The art of computer programming, volume 3: (2nd ed.) sorting and searching
The art of computer programming, volume 3: (2nd ed.) sorting and searching
Synchrony and desynchrony in integrate-and-fire oscillators
Neural Computation
The bifurcating neuron network 2: an analog associative memory
Neural Networks
Synchronization via multiplex spike-trains in digital pulse coupled networks
ICONIP'06 Proceedings of the 13th international conference on Neural information processing - Volume Part III
Sequence designs for ultra-wideband impulse radio with optimal correlation properties
IEEE Transactions on Information Theory
UWB wireless sensor networks: UWEN - a practical example
IEEE Communications Magazine
IEEE Transactions on Neural Networks
Synchronization phenomena in pulse-coupled networks driven by spike-train inputs
IEEE Transactions on Neural Networks
Fundamental Analysis of a Digital Spiking Neuron for Its Spike-Based Coding
Neural Information Processing
A novel hybrid spiking neuron: bifurcations, responses, and on-chip learning
IEEE Transactions on Circuits and Systems Part I: Regular Papers
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The digital spiking neuron (DSN) consists of digital state cells and behaves like a simplified neuron model. By adjusting wirings among the cells, the DSN can generate spike-trains with various characteristics. In this paper we present a theorem that clarifies basic relations between change of wirings and change of characteristics of the spike-train. Also, in order to explore learning potential of the DSN, we propose a learning algorithm for generating spike-trains that are suited to an application example. We then show significances and basic roles of the presented theorem in the learning dynamics.