The spike-timing dependent plasticity (STDP) of biological synapses, which is known to be a function of the formulated Hebbian learning rule of human cognition, learning and memory abilities, was emulated with two-phase change memory (2-PCM) cells built with 39 nm technology. For this, we designed a novel time-modulated voltage (TMV) scheme for changing the conductance of 2-PCM cells, that could produce both long-term potentiation (LTP) and long-term depression (LTD) by applying variable (decreasing/increasing) pulse voltages according to the sign and magnitude in time interval between pre- and post-spikes. Since such schemes can be easily modified to have a variety of pulse shapes and time intervals between pulses, it is expected to be a p...
Synaptic plasticity has been widely assumed to be the mechanism behind memory and learning, in which...
The Spike-Time-Dependent-Plasticity (STDP) learning rule is frequently associated with the memristor...
Current efforts to overcome such limitations are focused on employing material-optimization, or scal...
AbstractThe spike-timing dependent plasticity (STDP) of biological synapses, which is known to be a ...
In this work, we demonstrate how phase change memory (PCM) devices can be used to emulate biological...
International audiencePhase change memory can provide a remarkable artificial synapse for neuromorph...
This paper presents a design of electronic synapse with Spike Time Dependent Plasticity (STDP) based...
Memristive devices are popular among neuromorphic engineers for their ability to emulate forms of sp...
In this work, we will focus on the use of phase change memory (PCM) to emulate synaptic behavior in ...
Spike-Timing Dependent Plasticity (STDP) is believed to play an important role in learning and the f...
We present a novel one-transistor/one-resistor (1T1R) synapse for neuromorphic networks, based on ph...
Current advances in neuromorphic engineering have made it possible to emulate complex neuronal ion c...
Abstract Realization of brain-like computer has always been human’s ultimate dream. Today, the possi...
Spike-timing-dependent plasticity (STDP) is a fundamental synaptic learning rule observed in biology...
Emerging brain-inspired neuromorphic computing paradigms require devices that can emulate the comple...
Synaptic plasticity has been widely assumed to be the mechanism behind memory and learning, in which...
The Spike-Time-Dependent-Plasticity (STDP) learning rule is frequently associated with the memristor...
Current efforts to overcome such limitations are focused on employing material-optimization, or scal...
AbstractThe spike-timing dependent plasticity (STDP) of biological synapses, which is known to be a ...
In this work, we demonstrate how phase change memory (PCM) devices can be used to emulate biological...
International audiencePhase change memory can provide a remarkable artificial synapse for neuromorph...
This paper presents a design of electronic synapse with Spike Time Dependent Plasticity (STDP) based...
Memristive devices are popular among neuromorphic engineers for their ability to emulate forms of sp...
In this work, we will focus on the use of phase change memory (PCM) to emulate synaptic behavior in ...
Spike-Timing Dependent Plasticity (STDP) is believed to play an important role in learning and the f...
We present a novel one-transistor/one-resistor (1T1R) synapse for neuromorphic networks, based on ph...
Current advances in neuromorphic engineering have made it possible to emulate complex neuronal ion c...
Abstract Realization of brain-like computer has always been human’s ultimate dream. Today, the possi...
Spike-timing-dependent plasticity (STDP) is a fundamental synaptic learning rule observed in biology...
Emerging brain-inspired neuromorphic computing paradigms require devices that can emulate the comple...
Synaptic plasticity has been widely assumed to be the mechanism behind memory and learning, in which...
The Spike-Time-Dependent-Plasticity (STDP) learning rule is frequently associated with the memristor...
Current efforts to overcome such limitations are focused on employing material-optimization, or scal...