Update README.md

README.md

@@ -13,23 +13,23 @@ These subfolders include the following scripts and functions:
     - script_IN_sim_03_grid.m – grid simulation of a double dominant frequency in the summed composed signal of two coupled IN neurons (Fig 7);
     - script_IN_sim_04_1400Hz.m – simulation of a 4-fold natural frequency in the summed composed signal of four coupled IN neurons (Fig 10);
     - script_IN_sim_05_2000Hz.m – simulation of a 6-fold natural frequency in the summed composed signal of six coupled IN neurons (Fig 12);
-    - CoupledIN.txt - MatCont input used for bifurcation analysis of two coupled neurons, with parameters and equations (Fig 7). Can be adapted to 1 neuron.
+    - CoupledIN.txt – MatCont input used for bifurcation analysis of two coupled neurons, with parameters and equations (Fig 7). Can be adapted to 1 neuron.
 * ML (Morris–Lecar model)
     - script_ML_sim_01_IPS_APS.m – in-phase and anti-phase synchrony of two ML neurons (Fig 14, 15);
     - script_ML_sim_02_collective_APS.m – collective anti-phase behavior in an all-to-all coupled neuronal network composed of 50 ML neurons (Fig 5);
-    - script_ML_sim_03_collective_APS_transient - transient collective anti-phase behavior as a result of external input to an all-to-all coupled neuronal network composed of 50 ML neurons (Fig 6);
+    - script_ML_sim_03_collective_APS_transient – transient collective anti-phase behavior as a result of external input to an all-to-all coupled neuronal network composed of 50 ML neurons (Fig 6);
     - script_ML_sim_04_grid.m – grid simulation of a double dominant frequency in the summed composed signal of two coupled ML neurons (Fig 4);
-    - CoupledML.txt - MatCont input used for bifurcation analysis of two coupled neurons, with parameters and equations (Fig 3, 4). Can be adapted to 1 neuron (Fig 1).
+    - CoupledML.txt – MatCont input used for bifurcation analysis of two coupled neurons, with parameters and equations (Fig 3, 4). Can be adapted to 1 neuron (Fig 1).
 * Other scripts/routines used for pre/postprocessing and running simulations
-    - dp.m, interneuron.m, ml.m - functions determining the right-hand side of dynamical systems of coupled DP, IN, and ML neurons;
-    - getK.m - creates gap-junctional coupling matrix;
-    - em.m - integration routine for Euler–Maruyama;
-    - fmax.m - computes the dominant frequency for a signal (Fig 4, 7);
-    - getNeighbors.m - provides indices used to find anti-phase solutions in a grid search (Fig 4, 7);
-    - ml_EM.m - integration routine for a simulation of external input resulting in a transient collective anti-phase behavior (Fig 6);
-    - randtn.m - returns a truncated normal distribution for given parameters following Matlab documentation;
-    - simDP, simInterneuron, simML - simulates a corresponding neuronal network of coupled DP, IN, and ML neurons;
-    - simGridNode.m - refines the result of the grid search (Fig 4, 7), applied repeatedly till convergence.
+    - dp.m, interneuron.m, ml.m – functions determining the right-hand side of dynamical systems of coupled DP, IN, and ML neurons;
+    - getK.m – creates gap-junctional coupling matrix;
+    - em.m – integration routine for Euler–Maruyama;
+    - fmax.m – computes the dominant frequency for a signal (Fig 4, 7);
+    - getNeighbors.m – provides indices used to find anti-phase solutions in a grid search (Fig 4, 7);
+    - ml_EM.m – integration routine for a simulation of external input resulting in a transient collective anti-phase behavior (Fig 6);
+    - randtn.m – returns a truncated normal distribution for given parameters following Matlab documentation;
+    - simDP, simInterneuron, simML – simulates a corresponding neuronal network of coupled DP, IN, and ML neurons;
+    - simGridNode.m – refines the result of the grid search (Fig 4, 7), applied repeatedly till convergence.
 
 Software versions used: MATLAB2021a and MatCont7p3