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IBM simulates 530 billon neurons, 100 trillion synapses on supercomputer

IBM simulates 530 billon neurons, 100 trillion synapses on supercomputer
A network of neurosynaptic cores derived from long-distance wiring in the monkey brain: Neuro-synaptic cores are locally clustered into brain-inspired regions, and each core is represented as an individual point along the ring. Arcs are drawn from a source core to a destination core with an edge color defined by the color assigned to the source core. (Credit: IBM) Announced in 2008, DARPA’s SyNAPSE program calls for developing electronic neuromorphic (brain-simulation) machine technology that scales to biological levels, using a cognitive computing architecture with 1010 neurons (10 billion) and 1014 synapses (100 trillion, based on estimates of the number of synapses in the human brain) to develop electronic neuromorphic machine technology that scales to biological levels.” Simulating 10 billion neurons and 100 trillion synapses on most powerful supercomputer Neurosynaptic core (credit: IBM) Two billion neurosynaptic cores DARPA SyNAPSE Phase 0DARPA SyNAPSE Phase 1DARPA SyNAPSE Phase 2 Related:  Neural NetworkA bit about Neuro-computing (science)

DARPA SyNAPSE Program Last updated: Jan 11, 2013 SyNAPSE is a DARPA-funded program to develop electronic neuromorphic machine technology that scales to biological levels. More simply stated, it is an attempt to build a new kind of computer with similar form and function to the mammalian brain. SyNAPSE is a backronym standing for Systems of Neuromorphic Adaptive Plastic Scalable Electronics. The ultimate aim is to build an electronic microprocessor system that matches a mammalian brain in function, size, and power consumption. Latest news As of January 2013 the program is currently progressing through phase 2, the third of five phases. Background The following text is taken from the Broad Agency Announcement (BAA) published by DARPA in April 2008 (see the original document): Over six decades, modern electronics has evolved through a series of major developments (e.g., transistors, integrated circuits, memories, microprocessors) leading to the programmable electronic machines that are ubiquitous today. Phase 0

A Non-Mathematical Introduction to Using Neural Networks The goal of this article is to help you understand what a neural network is, and how it is used. Most people, even non-programmers, have heard of neural networks. There are many science fiction overtones associated with them. And like many things, sci-fi writers have created a vast, but somewhat inaccurate, public idea of what a neural network is. Most laypeople think of neural networks as a sort of artificial brain. Neural networks are one small part of AI. The human brain really should be called a biological neural network (BNN). There are some basic similarities between biological neural networks and artificial neural networks. Like I said, neural networks are designed to accomplish one small task. The task that neural networks accomplish very well is pattern recognition. Figure 1: A Typical Neural Network As you can see, the neural network above is accepting a pattern and returning a pattern. Neural Network Structure Neural networks are made of layers of similar neurons. Conclusion

Physicist Proposes New Way To Think About Intelligence (ISNS) -- A single equation grounded in basic physics principles could describe intelligence and stimulate new insights in fields as diverse as finance and robotics, according to new research. Alexander Wissner-Gross, a physicist at Harvard University and the Massachusetts Institute of Technology, and Cameron Freer, a mathematician at the University of Hawaii at Manoa, developed an equation that they say describes many intelligent or cognitive behaviors, such as upright walking and tool use. The researchers suggest that intelligent behavior stems from the impulse to seize control of future events in the environment. This is the exact opposite of the classic science-fiction scenario in which computers or robots become intelligent, then set their sights on taking over the world. "It's a provocative paper," said Simon DeDeo, a research fellow at the Santa Fe Institute, who studies biological and social systems. "It actually self-determines what its own objective is," said Wissner-Gross.

Classification du DSM-IV Diagnostics des troubles mentaux Classification du DSM-IV, Manuel diagnostique et statistique des troubles mentaux" publié par l'American Psychiatric Association en 1994 (version française: Masson, 1996). Cette classification est généralement adoptée par les professionnels de la santé en Amérique du Nord. Veuillez noter: cette classification est postée ici à titre de document d'information et outil de recherche, ceci ne confirme pas que l'AGIDD-SMQ adhère en tout ou en partie à cette classification. Il y a 15 catégories principales de diagnostics. 1) Troubles habituellement diagnostiqués pendant la petite enfance, la deuxième enfance ou l'adolescence Retard mental Troubles des apprentissages - Trouble de la lecture- Trouble du calcul- Trouble de l'expression écrite- Trouble des apprentissages non spécifié Troubles des habiletés motrices - Trouble de l'acquisition de la coordination Troubles de la communication Troubles envahissants du développement Troubles: tics Troubles du contrôle sphinctérien

Collective Intelligence in Neural Networks and Social Networks « 100 Trillion Connections Context for this post: I’m currently working on a social network application that demonstrates the value of connection strength and context for making networks more useful and intelligent. Connection strength and context are currently only rudimentarily and mushily implemented in social network apps. This post describes some of the underlying theory for why connection strength and context are key to next generation social network applications. A recent study of how behavioral decisions are made in the brain makes it clear how important strengths of connections are to the intelligence of networks. “Scientists at the University of Rochester, Washington University in St. Louis, and Baylor College of Medicine have unraveled how the brain manages to process the complex, rapidly changing, and often conflicting sensory signals to make sense of our world. “The answer lies in a simple computation performed by single nerve cells: a weighted average. 1. 2. 3. 4. Like this: Like Loading...

