This implements the simulation of a new proposed measure by G. Mijatovic, T. Loncar-Turukalo, N. Bozanic and L. Faes: "A measure of concurrent neural firing activity based on mutual information", 2020.
Concurrent neural firing index based on mutual information (MI), CFI_MI index:
The CFI_MI index estimates the degree of concomitant firing between two neural units based on a modified form of MI applied on a coarse, binary representations of firing activity (states of neural quiescence "0" and states of firing "1" unfolded in time).
The CFI-MI toolbox contains next functions:
- binary_representation.m: binary-state representation of the spiking activity (see REF0);
- function_CFI_MI.m: computing the CFI_MI index;
- spiSeMe_surrogate_jodi.m: assessment of statistical significance of the CFI_MI index index based on surrogate data analysis (this function is part of the SpiSeMe package, see REF2);
and script:
- demo.m: to demonstrate the estimaton of the CFI_MI index between two selected cells from a network of 1000 randomly coupled spiking neurons (see REF1); supported with assessment of its statistical significance (see REF2) and the corresponding visualization of spike trains and binary profiles.
REF0: Mijatovic G, Loncar-Turukalo T, Procyk E, Bajic D (2018): "A novel approach to probabilistic characterisation of neural firing patterns", Journal of neuroscience methods 305:67–81
REF1: Izhikevich EM (2003): "Simple model of spiking neurons", IEEE Transactions onneural networks 14(6):1569–1572
REF2: Ricci L, Castelluzzo M, Minati L, Perinelli A (2019): "Generation of surro-gate event sequences via joint distribution of successive inter-event intervals", Chaos: An Interdisciplinary Journal of Nonlinear Science 29(12):121102