A spiking neural network based on thalamo-cortical neurons for self-learning agent applications - Huderek, Damian; Szczęsny, Szymon; Pietrzak, Paweł; Rato, Raul; Przyborowski, Łukasz (Zielona Góra: Uniwersytet Zielonogórski)
The paper proposes a non-iterative training algorithm for a power efficient SNN classifier for applications in self-learning systems. The approach uses mechanisms of preprocessing of signals from sensory neurons typical of a thalamus in a diencephalon. The algorithm concept is based on a cusp catastrophe model and on training by routing. The algorithm guarantees a zero dispersion of connection weight values across the entire network, which is particularly important in the case of hardware implementation based on programmable logic devices.
Keywords: self-learning systems; classification; spiking neural networks; feature recognition; learning by routing