  The brain can reproduce memories from partial data; this ability is critical
for memory recall. The process of memory recall has been studied using
auto-associative networks such as the Hopfield model. This kind of model
reliably converges to stored patterns which contain the memory. However, it is
unclear how the behavior is controlled by the brain so that after convergence
to one configuration, it can proceed with recognition of another one. In the
Hopfield model this happens only through unrealistic changes of an effective
global temperature that destabilizes all stored configurations. Here we show
that spike frequency adaptation (SFA), a common mechanism affecting neuron
activation in the brain, can provide state dependent control of pattern
retrieval. We demonstrate this in a Hopfield network modified to include SFA,
and also in a model network of biophysical neurons. In both cases SFA allows
for selective stabilization of attractors with different basins of attraction,
and also for temporal dynamics of attractor switching that is not possible in
standard auto-associative schemes. The dynamics of our models give a plausible
account of different sorts of memory retrieval.
