Über das Produkt
Exploring neuron models, the neural code, decision making and learning, this textbook provides a thorough and up-to-date introduction to computational neuroscience for advanced undergraduate and beginning graduate students. With step-by-step explanations, end-of-chapter summaries and classroom-tested exercises, it is ideal for courses or for self-study.
Über den Autor und weitere Mitwirkende
Wulfram Gerstner is Director of the Laboratory of Computational Neuroscience and a Professor of Life Sciences and Computer Science at the École Polytechnique Fédérale de Lausanne (EPFL) in Switzerland. He studied physics in Tubingen and Munich and holds a PhD from the Technical University of Munich. His research in computational neuroscience concentrates on models of spiking neurons and synaptic plasticity. He teaches computational neuroscience to physicists, computer scientists, mathematicians, and life scientists. He is a co-author of Spiking Neuron Models (Cambridge University Press, 2002).
Werner M. Kistler received a Master's and PhD in physics from the Technical University of Munich. He previously worked as Assistant Professor in Rotterdam for computational neuroscience and he is the co-author of Spiking Neuron Models (Cambridge University Press, 2002). He is now working in Munich as a patent attorney. His scientific contributions are related to spiking neuron models, synaptic plasticity, and network models of the cerebellum and the inferior olive.
Richard Naud holds a PhD in computational neuroscience from the EPFL in Switzerland and a Bachelor's degree in physics from McGill University, Canada. He has published several scientific articles and book chapters on the dynamics of neurons. He is now a postdoctoral researcher.
Liam Paninski is a Professor in the Department of Statistics at Columbia University and co-director of the Grossman Center for the Statistics of Mind. He is also a member of the Center for Theoretical Neuroscience, the Kavli Institute for Brain Science and the doctoral program in neurobiology and behavior. He holds a PhD in neuroscience from New York University and a Bachelor's from Brown University. His work focuses on neuron models, estimation methods, neural coding and neural decoding. He teaches courses on computational statistics, inference, and statistical analysis of neural data.