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» Lattice Neural Networks with Spike Trains
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NN
2007
Springer
14 years 1 months ago
Impact of Higher-Order Correlations on Coincidence Distributions of Massively Parallel Data
The signature of neuronal assemblies is the higher-order correlation structure of the spiking activity of the participating neurons. Due to the rapid progress in recording technol...
Sonja Grün, Moshe Abeles, Markus Diesmann
IJCNN
2007
IEEE
14 years 1 months ago
Theta Neuron Networks: Robustness to Noise in Embedded Applications
- In this paper, we train a one-layer Theta Neuron Network (TNN) to perform a Braitenberg obstacle avoidance algorithm on a Khepera robot. The Theta neuron model is more biological...
Sam McKennoch, Preethi Sundaradevan, Linda G. Bush...
NECO
2010
147views more  NECO 2010»
13 years 5 months ago
Connectivity, Dynamics, and Memory in Reservoir Computing with Binary and Analog Neurons
Abstract: Reservoir Computing (RC) systems are powerful models for online computations on input sequences. They consist of a memoryless readout neuron which is trained on top of a ...
Lars Büsing, Benjamin Schrauwen, Robert A. Le...
ISCAS
2006
IEEE
144views Hardware» more  ISCAS 2006»
14 years 1 months ago
A VLSI spike-driven dynamic synapse which learns only when necessary
— We describe an analog VLSI circuit implementing spike-driven synaptic plasticity, embedded in a network of integrate-and-fire neurons. This biologically inspired synapse is hi...
S. Mitra, Stefano Fusi, Giacomo Indiveri
NIPS
2008
13 years 8 months ago
On Computational Power and the Order-Chaos Phase Transition in Reservoir Computing
Randomly connected recurrent neural circuits have proven to be very powerful models for online computations when a trained memoryless readout function is appended. Such Reservoir ...
Benjamin Schrauwen, Lars Buesing, Robert A. Legens...