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ENGL
2008
142views more  ENGL 2008»
13 years 7 months ago
A Solid-State Neuron for Spiking Neural Network Implementation
This paper presents a compact analog neuron cell incorporating an array of charge-coupled synapses connected via a common output terminal. The novel silicon synapse is based on a t...
Yajie Chen, Steve Hall, Liam McDaid, Octavian Buiu...
IJON
2007
93views more  IJON 2007»
13 years 7 months ago
How much can we trust neural simulation strategies?
Despite a steady improvement of computational hardware, results of numerical simulation are still tightly bound to the simulation tool and strategy used, and may substantially var...
Michelle Rudolph, Alain Destexhe
IWANN
2005
Springer
14 years 1 months ago
Real-Time Spiking Neural Network: An Adaptive Cerebellar Model
Abstract. A spiking neural network modeling the cerebellum is presented. The model, consisting of more than 2000 conductance-based neurons and more than 50 000 synapses, runs in re...
Christian Boucheny, Richard R. Carrillo, Eduardo R...
ICES
2010
Springer
277views Hardware» more  ICES 2010»
13 years 5 months ago
An Efficient, High-Throughput Adaptive NoC Router for Large Scale Spiking Neural Network Hardware Implementations
Recently, a reconfigurable and biologically inspired paradigm based on network-on-chip (NoC) and spiking neural networks (SNNs) has been proposed as a new method of realising an ef...
Snaider Carrillo, Jim Harkin, Liam McDaid, Sandeep...
ICANN
1997
Springer
13 years 11 months ago
On-Line Hebbian Learning for Spiking Neurons: Architecture of the Weight-Unit of NESPINN
: We present the implementation of on-line Hebbian learning for NESPINN, the Neurocomputer for the simulation of spiking neurons. In order to support various forms of Hebbian learn...
Ulrich Roth, Axel Jahnke, Heinrich Klar