Stability of working memory in continuous attractor networks under the control of short-term plasticity

Abstract

Continuous attractor models of working-memory store continuous-valued information in continuous state-spaces, but are sensitive to noise processes that degrade memory retention. Short-term synaptic plasticity of recurrent synapses has previously been shown to affect continuous attractor systems: short-term facilitation can stabilize memory retention, while short-term depression possibly increases continuous attractor volatility. However, it currently remains unclear to which degree these two short-term plasticity mechanisms interact, what their combined quantitative effect on working memory stability is, and whether these effects persist in neuronal networks with spike-based transmission. Here, we present a comprehensive description of the effects of short-term plasticity on noise-induced memory degradation in one-dimensional continuous attractor models. Our theoretical description, applicable to spiking and rate-based models alike, accurately describes the slow dynamics of stored memory positions in separate processes of diffusion due to spiking variability and drift due to sparse connectivity and neuronal heterogeneity. We find that facilitation decreases both diffusion and directed drifts, while short-term depression tends to increase both. Using mutual information, we evaluate the combined impact of short-term facilitation and depression on the ability of networks to retain stable working memory. Finally, our theory establishes links to experiments: we are able to predict the sensitivity of continuous working memory to distractor inputs and place constraints on network and synapse properties necessary to implement stable working memory.\n\nAuthor summaryThe ability to transiently memorize positions in the visual field is crucial for behavior. Models and experiments have shown that such memories can be maintained in networks of cortical neurons with a continuum of possible activity states, that reflects the continuum of positions in the environment. However, the accuracy of positions stored in such networks will degrade over time due to the noisiness of neuronal signaling and imperfections of the biological substrate. Previous work in simplified models has shown that synaptic short-term plasticity could stabilize this degradation by dynamically up- or down-regulating the strength of synaptic connections, thereby \"pinning down\" memorized positions. Here, we present a general theory that accurately predicts the extent of this \"pinning down\" by short-term plasticity in a broad class of biologically plausible models, thereby untangling the interplay of varying biological sources of noise with short-term plasticity. Importantly, our work provides a direct and novel theoretical link from the microscopic substrate of working memory - neurons and synaptic connections - to observable behavioral correlates. This allows us to constrain properties of cortical networks that are currently hard to assess experimentally, which we hope will help guide future theoretical and experimental work.