• _neurolib_deprecated: A library for neuroscientists by neuroscientists
• ed: A Jekyll theme for minimal editions :book:
• course-website: Spring 2020 | Python for Neuroscience Data
• PythonDataCourse: [WIP] Course on Python data pipelines
• django-jsonapi-training: Columbia University IT developer training on using Django, REST and {json:api}
• CNMF_E: Constrained Nonnegative Matrix Factorization for microEndoscopic data
• neurocaas: IaC codebase for the NeuroCAAS Platform
• TME: This code package is for the Tensor-Maximum-Entropy (TME) method. This method generates random surrogate data that preserves a specified set of first and second order marginal moments of a data tensor, which makes it well equipped to test for the null hypothesis that a structure in data is an epiphenomenon of these specified set of primary features of the data tensor. The random surrogate data are sampled from a maximum entropy distribution. This distribution unlike traditional maximum entropy method have constraints on the marginal first and second moments of the tensor mode.