Unsupervised Learning by Competing Hidden Units
This addresses the problem of biologically implausible learning mechanisms in AI for researchers in computational neuroscience and machine learning, though it is incremental as it builds on existing unsupervised and supervised methods.
The paper tackles the biological implausibility of backpropagation in neural networks by proposing an unsupervised learning rule based on local synaptic changes and global inhibition, which learns early feature detectors that enable full network performance comparable to standard backpropagation-trained networks.
It is widely believed that the backpropagation algorithm is essential for learning good feature detectors in early layers of artificial neural networks, so that these detectors are useful for the task performed by the higher layers of that neural network. At the same time, the traditional form of backpropagation is biologically implausible. In the present paper we propose an unusual learning rule, which has a degree of biological plausibility, and which is motivated by Hebb's idea that change of the synapse strength should be local - i.e. should depend only on the activities of the pre and post synaptic neurons. We design a learning algorithm that utilizes global inhibition in the hidden layer, and is capable of learning early feature detectors in a completely unsupervised way. These learned lower layer feature detectors can be used to train higher layer weights in a usual supervised way so that the performance of the full network is comparable to the performance of standard feedforward networks trained end-to-end with a backpropagation algorithm.