49 research outputs found

    Parallel computing for brain simulation

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    [Abstract] Background: The human brain is the most complex system in the known universe, it is therefore one of the greatest mysteries. It provides human beings with extraordinary abilities. However, until now it has not been understood yet how and why most of these abilities are produced. Aims: For decades, researchers have been trying to make computers reproduce these abilities, focusing on both understanding the nervous system and, on processing data in a more efficient way than before. Their aim is to make computers process information similarly to the brain. Important technological developments and vast multidisciplinary projects have allowed creating the first simulation with a number of neurons similar to that of a human brain. Conclusion: This paper presents an up-to-date review about the main research projects that are trying to simulate and/or emulate the human brain. They employ different types of computational models using parallel computing: digital models, analog models and hybrid models. This review includes the current applications of these works, as well as future trends. It is focused on various works that look for advanced progress in Neuroscience and still others which seek new discoveries in Computer Science (neuromorphic hardware, machine learning techniques). Their most outstanding characteristics are summarized and the latest advances and future plans are presented. In addition, this review points out the importance of considering not only neurons: Computational models of the brain should also include glial cells, given the proven importance of astrocytes in information processing.Galicia. Conseller铆a de Cultura, Educaci贸n e Ordenaci贸n Universitaria; GRC2014/049Galicia. Conseller铆a de Cultura, Educaci贸n e Ordenaci贸n Universitaria; R2014/039Instituto de Salud Carlos III; PI13/0028

    Deep Artificial Neural Networks and Neuromorphic Chips for Big Data Analysis: Pharmaceutical and Bioinformatics Applications

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    [Abstract] Over the past decade, Deep Artificial Neural Networks (DNNs) have become the state-of-the-art algorithms in Machine Learning (ML), speech recognition, computer vision, natural language processing and many other tasks. This was made possible by the advancement in Big Data, Deep Learning (DL) and drastically increased chip processing abilities, especially general-purpose graphical processing units (GPGPUs). All this has created a growing interest in making the most of the potential offered by DNNs in almost every field. An overview of the main architectures of DNNs, and their usefulness in Pharmacology and Bioinformatics are presented in this work. The featured applications are: drug design, virtual screening (VS), Quantitative Structure鈥揂ctivity Relationship (QSAR) research, protein structure prediction and genomics (and other omics) data mining. The future need of neuromorphic hardware for DNNs is also discussed, and the two most advanced chips are reviewed: IBM TrueNorth and SpiNNaker. In addition, this review points out the importance of considering not only neurons, as DNNs and neuromorphic chips should also include glial cells, given the proven importance of astrocytes, a type of glial cell which contributes to information processing in the brain. The Deep Artificial Neuron鈥揂strocyte Networks (DANAN) could overcome the difficulties in architecture design, learning process and scalability of the current ML methods.Galicia. Conseller铆a de Cultura, Educaci贸n e Ordenaci贸n Universitaria; GRC2014/049Galicia. Conseller铆a de Cultura, Educaci贸n e Ordenaci贸n Universitaria; R2014/039Instituto de Salud Carlos III; PI13/0028

    Many-core and heterogeneous architectures: programming models and compilation toolchains

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    1noL'abstract 猫 presente nell'allegato / the abstract is in the attachmentopen677. INGEGNERIA INFORMATInopartially_openembargoed_20211002Barchi, Francesc

    Neuromorphic Electronic Circuits for Building Autonomous Cognitive Systems

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    Chicca E, Stefanini F, Bartolozzi C, Indiveri G. Neuromorphic Electronic Circuits for Building Autonomous Cognitive Systems. In: Proceedings of the IEEE. Proceedings of the IEEE. Vol 102. Piscataway, NJ: IEEE; 2014: 1367-1388.Several analog and digital brain-inspired electronic systems have been recently proposed as dedicated solutions for fast simulations of spiking neural networks. While these architectures are useful for exploring the computational properties of large-scale models of the nervous system, the challenge of building low-power compact physical artifacts that can behave intelligently in the real world and exhibit cognitive abilities still remains open. In this paper, we propose a set of neuromorphic engineering solutions to address this challenge. In particular, we review neuromorphic circuits for emulating neural and synaptic dynamics in real time and discuss the role of biophysically realistic temporal dynamics in hardware neural processing architectures; we review the challenges of realizing spike-based plasticity mechanisms in real physical systems and present examples of analog electronic circuits that implement them; we describe the computational properties of recurrent neural networks and show how neuromorphic winner-take-all circuits can implement working-memory and decision-making mechanisms. We validate the neuromorphic approach proposed with experimental results obtained from our own circuits and systems, and argue how the circuits and networks presented in this work represent a useful set of components for efficiently and elegantly implementing neuromorphic cognition
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