This ambiguity is particularly challenging in continuous settings in which a continuum of explanations exist for the same observation.
Assessing the validity of a real-world system with respect to given quality criteria is a common yet costly task in industrial applications due to the vast number of required real-world tests.
Understanding physical phenomena oftentimes means understanding the underlying dynamical system that governs observational measurements.
Neural Stochastic Differential Equations model a dynamical environment with neural nets assigned to their drift and diffusion terms.
Gaussian Processes (GPs) are a generic modelling tool for supervised learning.
Second-order maximum-entropy models have recently gained much interest for describing the statistics of binary spike trains.
In this way, we provide a new rigorous framework for assessing the functional consequences of noise correlation structures for the representational accuracy of neural population codes that is in particular applicable to short-time population coding.
Imaging techniques such as optical imaging of intrinsic signals, 2-photon calcium imaging and voltage sensitive dye imaging can be used to measure the functional organization of visual cortex across different spatial scales.