KEJIE HUANG | Singapore University of Technology and Design (SUTD) (original) (raw)
Principal Investigator in Zhejiang University
Address: Singapore, Singapore
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Papers by KEJIE HUANG
Advanced materials (Deerfield Beach, Fla.), Jan 26, 2014
A new methodology for manipulating transient-amorphous states of phase-change memory (PCM) materi... more A new methodology for manipulating transient-amorphous states of phase-change memory (PCM) materials is reported as a viable means to boost the speed, yet reduce the power consumption of PC memories, and is applicable to new forms of PCM-based neuromorphic devices. Controlling multiple-pulse interactions with PC materials may provide an opportunity toward developing a new paradigm for ultra-fast neuromorphic computing.
Applied Biomedical Engineering, 2011
Lecture Notes in Computer Science, 2011
We present a minimal neuron model that captures the essence of the persistent firing behavior of ... more We present a minimal neuron model that captures the essence of the persistent firing behavior of interneurons as discovered recently in the field of Neuroscience. The mathematical model reproduces the phenomenon that slow integration in distal axon of interneurons on a timescale of tens of seconds to minutes, leads to persistent firing of axonal action potentials lasted for similar duration. In this model, we consider the axon as a slow leaky integrator, which is capable of dynamically switching the neuronal firing states between normal firing and persistent firing, through axonal computation. This model is based on the Izhikevich neuron model and includes additional equations and parameters to represent the persistent firing dynamics, making it computationally efficient yet bio-plausible, and thus well suitable for large scale spiking network simulations.
Scientific Reports, 2014
Learning scheme is the key to the utilization of spike-based computation and the emulation of neu... more Learning scheme is the key to the utilization of spike-based computation and the emulation of neural/ synaptic behaviors toward realization of cognition. The biological observations reveal an integrated spike time-and spike rate-dependent plasticity as a function of presynaptic firing frequency. However, this integrated rate-temporal learning scheme has not been realized on any nano devices. In this paper, such scheme is successfully demonstrated on a memristor. Great robustness against the spiking rate fluctuation is achieved by waveform engineering with the aid of good analog properties exhibited by the iron oxide-based memristor. The spike-time-dependence plasticity (STDP) occurs at moderate presynaptic firing frequencies and spike-rate-dependence plasticity (SRDP) dominates other regions. This demonstration provides a novel approach in neural coding implementation, which facilitates the development of bio-inspired computing systems.
2014 IEEE/ACM International Symposium on Nanoscale Architectures (NANOARCH), 2014
ABSTRACT
IEEE Transactions on Circuits and Systems I: Regular Papers, 2000
International Journal of Intelligence Science, 2015
Neurons are believed to be the brain computational engines of the brain. A recent discovery in ne... more Neurons are believed to be the brain computational engines of the brain. A recent discovery in neurophysiology reveals that interneurons can slowly integrate spiking, share the output across a coupled network of axons and respond with persistent firing even in the absence of input to the soma or dendrites, which has not been understood and could be very important for exploring the mechanism of human cognition. The conventional models are incapable of simulating the important newly-discovered phenomenon of persistent firing induced by axonal slow integration. In this paper, we propose a computationally efficient model of neurons through modeling the axon as a slow leaky integrator, which captures almost all-known neural behaviors. The model controls the switching of axonal firing dynamics between passive conduction mode and persistent firing mode. The interplay between the axonal integrated potential and its multiple thresholds in axon precisely determines the persistent firing dynamics of neurons. We also present a persistent firing polychronous spiking network which exhibits asynchronous dynamics indicating that this computationally efficient model is not only bio-plausible, but also suitable for large scale spiking network simulations. The implications of this network and the analog circuit design for exploring the relationship between working memory and persistent firing enable developing a spiking networkbased memory and bio-inspired computer systems.
Advanced materials (Deerfield Beach, Fla.), Jan 26, 2014
A new methodology for manipulating transient-amorphous states of phase-change memory (PCM) materi... more A new methodology for manipulating transient-amorphous states of phase-change memory (PCM) materials is reported as a viable means to boost the speed, yet reduce the power consumption of PC memories, and is applicable to new forms of PCM-based neuromorphic devices. Controlling multiple-pulse interactions with PC materials may provide an opportunity toward developing a new paradigm for ultra-fast neuromorphic computing.
Applied Biomedical Engineering, 2011
Lecture Notes in Computer Science, 2011
We present a minimal neuron model that captures the essence of the persistent firing behavior of ... more We present a minimal neuron model that captures the essence of the persistent firing behavior of interneurons as discovered recently in the field of Neuroscience. The mathematical model reproduces the phenomenon that slow integration in distal axon of interneurons on a timescale of tens of seconds to minutes, leads to persistent firing of axonal action potentials lasted for similar duration. In this model, we consider the axon as a slow leaky integrator, which is capable of dynamically switching the neuronal firing states between normal firing and persistent firing, through axonal computation. This model is based on the Izhikevich neuron model and includes additional equations and parameters to represent the persistent firing dynamics, making it computationally efficient yet bio-plausible, and thus well suitable for large scale spiking network simulations.
Scientific Reports, 2014
Learning scheme is the key to the utilization of spike-based computation and the emulation of neu... more Learning scheme is the key to the utilization of spike-based computation and the emulation of neural/ synaptic behaviors toward realization of cognition. The biological observations reveal an integrated spike time-and spike rate-dependent plasticity as a function of presynaptic firing frequency. However, this integrated rate-temporal learning scheme has not been realized on any nano devices. In this paper, such scheme is successfully demonstrated on a memristor. Great robustness against the spiking rate fluctuation is achieved by waveform engineering with the aid of good analog properties exhibited by the iron oxide-based memristor. The spike-time-dependence plasticity (STDP) occurs at moderate presynaptic firing frequencies and spike-rate-dependence plasticity (SRDP) dominates other regions. This demonstration provides a novel approach in neural coding implementation, which facilitates the development of bio-inspired computing systems.
2014 IEEE/ACM International Symposium on Nanoscale Architectures (NANOARCH), 2014
ABSTRACT
IEEE Transactions on Circuits and Systems I: Regular Papers, 2000
International Journal of Intelligence Science, 2015
Neurons are believed to be the brain computational engines of the brain. A recent discovery in ne... more Neurons are believed to be the brain computational engines of the brain. A recent discovery in neurophysiology reveals that interneurons can slowly integrate spiking, share the output across a coupled network of axons and respond with persistent firing even in the absence of input to the soma or dendrites, which has not been understood and could be very important for exploring the mechanism of human cognition. The conventional models are incapable of simulating the important newly-discovered phenomenon of persistent firing induced by axonal slow integration. In this paper, we propose a computationally efficient model of neurons through modeling the axon as a slow leaky integrator, which captures almost all-known neural behaviors. The model controls the switching of axonal firing dynamics between passive conduction mode and persistent firing mode. The interplay between the axonal integrated potential and its multiple thresholds in axon precisely determines the persistent firing dynamics of neurons. We also present a persistent firing polychronous spiking network which exhibits asynchronous dynamics indicating that this computationally efficient model is not only bio-plausible, but also suitable for large scale spiking network simulations. The implications of this network and the analog circuit design for exploring the relationship between working memory and persistent firing enable developing a spiking networkbased memory and bio-inspired computer systems.