K
Kenneth D. Miller
Researcher at Columbia University
Publications - 125
Citations - 12153
Kenneth D. Miller is an academic researcher from Columbia University. The author has contributed to research in topics: Visual cortex & Receptive field. The author has an hindex of 45, co-authored 121 publications receiving 10676 citations. Previous affiliations of Kenneth D. Miller include University of California & University of Southern California.
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Journal ArticleDOI
Competitive Hebbian learning through spike-timing-dependent synaptic plasticity
TL;DR: In modeling studies, it is found that this form of synaptic modification can automatically balance synaptic strengths to make postsynaptic firing irregular but more sensitive to presynaptic spike timing.
Journal ArticleDOI
Neural Mechanisms of Orientation Selectivity in the Visual Cortex
David Ferster,Kenneth D. Miller +1 more
TL;DR: The origin of orientation selectivity in the responses of simple cells in cat visual cortex serves as a model problem for understanding cortical circuitry and computation and the modified feed-forward and the feedback models ascribe fundamentally different functions to cortical processing.
Journal ArticleDOI
Ocular dominance column development: analysis and simulation
TL;DR: A mathematical model of several biological mechanisms that can account for ocular dominance segregation and the resulting patch width is presented and can be used to predict the results of proposed experiments and to discriminate among various mechanisms of plasticity.
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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
The role of constraints in Hebbian learning
TL;DR: These results may be used to understand constraints both over output cells and over input cells, and a variety of rules that can implement constrained dynamics are discussed.