Theories of Error Back-Propagation in the Brain
By James C. R. Whittington, Rafal Bogacz. Trends in Cognitive Sciences, 2019
444 citations recorded by the source corpus.
This review article summarises recently proposed theories on how neural circuits in the brain could approximate the error back-propagation algorithm used by artificial neural networks. Computational models implementing these theories achieve learning as efficient as artificial neural networks, but they use simple synaptic plasticity rules based on activity of presynaptic and postsynaptic neurons. The models have similarities, such as including both feedforward and feedback connections, allowing information about error to propagate throughout the network. Furthermore, they incorporate experimental evidence on neural connectivity, responses, and plasticity. These models provide insights on how brain networks might be organised such that modification of synaptic weights on multiple levels of cortical hierarchy leads to improved performance on tasks.
Subfields: Cognitive Neuroscience
View Paper: https://www.semanticscholar.org/paper/d6e2b093ee5cb5f6533bd41eaecdb9e4a57eee67
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Cognitive Neuroscience
Citations in the corpus
- The Rhetoric and Reality of Anthropomorphism in Artificial Intelligence (2019, methodology): “Predictive coding has also been conceptualized as a sort of backpropagation algorithm (Whittington and Bogacz 2019), in reference to the method by which neural network parameters are trained.”
- Convolutional Neural Networks as a Model of the Visual System: Past, Present, and Future (2020, background): “That being said, several researchers are investigating means by which the brain could perform something like backpropagation[111, 112, 113, 114].”
- Synaptic Plasticity Forms and Functions. (2020, background): “…an active research area, and recent work suggests that there may be more compatibilities NE43CH05_Magee ARjats.cls February 6, 2020 10:7 between the biology and theory than were originally appreciated (Bengio et al. 2016, Lillicrap et al. 2016, Roelfsema & Holtmatt 2018, Whittington & Bogacz 2019).”