Multistable irregular activity in large winner-take-all networks

Identifying multistable network models producing cortical-like irregular spiking is an unsolved challenge in neuroscience. Multistability, thought to support working memory, is usually modeled via persistent states in which certain neurons receive elevated mean inputs. In contrast, irregular spiking is usually modeled in large networks in which neurons receive inputs that strongly fluctuate around a vanishingly small mean. This suggests stable states might instead be distinguished by higher-order input statistics, but networks operating in such a regime are not well understood. Here we show that a network of winner-take-all-like neuron groups can create a landscape of steady states in which sparse, irregular spiking stabilizes memories of past external inputs. Via simulation and theory we show this is not a finite-size effect but results from a symmetry breaking in networks driven by multi-dimensional input fluctuations. Our results survive randomization of the competitive interactions, suggesting fine-tuned reciprocal connections are not needed. Thus, explicit competition among neurons, potentially via fast lateral inhibition, can enable irregular spiking to protect rather than degrade information, illustrating a novel collective mechanism that could support working memory.