Synchronous inhibition as a mechanism for unbiased selective gain control


While there are many experiments providing evidence for synchronized neuronal activity, there is little agreement about its functional role. Since many proposals rely on the assumption that neuronal activity can be modulated by top-down or feedback signals in a multiplicative way, it is a critical question how the dynamics of neurons may account for a selective control of their gain. In this paper we present a novel gain control mechanism based on the interplay of synaptic depression and synchronous inhibition. From simulations of a two-layered model of populations of integrate-and-fire neurons connected by stochastic depressing synapses, we conclude that synchronous inhibition can act as a selective gain control signal, which may be relevant, in particular when sensory processing reflects an ongoing process of hypotheses testing.

Matthias Bethge
Matthias Bethge
Professor for Computational Neuroscience and Machine Learning & Director of the Tübingen AI Center

Matthias Bethge is Professor for Computational Neuroscience and Machine Learning at the University of Tübingen and director of the Tübingen AI Center, a joint center between Tübingen University and MPI for Intelligent Systems that is part of the German AI strategy.