C
Claudia Clopath
Researcher at Imperial College London
Publications - 166
Citations - 11996
Claudia Clopath is an academic researcher from Imperial College London. The author has contributed to research in topics: Computer science & Biology. The author has an hindex of 30, co-authored 134 publications receiving 7728 citations. Previous affiliations of Claudia Clopath include Columbia University & Royal School of Mines.
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Overcoming catastrophic forgetting in neural networks
James Kirkpatrick,Razvan Pascanu,Neil C. Rabinowitz,Joel Veness,Guillaume Desjardins,Andrei Rusu,Kieran Milan,John Quan,Tiago Ramalho,Agnieszka Grabska-Barwinska,Demis Hassabis,Claudia Clopath,Dharshan Kumaran,Raia Hadsell +13 more
TL;DR: It is shown that it is possible to overcome the limitation of connectionist models and train networks that can maintain expertise on tasks that they have not experienced for a long time and selectively slowing down learning on the weights important for previous tasks.
Journal ArticleDOI
Overcoming catastrophic forgetting in neural networks
James Kirkpatrick,Razvan Pascanu,Neil C. Rabinowitz,Joel Veness,Guillaume Desjardins,Andrei Rusu,Kieran Milan,John Quan,Tiago Ramalho,Agnieszka Grabska-Barwinska,Demis Hassabis,Claudia Clopath,Dharshan Kumaran,Raia Hadsell +13 more
TL;DR: In this paper, the authors show that it is possible to train networks that can maintain expertise on tasks that they have not experienced for a long time by selectively slowing down learning on the weights important for those tasks.
Journal ArticleDOI
Inhibitory Plasticity Balances Excitation and Inhibition in Sensory Pathways and Memory Networks
Tim P. Vogels,Henning Sprekeler,Friedemann Zenke,Claudia Clopath,Claudia Clopath,Wulfram Gerstner +5 more
TL;DR: An essential role of inhibitory plasticity is suggested in the formation and maintenance of functional cortical circuitry that can accommodate synaptic memories with activity patterns that become indiscernible from the background state but can be reactivated by external stimuli.
Journal ArticleDOI
A deep learning framework for neuroscience
Blake A. Richards,Timothy P. Lillicrap,Philippe Beaudoin,Yoshua Bengio,Yoshua Bengio,Rafal Bogacz,Amelia J. Christensen,Claudia Clopath,Rui Ponte Costa,Rui Ponte Costa,Archy O. de Berker,Surya Ganguli,Surya Ganguli,Colleen J Gillon,Danijar Hafner,Danijar Hafner,Adam Kepecs,Nikolaus Kriegeskorte,Peter E. Latham,Grace W. Lindsay,Kenneth D. Miller,Richard Naud,Christopher C. Pack,Panayiota Poirazi,Pieter R. Roelfsema,João Sacramento,Andrew M. Saxe,Benjamin Scellier,Anna C. Schapiro,Walter Senn,Greg Wayne,Daniel L. K. Yamins,Friedemann Zenke,Friedemann Zenke,Joel Zylberberg,Joel Zylberberg,Denis Therien,Konrad P. Kording,Konrad P. Kording +38 more
TL;DR: It is argued that a deep network is best understood in terms of components used to design it—objective functions, architecture and learning rules—rather than unit-by-unit computation.
Journal ArticleDOI
Connectivity reflects coding: a model of voltage-based STDP with homeostasis
Claudia Clopath,Lars Holger Büsing,Lars Holger Büsing,Eleni Vasilaki,Eleni Vasilaki,Wulfram Gerstner +5 more
TL;DR: A model of spike timing–dependent plasticity (STDP) in which synaptic changes depend on presynaptic spike arrival and the postsynaptic membrane potential, filtered with two different time constants is created and found that the plasticity rule led not only to development of localized receptive fields but also to connectivity patterns that reflect the neural code.