Resources

Spiking Variability Decomposition

The code for parsing spiking variability: within trial variability nψ (aka φ)  and between trial variability (nRV) using a pricinpled approach for a doubly stochastic process.

Reference: Fayyaz, S., Fakharian, M., & Ghazizadeh, A. (2021). Stimulus presentation can enhance spiking irregularity across subcortical and cortical regions. bioRxiv

Code: var_decom (courtesy of MohammadAmin Fakharian)

Graph Learning

The code for constructing graph between nodes based on partial correlations and using PC-stable algorithm

References: 

1) Ghazizadeh, A., Fakharian, M. A., Amini, A., Griggs, W., Leopold, D. A., & Hikosaka, O. (2020). Brain networks sensitive to object novelty, value, and their combination. Cerebral cortex communications, 1(1), tgaa034

2) Attary T, Ghazizadeh A, (2021) Localizing sensory processing sensitivity and its subdomains within its relevant trait space: a data driven approach, Scientific Report

Code: PC_Graph (courtesy of MohammadAmin Fakharian and Taraneh Attary)

For Attary et al 2021 data contact us and include your information and intended use.

Neural Spiking Deconvolution

The code for parsing neural activity to events that are closely spaced Reference: Ghazizadeh, A., Fields, H. L., & Ambroggi, F. (2010). Isolating event-related neuronal responses by deconvolution. Journal of neurophysiology, 104(3), 1790-1802

Code: Full and Partial deconvolution functions (courtesy of Setayesh Radkani)

Fractal Generation Code

The code for generating random fractal objects

Reference: Ghazizadeh, A, et al “Ecological origins of object salience: Reward, uncertainty, aversiveness, and novelty.” Frontiers in neuroscience 10 (2016): 378

Code: fractalmaker (courtesy of Shinya Yamamoto)