The informational properties of a neural network model of an autoassoc
iative memory based on binary Hebbian synapses are investigated. The m
odel is a modification of the Willshaw network with a floating thresho
ld which keeps approximately constant the number of active neurons (wi
nners) at each time step. In the asymptotic case of large number of ne
urons, informational characteristics have been calculated analytically
for single-step correction. Comparison with simulations shows that th
e maximal correction efficiency attains its asymptotic values for netw
orks with surprisingly small number of neurons. Simulation results for
multistep correction show considerable improvement over the single-st
ep case.