Mathematical and statistical models have played important roles in neuroscience, especially by describing the electrical activity of neurons recorded individually, or collectively across large networks. As the field moves forward rapidly, new challenges are emerging. For maximal effectiveness, those working to advance computational neuroscience will need to appreciate and exploit the complementary strengths of mechanistic theory and the statistical paradigm.National Science Foundation (U.S.) (Grant DMS-1127914)National Institute of Mental Health (U.S.) (Grant MH064537
© 2016 Elsevier Ltd.Computational modeling and associated methods have greatly advanced our understa...
Neuroscience has become a very broad field indeed: each year around 30,000 researchers and student...
Population density techniques are efficient simulation techniques for modeling large homogeneous pop...
Mathematical and statistical models have played important roles in neuroscience, especially by descr...
The ultimate aim of computational neuroscience is to explain how electrical and chemical signals are...
This volume gathers contributions from theoretical, experimental and computational researchers who a...
Technological advances in experimental neuroscience are generating vast quantities of data, from the...
Computational Neuroscience deals with the study of information dynamics inside brain. Neuron as unit...
This book has two main aims: to teach how mathematical models that illuminate some parts of neurosci...
Theoretical approaches have long shaped neuroscience, but current needs for theory are elevated and ...
Computational neuroscience is an interdisciplinary field that incorporates an analysis of brain func...
This book is intended for use in advanced graduate courses in statistics / machine learning, as well...
AbstractThis article reviews the substantial impact computational neuroscience has had on neuroimagi...
International audienceThe CNS, like all complex systems, features a large variety of spatial and tem...
What is mathematical neuroscience? Is it simply an interdisciplinary field that aims at modelling ne...
© 2016 Elsevier Ltd.Computational modeling and associated methods have greatly advanced our understa...
Neuroscience has become a very broad field indeed: each year around 30,000 researchers and student...
Population density techniques are efficient simulation techniques for modeling large homogeneous pop...
Mathematical and statistical models have played important roles in neuroscience, especially by descr...
The ultimate aim of computational neuroscience is to explain how electrical and chemical signals are...
This volume gathers contributions from theoretical, experimental and computational researchers who a...
Technological advances in experimental neuroscience are generating vast quantities of data, from the...
Computational Neuroscience deals with the study of information dynamics inside brain. Neuron as unit...
This book has two main aims: to teach how mathematical models that illuminate some parts of neurosci...
Theoretical approaches have long shaped neuroscience, but current needs for theory are elevated and ...
Computational neuroscience is an interdisciplinary field that incorporates an analysis of brain func...
This book is intended for use in advanced graduate courses in statistics / machine learning, as well...
AbstractThis article reviews the substantial impact computational neuroscience has had on neuroimagi...
International audienceThe CNS, like all complex systems, features a large variety of spatial and tem...
What is mathematical neuroscience? Is it simply an interdisciplinary field that aims at modelling ne...
© 2016 Elsevier Ltd.Computational modeling and associated methods have greatly advanced our understa...
Neuroscience has become a very broad field indeed: each year around 30,000 researchers and student...
Population density techniques are efficient simulation techniques for modeling large homogeneous pop...