Interpretable Convolutional Neural Networks for Decoding and Analyzing Neural Time Series Data

Machine learning is widely adopted to decode multi-variate neural time series, including electroencephalographic (EEG) and single-cell recordings. Recent solutions based on deep learning (DL) outperformed traditional decoders by automatically extracting relevant discriminative features from raw or minimally pre-processed signals. Convolutional Neural Networks (CNNs) have been successfully applied to EEG and are the most common DL-based EEG decoders in the state-of-the-art (SOA). However, the current research is affected by some limitations. SOA CNNs for EEG decoding usually exploit deep and heavy structures with the risk of overfitting small datasets, and architectures are often defined empirically. Furthermore, CNNs are mainly validated by designing within-subject decoders. Crucially, the automatically learned features mainly remain unexplored; conversely, interpreting these features may be of great value to use decoders also as analysis tools, highlighting neural signatures underlying the different decoded brain or behavioral states in a data-driven way. Lastly, SOA DL-based algorithms used to decode single-cell recordings rely on more complex, slower to train and less interpretable networks than CNNs, and the use of CNNs with these signals has not been investigated. This PhD research addresses the previous limitations, with reference to P300 and motor decoding from EEG, and motor decoding from single-neuron activity. CNNs were designed light, compact, and interpretable. Moreover, multiple training strategies were adopted, including transfer learning, which could reduce training times promoting the application of CNNs in practice. Furthermore, CNN-based EEG analyses were proposed to study neural features in the spatial, temporal and frequency domains, and proved to better highlight and enhance relevant neural features related to P300 and motor states than canonical EEG analyses. Remarkably, these analyses could be used, in perspective, to design novel EEG biomarkers for neurological or neurodevelopmental disorders. Lastly, CNNs were developed to decode single-neuron activity, providing a better compromise between performance and model complexity

Pattern Recognition

A wealth of advanced pattern recognition algorithms are emerging from the interdiscipline between technologies of effective visual features and the human-brain cognition process. Effective visual features are made possible through the rapid developments in appropriate sensor equipments, novel filter designs, and viable information processing architectures. While the understanding of human-brain cognition process broadens the way in which the computer can perform pattern recognition tasks. The present book is intended to collect representative researches around the globe focusing on low-level vision, filter design, features and image descriptors, data mining and analysis, and biologically inspired algorithms. The 27 chapters coved in this book disclose recent advances and new ideas in promoting the techniques, technology and applications of pattern recognition

Mécanismes cognitifs dans la catégorisation d'objets visuels

La catégorisation est un processus fondamental de la reconnaissance d'objets. Pour comprendre ses mécanismes sous-jacents, cette thèse interroge le rôle du niveau de catégorisation, de l'attention, de la mémoire, et de la relation entre les catégories d'objets, dans la catégorisation de scènes naturelles. Les résultats de la première étude indiquent que les performances de catégorisation sont influencées par les caractéristiques diagnostiques de la tâche. Une seconde étude montre que des objets naturels peuvent être catégorisés en quasi-absence d'attention. Les résultats de la troisième étude indiquent que les scènes sont encodées en mémoire à long-terme sans instruction explicite et catégorisées automatiquement. La dernière étude explore les interactions entre deux représentations d'objets actives simultanément. Plus le degré de relation entre deux objets est élevé, plus le traitement du second objet est affecté. Pour expliquer ces résultats un modèle, inspiré de la physiologie, est proposé qui postule que le niveau d'interaction entre des catégories d'objet actives simultanément dépend du niveau de chevauchement entre les patterns d'activité du cortex inféro-temporal produits par chacun des objets. Les résultats de cette thèse sont compatibles avec l'idée que les caractéristiques visuelles des objets sont traitées automatiquement (étude 3) en quasi-absence d'attention (étude 2) et représentées dans la voie visuelle ventrale de façon distribuée et continue. Les performances de catégorisation dépendraient de la similarité des catégories cibles et distracteurs (étude 1) ou de la similarité entre les représentations actives de deux objets (étude 4).Categorization is a fundamental process of object recognition. To determine its underlying mechanisms, a series of experiments examined the roles of stimulus properties, categorization level, attention, memory, and category-relatedness in natural scene categorization tasks. The results of the first study suggest that categorization performance is driven by characteristics that are diagnostic for the task. A second study shows that visual objects embedded in complex natural scenes can be categorized in the near-absence of attention. The results of a third study suggest that long-term encoding of complex scenes happens without any explicit instruction, and information about object categories is processed automatically. The final study explores the interaction between two concurrently active category representations by presenting two objects in a rapid sequence. The greater the degree of relatedness between two objects, the more affected the processing of the second object is. To explain these results a physiologically inspired model is proposed, which posits that the extent of interaction between concurrently active objects depends on the extent of overlap between the activity patterns in the infero-temporal cortex elicited by the two objects. The results of this thesis support the idea that visual object characteristics are processed automatically (study 3) in the near-absence of attention (study 2) and represented in the ventral stream in a distributed and continuous manner. Categorization performance would depend on the similarity between and within the target and the distractor categories (study 1) or on the similarity between two active object representations (study 4)

