60 research outputs found
AI-Driven Assessment of Students: Current Uses and Research Trends
During the last decade, the use of AIs is being incorporated into the
educational field whether to support the analysis of human behavior in teachinglearning
contexts, as didactic resource combined with other technologies or as a
tool for the assessment of the students.
This proposal presents a Systematic Literature Review and mapping study on
the use of AIs for the assessment of students that aims to provide a general
overview of the state of the art and identify the current areas of research by
answering 6 research questions related with the evolution of the field, and the
geographic and thematic distribution of the studies.
As a result of the selection process this study identified 20 papers focused on
the research topic in the repositories SCOPUS and Web of Science from an
initial amount of 129.
The analysis of the papers allowed the identification of three main thematic
categories: assessment of student behaviors, assessment of student sentiments
and assessment of student achievement as well as several gaps in the literature
and future research lines addressed in the discussion
Mechanical properties of freely suspended semiconducting graphene-like layers based on MoS2
We fabricate freely suspended nanosheets of molybdenum disulphide (MoS2) which are characterized by quantitative optical microscopy and high-resolution friction force microscopy. We study the elastic deformation of freely suspended nanosheets of MoS2 using an atomic force microscope. The Young's modulus and the initial pre-tension of the nanosheets are determined by performing a nanoscopic version of a bending test experiment. MoS2 sheets show high elasticity and an extremely high Young's modulus (0.30 TPa, 50% larger than steel). These results make them a potential alternative to graphene in applications requiring flexible semiconductor materials
Different Origins of Gamma Rhythm and High-Gamma Activity in Macaque Visual Cortex
High-gamma (80–200 Hz) activity can be dissociated from gamma rhythms in
the monkey cortex, and appears largely to reflect spiking activity in the
vicinity of the electrode
Power-Law Scaling in the Brain Surface Electric Potential
Recent studies have identified broadband phenomena in the electric potentials produced by the brain. We report the finding of power-law scaling in these signals using subdural electrocorticographic recordings from the surface of human cortex. The power spectral density (PSD) of the electric potential has the power-law form from 80 to 500 Hz. This scaling index, , is conserved across subjects, area in the cortex, and local neural activity levels. The shape of the PSD does not change with increases in local cortical activity, but the amplitude, , increases. We observe a “knee” in the spectra at , implying the existence of a characteristic time scale . Below , we explore two-power-law forms of the PSD, and demonstrate that there are activity-related fluctuations in the amplitude of a power-law process lying beneath the rhythms. Finally, we illustrate through simulation how, small-scale, simplified neuronal models could lead to these power-law observations. This suggests a new paradigm of non-oscillatory “asynchronous,” scale-free, changes in cortical potentials, corresponding to changes in mean population-averaged firing rate, to complement the prevalent “synchronous” rhythm-based paradigm
Context Matters: The Illusive Simplicity of Macaque V1 Receptive Fields
Even in V1, where neurons have well characterized classical receptive fields (CRFs), it has been difficult to deduce which features of natural scenes stimuli they actually respond to. Forward models based upon CRF stimuli have had limited success in predicting the response of V1 neurons to natural scenes. As natural scenes exhibit complex spatial and temporal correlations, this could be due to surround effects that modulate the sensitivity of the CRF. Here, instead of attempting a forward model, we quantify the importance of the natural scenes surround for awake macaque monkeys by modeling it non-parametrically. We also quantify the influence of two forms of trial to trial variability. The first is related to the neuron’s own spike history. The second is related to ongoing mean field population activity reflected by the local field potential (LFP). We find that the surround produces strong temporal modulations in the firing rate that can be both suppressive and facilitative. Further, the LFP is found to induce a precise timing in spikes, which tend to be temporally localized on sharp LFP transients in the gamma frequency range. Using the pseudo R[superscript 2] as a measure of model fit, we find that during natural scene viewing the CRF dominates, accounting for 60% of the fit, but that taken collectively the surround, spike history and LFP are almost as important, accounting for 40%. However, overall only a small proportion of V1 spiking statistics could be explained (R[superscript 2]~5%), even when the full stimulus, spike history and LFP were taken into account. This suggests that under natural scene conditions, the dominant influence on V1 neurons is not the stimulus, nor the mean field dynamics of the LFP, but the complex, incoherent dynamics of the network in which neurons are embedded.National Institutes of Health (U.S.) (K25 NS052422-02)National Institutes of Health (U.S.) (DP1 ODOO3646
Synchronous chaos and broad band gamma rhythm in a minimal multi-layer model of primary visual cortex
Visually induced neuronal activity in V1 displays a marked gamma-band
component which is modulated by stimulus properties. It has been argued that
synchronized oscillations contribute to these gamma-band activity [...
however,] even when oscillations are observed, they undergo temporal
decorrelation over very few cycles. This is not easily accounted for in
previous network modeling of gamma oscillations. We argue here that
interactions between cortical layers can be responsible for this fast
decorrelation. We study a model of a V1 hypercolumn, embedding a simplified
description of the multi-layered structure of the cortex. When the stimulus
contrast is low, the induced activity is only weakly synchronous and the
network resonates transiently without developing collective oscillations. When
the contrast is high, on the other hand, the induced activity undergoes
synchronous oscillations with an irregular spatiotemporal structure expressing
a synchronous chaotic state. As a consequence the population activity undergoes
fast temporal decorrelation, with concomitant rapid damping of the oscillations
in LFPs autocorrelograms and peak broadening in LFPs power spectra. [...]
Finally, we argue that the mechanism underlying the emergence of synchronous
chaos in our model is in fact very general. It stems from the fact that gamma
oscillations induced by local delayed inhibition tend to develop chaos when
coupled by sufficiently strong excitation.Comment: 49 pages, 11 figures, 7 table
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