Work

KDE spike-phase and history models

Year: 2022

Type: article

Abstract: Here you will find a series of scripts and functions to fit spike-phase models and spike histories. Phase models rely on fitting kernel density estimators to the distribution of rhythmic phase values that coincide with a neuron's spikes. The kernel bandwidth is optimized via cross-validation. History models are fit via logistic regression. The scripts to run the models all begin with the "run_models_" prefix, and they vary according to the format the data are in (.mat files versus .csv files) as well as whether there is an option for leave-one-out cross-validation to find the optimal KDE kernel bandwidth. For instance, "run_all_models_simcells.py" will expect data to be already organized as a dataframe, while "run_all_models_realcells.py" will expect data to come in as a .mat file.

Source: Zenodo (CERN European Organization for Nuclear Research)

DOI: 10.5281/zenodo.6837167

Authors: Riviere, Pamela D, Schamberg, Gabriel, Coleman, Todd P, Rangel, Lara

Institutions: University of California San Diego, Massachusetts Institute of Technology, Stanford University

Language: English

Topic: Neural dynamics and brain function

Subfield: Cognitive Neuroscience

Field: Neuroscience

Domain: Life Sciences

Open Access status: green

OpenAlex ID: W4285514143

FWCI:

Cites:

Cited by:

Related to: 10