Abstract
Sequential and attractor-based models are two prominent models of short-term memory in neural circuits. In attractor-based models, memories are represented in persistent or nearly persistent activity patterns across a population of neurons, whereas in sequential models, memories are represented dynamically by a sequential pattern of activity across the population. Experimental evidence for both types of model in the brain has been reported previously. However, it has been unclear under what conditions these two qualitatively different types of solutions emerge in neural circuits. Here, we address this question by training recurrent neural networks on several short-term memory tasks under a wide range of circuit and task manipulations. We show that fixed delay durations, tasks with higher temporal complexity, strong network coupling and motion-related dynamic inputs favor sequential solutions, whereas variable delay durations, tasks with low temporal complexity, weak network coupling and symmetric Hebbian short-term synaptic plasticity favor more persistent solutions. Our results clarify some seemingly contradictory experimental results on the existence of sequential vs. attractor-like memory mechanisms in the brain.