Minimising Contrastive Divergence in Noisy, Mixed-mode VLSI Neurons

This paper presents VLSI circuits with continuous-valued proba-bilistic behaviour realized by injecting noise into each computing unit(neuron). Interconnecting the noisy neurons forms a Contin-uous Restricted Boltzmann Machine (CRBM), which has shown promising performance in modelling and classifying noisy biomed-ical data. The Minimising-Contrastive-Divergence learning algo-rithm for CRBM is also implemented in mixed-mode VLSI, to adapt the noisy neurons ’ parameters on-chip.