Latching dynamics in neural networks with synaptic depression

International audiencePrediction is the ability of the brain to quickly activate a target concept in response to arelated stimulus (prime). Experiments point to the existence of an overlap between the populationsof the neurons coding for different stimuli, and other experiments show that prime-targetrelations arise in the process of long term memory formation. The classical modellingparadigm is that long term memories correspond to stable steady states of a Hopfield networkwith Hebbian connectivity. Experiments show that short term synaptic depressionplays an important role in the processing of memories. This leads naturally to a computationalmodel of priming, called latching dynamics; a stable state (prime) can become unstableand the system may converge to another transiently stable steady state (target).Hopfield network models of latching dynamics have been studied by means of numericalsimulation, 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 confirmthat latching dynamics can exist in the context of a symmetric Hebbian learning rule, howeverlacks robustness and imposes a number of biologically unrealistic restrictions on themodel. In particular our work shows that the symmetry of the Hebbian rule is not an obstructionto the existence of latching dynamics, however fine tuning of the parameters of themodel is needed