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PubMed2006

Backaction noise in strongly interacting systems: the dc SQUID and the interacting quantum point contact.

We study the backaction noise and measurement the efficiency (i.e., noise temperature) of a dc SQUID amplifier and, equivalently, a quantum point-contact detector formed in a Luttinger liquid. Using a mapping to a dissipative tight-binding model, we show that these systems are able to reach the quantum limit even in regimes where several independent transport processes contribute to the current. We suggest how this is related to the underlying integrability of these systems.

Journal Article↗
PubMed2006

Learning lateral interactions for feature binding and sensory segmentation from prototypic basis interactions.

We present a hybrid learning method bridging the fields of recurrent neural networks, unsupervised Hebbian learning, vector quantization, and supervised learning to implement a sophisticated image and feature segmentation architecture. This architecture is based on the competitive layer model (CLM), a dynamic feature binding model, which is applicable on a wide range of perceptual grouping and segmentation problems. A predefined target segmentation can be achieved as attractor states of this linear threshold recurrent network, if the lateral weights are chosen by Hebbian learning. The weight matrix is given by the correlation matrix of special pattern vectors with a structure dependent on the target labeling. Generalization is achieved by applying vector quantization on pair-wise feature relations, like proximity and similarity, defined by external knowledge. We show the successful application of the method to a number of artifical test examples and a medical image segmentation problem of fluorescence microscope cell images.

Image Processing, Computer-Assisted↗