Ryo Aoki's repositories
AP_histology
Histology processing
dPCA
An implementation of demixed Principal Component Analysis (a supervised linear dimensionality reduction technique)
GLMspiketools
Fitting and simulation of Poisson generalized linear model for single and multi-neuron spike trains (Pillow et al 2008).
iblrig
Main repository for IBL rig code
matnwb
A Matlab interface for reading and writing NWB files
prednet
Code and models accompanying "Deep Predictive Coding Networks for Video Prediction and Unsupervised Learning"
snntorch
Deep and online learning with spiking neural networks in Python
spikes
cortex lab code for electrophysiology
swdb_2018_tools
A collaborative Python package built by participants of the Summer Workshop on the Dynamic Brain
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.