Mark Kramer

Robert E. Kass

Sonja Grün

Markus Diesmann

Peter J. Thomas

Shun-Ichi Amari

Kensuke Arai

Emery Brown

Casey O. Diekman

Brent Doiron

Uri Eden

Adrienne Fairhall

Grant M. Fiddyment

Tomoki Fukai

Matthew T. Harrison

Moritz Helias

Hiroyuki Nakahara

Jun-nosuke Teramae

Mark Reimers

Jordan Rodu

Horacio G. Rotstein

Eric Shea-Brown

Hideaki Shimazaki

Shigeru Shinomoto

Byron M. Yu

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.

Mathematical and Statistical Perspectives

Computational Neuroscience

Andrew P. Davison

 Eilif Muller

James A. Bednar

Marc-Oliver Gewaltig

Michael Hines

Python is rapidly becoming the de facto standard language for systems integration. Python has a large user and developer-base external to theneuroscience community, and a vast module library that facilitates rapid and maintainable development of complex and intricate systems. In this Research Topic, we highlight recent efforts to develop Python modules for the domain of neuroscience software and neuroinformatics: - simulators and simulator interfaces - data collection and analysis - sharing, re-use, storage and databasing of models and data - stimulus generation - parameter search and optimization - visualization - VLSI hardware interfacing. Moreover, we seek to provide a representative overview of existing mature Python modules for neuroscience and neuroinformatics, to demonstrate a critical mass and show that Python is an appropriate choice of interpreter interface for future neuroscience software development.

Python in Neuroscience

Markus Diesmann

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