slinderman

pyhawkes

Python framework for inference in Hawkes processes.

stats320

STATS320: Statistical Methods for Neural Data Analysis

recurrent-slds

Recurrent Switching Linear Dynamical Systems

pypolyagamma

Fast C code for sampling Polya-gamma random variates. Builds on Jesse Windle's BayesLogit library.

pyglm

Interpretable neural spike train models with fully-Bayesian inference algorithms

stats215

stats271sp2021

Material for STATS271: Applied Bayesian Statistics (Spring 2021)

stats305c

STATS305C: Applied Statistics III (Spring, 2023)

thesis

My PhD Thesis

pyhsmm_spiketrains

Code for fitting neural spike trains with nonparametric hidden Markov and semi-Markov models built upon mattjj's PyHSMM framework.

graphistician

Generative random network models and Bayesian inference algorithms

tdlds

Reducing the temporal-difference learning theory of dopamine to a linear dynamical system

gslrandom

Cython wrapper for GSL random number generators

ml4nd

Machine Learning Methods for Neural Data Analysis

stats305b

STATS 305B: Applied Statistics II. Models and Algorithms for Discrete Data.

neymanscott

Bayesian inference for Neyman-Scott processes

course-content

NMA Computational Neuroscience course

birkhoff

Reparametrizing the Birkhoff Polytope

cython_openmp_mwe

Minimum working example of OpenMP with Cython

numpyro

Probabilistic programming with NumPy powered by JAX for autograd and JIT compilation to GPU/TPU/CPU.

AllenSDK

code for reading and processing Allen Institute for Brain Science data

CaImAn

Computational toolbox for large scale Calcium Imaging Analysis, including movie handling, motion correction, source extraction, spike deconvolution and result visualization.

pybasicbayes

svae

code for Structured Variational Autoencoders

torchhmm

Pytorch extension to compute gradients through HMM message passing

variational_autoencoder

Playing with Matt's VAE code

bbmi4s

A work in progress

blackjax

BlackJAX is a sampling library designed for ease of use, speed and modularity.

jax

Composable transformations of Python+NumPy programs: differentiate, vectorize, JIT to GPU/TPU, and more

zimmer

Working with Kato et al C Elegans data