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» Evolving a neural network using dyadic connections
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ECAL
1995
Springer
13 years 11 months ago
Evolving Artificial Neural Networks that Develop in Time
Although recently there has been an increasing interest in studing genetically-based development using Artificial Life models, the mapping of the genetic information into the phen...
Stefano Nolfi, Domenico Parisi
SIAMADS
2010
121views more  SIAMADS 2010»
13 years 2 months ago
Binocular Rivalry in a Competitive Neural Network with Synaptic Depression
We study binocular rivalry in a competitive neural network with synaptic depression. In particular, we consider two coupled hypercolums within primary visual cortex (V1), represent...
Zachary P. Kilpatrick, Paul C. Bressloff
CONNECTION
2004
92views more  CONNECTION 2004»
13 years 7 months ago
High capacity associative memories and connection constraints
: High capacity associative neural networks can be built from networks of perceptrons, trained using simple perceptron training. Such networks perform much better than those traine...
Neil Davey, Rod Adams
NEUROSCIENCE
2001
Springer
14 years 5 days ago
Finite-State Computation in Analog Neural Networks: Steps towards Biologically Plausible Models?
Abstract. Finite-state machines are the most pervasive models of computation, not only in theoretical computer science, but also in all of its applications to real-life problems, a...
Mikel L. Forcada, Rafael C. Carrasco
IJCNN
2006
IEEE
14 years 1 months ago
Learning using Dynamical Regime Identification and Synchronization
—This study proposes to generalize Hebbian learning by identifying and synchronizing the dynamical regimes of individual nodes in a recurrent network. The connection weights are ...
Nicolas Brodu