STM publisher Elsevier has announced the publication of Neural Data Science: A Primer with MATLAB® and Python™ by Erik Lee Nylen and Pascal Wallisch. The book offers a beginner's introduction to the principles of computation and data analysis in neuroscience, using both Python and MATLAB in parallel, giving readers the ability to transcend platform tribalism and enable coding versatility. Elsevier also announced the publication of six additional neuroscience books.
Neural Data Science: A Primer with MATLAB® and Python™ introduces the canonical data analysis cascade, standardizing the data analysis flow. The book also presents methods that strategically, tactically and algorithmically help improve the organization of code. It is a valuable resource for students, researchers and instructors in systems, cognitive and behavioural neuroscience, and cognitive psychology.
The seven new neuroscience titles are: Neural Data Science: A Primer with MATLAB® and Python™ by Erik Lee Nylen and Pascal Wallisch; Molecular and Cellular Therapies for Motor Neuron Diseases by Nicholas Boulis, Deirdre O’Connor and Anthony Donsante; Stress: Neuroendocrinology and Neurobiology by George Fink; Neuroprotection in Alzheimer's Disease by Illana Gozes; Mathematics for Neuroscientists, Second Edition by Fabrizio Gabbiani and Steven J. Cox; Primer on Cerebrovascular Diseases, Second Edition by Louis R. Caplan, José Biller, Megan Leary, Eng Lo, Ajith Thomas, Midori Yenari and John Zhang; and Translational Immunotherapy of Brain Tumors by John H. Sampson.
In order to meet content needs in neuroscience, Elsevier uses proprietary tools to identify the gaps in coverage of the topics. Editorial teams strategically fill those gaps with content written by key influencers in the field, giving students, faculty and researchers the content they need to answer challenging questions and improve outcomes. These new books will seek to educate the next generation of neuroscientists, and provide critical foundational content for information professionals.
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