Neural network models and deep learning - a primer for biologists
February 13, 2019 ยท Declared Dead ยท ๐ Current Biology
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Nikolaus Kriegeskorte, Tal Golan
arXiv ID
1902.04704
Category
q-bio.NC
Cross-listed
cs.LG,
cs.NE
Citations
441
Venue
Current Biology
Last Checked
2 months ago
Abstract
Originally inspired by neurobiology, deep neural network models have become a powerful tool of machine learning and artificial intelligence, where they are used to approximate functions and dynamics by learning from examples. Here we give a brief introduction to neural network models and deep learning for biologists. We introduce feedforward and recurrent networks and explain the expressive power of this modeling framework and the backpropagation algorithm for setting the parameters. Finally, we consider how deep neural networks might help us understand the brain's computations.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
๐ Similar Papers
In the same crypt โ q-bio.NC
R.I.P.
๐ป
Ghosted
R.I.P.
๐ป
Ghosted
SuperSpike: Supervised learning in multi-layer spiking neural networks
R.I.P.
๐ป
Ghosted
Generic decoding of seen and imagined objects using hierarchical visual features
R.I.P.
๐ป
Ghosted
Convolutional Neural Networks as a Model of the Visual System: Past, Present, and Future
R.I.P.
๐ป
Ghosted
A probabilistic atlas of the human thalamic nuclei combining ex vivo MRI and histology
R.I.P.
๐ป
Ghosted
Why Neurons Have Thousands of Synapses, A Theory of Sequence Memory in Neocortex
Died the same way โ ๐ป Ghosted
R.I.P.
๐ป
Ghosted
Language Models are Few-Shot Learners
R.I.P.
๐ป
Ghosted
PyTorch: An Imperative Style, High-Performance Deep Learning Library
R.I.P.
๐ป
Ghosted
XGBoost: A Scalable Tree Boosting System
R.I.P.
๐ป
Ghosted