Bio-NICA: A biologically inspired single-layer network for Nonnegative Independent Component Analysis
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Blind source separation, the problem of separating mixtures of unknown signals into their distinct sources, is an important problem for both biological and engineered signal processing systems. Nonnegative Independent Component Analysis (NICA) is a special case of blind source separation that assumes the mixture is a linear combination of independent, nonnegative sources. In this work, we derive a single-layer neural network implementation of NICA satisfying the following 3 constraints, which are relevant for biological systems and the design of neuromorphic hardware: (i) the network operates in the online setting, (ii) the synaptic learning rules are local, and (iii) the neural outputs are nonnegative.
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