Yingxue Wang

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This demonstration shows the first implementation of a real-time spike-based convolution processing system which combines a spike based dynamic vision sensor (DVS) with parallel graphics processor unit (GPU) computation. Moving objects with different features (shape and size) are presented to the system. In the first demo, the system responses in real time(More)
— We describe a formalism for quantifying the performance of spike-based winner-take-all (WTA) VLSI chips. The WTA function non-linearly amplifies the output responses of pixels/neurons dependent on the input magnitudes in a decision or selection task. In this work, we show a theoretical description of this winner-take-all computation which takes into(More)
With the advent of new experimental evidence showing that dendrites play an active role in processing a neuron's inputs, we revisit the question of a suitable abstraction for the computing function of a neuron in processing spatiotemporal input patterns. Although the integrative role of a neuron in relation to the spatial clustering of synaptic inputs can(More)
—We describe a spiking neuronal network which allows local synaptic weights to be assigned to individual synapses. In previous implementations of neuronal networks, the biases that control the parameters of a particular synapse are global to all synapses of the same type regardless of the target neuron. In this new implementation, the parameters for a(More)
Capturing the functionality of active dendritic processing into abstract mathematical models will help us to understand the role of complex biophysical neurons in neuronal computation and to build future useful neuromorphic analog Very Large Scale Integrated (aVLSI) neuronal devices. Previous work based on an aVLSI multi-compartmental neuron model(More)
This paper proposes a new speech bandwidth expansion method, which uses Deep Neural Networks (DNNs) to build high-order eigenspaces between the low frequency components and the high frequency components of the speech signal. A four-layer DNN is trained layer-by-layer from a cascade of Neural Networks (NNs) and two Gaussian-Bernoulli Restricted Boltzmann(More)