Topic: linear rate models

To successfully inform behavior, cortical circuits must produce consistent patterns of neuronal activity within and across trials that allow for reliable stimulus discrimination. Under this perspective, any response variability appears detrimental to sensory systems as it severely limits stimulus discriminability. Here, we propose a beneficial role of response variability: to facilitate faster learning in feedforward-recurrent networks through a self-stabilizing feedback mechanism. We explore this hypothesis in the framework of learning novel stimulus representations in pre-structured recurren

Cortical neurons exhibit rich, time-dependent patterns of activity that are often selective to specific stimuli. From a dynamical systems perspective, such complex activity can be conceptualized as ON/OFF responses, or also as transient amplification. It is still unclear if transient amplification is an artifact of a carefully tuned model, or an intrinsically occurring phenomenon. Moreover, minute changes in the connectivity of amplifying networks can cause catastrophic dynamic instabilities. How biological circuits could settle into stable, yet amplifying connectivity regimes in an ever-chang