Search results for "Intelligence"
showing 10 items of 6959 documents
SpCLUST: Towards a fast and reliable clustering for potentially divergent biological sequences
2019
International audience; This paper presents SpCLUST, a new C++ package that takes a list of sequences as input, aligns them with MUSCLE, computes their similarity matrix in parallel and then performs the clustering. SpCLUST extends a previously released software by integrating additional scoring matrices which enables it to cover the clustering of amino-acid sequences. The similarity matrix is now computed in parallel according to the master/slave distributed architecture, using MPI. Performance analysis, realized on two real datasets of 100 nucleotide sequences and 1049 amino-acids ones, show that the resulting library substantially outperforms the original Python package. The proposed pac…
Principal components analysis: theory and application to gene expression data analysis
2018
Advances in computational power have enabled research to generate significant amounts of data related to complex biological problems. Consequently, applying appropriate data analysis techniques has become paramount to tackle this complexity. However, theoretical understanding of statistical methods is necessary to ensure that the correct method is used and that sound inferences are made based on the analysis. In this article, we elaborate on the theory behind principal components analysis (PCA), which has become a favoured multivariate statistical tool in the field of omics-data analysis. We discuss the necessary prerequisites and steps to produce statistically valid results and provide gui…
Deep Learning Architectures for DNA Sequence Classification
2017
DNA sequence classification is a key task in a generic computational framework for biomedical data analysis, and in recent years several machine learning technique have been adopted to successful accomplish with this task. Anyway, the main difficulty behind the problem remains the feature selection process. Sequences do not have explicit features, and the commonly used representations introduce the main drawback of the high dimensionality. For sure, machine learning method devoted to supervised classification tasks are strongly dependent on the feature extraction step, and in order to build a good representation it is necessary to recognize and measure meaningful details of the items to cla…
Dynamic large-scale network synchronization from perception to action
2018
Sensory-guided actions entail the processing of sensory information, generation of perceptual decisions, and the generation of appropriate actions. Neuronal activity underlying these processes is distributed into sensory, fronto-parietal, and motor brain areas, respectively. How the neuronal processing is coordinated across these brain areas to support functions from perception to action remains unknown. We investigated whether phase synchronization in large-scale networks coordinate these processes. We recorded human cortical activity with magnetoencephalography (MEG) during a task in which weak somatosensory stimuli remained unperceived or were perceived. We then assessed dynamic evolutio…
The Active Inference Approach to Ecological Perception: General Information Dynamics for Natural and Artificial Embodied Cognition
2018
The emerging neurocomputational vision of humans as embodied, ecologically embedded, social agents – who shape and are shaped by their environment – offers a golden opportunity to revisit and revise ideas about the physical and information-theoretic underpinnings of life, mind, and consciousness itself. In particular, the active inference framework (AIF) makes it possible to bridge connections from computational neuroscience and robotics/AI to ecological psychology and phenomenology, revealing common underpinnings and overcoming key limitations. AIF opposes the mechanistic to the reductive, while staying fully grounded in a naturalistic and information theoretic foundation, using the princi…
Patch-based Carcinoma Detection on Confocal Laser Endomicroscopy Images -- A Cross-Site Robustness Assessment
2017
Deep learning technologies such as convolutional neural networks (CNN) provide powerful methods for image recognition and have recently been employed in the field of automated carcinoma detection in confocal laser endomicroscopy (CLE) images. CLE is a (sub-)surface microscopic imaging technique that reaches magnifications of up to 1000x and is thus suitable for in vivo structural tissue analysis. In this work, we aim to evaluate the prospects of a priorly developed deep learning-based algorithm targeted at the identification of oral squamous cell carcinoma with regard to its generalization to further anatomic locations of squamous cell carcinomas in the area of head and neck. We applied the…
Brain-like large scale cognitive networks and dynamics
2018
A new approach to the study of the brain and its functions known as Human Connectomics has been recently established. Starting from magnetic resonance images (MRI) of brain scans, it is possible to identify the fibers that link brain areas and to build an adjacency matrix that connects these areas, thus creating the brain connectome. The topology of these networks provides a lot of information about the organizational structure of the brain (both structural and functional). Nevertheless this knowledge is rarely used to investigate the possible emerging brain dynamics linked to cognitive functions. In this work, we implement finite state models on neural networks to display the outcoming bra…
Autism spectrum disorders in children affected by Duchenne muscular dystrophy
2018
Background Duchenne muscular dystrophy (DMD) is the most frequent and severe form of the dystrophinopathies. The literature shows that about 30-40% of DMD subjects have intellectual disability. In males with Duchenne muscular dystrophy, neuropsychiatric disorders have also been observed: attention deficit disorder and hyperactivity, autism spectrum disorders, and obsessive-compulsive disorder. Duchenne muscular dystrophy is not just a muscle disorder, but also a disease that affects the brain. The aim of the present study was to describe a case series of children with Duchenne muscular dystrophy that have also the presence of autism spectrum disorders (ASDs). They have been assessed by mean…
A deeper look into natural sciences with physics-based and data-driven measures
2021
Summary With the development of machine learning in recent years, it is possible to glean much more information from an experimental data set to study matter. In this perspective, we discuss some state-of-the-art data-driven tools to analyze latent effects in data and explain their applicability in natural science, focusing on two recently introduced, physics-motivated computationally cheap tools—latent entropy and latent dimension. We exemplify their capabilities by applying them on several examples in the natural sciences and show that they reveal so far unobserved features such as, for example, a gradient in a magnetic measurement and a latent network of glymphatic channels from the mous…
In Situ Representations and Access Consciousness in Neural Blackboard or Workspace Architectures
2018
Phenomenal theories of consciousness assert that consciousness is based on specific neural correlates in the brain, which can be separated from all cognitive functions we can perform. If so, the search for robot consciousness seems to be doomed. By contrast, theories of functional or access consciousness assert that consciousness can be studied only with forms of cognitive access, given by cognitive processes. Consequently, consciousness and cognitive access cannot be fully dissociated. Here, the global features of cognitive access of consciousness are discussed based on neural blackboard or (global) workspace architectures, combined with content addressable or "in situ" representations as …