Topic: excitatory and inhibitory cells

Recurrent neural networks have been trained to perform a wide variety of experimental tasks in order to understand the underlying dynamics employed by the brain to solve those tasks. However, some of the most commonly trained network architectures have not been biologically realistic, typically limiting their analogy with the brain to the level of abstract dynamical motifs. For example, they had noiseless dynamics or no separation of excitatory and inhibitory cells. The stochastic stabilized supralinear network (SSN) incorporates these features, as well as a rectified-superlinear activation f

Behavioral states in humans and animals vary over time, and these shifts alter cortical processing and cognitive function. For example, one striking result is an observed inverted-U relationship between arousal and performance on signal detection tasks. However, there is currently a need for mechanistic models that can explain these state-dependent effects. Here we address this challenge in the context of arousal-dependent auditory processing in rodents. In experiments where mice were presented tones of different frequencies, we found that frequency information could be best decoded from popu