Hamiltonian dynamics of neural networks
U Ramacher - Neurobionics, 1993 - Elsevier
The activation and weight dynamics of Artificial Neural Networks are derived from a partial
differential equation (PDE) that may incorporate weights either as parameters or variables. It
is shown that a single first-order Hamilton-Jacobi 'parametrical'PDE suffices to derive the
various neurodynamical paradigms used today. In the case that weights are taken as
variables, a new type of neurodynamics is discovered: A Hamilton function is derived so that
the weights obey a 2-order ordinary differential equation (ODE). As this ODE models the …
differential equation (PDE) that may incorporate weights either as parameters or variables. It
is shown that a single first-order Hamilton-Jacobi 'parametrical'PDE suffices to derive the
various neurodynamical paradigms used today. In the case that weights are taken as
variables, a new type of neurodynamics is discovered: A Hamilton function is derived so that
the weights obey a 2-order ordinary differential equation (ODE). As this ODE models the …
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