Scott Linderman's Projects
code for reading and processing Allen Institute for Brain Science data
A work in progress
Reparametrizing the Birkhoff Polytope
BlackJAX is a sampling library designed for ease of use, speed and modularity.
Computational toolbox for large scale Calcium Imaging Analysis, including movie handling, motion correction, source extraction, spike deconvolution and result visualization.
NMA Computational Neuroscience course
Graph models with MCMC
Minimum working example of OpenMP with Cython
Generative random network models and Bayesian inference algorithms
Cython wrapper for GSL random number generators
Composable transformations of Python+NumPy programs: differentiate, vectorize, JIT to GPU/TPU, and more
Machine Learning Methods for Neural Data Analysis
Bayesian inference for Neyman-Scott processes
Probabilistic programming with NumPy powered by JAX for autograd and JIT compilation to GPU/TPU/CPU.
Interpretable neural spike train models with fully-Bayesian inference algorithms
Python framework for inference in Hawkes processes.
Code for fitting neural spike trains with nonparametric hidden Markov and semi-Markov models built upon mattjj's PyHSMM framework.
Fast C code for sampling Polya-gamma random variates. Builds on Jesse Windle's BayesLogit library.
Recurrent Switching Linear Dynamical Systems
Material for STATS271: Applied Bayesian Statistics (Spring 2021)
STATS 305B: Applied Statistics II. Models and Algorithms for Discrete Data.
STATS305C: Applied Statistics III (Spring, 2023)
STATS320: Statistical Methods for Neural Data Analysis
code for Structured Variational Autoencoders
Reducing the temporal-difference learning theory of dopamine to a linear dynamical system
Generalized linear models for neural spike train modeling, in Python! With GPU-accelerated fully-Bayesian inference, MAP inference, and network priors.