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July 2017

“Innate Immunity in Injury and Disease in the Nervous System” – Professor Shlomo Rotshenker,The Hebrew University of Jerusalem

July 5 @ 12:00 pm - 1:00 pm

A short bio: Professor Shlomo Rotshenker is the DR. LEON SIMON GOLDMAN PROFESSOR OF MEDICINE . A highly accomplished neuroimmunologist, his lab investigates the involvement of the immune system in degeneration and regeneration of the nervous system. In particularly, he is interested on the role of the innate immune system and has made seminal contributions to our understanding of macrophage phagocytosis in the context of injury.

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Localist models are compatible with information measures, sparseness indices and complementary learning systems in the brain – Dr Mike Page, University of Hertfordshire

July 5 @ 12:30 pm - 1:30 pm

Abstract not available

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A simple neural network module for relational reasoning – David Barrett, DeepMind

July 6 @ 11:00 am - 12:00 pm

Relational reasoning is a central component of generally intelligent behavior, but has proven difficult for neural networks to learn. In this paper we describe how to use Relation Networks (RNs) as a simple plug-and-play module to solve problems that fundamentally hinge on relational reasoning. We tested RN-augmented networks on three tasks: visual question answering using a challenging dataset called CLEVR, on which we achieve state-of-the-art, super-human performance; text-based question answering using the bAbI suite of tasks; and complex reasoning about…

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Title to be confirmed – Dr John-Paul Taylor, Senior Clincial Lecturer, Institute of Neuroscience, University of Newcastle

July 6 @ 12:30 pm
Seminar Room, Herchel Smith Building, Forvie Site.,

Abstract not available

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From neuroscience to ultra-brief treatments for anxiety disorders – Dr Andrea Reinecke, Senior Research Fellow, Department of Psychiatry, University of Oxford

July 6 @ 12:30 pm - 1:30 pm

Abstract not available

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Title to be confirmed – Dr Andrea Reinecke, Research Clinical Psychologist, Department of Psychiatry, University of Oxford

July 6 @ 12:30 pm - 1:30 pm

Abstract not available

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Computational Neuroscience Journal Club – Timothy O’Leary (Control Group)

July 11 @ 4:00 pm - 5:00 pm

Timothy O'Leary will cover: * Flexible information routing by transient synchrony * Agostina Palmigiano, Theo Geisel, Fred Wolf, Demian Battaglia * Nature Neuroscience (May 2017) * http://www.nature.com/neuro/journal/v20/n7/full/nn.4569.html Abstract: Perception, cognition and behavior rely on flexible communication between microcircuits in distinct cortical regions. The mechanisms underlying rapid information rerouting between such microcircuits are still unknown. It has been proposed that changing patterns of coherence between local gamma rhythms support flexible information rerouting. The stochastic and transient nature of gamma oscillations in…

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Weighing the past against the present: computational approaches to understand learning and uncertainty autism – Dr Rebecca Lawson, Research Associate, Imaging Neuroscience, Institute of Neurology, Faculty of Brain Sciences, University College London

July 12 @ 10:30 am - 11:30 am

Abstract not available

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University High Performance Hub for Informatics Q&A – Professor John Suckling, Department of Psychiatry, University of Cambridge

July 13 @ 12:30 pm - 1:30 pm

Opened in October 2016, the High Performance Hub for Informatics (HPHI) is funded by a MRC Clinical Research Infrastructure Award to provide storage and processing facilities. It is part of the BioCloud initiative integrating medical and biological datasets with high performance computing facilities. The HPHI equipment is hosted in the West Cambridge Data Centre, with offsite backup, reducing energy usage and thus the University’s carbon footprint. The vast majority of neuroimaging data processing still takes place on the BCNI. The…

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Understanding Black-box Predictions via Influence Functions – Pang Wei Koh, Stanford University

July 20 @ 2:00 pm - 3:00 pm

How can we explain the predictions of a black-box model? In this paper, we use influence functions -- a classic technique from robust statistics -- to trace a model’s prediction through the learning algorithm and back to its training data, thereby identifying training points most responsible for a given prediction. To scale up influence functions to modern machine learning settings, we develop a simple, efficient implementation that requires only oracle access to gradients and Hessian-vector products. We show that even…

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