Add to ORKGIn this paper, we introduce a deep spiking delayed feedback reservoir (DFR) model to combine DFR with spiking neuros: DFRs are a new type of recurrent neural networks (RNNs) that are able to capture the temporal correlations in time series while spiking neurons are energy-efficient and biologically plausible neurons models. The introduced deep spiking DFR model is energy-efficient and has the capability of analyzing time series signals. The corresponding field programmable gate arrays (FPGA)-based hardware implementation of such deep spiking DFR model is introduced and the underlying energy-efficiency and recourse utilization are evaluated. Various spike encoding schemes are explored and the optimal spike encoding scheme to analyze the time...
We present a neuromorphic spiking neural network, the DELTRON, that can remember and store patterns ...
This thesis describes the design and implementation of two pattern recognition systems on field-prog...
Spiking Neural Networks (SNNs) have recently attracted significant research interest as the third ge...
In this paper, we introduce a deep spiking delayed feedback reservoir (DFR) model to combine DFR wit...
This chapter introduces the novel applications of deep reservoir computing (RC) systems in cyber-sec...
Implementation of a neuron like information processing structure at hardware level is a burning rese...
Conventional reservoir computing (RC) is a shallow recurrent neural network (RNN) with fixed high di...
With the continuous development of deep learning, the scientific community continues to propose new ...
Spiking sensors such as the silicon retina and cochlea encode analog signals into massively parallel...
Spectrum sensing plays a critical role in dynamic spectrum sharing, a promising technology to addre...
The object of this thesis is to investigate polychronous spiking neural networks using neuromorphic ...
Reservoir computing (RC) is a class of neuromorphic computing approaches that deals particularly wel...
© 1972-2012 IEEE. Spectrum sensing plays a critical role in dynamic spectrum sharing, a promising te...
Spiking neural networks have been widely used for supervised pattern recognition exploring the under...
Learning is central to infusing intelligence to any biologically inspired system. This study introdu...
We present a neuromorphic spiking neural network, the DELTRON, that can remember and store patterns ...
This thesis describes the design and implementation of two pattern recognition systems on field-prog...
Spiking Neural Networks (SNNs) have recently attracted significant research interest as the third ge...
In this paper, we introduce a deep spiking delayed feedback reservoir (DFR) model to combine DFR wit...
This chapter introduces the novel applications of deep reservoir computing (RC) systems in cyber-sec...
Implementation of a neuron like information processing structure at hardware level is a burning rese...
Conventional reservoir computing (RC) is a shallow recurrent neural network (RNN) with fixed high di...
With the continuous development of deep learning, the scientific community continues to propose new ...
Spiking sensors such as the silicon retina and cochlea encode analog signals into massively parallel...
Spectrum sensing plays a critical role in dynamic spectrum sharing, a promising technology to addre...
The object of this thesis is to investigate polychronous spiking neural networks using neuromorphic ...
Reservoir computing (RC) is a class of neuromorphic computing approaches that deals particularly wel...
© 1972-2012 IEEE. Spectrum sensing plays a critical role in dynamic spectrum sharing, a promising te...
Spiking neural networks have been widely used for supervised pattern recognition exploring the under...
Learning is central to infusing intelligence to any biologically inspired system. This study introdu...
We present a neuromorphic spiking neural network, the DELTRON, that can remember and store patterns ...
This thesis describes the design and implementation of two pattern recognition systems on field-prog...
Spiking Neural Networks (SNNs) have recently attracted significant research interest as the third ge...