Synapses plays an important role of learning in a neural network; the learning rules which modify the synaptic strength based on the timing difference between the pre- and post-synaptic spike occurrence is termed as Spike Time Dependent Plasticity (STDP). This paper describes the compact implementation of a synapse using single floating-gate (FG) transistor (and two additional high voltage transistors) that can store a weight in a non-volatile manner and demonstrate the triplet STDP (T-STDP) learning rule developed to explain biologically observed plasticity. We describe a mathematical procedure to obtain control voltages for the FG device for T-STDP and also show measurement results, from a FG synapse fabricated in TSMC 0.35μm CMOS process...
To endow large scale VLSI networks of spiking neurons with learning abilities it is important to dev...
We propose a CMOS architecture for spiking neural networks with permanent memory and online learning...
Abstract — We present a novel two transistor synapse (“2TS”) that exhibits spike timing dependent pl...
Synapses plays an important role of learning in a neural network; the learning rules which modify th...
Learning in a neural network typically happens with the modification or plasticity of synaptic weigh...
This brief describes the neuromorphic very large scale integration implementation of a synapse utili...
One of the major areas of research by neurobiologists is long term synaptic modification or plastici...
AbstractSpike timing dependent plasticity (STDP) forms the basis of learning within neural networks....
Spike-Timing Dependent Plasticity (STDP) is believed to play an important role in learning and the f...
Spike Timing-Dependent Plasticity (STDP) is one of several plasticity rules that is believed to play...
Triplet-based Spike Timing Dependent Plasticity (TSTDP) is a powerful synaptic plasticity rule that ...
Triplet-based Spike Timing Dependent Plasticity (TSTDP) is a powerful synaptic plasticity rule that ...
Spike Timing Dependent Plasticity (STDP) is a time-based synaptic plasticity rule that has generated...
Spike Timing Dependent Plasticity (STDP) is a time-based synaptic plasticity rule that has generated...
To endow large scale VLSI networks of spiking neurons with learning abilities it is important to dev...
We propose a CMOS architecture for spiking neural networks with permanent memory and online learning...
Abstract — We present a novel two transistor synapse (“2TS”) that exhibits spike timing dependent pl...
Synapses plays an important role of learning in a neural network; the learning rules which modify th...
Learning in a neural network typically happens with the modification or plasticity of synaptic weigh...
This brief describes the neuromorphic very large scale integration implementation of a synapse utili...
One of the major areas of research by neurobiologists is long term synaptic modification or plastici...
AbstractSpike timing dependent plasticity (STDP) forms the basis of learning within neural networks....
Spike-Timing Dependent Plasticity (STDP) is believed to play an important role in learning and the f...
Spike Timing-Dependent Plasticity (STDP) is one of several plasticity rules that is believed to play...
Triplet-based Spike Timing Dependent Plasticity (TSTDP) is a powerful synaptic plasticity rule that ...
Triplet-based Spike Timing Dependent Plasticity (TSTDP) is a powerful synaptic plasticity rule that ...
Spike Timing Dependent Plasticity (STDP) is a time-based synaptic plasticity rule that has generated...
Spike Timing Dependent Plasticity (STDP) is a time-based synaptic plasticity rule that has generated...
To endow large scale VLSI networks of spiking neurons with learning abilities it is important to dev...
We propose a CMOS architecture for spiking neural networks with permanent memory and online learning...
Abstract — We present a novel two transistor synapse (“2TS”) that exhibits spike timing dependent pl...