On learning and energy-entropy dependence in recurrent and nonrecurrent signed networks
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Learning of patterns by neural networks obeying general rules of sensory transduction and of converting membrane potentials to spiking frequencies is considered. Any finite number of cellsA can sample a pattern playing on any finite number of cells ∇ without causing irrevocable sampling bias ifA = ℬ orA ∩ ℬ =. Total energy transfer from inputs ofA to outputs of ℬ depends on the entropy of the input distribution. Pattern completion on recall trials can occur without destroying perfect memory even ifA = ℬ by choosing the signal thresholds sufficiently large. The mathematical results are global limit and oscillation theorems for a class of nonlinear functional-differential systems.
Key wordslearning stimulus sampling nonlinear difference-differential equations global limits and oscillations flows on signed networks functional-differential systems energy-entropy dependence pattern completion recurrent and nonrecurrent anatomy sensory transduction rules ratio limit theorems
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