis was published in Australian and New Zealand Journal of Statistics. That genealogy consisted of more than 270 key books and articles and focused on an historical development of the correspondence analysis,a statistical tool which provides the analyst with a visual inspection of the association between two or more categorical variables. In this new genealogy, we provide a brief overview of over 30 variants of correspondence analysis that now exist outside of the traditional approaches used to analysethe association between two or more categorical variables. It comprises of a bibliography of a more than 300 books and articles that were not included in the 2012 bibliography and highlights the growth in the development ofcorrespondence analysis across all areas of research

State-Space Analysis of Time-Varying Higher-Order Spike Correlation for Multiple Neural Spike Train Data

Precise spike coordination between the spiking activities of multiple neurons is suggested as an indication of coordinated network activity in active cell assemblies. Spike correlation analysis aims to identify such cooperative network activity by detecting excess spike synchrony in simultaneously recorded multiple neural spike sequences. Cooperative activity is expected to organize dynamically during behavior and cognition; therefore currently available analysis techniques must be extended to enable the estimation of multiple time-varying spike interactions between neurons simultaneously. In particular, new methods must take advantage of the simultaneous observations of multiple neurons by addressing their higher-order dependencies, which cannot be revealed by pairwise analyses alone. In this paper, we develop a method for estimating time-varying spike interactions by means of a state-space analysis. Discretized parallel spike sequences are modeled as multi-variate binary processes using a log-linear model that provides a well-defined measure of higher-order spike correlation in an information geometry framework. We construct a recursive Bayesian filter/smoother for the extraction of spike interaction parameters. This method can simultaneously estimate the dynamic pairwise spike interactions of multiple single neurons, thereby extending the Ising/spin-glass model analysis of multiple neural spike train data to a nonstationary analysis. Furthermore, the method can estimate dynamic higher-order spike interactions. To validate the inclusion of the higher-order terms in the model, we construct an approximation method to assess the goodness-of-fit to spike data. In addition, we formulate a test method for the presence of higher-order spike correlation even in nonstationary spike data, e.g., data from awake behaving animals. The utility of the proposed methods is tested using simulated spike data with known underlying correlation dynamics. Finally, we apply the methods to neural spike data simultaneously recorded from the motor cortex of an awake monkey and demonstrate that the higher-order spike correlation organizes dynamically in relation to a behavioral demand

14th Conference on DATA ANALYSIS METHODS for Software Systems

DAMSS-2023 is the 14th International Conference on Data Analysis Methods for Software Systems, held in Druskininkai, Lithuania. Every year at the same venue and time. The exception was in 2020, when the world was gripped by the Covid-19 pandemic and the movement of people was severely restricted. After a year’s break, the conference was back on track, and the next conference was successful in achieving its primary goal of lively scientific communication. The conference focuses on live interaction among participants. For better efficiency of communication among participants, most of the presentations are poster presentations. This format has proven to be highly effective. However, we have several oral sections, too. The history of the conference dates back to 2009 when 16 papers were presented. It began as a workshop and has evolved into a well-known conference. The idea of such a workshop originated at the Institute of Mathematics and Informatics, now the Institute of Data Science and Digital Technologies of Vilnius University. The Lithuanian Academy of Sciences and the Lithuanian Computer Society supported this idea, which gained enthusiastic acceptance from both the Lithuanian and international scientific communities. This year’s conference features 84 presentations, with 137 registered participants from 11 countries. The conference serves as a gathering point for researchers from six Lithuanian universities, making it the main annual meeting for Lithuanian computer scientists. The primary aim of the conference is to showcase research conducted at Lithuanian and foreign universities in the fields of data science and software engineering. The annual organization of the conference facilitates the rapid exchange of new ideas within the scientific community. Seven IT companies supported the conference this year, indicating the relevance of the conference topics to the business sector. In addition, the conference is supported by the Lithuanian Research Council and the National Science and Technology Council (Taiwan, R. O. C.). The conference covers a wide range of topics, including Applied Mathematics, Artificial Intelligence, Big Data, Bioinformatics, Blockchain Technologies, Business Rules, Software Engineering, Cybersecurity, Data Science, Deep Learning, High-Performance Computing, Data Visualization, Machine Learning, Medical Informatics, Modelling Educational Data, Ontological Engineering, Optimization, Quantum Computing, Signal Processing. This book provides an overview of all presentations from the DAMSS-2023 conference

The role of previous experience in conscious perception

Which factors determine whether a stimulus is consciously perceived or unconsciously processed? Here, I investigate how previous experience on two different time scales – long term experience over the course of several days, and short term experience based on the previous trial – impact conscious perception. Regarding long term experience, I investigate how perceptual learning does not only change the capacity to process stimuli, but also the capacity to consciously perceive them. To this end, subjects are trained extensively to discriminate between masked stimuli, and concurrently rate their subjective experience. Both the ability to discriminate the stimuli as well as subjective awareness of the stimuli increase as a function of training. However, these two effects are not simple byproducts of each other. On the contrary, they display different time courses, with above chance discrimination performance emerging before subjective experience; importantly, the two learning effects also rely on different circuits in the brain: Moving the stimuli outside the trained receptive field size abolishes the learning effects on discrimination ability, but preserves the learning effects on subjective awareness. This indicates that the receptive fields serving subjective experience are larger than the ones serving objective performance, and that the channels through which they receive their information are arranged in parallel. Regarding short term experience, I investigate how memory based predictions arising from information acquired on the trial before affect visibility and the neural correlates of consciousness. To this end, I vary stimulus evidence as well as predictability and acquire electroencephalographic data. A comparison of the neural processes distinguishing consciously perceived from unperceived trials with and without predictions reveals that predictions speed up processing, thus shifting the neural correlates forward in time. Thus, the neural correlates of consciousness display a previously unappreciated flexibility in time and do not arise invariably late as had been predicted by some theorists. Admittedly, however, previous experience does not always stabilize perception. Instead, previous experience can have the reverse effect: Seeing the opposite of what was there, as in so-called repulsive aftereffects. Here, I investigate what determines the direction of previous experience using multistable stimuli. In a functional magnetic resonance imaging experiment, I find that a widespread network of frontal, parietal, and ventral occipital brain areas is involved in perceptual stabilization, whereas the reverse effect is only evident in extrastriate cortex. This areal separation possibly endows the brain with the flexibility to switch between exploiting already available information and emphasizing the new. Taken together, my data show that conscious perception and its neuronal correlates display a remarkable degree of flexibility and plasticity, which should be taken into account in future theories of consciousness

Modeling and Analysis of Neural Spike Trains

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