The maintenance of short-term memories is critical for survival in a dynamically changing world. Previous studies suggest that this memory can be stored in the form of persistent neural activity or using a synaptic mechanism, such as with short-term plasticity. Here, we compare the predictions of these two mechanisms to neural and behavioral measurements in a visual change detection task. Mice were trained to respond to changes in a repeated sequence of natural images while neural activity was recorded using two-photon calcium imaging. We trained two different models to perform the same visual change detection task. Compared to a recurrent neural network (RNN), we find that a feedforward neural network with short-term synaptic depression (STPNet) is more consistent with the pattern of behavioral responses to image changes in mice and the observed adaptation in neural responses across repeated image presentations. These results suggest that simple neural adaptation mechanisms may serve as an important bottom-up memory signal in this task, which can be used by downstream areas in the decision-making process.

Adaptation supports short-term memory in a visual change detection task