A Diverse Range of Factors Affect the Nature of Neural Representations Underlying Short-Term Memory

Sequential and persistent activity models are two prominent models of short-term memory in neural circuits. In persistent activity 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 sequential and nearly persistent solutions are both part of a spectrum that emerges naturally in trained networks under different conditions. Fixed delay durations, tasks with higher temporal complexity, strong network coupling, motion-related dynamic inputs and prior training in a different task favor more 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 help clarify some seemingly contradictory experimental results on the existence of sequential vs. persistent activity based memory mechanisms in the brain.