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Simon Laughlin, Ph.D. (ANU)

Affiliations: 
Zoology University of Cambridge, Cambridge, England, United Kingdom 
Area:
Visual system, energy efficiency, neural circuit design, natural image statistics, information theory, noise constraints, optimal coding
Website:
http://www.zoo.cam.ac.uk/zoostaff/laughlin/
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"Simon Laughlin"
Mean distance: 12.89 (cluster 39)
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Parents

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Randolf Menzel grad student 1971-1973 Neurobiology Australian National University
 (A bold new perspective)
G. Adrian Horridge grad student 1970-1974 ANU
 (A formative experience)

Children

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Roger C. Hardie grad student Cambridge
Stephen Huston grad student Cambridge
Susanne Schreiber grad student Cambridge
Jonathon Howard grad student 1978-1981 Neurobiology Australian National University
Brian G. Burton grad student 1997-2001 Cambridge
Robert Harris grad student 1998-2001 Biology, University of Sussex
Aldo A. Faisal grad student 2000-2004 Cambridge
Matthew M. Parsons grad student 2004-2009 Cambridge
Anne M.M. Fransen grad student 2009-2010 Cambridge
Biswa Sengupta grad student 2007-2011 Cambridge
Nikon A. Rasumov grad student 2008-2012 Cambridge
Arjun Bharioke grad student 2008-2015 Cambridge
Francisco J. H Heras grad student 2011-2016
Rob de Ruyter van Steveninck post-doc Cambridge
Peter Neri post-doc Cambridge
David Charles O'Carroll post-doc Cambridge
Matti Weckstrom post-doc University of Oulu
Jeremy E. Niven post-doc 2003-2005 Cambridge

Collaborators

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Roger C. Hardie collaborator Cambridge
Holger Krapp collaborator Cambridge
Matti Weckstrom collaborator University of Oulu
John A. White collaborator Cambridge
Peter Sterling collaborator 1999- Cambridge
Gordon L. Fain collaborator 2008- Cambridge
Doekele G. Stavenga collaborator 1975-1976 Neurobiology Australian National University
 (applied information theory to vision)
Allan W. Snyder collaborator 1971-1984 Applied Mathematics, Australian National University
David Attwell collaborator 1999-2003 Cambridge
BETA: Related publications

Publications

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Sterling P, Laughlin S. (2023) Why an animal needs a brain. Animal Cognition
Heras FJ, Anderson J, Laughlin SB, et al. (2017) Voltage-dependent K(+) channels improve the energy efficiency of signalling in blowfly photoreceptors. Journal of the Royal Society, Interface. 14
Heras FJ, Laughlin SB, Niven JE. (2016) Shunt peaking in neural membranes. Journal of the Royal Society, Interface. 13
Sterling P, Laughlin S. (2015) Principles of neural design Principles of Neural Design. 1-542
Sengupta B, Laughlin SB, Niven JE. (2014) Consequences of converting graded to action potentials upon neural information coding and energy efficiency. Plos Computational Biology. 10: e1003439
Sengupta B, Laughlin SB, Niven JE. (2013) Balanced excitatory and inhibitory synaptic currents promote efficient coding and metabolic efficiency. Plos Computational Biology. 9: e1003263
Sengupta B, Faisal AA, Laughlin SB, et al. (2013) The effect of cell size and channel density on neuronal information encoding and energy efficiency. Journal of Cerebral Blood Flow and Metabolism : Official Journal of the International Society of Cerebral Blood Flow and Metabolism. 33: 1465-73
Laughlin S. (2013) The influence of metabolic energy on neural computation Bmc Neuroscience. 14
Rasumov N, Niven J, Baker M, et al. (2011) Sensory adaptation in the light of information and energy Nature Precedings. 1-1
Stemmler M, Sengupta B, Laughlin S, et al. (2011) Energetically optimal action potentials Advances in Neural Information Processing Systems 24: 25th Annual Conference On Neural Information Processing Systems 2011, Nips 2011
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