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NN
1998
Springer
102views Neural Networks» more  NN 1998»
9 years 1 months ago
A learning model for oscillatory networks
A learning model for coupled oscillators is proposed. The proposed learning rule takes a simple form by which the intrinsic frequencies of the component oscillators and the coupli...
Jun Nishii
IJON
2002
74views more  IJON 2002»
9 years 1 months ago
Optimal spontaneous activity in neural network modeling
We consider the origin of the high-dimensional input space as a variable which can be optimized before or during neuronal learning. This set of variables acts as a translation on ...
Daniel Remondini, Nathan Intrator, Gastone C. Cast...
IJON
2006
70views more  IJON 2006»
9 years 1 months ago
A self-organizing map with homeostatic synaptic scaling
Hebbian learning has been a staple of neural-network models for many years. It is well known that the most straight-forward implementations of this popular learning rule lead to u...
Thomas J. Sullivan, Virginia R. de Sa
NIPS
2000
9 years 2 months ago
Temporally Dependent Plasticity: An Information Theoretic Account
The paradigm of Hebbian learning has recently received a novel interpretation with the discovery of synaptic plasticity that depends on the relative timing of pre and post synapti...
Gal Chechik, Naftali Tishby
NIPS
2004
9 years 2 months ago
Rate- and Phase-coded Autoassociative Memory
Areas of the brain involved in various forms of memory exhibit patterns of neural activity quite unlike those in canonical computational models. We show how to use well-founded Ba...
Máté Lengyel, Peter Dayan
NIPS
2007
9 years 2 months ago
An online Hebbian learning rule that performs Independent Component Analysis
Independent component analysis (ICA) is a powerful method to decouple signals. Most of the algorithms performing ICA do not consider the temporal correlations of the signal, but o...
Claudia Clopath, André Longtin, Wulfram Ger...
NIPS
2007
9 years 2 months ago
Theoretical Analysis of Learning with Reward-Modulated Spike-Timing-Dependent Plasticity
Reward-modulated spike-timing-dependent plasticity (STDP) has recently emerged as a candidate for a learning rule that could explain how local learning rules at single synapses su...
Robert A. Legenstein, Dejan Pecevski, Wolfgang Maa...
ICIAP
1999
ACM
9 years 5 months ago
Texture Segmentation by Frequency-Sensitive Elliptical Competitive Learning
In this paper a new learning algorithm is proposed with the purpose of texture segmentation. The algorithm is a competitive clustering scheme with two specific features: elliptic...
Steve De Backer, Paul Scheunders
IJCNN
2006
IEEE
9 years 7 months ago
Backpropagation for Population-Temporal Coded Spiking Neural Networks
Abstract— Supervised learning rules for spiking neural networks are currently only able to use time-to-first-spike coding and are plagued by very irregular learning curves due t...
Benjamin Schrauwen, Jan M. Van Campenhout
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