Latching dynamics in neural networks with synaptic depression

TL;DR

This paper investigates the conditions under which latching dynamics, involving transitions between stable states in neural networks with synaptic depression, can occur, revealing limitations in robustness and biological realism.

Contribution

It combines analytic and numerical methods to clarify the conditions for latching dynamics in Hopfield networks with Hebbian learning, highlighting the need for fine tuning.

Findings

Latching dynamics can exist with Hebbian learning.

Such dynamics lack robustness and require fine tuning.

Symmetry of Hebbian rule does not prevent latching.

Abstract

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.