Methods and Approaches for Characterizing Learning Related Changes Observed in functional MRI Data — A Review

Brain imaging data have so far revealed a wealth of information about neuronal circuits involved in higher mental functions like memory, attention, emotion, language etc. Our efforts are toward understanding the learning related effects in brain activity during the acquisition of visuo-motor sequential skills. The aim of this paper is to survey various methods and approaches of analysis that allow the characterization of learning related changes in fMRI data. Traditional imaging analysis using the Statistical Parametric Map (SPM) approach averages out temporal changes and presents overall differences between different stages of learning. We outline other potential approaches for revealing learning effects such as statistical time series analysis, modelling of haemodynamic response function and independent component analysis. We present example case studies from our visuo-motor sequence learning experiments to describe application of SPM and statistical time series analyses. Our review highlights that the problem of characterizing learning induced changes in fMRI data remains an interesting and challenging open research problem

A Multi-disciplinary Approach to the Investigation of Aspects of Serial Order in Cognition

Serial order processing or Sequence processing underlies many human activities such as speech, language, skill learning, planning, problem solving, etc. Investigating the\ud neural bases of sequence processing enables us to understand serial order in cognition and helps us building intelligent devices. In the current paper, various\ud cognitive issues related to sequence processing will be discussed with examples. Some of the issues are: distributed versus local representation, pre-wired versus\ud adaptive origins of representation, implicit versus explicit learning, fixed/flat versus hierarchical organization, timing aspects, order information embedded in sequences, primacy versus recency in list learning and aspects of sequence perception such as recognition, recall and generation. Experimental results that give evidence for the involvement of various brain areas will be described. Finally, theoretical frameworks based on Markov models and Reinforcement Learning paradigm will be presented. These theoretical ideas are useful for studying sequential phenomena in a principled way

Extreme events prediction from nonlocal partial information in a spatiotemporally chaotic microcavity laser

The forecasting of high-dimensional, spatiotemporal nonlinear systems has made tremendous progress with the advent of model-free machine learning techniques. However, in real systems it is not always possible to have all the information needed; only partial information is available for learning and forecasting. This can be due to insufficient temporal or spatial samplings, to inaccessible variables or to noisy training data. Here, we show that it is nevertheless possible to forecast extreme events occurrence in incomplete experimental recordings from a spatiotemporally chaotic microcavity laser using reservoir computing. Selecting regions of maximum transfer entropy, we show that it is possible to get higher forecasting accuracy using nonlocal data vs local data thus allowing greater warning times, at least twice the time horizon predicted from the nonlinear local Lyapunov exponent

Continuous Interaction with a Virtual Human

Attentive Speaking and Active Listening require that a Virtual Human be capable of simultaneous perception/interpretation and production of communicative behavior. A Virtual Human should be able to signal its attitude and attention while it is listening to its interaction partner, and be able to attend to its interaction partner while it is speaking – and modify its communicative behavior on-the-fly based on what it perceives from its partner. This report presents the results of a four week summer project that was part of eNTERFACE’10. The project resulted in progress on several aspects of continuous interaction such as scheduling and interrupting multimodal behavior, automatic classification of listener responses, generation of response eliciting behavior, and models for appropriate reactions to listener responses. A pilot user study was conducted with ten participants. In addition, the project yielded a number of deliverables that are released for public access

A Green Approach for the Synthesis of Iron Oxide Nanoparticles by Using Roots of A. Racemosus and Its Deg-radation of Dye Methyl Orange.

Metal oxide nanoparticles have been used in various fields ranging from catalysis and opto-electronic materials to sensors, environmental remediation and biomedicine. The present study reports green synthesized Fe2O3 nanoparticles by using roots of A. racemosus. This approach involves ecofriendly and non-toxic method. Powder X-ray diffraction, scanning electron microscope and transmission electron microscope analysis revealed that synthesized Fe2O3 nanoparticles are in spherical shape with an average particle size of 40 nm. The synthesized iron oxide nanoparticles are utilized as green catalyst for the effective degradation of dye methyl orange

Antibacterial Effect of Fe2o3 Nanoparticles Synthesized by B. Diffusa Herbal Extract

The present study was carried out in order to evaluate the potential antibacterial activity of iron oxide nanoparticles (Fe2O3) synthesized by green synthesis against gram negative and gram positive bacteria. The synthesized iron oxide nanoparticles were characterized by scanning electron microscope (SEM-EDS), X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FTIR). Antibacterial activity was investigated using agar well diffusion method. The characterized nanoparticles were average size 34 nm with spherical shape. The green synthesized iron oxide nanoparticles show good antibacterial effect on gram positive bacteria compare to gram negative bacteria

Is loss-aversion magnitude-dependent? Measuring prospective affective judgments regarding gains and losses

Prospect Theory proposed that the (dis)utility of losses is always more than gains due to a phenomena called ‘loss-aversion’, a result obtained in multiple later studies over the years. However, some researchers found reversed or no loss-aversion for affective judgments of small monetary amounts but, those findings have been argued to stem from the way gains versus losses were measured. Thus, it was not clear whether loss-aversion does not show with affective judgments for smaller magnitudes, or it is a measurement error. This paper addresses the debate concerning loss-aversion (in the prospect theoretic sense) and judgments about the intensity of gains and losses. We measured affective prospective judgments for monetary amounts using measurement scales that have been argued to be suitable for measuring loss-aversion and hence rule out any explanations regarding measurement. Both in a gambling scenario (Experiments 1 and 2) and in the context of fluctuating prices (Experiments 3a and 3b), potential losses never loomed larger than gains for low magnitudes, indicating that it is not simply a measurement error. Moreover, for the same participant, loss aversion was observable at high magnitudes. Further, we show that loss-aversion disappears even for higher monetary values, if contextually an even larger anchor is provided. The results imply that Prospect Theory’s value function is contextually dependent on magnitudes

Neuroinformatics Tools for Functional MRI: Experimental Design and Data Analysis

Neuroimaging in vivo is becoming popular from the last two decades. The primary quest of neuroimaging is to better-understanding the functions of various brain areas pertaining to various cognitive processes of interest. Though there are several neuroimaging techniques available currently, the functional Magnetic Resonance Imaging (fMRI) is playing an important role in the field of Imaging Neuroscience. In this paper an introduction to fMRI, the issues related to experimental design and analysis will be presented. This paper also discusses some of the neuroinformatics tools available for fMRI research