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IJCNN
2006
IEEE
14 years 9 days 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
ECAL
2007
Springer
14 years 13 days ago
Developmental Neural Heterogeneity Through Coarse-Coding Regulation
Abstract. A coarse-coding regulatory model that facilitates neural heterogeneity through a morphogenetic process is presented. The model demonstrates cellular and tissue extensibil...
Jekanthan Thangavelautham, Gabriele M. T. D'Eleute...
IJCES
2002
100views more  IJCES 2002»
13 years 6 months ago
Neural Network Decoders for Linear Block Codes
This paper presents a class of neural networks suitable for the application of decoding error-correcting codes.The neural model is basically a perceptron with a high-order polynom...
Ja-Ling Wu, Yuen-Hsien Tseng, Yuh-Ming Huang
IJCNN
2008
IEEE
14 years 20 days ago
Biologically realizable reward-modulated hebbian training for spiking neural networks
— Spiking neural networks have been shown capable of simulating sigmoidal artificial neural networks providing promising evidence that they too are universal function approximat...
Silvia Ferrari, Bhavesh Mehta, Gianluca Di Muro, A...
NIPS
2008
13 years 7 months ago
Interpreting the neural code with Formal Concept Analysis
We propose a novel application of Formal Concept Analysis (FCA) to neural decoding: instead of just trying to figure out which stimulus was presented, we demonstrate how to explor...
Dominik Endres, Peter Földiák