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Artificial neural networks for neuroscientists: A primer
- Publication Year :
- 2020
- Publisher :
- arXiv, 2020.
-
Abstract
- Artificial neural networks (ANNs) are essential tools in machine learning that have drawn increasing attention in neuroscience. Besides offering powerful techniques for data analysis, ANNs provide a new approach for neuroscientists to build models for complex behaviors, heterogeneous neural activity and circuit connectivity, as well as to explore optimization in neural systems, in ways that traditional models are not designed for. In this pedagogical Primer, we introduce ANNs and demonstrate how they have been fruitfully deployed to study neuroscientific questions. We first discuss basic concepts and methods of ANNs. Then, with a focus on bringing this mathematical framework closer to neurobiology, we detail how to customize the analysis, structure, and learning of ANNs to better address a wide range of challenges in brain research. To help the readers garner hands-on experience, this Primer is accompanied with tutorial-style code in PyTorch and Jupyter Notebook, covering major topics.
- Subjects :
- 0301 basic medicine
FOS: Computer and information sciences
Computer Science - Machine Learning
Computer science
Models, Neurological
Computer Science::Neural and Evolutionary Computation
Brain research
Machine Learning (cs.LG)
03 medical and health sciences
Neural activity
0302 clinical medicine
medicine
Animals
Humans
Neural system
030304 developmental biology
Structure (mathematical logic)
0303 health sciences
Artificial neural network
Quantitative Biology::Neurons and Cognition
business.industry
General Neuroscience
Brain
medicine.anatomical_structure
030104 developmental biology
Quantitative Biology - Neurons and Cognition
FOS: Biological sciences
Neurons and Cognition (q-bio.NC)
Neuron
Neural Networks, Computer
Artificial intelligence
Primer (molecular biology)
business
Neuroscience
030217 neurology & neurosurgery
Subjects
Details
- Database :
- OpenAIRE
- Accession number :
- edsair.doi.dedup.....79e00e20411db4081f1e007419d67498
- Full Text :
- https://doi.org/10.48550/arxiv.2006.01001