diff --git a/README.md b/README.md
index 7c3ba80..e5eeb2b 100644
--- a/README.md
+++ b/README.md
@@ -10,22 +10,22 @@ ai_feynman_example.py contains an example of running a code on some examples (fo
 
 The main function of the code, called by the user, has the following parameters:
 
-pathdir - path to the directory containing the data file
-filename - the name of the file containing the data
-BF_try_time - time limit for each brute force call (set by default to 60 seconds)
-BF_ops_file_type - file containing the symbols to be used in the brute force code (set by default to "14ops.txt")
-polyfit_deg - maximum degree of the polynomial tried by the polynomial fit routine (set be default to 4)
-NN_epochs - number of epochs for the training (set by default to 4000)
-vars_name - name of the variables appearing in the equation (inluding the name ofthe output variable). This should be passed as a list of strings, with the name of the variables appearing in the same order as they are in the file containing the data
-test_percentage - percentage of the input data to be kept aside and used as the test set
+* pathdir - path to the directory containing the data file
+* filename - the name of the file containing the data
+* BF_try_time - time limit for each brute force call (set by default to 60 seconds)
+* BF_ops_file_type - file containing the symbols to be used in the brute force code (set by default to "14ops.txt")
+* polyfit_deg - maximum degree of the polynomial tried by the polynomial fit routine (set be default to 4)
+* NN_epochs - number of epochs for the training (set by default to 4000)
+* vars_name - name of the variables appearing in the equation (inluding the name ofthe output variable). This should be passed as a list of strings, with the name of the variables appearing in the same order as they are in the file containing the data
+* test_percentage - percentage of the input data to be kept aside and used as the test set
 
 The data file to be analyzed should be a text file with each column containing the numerical values of each (dependent and independent) variable. The solution file will be saved in the directory called "results" under the name solution_{filename}. The solution file will contain several rows (corresponding to each point on the Pareto frontier), each row showing: 
 
-- the mean logarithm in based 2 of the error of the discovered equation applied to the input data (this can be though of as the average error in bits)
-- the cummulative logarithm in based 2 of the error of the discovered equation applied to the input data (this can be though of as the cummulative error in bits)
-- the complexity of the discovered equation (in bits)
-- the error of the discovered equation applied to the input data
-- the symbolic expression of the discovered equation
+* the mean logarithm in based 2 of the error of the discovered equation applied to the input data (this can be though of as the average error in bits)
+* the cummulative logarithm in based 2 of the error of the discovered equation applied to the input data (this can be though of as the cummulative error in bits)
+* the complexity of the discovered equation (in bits)
+* the error of the discovered equation applied to the input data
+* the symbolic expression of the discovered equation
 
 If test_percentage is different than zero, one more number is added in the beginning of each row, showing the error of the discovered equation on the test set.