Systems/Circuits OptimizingWorking Memory with Heterogeneity of Recurrent Cortical Excitation

A neural correlate of parametric working memory is a stimulus-specific rise in neuron firing rate that persists long after the stimulus is removed. Networkmodelswith local excitation and broad inhibition support persistent neural activity, linking network architecture and parametric workingmemory. Cortical neurons receive noisy input fluctuations that cause persistent activity to diffusively wander about the network, degrading memory over time. We explore how cortical architecture that supports parametric working memory affects the diffusion of persistent neural activity. Studying both a spiking network and a simplified potential well model, we show that spatially heterogeneous excitatory coupling stabilizes a discrete number of persistent states, reducing the diffusion of persistent activity over the network. However, heterogeneous coupling also coarse-grains the stimulus representation space, limiting the storage capacity of para-metricworkingmemory. The storage errors due to coarse-graining anddiffusion tradeoff so that information transfer between the initial and recalled stimulus is optimized at a fixed network heterogeneity. For sufficiently long delay times, the optimal number of attractors is less than thenumberofpossible stimuli, suggesting thatmemorynetworks canunder-represent stimulus space tooptimizeperformance. Our results clearly demonstrate the combined effects of network architecture and stochastic fluctuations onparametricmemory storage