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cond-mat.dis-nn2004
Slowly evolving random graphs II: Adaptive geometry in finite-connectivity Hopfield models
B. Wemmenhove, N. S. Skantzos
We present an analytically solvable random graph model in which the connections between the nodes can evolve in time, adiabatically slowly compared to the dynamics of the nodes. We…
cond-mat.dis-nn2004
Slowly evolving geometry in recurrent neural networks I: extreme dilution regime
B. Wemmenhove, N. S. Skantzos, A. C. C. Coolen
We study extremely diluted spin models of neural networks in which the connectivity evolves in time, although adiabatically slowly compared to the neurons, according to stochastic…