  Priming is the ability of the brain to more quickly activate a target concept
in response to a related stimulus (prime). Experiments point to the existence
of an overlap between the populations of the neurons coding for different
stimuli. Other experiments show that prime-target relations arise in the
process of long term memory formation. The classical modelling paradigm is that
long term memories correspond to stable steady states of a Hopfield network
with Hebbian connectivity. Experiments show that short term synaptic depression
plays an important role in the processing of memories. This leads naturally to
a computational model of priming, called latching dynamics; a stable state
(prime) can become unstable and the system may converge to another transiently
stable steady state (target). Hopfield network models of latching dynamics have
been studied by means of numerical simulation, however the conditions for the
existence of this dynamics have not been elucidated. In this work we use a
combination of analytic and numerical approaches to confirm that latching
dynamics can exist in the context of Hebbian learning, however lacks robustness
and imposes a number of biologically unrealistic restrictions on the model. In
particular our work shows that the symmetry of the Hebbian rule is not an
obstruction to the existence of latching dynamics, however fine tuning of the
parameters of the model is needed.
