Online Linear Subspace Learning in an Analog Array Computing Architecture

We map to an analog array computing framework a recently presented algorithm for online learning of linear subspaces using local learning rules. We demonstrate that the considered algorithm is well suited for implementation in analog array computing architectures, and that the computation of neural activity dynamics required in the learning can be realized in an efficient homeostatic manner using an analog continuoustime circuit.