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» Modeling spiking neural networks
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JCNS
2007
89views more  JCNS 2007»
13 years 10 months ago
Synchronous and asynchronous bursting states: role of intrinsic neural dynamics
Brain signals such as local field potentials often display gamma-band oscillations (30–70 Hz) in a variety of cognitive tasks. These oscillatory activities possibly reflect sy...
Takashi Takekawa, Toshio Aoyagi, Tomoki Fukai
ICANN
1997
Springer
14 years 2 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
ISCAS
2006
IEEE
102views Hardware» more  ISCAS 2006»
14 years 4 months ago
A low power merge cell processor for real-time spike sorting in implantable neural prostheses
Extremely low power consumption is the critical constraint for designing implantable neural decoders that inter- Desired face directly with the nervous system. Typically a system w...
M. D. Linderman, T. H. Meng
IJCNN
2006
IEEE
14 years 4 months ago
Spatiotemporal Pattern Recognition via Liquid State Machines
— The applicability of complex networks of spiking neurons as a general purpose machine learning technique remains open. Building on previous work using macroscopic exploration o...
Eric Goodman, Dan Ventura
NIPS
2003
13 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