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» The Secrets of a Functional Synapse - From a Computational a...
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NIPS
2003
14 years 11 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»
15 years 1 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»
15 years 4 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»
14 years 9 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»
14 years 9 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...