Link to project: https://github.com/Siaan/kalmanfilter
Neurocaas is an open source platform that separates the scientist from the engineer. It hosts powerful data analysis tools for the computational neuroscience community, however it is still in early stages of development and lacks many tools that are needed by the scientific community. One main analysis being hypothesis guideded dimensionality reduction models such as Kalman Filter.
Kalman Filter, also known as Hidden Markov Model, is a dimensionality reduction model which assumes that data follows an underlying latent linear dynamical system. It projects high dimensional data into a low dimensional represenation while preserving the main features/variables (better known as explanatory variables) which it believes explains the data well.
I will be implementing Kalman Filter with the filterpy package to allow for model analysis, and visualization of the low dimensional output.