Topic: Allen Institute Synaptic Physiology data set

Synapses show preferential responses to particular temporal patterns of activity. Across synapses, however, there is a large degree of response heterogeneity that is routinely and tacitly separated into classes. Furthermore, feedforward neural networks incorporating simple models of these dynamic responses outperform similar recurrent neural network (RNN) models in several neuroscience related tasks. Here we combined a kernel-based model and machine learning techniques to infer biologically grounded, and functionally distinct classes of short-term synaptic dynamics in data from the Allen Insti