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» The Secrets of a Functional Synapse - From a Computational a...
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NIPS
2003
13 years 6 months ago
A Summating, Exponentially-Decaying CMOS Synapse for Spiking Neural Systems
Synapses are a critical element of biologically-realistic, spike-based neural computation, serving the role of communication, computation, and modification. Many different circui...
Rock Z. Shi, Timothy K. Horiuchi
GLVLSI
2009
IEEE
170views VLSI» more  GLVLSI 2009»
13 years 8 months ago
Physical unclonable function and true random number generator: a compact and scalable implementation
Physical Unclonable Functions (PUF) and True Random Number Generators (TRNG) are two very useful components in secure system design. PUFs can be used to extract chip-unique signat...
Abhranil Maiti, Raghunandan Nagesh, Anand Reddy, P...
DEXAW
2009
IEEE
124views Database» more  DEXAW 2009»
13 years 11 months ago
Performance Comparison of Secure Comparison Protocols
Secure Multiparty Computation (SMC) has gained tremendous importance with the growth of the Internet and E-commerce, where mutually untrusted parties need to jointly compute a fun...
Florian Kerschbaum, Debmalya Biswas, Sebastiaan de...
NECO
2007
258views more  NECO 2007»
13 years 4 months ago
Reinforcement Learning Through Modulation of Spike-Timing-Dependent Synaptic Plasticity
The persistent modification of synaptic efficacy as a function of the relative timing of pre- and postsynaptic spikes is a phenomenon known as spiketiming-dependent plasticity (...
Razvan V. Florian
NN
2002
Springer
208views Neural Networks» more  NN 2002»
13 years 4 months ago
A spiking neuron model: applications and learning
This paper presents a biologically-inspired, hardware-realisable spiking neuron model, which we call the Temporal Noisy-Leaky Integrator (TNLI). The dynamic applications of the mo...
Chris Christodoulou, Guido Bugmann, Trevor G. Clar...