Update 'README.md'

README.md

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 Data for "Neuronal population representation of human emotional memory"
 
-Follow [this tutorial](https://gin.g-node.org/G-Node/Info/wiki/GIN+CLI+Setup) to download the dataset using the Gin client. Downloading the zip file will not download large files (if you get an error, check if files are very small). After installing the GIN client, download this repository using `gin download --content` to ensure you get the full files.
+Please follow [this tutorial](https://gin.g-node.org/G-Node/Info/wiki/GIN+CLI+Setup) to download the dataset using Gin client. Downloading the zip file will cause a Matlab error because files will be too small. After installing the GIN client, download this repository using `gin download --content` to ensure you get the full files.
 
-Neuronal spike trains were obtained using [Combinato](https://github.com/jniediek/combinato) and the outputs of this algorithm are provided for the 5 subjects in the publication.  After downloading the dataset, all results and figures can be reproduced using the code located in the [GitHub repository](https://github.com/dustinf1989/emotional_memory_neuronal_analyses/). Basic details about the files are given below and further described by comments in the code.
+Neuronal spike trains were obtained using [Combinato](https://github.com/jniediek/combinato) and its outputs are provided for the 5 subjects in the publication.  After downloading the dataset, all results and figures can be reproduced using the code located in the [GitHub repository](https://github.com/dustinf1989/emotional_memory_neuronal_analyses/). 
 
 ## Description of dataset and its organization
 
@@ -20,14 +20,14 @@ For each subject, there are folders called uXXX# where u stands for microwire, X
 * Entorhinal Cortex = "u" + ("ECR"|"ECL");
 * Pararhinal cortex = "uPCL" (Only located in Patient1, not used)
 
-Inside each folder, there are spike sorting results for both negative and positive spikes saved in Combinato format (for more details see their GitHub). Additionally, spike waveforms and spike times were saved in a mat file called *py_spikes_posneg_uXXX#.mat* for each microwire with detected waveforms. These files are required for the corresponding code.
+Inside each folder, there are spike sorting results for both negative and positive spikes saved in Combinato format (for more details see their GitHub). Additionally, spike waveforms and spike times were saved in *py_spikes_posneg_uXXX#.mat* for each microwire. 
 
 ### **Behavior**
 
 The file *all_images.txt* contains the image identities used in all sessions. The 229 images from the IAPS dataset are owned by the [Center for the Study of Emotion and Attention](https://csea.phhp.ufl.edu/) and can only be obtained from them directly, and therefore, we only share the image identities.  The additional 11 (neutral) images used in our study are provided in the 'images' folder. Additional files to understand the trial types are located inside the behavioral folder _Enc_ for encoding and _Rec_ for recognition e.g., emotional_memory_neuronal_data\rec\Patient1\Rec and described below:
 
 * *ft_trial_PatientID.mat* contains the FieldTrip header from the NeuraLynx files, FieldTrip event structure, FieldTrip trial structure (trl in samples and trl_sec in seconds). This is used to synchronize behavior and electrophysiological data.
-* *log.res*  contains 3 columns corresponding to response (Remember, Know, Forgot or No Response), Reaction time (in milliseconds), and total elapsed time (in msec, unused). 
+* *log.res*  contains 3 columns corresponding to response (Remember, Know, Forgot or No Response), Reaction time (in msec), and total elapsed time (in msec, unused). 
 * *IAPS_enc.mat* and *IAPS_rec.mat* file contains indices (to index all_images.txt) for the image identity of each trial.
-* *enc_onsets.mat* and *rec_onsets.mat* are MATLAB structs with fields containing the trial numbers for all trial types (e.g., eR: emotional remembered, nMiss: neutral missed, etc.). This is used to indicate both the emotional and memory trial types.
-* *encode_list.txt* contains the image identities for each trial. The recognition images are identified using IAPS_rec.mat to index *all_images.txt*
+* *enc_onsets.mat* and *rec_onsets.mat* are MATLAB structs with fields containing the trial numbers for all trial types (e.g., eR: emotional remembered, nMiss: neutral missed, etc.).
+* *encode_list.txt* contains the image identities for each trial. The recognition images are identified using IAPS_rec.mat to index *all_images.txt*.