Using MapReduce Streaming for Distributed Life Simulation on the Cloud

Distributed software simulations are indispensable in the study of large-scale life models but often require the use of technically complex lower-level distributed computing frameworks, such as MPI. We propose to overcome the complexity challenge by applying the emerging MapReduce (MR) model to distributed life simulations and by running such simulations on the cloud. Technically, we design optimized MR streaming algorithms for discrete and continuous versions of Conway’s life according to a general MR streaming pattern. We chose life because it is simple enough as a testbed for MR’s applicability to a-life simulations and general enough to make our results applicable to various lattice-based a-life models. We implement and empirically evaluate our algorithms’ performance on Amazon’s Elastic MR cloud. Our experiments demonstrate that a single MR optimization technique called strip partitioning can reduce the execution time of continuous life simulations by 64%. To the best of our knowledge, we are the first to propose and evaluate MR streaming algorithms for lattice-based simulations. Our algorithms can serve as prototypes in the development of novel MR simulation algorithms for large-scale lattice-based a-life models.https://digitalcommons.chapman.edu/scs_books/1014/thumbnail.jp

To which extent can attention and/or modulation explains deficits in dyslexia?

This thesis investigates the visual deficits associated with developmental dyslexia, particularly that of visual attention. Visual attention has previously been investigated in a wide array of behavioural and psychophysical (amongst others) studies but not many have produced consistent findings. Attention processes are believed to play an integral part in depicting the overall "extent" of reading deficits in dyslexia, so it was of paramount importance to aim at such attention mechanisms in this research. The experiments in this thesis focused on signal enhancement and noise (distractor) exclusion. Given the flexibility of the visual search paradigms employed in this research, factors such as visual crowding and attention distribution was also investigated. The experiments systematically manipulated noise (by increasing distractor count, i.e. set-size), crowding (varying the spacing between distractors), attention allocation (use of peripheral cues to direct attention), and attention distribution (influence of one visual field over the other), all of which were tied to a critical factor, the "location/spatial/decisional uncertainty". Adults with dyslexia were: (i) able to modulate attention appropriately using peripheral pre-cues, (ii) severely affected by crowding, and (iii) unable to counteract increased set-sizes when post or un-cued, the latter signifying poor distractor (noise) suppression. By controlling for location uncertainty, the findings confirmed that adults with dyslexia were yet again affected by crowding and set-size, in addition to an asymmetric attention distribution. Confounding effects of ADHD symptoms did not explain a significant independent variance in performance, suggesting that the difficulty shown by adult dyslexics were not accounted for by co-morbid ADHD. Furthermore, the effects of crowding, set-size and asymmetric attention correlated significantly with literacy, but not ADHD measures. It is believed that a more diffuse and an asymmetric attention system (in dyslexia) to be the limiting factor concerning noise exclusion and attention distribution. The findings from this thesis add to the current understanding of the potential role of deficits in visual attention in dyslexia and in the literacy difficulties experienced by this population

Object Recognition

Vision-based object recognition tasks are very familiar in our everyday activities, such as driving our car in the correct lane. We do these tasks effortlessly in real-time. In the last decades, with the advancement of computer technology, researchers and application developers are trying to mimic the human's capability of visually recognising. Such capability will allow machine to free human from boring or dangerous jobs

Neuromodulation of Spatial Associations: Evidence from Choice Reaction Tasks During Transcranial Direct Current Stimulation

Various portions of human behavior and cognition are influenced by covert implicit processes without being necessarily available to intentional planning. Implicit cognitive biases can be measured in behavioral tasks yielding SNARC effects for spatial associations of numerical and non-numerical sequences, or yielding the implicit association test effect for associations between insect-flower and negative-positive categories. By using concurrent neuromodulation with transcranial direct current stimulation (tDCS), subthreshold activity patterns in prefrontal cortical regions can be experimentally manipulated to reduce implicit processing. Thus, the application of tDCS can test neurocognitive hypotheses on a unique neurocognitive origin of implicit cognitive biases in different spatial-numerical and non-numerical domains. However, the effects of tDCS are not only determined by superimposed electric fields, but also by task characteristics. To outline the possibilities of task-specific targeting of tDCS, task characteristics and instructions can be varied systematically when combined with neuromodulation. In the present thesis, implicit cognitive processes are assessed in different paradigms concurrent to left-hemispheric prefrontal tDCS to investigate a verbal processing hypothesis for implicit associations in general. In psychological experiments, simple choice reaction tasks measure implicit SNARC and SNARC-like effects as relative left-hand vs. right-hand latency advantages for responding to smaller number or ordinal sequence targets. However, different combinations of polarity-dependent tDCS with stimuli and task procedures also reveal domain-specific involvements and dissociations. Discounting previous unified theories on the SNARC effect, polarity-specific neuromodulation effects dissociate numbers and weekday or month ordinal sequences. By considering also previous results and patient studies, I present a hybrid and augmented working memory account and elaborate the linguistic markedness correspondence principle as one critical verbal mechanism among competing covert coding mechanisms. Finally, a general stimulation rationale based on verbal working memory is tested in separate experiments extending also to non-spatial implicit association test effects. Regarding cognitive tDCS effects, the present studies show polarity asymmetry and task-induced activity dependence of state-dependent neuromodulation. At large, distinct combinations of the identical tDCS electrode configuration with different tasks influences behavioral outcomes tremendously, which will allow for improved task- and domain-specific targeting