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June 2018

Investigating directed phase couplings from multivariate EEG/MEG time series – Alessio Basti, MRC CBU

June 4 @ 12:30 pm - 1:30 pm

Recent works suggest that the use of bivariate methods to analyse EEG/MEG time series is suboptimal when functional connectivity is the target. Indeed, the application of dimensionality reduction approaches to multivariate time series, such as parcel signals, unavoidably leads to discard a large part of the information. In my talk I will present a computational method called Multivariate Phase Slope Index. This method allows to directly investigate the directionality of functional interactions between brain regions from multivariate EEG/MEG time series.

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Why are patients with Parkinson’s disease and Dementia impulsive – and what can we do about it? – Prof James Rowe (U. of Cambridge, MRC CBU & Dept. of Clinical Neuroscience)

Abstract not available

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Development and activity-dependent plasticity of olfactory bulb dopaminergic neurons – Elisa Galliano KCL/University of Cambridge

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How can we improve and maintain child mental health; lessons from interventional epidemiology – Dr Tamsin Ford, Professor of Child and Adolescent Psychiatry, University of Exeter Medical School

Abstract not available

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The neural bases of declarative memory and primary using studies of brain damaged patients – Daniela Montaldi (U. of Manchester)

Abstract not available

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Computational Neuroscience Journal Club – Xizi Li (CBL)

Xizi Li will cover: • Vector-based navigation using grid-like representations in artificial agents • Banino et al (DeepMind) • Nature (2018) • https://www.nature.com/articles/s41586-018-0102-6 Abstract: Deep neural networks have achieved impressive successes in fields ranging from object recognition to complex games such as Go. Navigation, however, remains a substantial challenge for artificial agents, with deep neural networks trained by reinforcement learning failing to rival the proficiency of mammalian spatial behaviour, which is underpinned by grid cells in the entorhinal cortex. Grid…

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The dynamics of functional brain networks: Examining the role of noradrenaline – James Mac Shine (U. of Sydney)

Abstract not available

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Preventing Fairness Gerrymandering: Auditing and Learning for Subgroup Fairness – Prof. Michael Kearns (University of Pennsylvania)

The most prevalent notions of fairness in machine learning are statistical and coarse: they fix a small collection of pre-defined groups or attributes (such as race or gender), and then ask for parity of some statistic of the classifier (such as false positive rate) across these groups. Constraints of this form are susceptible to intentional or inadvertent "fairness gerrymandering", in which a classifier appears to be fair on each individual group, but badly violates fairness on one or more subgroups…

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Concept learning as compression – Bradley Love (UCL)

Abstract not available

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Foster Talk – "Integrating structural and functional approaches to decipher AMPA receptor signaling in synaptic transmission and plasticity" – Dr Ingo Greger, MRC-Laboratory of Molecular Biology, Cambridge

Abstract not available

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