Topic: mean firing rate

Neural responses are variable: even under identical experimental conditions, single neuron and population responses typically differ from trial to trial and across time. Recent work demonstrated that this variability can be modulated by specific stimulus features, most prominently quenched by stimulus onset. However, it is unknown how ubiquitous this effect is across stimulus features, and indeed whether it exists in brain areas to which classical neural coding approaches cannot be applied due to the lack of trials with repeatable experimental stimuli. To address these questions, we develop a

Our recent work presents a stochastic process model of the activations within an ANN and shows a promising indicator to distinguish memorizing from generalizing ANNs. The average λ, or mean firing rate (MFR), of a hidden layer, shows stable differences between memorizing and generalizing networks, comparatively independent of the underlying data used for evaluation. We first show the performance of this indicator during training on benchmark computer vision datasets such as MNIST and CIFAR-10. In a second step, we extend the work to the real-life use case of calibrating a pre-trained model to