Theories of error back-propagation in the brain
This review summarizes recently proposed theories on how neural circuits in the brain could approximate the error back-propagation algorithm employed by artificial neural networks. Computational models implementing these theories achieve learning as efficient as artificial neural networks but they u...
| Main Authors: | , |
|---|---|
| Format: | Journal article |
| Published: |
Elsevier
2019
|
| Summary: | This review summarizes recently proposed theories on how neural circuits in the brain could approximate the error back-propagation algorithm employed 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 pre-synaptic 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 organized such that modification of synaptic weights on multiple levels of cortical hierarchy leads to improved performance on tasks. |
|---|