Introduction aux Réseaux de Neurones Artificiels Feed Forward Plongeons-nous dans l'univers de la reconnaissance de formes. Plus particulièrement, nous allons nous intéresser à la reconnaissance des chiffres (0, 1, ..., 9). Imaginons un programme qui devrait reconnaître, depuis une image, un chiffre. On présente donc au programme une image d'un "1" manuscrit par exemple et lui doit pouvoir nous dire "c'est un 1". Supposons que les images que l'on montrera au programme soient toutes au format 200x300 pixels. De façon plus générale, un réseau de neurone permet l'approximation d'une fonction. Dans la suite de l'article, on notera un vecteur dont les composantes sont les n informations concernant un exemple donné. Voyons maintenant d'où vient la théorie des réseaux de neurones artificiels. Comment l'homme fait-il pour raisonner, parler, calculer, apprendre... ? Approches adoptée en recherche en Intelligence Artificielle procéder d'abord à l'analyse logique des tâches relevant de la cognition humaine et tenter de les reconstituer par programme. III-1.

Intelligent Robots Will Overtake Humans by 2100, Experts Say | The Singularity & Artificial Intelligence Are you prepared to meet your robot overlords? The idea of superintelligent machines may sound like the plot of "The Terminator" or "The Matrix," but many experts say the idea isn't far-fetched. Some even think the singularity — the point at which artificial intelligence can match, and then overtake, human smarts — might happen in just 16 years. But nearly every computer scientist will have a different prediction for when and how the singularity will happen. Some believe in a utopian future, in which humans can transcend their physical limitations with the aid of machines. Singularity near? In his book "The Singularity is Near: When Humans Transcend Biology" (Viking, 2005), futurist Ray Kurzweil predicted that computers will be as smart as humans by 2029, and that by 2045, "computers will be billions of times more powerful than unaided human intelligence," Kurzweil wrote in an email to LiveScience. But other AI researchers are skeptical. Infinite abilities Earth's destruction?

La musique rend intelligent Pour en savoir plus S. Moreno et al., Psychol. Science, à paraître. C. Lima et S. L'auteur Sébastien Bohler est journaliste à Cerveau&Psycho Du même auteur Selon une étude de l’Université de Toronto au Canada, la pratique de la musique augmenterait l’intelligence verbale des enfants, c’est-à-dire leur capacité de compréhension du discours d’autrui et leur propre expression. La première étude a consisté à faire participer des enfants âgés de quatre à cinq ans à des programmes d’initiation à la musique, où ils écoutaient des mélodies, apprenaient à les reconnaître, à identifier le timbre des instruments, etc. Dans la seconde étude, les psychologues ont fait écouter à des adultes âgés de 18 à 30 ans ou de 40 à 60 ans des phrases enregistrées, dont le ton exprimait six émotions différentes : la peur, la colère, le dégoût, la joie, la tristesse ou la surprise.

Learning and neural networks Artificial Intelligence: History of AI | Intelligent Agents | Search techniques | Constraint Satisfaction | Knowledge Representation and Reasoning | Logical Inference | Reasoning under Uncertainty | Decision Making | Learning and Neural Networks | Bots An Overview of Neural Networks[edit] The Perceptron and Backpropagation Neural Network Learning[edit] Single Layer Perceptrons[edit] A Perceptron is a type of Feedforward neural network which is commonly used in Artificial Intelligence for a wide range of classification and prediction problems. We can classify people in this problem using a single layer perceptron A perceptron learns by a trial and error like method. To summarize[edit] The neural network starts out kind of dumb, but we can tell how wrong it is and based on how far off its answers are, we adjust the weights a little to make it more correct the next time. . Note: The difference between and is that is what you want the network to produce while is what it actually outputs. and

Les architectures neuromorphiques Les ordinateurs sont vraiment loin d'être les seuls systèmes capables de traiter de l'information. Les automates mécaniques furent les premiers à en être capables : les ancêtres des calculettes étaient bel et bien des automates basés sur des pièces mécaniques, et n'étaient pas programmables. Par la suite, ces automates furent remplacés par les circuits électroniques analogiques et numériques non-programmables. La suite logique fût l'introduction de la programmation : l'informatique était née. De nos jours, de nouveaux types de circuits de traitement de l’information ont vu le jour. On peut citer des circuits électroniques, qui sont souvent reconfigurables, mais pas programmables. Dans ce qui va suivre, nous allons voir : Réseaux de neurones matériels Ces architectures s'inspirent fortement du cerveau humain, et du fonctionnement du système nerveux. Simulation matérielle de systèmes nerveux Au départ, les neurones simulés étaient simples, et cela suffisait. Les accélérateurs matériels

Advances in Cultural Neuroscience A lot of good stuff coming out around cultural neuroscience right now. Here are the three main things up front, so people can have them. Then I’ll go over them in turn. And finally, a reflective comment at the end highlighting potential differences between cultural neuroscience and neuroanthropology. Cultural Neuroscience special issue in Psychological Inquiry, with a target article by Joan Chiao and colleagues and commentaries by leaders in the field. The inaugural issue of the new journal Culture and Brain , with Shihui Han serving as editor-in-chief A 2013 Annual Review of Psychology article, A Cultural Neuroscience Approach to the Biosocial Nature of the Human Brain , also by Shihui Han and a long-list of leaders in cultural neuroscience Cultural Neuroscience: Progress and Promise The abstract for the Chaio et al. review: Contemporary advances in cultural and biological sciences provide unique opportunities for the emerging field of cultural neuroscience. New journal Culture and Brain

neuralview [OProj - Open Source Software] Bitbucket is a code hosting site with unlimited public and private repositories. We're also free for small teams! Sign up for freeClose NeuralView is a graphical interface for FANN 1, making possible to graphically design, train, and test artificial neural networks.

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