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Full-Text Articles in Physical Sciences and Mathematics
Learning Two-Input Linear And Nonlinear Analog Functions With A Simple Chemical System, Peter Banda, Christof Teuscher
Learning Two-Input Linear And Nonlinear Analog Functions With A Simple Chemical System, Peter Banda, Christof Teuscher
Computer Science Faculty Publications and Presentations
The current biochemical information processing systems behave in a predetermined manner because all features are defined during the design phase. To make such unconventional computing systems reusable and programmable for biomedical applications, adaptation, learning, and self-modification baaed on external stimuli would be highly desirable. However, so far, it haa been too challenging to implement these in real or simulated chemistries. In this paper we extend the chemical perceptron, a model previously proposed by the authors, to function as an analog instead of a binary system. The new analog asymmetric signal perceptron learns through feedback and supports MichaelisMenten kinetics. The results …
The Role Of Prototype Learning In Hierarchical Models Of Vision, Michael David Thomure
The Role Of Prototype Learning In Hierarchical Models Of Vision, Michael David Thomure
Dissertations and Theses
I conduct a study of learning in HMAX-like models, which are hierarchical models of visual processing in biological vision systems. Such models compute a new representation for an image based on the similarity of image sub-parts to a number of specific patterns, called prototypes. Despite being a central piece of the overall model, the issue of choosing the best prototypes for a given task is still an open problem. I study this problem, and consider the best way to increase task performance while decreasing the computational costs of the model. This work broadens our understanding of HMAX and related hierarchical …
Damage Spreading In Spatial And Small-World Random Boolean Networks, Qiming Lu, Christof Teuscher
Damage Spreading In Spatial And Small-World Random Boolean Networks, Qiming Lu, Christof Teuscher
Computer Science Faculty Publications and Presentations
The study of the response of complex dynamical social, biological, or technological networks to external perturbations has numerous applications. Random Boolean networks (RBNs) are commonly used as a simple generic model for certain dynamics of complex systems. Traditionally, RBNs are interconnected randomly and without considering any spatial extension and arrangement of the links and nodes. However, most real-world networks are spatially extended and arranged with regular, power-law, small-world, or other nonrandom connections. Here we explore the RBN network topology between extreme local connections, random small-world, and pure random networks, and study the damage spreading with small perturbations. We find that …
Learning General Features From Images And Audio With Stacked Denoising Autoencoders, Nathaniel H. Nifong
Learning General Features From Images And Audio With Stacked Denoising Autoencoders, Nathaniel H. Nifong
Dissertations and Theses
One of the most impressive qualities of the brain is its neuro-plasticity. The neocortex has roughly the same structure throughout its whole surface, yet it is involved in a variety of different tasks from vision to motor control, and regions which once performed one task can learn to perform another. Machine learning algorithms which aim to be plausible models of the neocortex should also display this plasticity. One such candidate is the stacked denoising autoencoder (SDA). SDA's have shown promising results in the field of machine perception where they have been used to learn abstract features from unlabeled data. In …