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Hardware implementations of spiking neurons can be extremely useful for a large variety of applications, ranging from high-speed modeling of large-scale neural systems to real-time behaving systems, to bidirectional brain-machine interfaces. The specific circuit solutions used to implement silicon neurons depend on the application requirements. In this(More)
Sensory cue inputs and memory-related internal brain activities govern the firing of hippocampal neurons, but which specific firing patterns are induced by either of the two processes remains unclear. We found that sensory cues guided the firing of neurons in rats on a timescale of seconds and supported the formation of spatial firing fields. Independently(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)
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)
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)