Blind Bounded Source Separation Using Neural Networks with Local Learning Rules
This addresses blind source separation for bounded signals, with potential applications in biological and neuromorphic systems, though it appears incremental as it builds on similarity matching methods.
The paper tackles blind source separation for bounded sources by proposing a new optimization problem called Bounded Similarity Matching (BSM), resulting in a recurrent neural network with clipping nonlinearity that adapts using local learning rules.
An important problem encountered by both natural and engineered signal processing systems is blind source separation. In many instances of the problem, the sources are bounded by their nature and known to be so, even though the particular bound may not be known. To separate such bounded sources from their mixtures, we propose a new optimization problem, Bounded Similarity Matching (BSM). A principled derivation of an adaptive BSM algorithm leads to a recurrent neural network with a clipping nonlinearity. The network adapts by local learning rules, satisfying an important constraint for both biological plausibility and implementability in neuromorphic hardware.