Neuroscience-inspired online unsupervised learning algorithms
This work addresses the problem of creating more brain-like AI systems for researchers in neuroscience and machine learning, though it appears incremental in applying known principles to new network designs.
The authors tackled the biological implausibility of deep learning by developing a family of biologically plausible artificial neural networks for unsupervised learning, achieving tasks like dimensionality reduction and clustering through similarity-based objective functions optimized with local learning rules.
Although the currently popular deep learning networks achieve unprecedented performance on some tasks, the human brain still has a monopoly on general intelligence. Motivated by this and biological implausibility of deep learning networks, we developed a family of biologically plausible artificial neural networks (NNs) for unsupervised learning. Our approach is based on optimizing principled objective functions containing a term that matches the pairwise similarity of outputs to the similarity of inputs, hence the name - similarity-based. Gradient-based online optimization of such similarity-based objective functions can be implemented by NNs with biologically plausible local learning rules. Similarity-based cost functions and associated NNs solve unsupervised learning tasks such as linear dimensionality reduction, sparse and/or nonnegative feature extraction, blind nonnegative source separation, clustering and manifold learning.