LETTER Communicated by Misha Tsodyks Computing and Stability in Cortical Networks

Cortical neurons are predominantly excitatory and highly interconnected. In spite of this, the cortex is remarkably stable: normal brains do not ex-hibit the kind of runaway excitation one might expect of such a system. How does the cortex maintain stability in the face of this massive exci-tatory feedback? More importantly, how does it do so during computa-tions, which necessarily involve elevated firing rates? Here we address these questions in the context of attractor networks—networks that ex-hibit multiple stable states, or memories. We find that such networks can be stabilized at the relatively low firing rates observed in vivo if two conditions are met: (1) the background state, where all neurons are firing at low rates, is inhibition dominated, and (2) the fraction of neurons in-volved in a memory is above some threshold, so that there is sufficient coupling between the memory neurons and the background. This allows “dynamical stabilization ” of the attractors, meaning feedback from the pool of background neurons stabilizes what would otherwise be an unsta-ble state. We suggest that dynamical stabilization may be a strategy used for a broad range of computations, not just those involving attractors.