Question about Hopfield&Tank nets

[Mark.Derthick@G.GP.CS.CMU.EDU]

Using the Hopfield and Tank energy function

E = -1/2 SUMi SUMj Tij Vi Vj  +  SUMi 1/R INTEGRAL g-inverse  + SUMi Ii Vi

one COULD calculate dE/dVi for each output and do steepest descent.
Instead, Hopfield and Tank introduce a new variable, u=g-inverse(V)
representing the input voltage to an amplifier with finite resistance
and capacitance.  The energy function is still a Liapunov function for
their circuit, but the circuit doesn't do steepest descent; it moves in
a direction obtained from the gradient by warping with the sigmoid
function: delta-Vi is shrunk for those Vi which take on values
near zero or one.

Hopfield and Tanks's motivation seems to be fidelity to real neurons.
If one doesn't care about this, is there any reason to prefer their
algorithm to steepest descent?

Mark