Rapid modulation of sensory processing induced by stimulus conflict

Humans are constantly confronted with environmental stimuli that conflict with task goals and can interfere with successful behavior. Prevailing theories propose the existence of cognitive control mechanisms that can suppress the processing of conflicting input and enhance that of the relevant input. However, the temporal cascade of brain processes invoked in response to conflicting stimuli remains poorly understood. By examining evoked electrical brain responses in a novel, hemifield-specific, visual-flanker task, we demonstrate that task-irrelevant conflicting stimulus input is quickly detected in higher level executive regions while simultaneously inducing rapid, recurrent modulation of sensory processing in the visual cortex. Importantly, however, both of these effects are larger for individuals with greater incongruency-related RT slowing. The combination of neural activation patterns and behavioral interference effects suggest that this initial sensory modulation induced by conflicting stimulus inputs reflects performance-degrading attentional distraction because of their incompatibility rather than any rapid task-enhancing cognitive control mechanisms. The present findings thus provide neural evidence for a model in which attentional distraction is the key initial trigger for the temporal cascade of processes by which the human brain responds to conflicting stimulus input in the environment

Grey matter alterations co-localize with functional abnormalities in developmental dyslexia : an ALE meta-analysis

The neural correlates of developmental dyslexia have been investigated intensively over the last two decades and reliable evidence for a dysfunction of left-hemispheric reading systems in dyslexic readers has been found in functional neuroimaging studies. In addition, structural imaging studies using voxel-based morphometry (VBM) demonstrated grey matter reductions in dyslexics in several brain regions. To objectively assess the consistency of these findings, we performed activation likelihood estimation (ALE) meta-analysis on nine published VBM studies reporting 62 foci of grey matter reduction in dyslexic readers. We found six significant clusters of convergence in bilateral temporo-parietal and left occipito-temporal cortical regions and in the cerebellum bilaterally. To identify possible overlaps between structural and functional deviations in dyslexic readers, we conducted additional ALE meta-analyses of imaging studies reporting functional underactivations (125 foci from 24 studies) or overactivations (95 foci from 11 studies ) in dyslexics. Subsequent conjunction analyses revealed overlaps between the results of the VBM meta-analysis and the meta-analysis of functional underactivations in the fusiform and supramarginal gyri of the left hemisphere. An overlap between VBM results and the meta-analysis of functional overactivations was found in the left cerebellum. The results of our study provide evidence for consistent grey matter variations bilaterally in the dyslexic brain and substantial overlap of these structural variations with functional abnormalities in left hemispheric regions

A Comprehensive Survey of Deep Learning in Remote Sensing: Theories, Tools and Challenges for the Community

In recent years, deep learning (DL), a re-branding of neural networks (NNs), has risen to the top in numerous areas, namely computer vision (CV), speech recognition, natural language processing, etc. Whereas remote sensing (RS) possesses a number of unique challenges, primarily related to sensors and applications, inevitably RS draws from many of the same theories as CV; e.g., statistics, fusion, and machine learning, to name a few. This means that the RS community should be aware of, if not at the leading edge of, of advancements like DL. Herein, we provide the most comprehensive survey of state-of-the-art RS DL research. We also review recent new developments in the DL field that can be used in DL for RS. Namely, we focus on theories, tools and challenges for the RS community. Specifically, we focus on unsolved challenges and opportunities as it relates to (i) inadequate data sets, (ii) human-understandable solutions for modelling physical phenomena, (iii) Big Data, (iv) non-traditional heterogeneous data sources, (v) DL architectures and learning algorithms for spectral, spatial and temporal data, (vi) transfer learning, (vii) an improved theoretical understanding of DL systems, (viii) high barriers to entry, and (ix) training and optimizing the DL.Comment: 64 pages, 411 references. To appear in Journal of Applied Remote Sensin