master

Guide to master repository. These scripts and functions were mostly written for macaque behavioral data and single-unit data from my time in the Ramachandran Lab (e.g. 2018-2022).

"CP" scripts for choice probability/detect probability analysis from Mackey et al. (2022)

Preprint here: https://doi.org/10.1101/2022.06.15.496306

J Neurophysiol paper here: https://doi.org/10.1152/jn.00439.2022

Uses:

    CP_Analysis_ScriptCM,
    CP_Calc,
    CP_ROC

Shell script is CP_Analysis_ScriptCM.m imports data and runs CP_Calc in a loop through all the data. CP_calc runs the CP function, which calculates CP using signal detection theoretic ROC analysis (CP_ROC function)

"MFdisc" and "samsmodlong" scripts for neuronal discrimination analysis and population modeling using single-unit responses to amplitude modulated noise

Preprint: https://doi.org/10.1101/2022.08.05.502987

Uses:

    samsmodlong_pooler_looper,
    samsmodlong_pooler_fxn,
    mfSort,
    MTF.m,
    mfROC or mfROC_pop,
    and (!!OPTIONAL!!) trimmer.m if the duration is less than 500 ms, which can trim response to nearest AM trough.
    some reviewers hated trimmer.m. - not currently using it.

Shell script is samsmodlong_pooler_looper.m, which imports data and runs samsmodlong_pooler_fxn - this function uses mfSort.m and MTF.m to pick out neurons and certain trials. At that point the responses are extracted and it does ROC analysis (mfROC for a single neuron or mfROCpop for populations) This is describing the population level analysis, but samsmodlong_noActX.m does all of this for a single neuron.

Drift diffusion model (jupyter notebook) mostly uses code from de Gee et al. (2021, eLife) but some was repurposed. Links to de Gee et al's code and papers are in the jupyter notebook "Chase_discrimination_DDM_clean2"

seemackey/master

master