Erik De Schutter
- Published in print:
- 2009
- Published Online:
- August 2013
- ISBN:
- 9780262013277
- eISBN:
- 9780262258722
- Item type:
- chapter
- Publisher:
- The MIT Press
- DOI:
- 10.7551/mitpress/9780262013277.003.0001
- Subject:
- Neuroscience, Techniques
This book concentrates on data-driven modeling, i.e., the use of fairly standardized modeling methods to replicate the behavior of neural systems at different levels of detail. The chapters are ...
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This book concentrates on data-driven modeling, i.e., the use of fairly standardized modeling methods to replicate the behavior of neural systems at different levels of detail. The chapters are structured in intuitive order and try to cover all aspects of neural modeling, from molecules to networks. Each addresses the equations needed to simulate these models, sources of data for the model parameters, approaches to validate the models, and a short review of relevant models. This chapter provides an overview of those that follow.Less
This book concentrates on data-driven modeling, i.e., the use of fairly standardized modeling methods to replicate the behavior of neural systems at different levels of detail. The chapters are structured in intuitive order and try to cover all aspects of neural modeling, from molecules to networks. Each addresses the equations needed to simulate these models, sources of data for the model parameters, approaches to validate the models, and a short review of relevant models. This chapter provides an overview of those that follow.
Erik De Schutter (ed.)
- Published in print:
- 2009
- Published Online:
- August 2013
- ISBN:
- 9780262013277
- eISBN:
- 9780262258722
- Item type:
- book
- Publisher:
- The MIT Press
- DOI:
- 10.7551/mitpress/9780262013277.001.0001
- Subject:
- Neuroscience, Techniques
This book offers an introduction to current methods in computational modeling in neuroscience, and describes realistic modeling methods at levels of complexity ranging from molecular interactions to ...
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This book offers an introduction to current methods in computational modeling in neuroscience, and describes realistic modeling methods at levels of complexity ranging from molecular interactions to large neural networks. A “how to” book rather than an analytical account, it focuses on the presentation of methodological approaches, including the selection of the appropriate method and its potential pitfalls. The book is intended for experimental neuroscientists and graduate students who have little formal training in mathematical methods, but will also be useful for scientists with theoretical backgrounds who want to start using data-driven modeling methods. The mathematics needed are kept to an introductory level; the first chapter explains the mathematical methods the reader needs to master to understand the rest of the book. The chapters are written by scientists who have successfully integrated data-driven modeling with experimental work, so all of the material is accessible to experimentalists and offers comprehensive coverage with little overlap, and extensive cross-references moving from basic building blocks to more complex applications.Less
This book offers an introduction to current methods in computational modeling in neuroscience, and describes realistic modeling methods at levels of complexity ranging from molecular interactions to large neural networks. A “how to” book rather than an analytical account, it focuses on the presentation of methodological approaches, including the selection of the appropriate method and its potential pitfalls. The book is intended for experimental neuroscientists and graduate students who have little formal training in mathematical methods, but will also be useful for scientists with theoretical backgrounds who want to start using data-driven modeling methods. The mathematics needed are kept to an introductory level; the first chapter explains the mathematical methods the reader needs to master to understand the rest of the book. The chapters are written by scientists who have successfully integrated data-driven modeling with experimental work, so all of the material is accessible to experimentalists and offers comprehensive coverage with little overlap, and extensive cross-references moving from basic building blocks to more complex applications.
Alain Barrat and Ciro Cattuto
- Published in print:
- 2018
- Published Online:
- December 2018
- ISBN:
- 9780198809456
- eISBN:
- 9780191847073
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/oso/9780198809456.003.0006
- Subject:
- Physics, Theoretical, Computational, and Statistical Physics
The chapter “Data Summaries and Representations: Definitions and Practical Use” examines data structures used to deal with complex networked data, using temporal networks as a concrete case. Complex ...
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The chapter “Data Summaries and Representations: Definitions and Practical Use” examines data structures used to deal with complex networked data, using temporal networks as a concrete case. Complex networked data has become available in a variety of contexts, describing a variety of systems with growing abundance of details, such as, for instance, links between individuals in social networks, or the temporal evolution of these links. However, data needs to be summarized and represented in simple forms. This chapter describes several commonly used data summaries and levels of representation of temporal networks, as well as novel data representations that have been developed through the MULTIPLEX project. It focuses in particular on the case of temporal networks of contacts between individuals and shows in a series of concrete use cases how different representations can be used to characterize and compare data, or feed data-driven models of epidemic spreading processes.Less
The chapter “Data Summaries and Representations: Definitions and Practical Use” examines data structures used to deal with complex networked data, using temporal networks as a concrete case. Complex networked data has become available in a variety of contexts, describing a variety of systems with growing abundance of details, such as, for instance, links between individuals in social networks, or the temporal evolution of these links. However, data needs to be summarized and represented in simple forms. This chapter describes several commonly used data summaries and levels of representation of temporal networks, as well as novel data representations that have been developed through the MULTIPLEX project. It focuses in particular on the case of temporal networks of contacts between individuals and shows in a series of concrete use cases how different representations can be used to characterize and compare data, or feed data-driven models of epidemic spreading processes.
