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- W2238585833 abstract "We study changes of coordinates that allow the representation of the ordinary differential equations describing continuous-time recurrent neural networks into differential equations describing predator-prey models—also called Lotka-Volterra systems. We transform the equations for the neural network first into quasi-monomial form, where we express the vector field of the dynamical system as a linear combination of products of powers of the variables. In practice, this transformation is possible only if the activation function is the hyperbolic tangent or the logistic sigmoid. From this quasi-monomial form, we can directly transform the system further into Lotka-Volterra equations. The resulting Lotka-Volterra system is of higher dimension than the original system, but the behavior of its first variables is equivalent to the behavior of the original neural network." @default.
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- W2238585833 date "1999-01-01" @default.
- W2238585833 modified "2023-09-25" @default.
- W2238585833 title "Representation of neural networks as Lotka-Volterra systems" @default.
- W2238585833 doi "https://doi.org/10.1063/1.58279" @default.
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