Community detection with spiking neural networks for neuromorphic hardware

We present results related to the performance of an algorithm for community detection which incorporates event-driven computation. We define a mapping which takes a graph G to a system of spiking neurons. Using a fully connected spiking neuron system, with both inhibitory and excitatory synaptic connections, the firing patterns of neurons within the same community can be distinguished from firing patterns of neurons in different communities. On a random graph with 128 vertices and known community structure we show that by using binary decoding and a Hamming-distance based metric, individual communities can be identified from spike train similarities. Using bipolar decoding and finite rate thresholding, we verify that inhibitory connections prevent the spread of spiking patterns.Comment: Conference paper presented at ORNL Neuromorphic Workshop 2017, 7 pages, 6 figure

Generation and measurement of surface plasmon coupled emission

Surface plasmon coupled emission (SPCE) is a process by which isotropic fluorescent emission, from a fluorescent dye or biological agent labeled with a fluorescent dye, is channeled into highly directional emission. This process relies on the interaction between fluorescent dye molecules and thin silver films in close proximity to each other. Recent studies have shown that SPCE greatly increases the sensitivity of spectroscopic methods by increasing the amount of light that can be collected from a fluorescent molecule. The work described in this thesis encompasses the study of new materials for SPCE sample manufacture as well as the design and construction of a compact, automated apparatus for SPCE measurement. Novel material sets have been explored to improve the adhesion of silver films to glass slides and to protect them against corrosion from the fluorescent dye coating. These sets are made with reactive gas sputtering of materials. Examples of the SPCE signals from these materials will be shown. The apparatus built to measure the angular distribution of SPCE signals measures 18 x12 x12 . Measurements of SPCE with angular resolutions as low as 0.5° are demonstrated with accurate, repeatable scans. With a 1.0° step size, a full angular scan through 180° can be completed in less than 5 minutes

Identifying overparameterization in Quantum Circuit Born Machines

In machine learning, overparameterization is associated with qualitative changes in the empirical risk landscape, which can lead to more efficient training dynamics. For many parameterized models used in statistical learning, there exists a critical number of parameters, or model size, above which the model is constructed and trained in the overparameterized regime. There are many characteristics of overparameterized loss landscapes. The most significant is the convergence of standard gradient descent to global or local minima of low loss. In this work, we study the onset of overparameterization transitions for quantum circuit Born machines, generative models that are trained using non-adversarial gradient-based methods. We observe that bounds based on numerical analysis are in general good lower bounds on the overparameterization transition. However, bounds based on the quantum circuit's algebraic structure are very loose upper bounds. Our results indicate that fully understanding the trainability of these models remains an open question.Comment: 11 pages, 16 figure