Artificial Neural Networks as Models of Neural Information Processing

393.3K

views

39

authors

14

articles

Artificial Neural Networks as Models of Neural Information Processing

393.3K

views

39

authors

14

articles

Editorial

19 December 2017

Editorial: Artificial Neural Networks as Models of Neural Information Processing

Marcel van Gerven

and

Sander Bohte

25,103 views

225 citations

Editors

2

Marcel van Gerven

Radboud University

Centrum Wiskunde & Informatica

Impact

About

Artificial neural networks (ANNs) are computational models that are loosely inspired by their biological counterparts. In recent years, major breakthroughs in ANN research have transformed the machine learning landscape from an engineering perspective. At the same time, scientists have started to revisit ANNs as models of neural information processing in biological agents. From an empirical point of view, neuroscientists have shown that ANNs provide state-of-the-art predictions of neural responses to naturalistic stimuli. From a theoretical point of view, computational neuroscientists have started to address the foundations of learning and inference in next-generation ANNs, identifying the desiderata that models of neural information processing should fulfill.

The goal of this Research Topic is to bring together key experimental and theoretical ANN research with the aim of providing new insights on information processing in biological neural networks through the use of artificial neural networks. We welcome contributions that are of direct relevance to neuroscientists that use ANNs as a model of neural information processing. This topic is timely given the recent exciting developments in the field and will be highly attractive to a wide community of brain researchers, as well as for the community at large.

Artificial neural networks (ANNs) are computational models that are loosely inspired by their biological counterparts. In recent years, major breakthroughs in ANN research have transformed the machine learning landscape from an engineering perspective. At the same time, scientists have started to revisit ANNs as models of neural information processing in biological agents. From an empirical point of view, neuroscientists have shown that ANNs provide state-of-the-art predictions of neural responses to naturalistic stimuli. From a theoretical point of view, computational neuroscientists have started to address the foundations of learning and inference in next-generation ANNs, identifying the desiderata that models of neural information processing should fulfill.

The goal of this Research Topic is to bring together key experimental and theoretical ANN research with the aim of providing new insights on information processing in biological neural networks through the use of artificial neural networks. We welcome contributions that are of direct relevance to neuroscientists that use ANNs as a model of neural information processing. This topic is timely given the recent exciting developments in the field and will be highly attractive to a wide community of brain researchers, as well as for the community at large.

Download ebook

Share

Editors

Marcel van Gerven

Radboud University

Centrum Wiskunde & Informatica

Impact

393,339 Total views

305,775 Article views

56,889 Article downloads

30,675 Topic views

Published In

Journal Thumbnail

Frontiers in Computational Neuroscience

About Frontiers Research Topics

With their unique mixes of varied contributions from Original Research to Review Articles, Research Topics unify the most influential researchers, the latest key findings and historical advances in a hot research area! Find out more on how to host your own Frontiers Research Topic or contribute to one as an author.

Suggest a topic