Demixing smells — fast inference in olfaction Supplementary Material ´ Agnieszka Grabska-Barwinska Gatsby Computational Neuroscience Unit

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NEUROSCI 612: Networks and Computational Neuroscience November 5 – December 10, 2013, Tues, Thurs & Friday 10am-12pm Coordinators: Victoria Booth, Associate Professor of Mathematics and Anesthesiology (

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A Multithreaded and Distributed System for the Simulation of Large Biological Neural Networks Diploma Thesis by Jochen Martin Eppler Submitted April 2006

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• Software
• Application software
• Computational neuroscience
• Applied mathematics
• Simulation software
• Computational biology
• NEST
• Artificial intelligence
• Computational statistics
• Simulation
• GENESIS
• Scientific modelling

Pairwise Maximum Entropy Models for Studying Large Biological Systems: When They Can Work and When They Can’t Yasser Roudi1,2, Sheila Nirenberg2, Peter E. Latham1* 1 Gatsby Computational Neuroscience Unit, University C

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Hindawi Publishing Corporation Computational Intelligence and Neuroscience Volume 2011, Article ID, 9 pages doi:Research Article

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Computational and Mathematical Neuroscience MATH 559/BIOINF 800, Fall 2010 Meeting times: T,Th 8:30-10am, EH 4088 Instructor: Victoria Booth Math Dept Office: East Hall 4075, Email:

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DOC Document

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• Applied mathematics
• Artificial neural network
• Computational neuroscience
• Mathematical psychology
• Transmission Control Protocol
• Quotation mark

PDF Document

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• Neuroscience
• Cognitive science
• Academia
• Neuropsychology
• Computational neuroscience
• Coaching
• Educational psychology
• Life coaching
• Mind
• Consciousness
• Artificial neural network
• Lateralization of brain function

Demixing odors — fast inference in olfaction ´ Agnieszka Grabska-Barwinska Gatsby Computational Neuroscience Unit UCL

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Bayesian Rose Trees Charles Blundell, Yee Whye Teh Gatsby Computational Neuroscience Unit, University College London, London, UK

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