diff --git a/database.py b/database.py
index ebafd93..d3b8546 100644
--- a/database.py
+++ b/database.py
@@ -19,6 +19,8 @@ class Dataset(dj.Manual):
        data_source : varchar(512) # path to the dataset
        experimenter : varchar(512) 
        recording_date : date
+       quality : varchar(512)
+       comment : varchar(1024)
        has_nix : bool
        """
 
@@ -27,7 +29,8 @@ class Dataset(dj.Manual):
         if id is not None:
             d = dict((Dataset() & {"dataset_id": id}).fetch1())
             return d
-        return dict(dataset_id=None, data_source="", experimenter="", recording_date=None, has_nix=False)
+        return dict(dataset_id=None, data_source="", experimenter="", recording_date=None,
+                    quality="", comment="", has_nix=False)
 
     @staticmethod
     def get_nix_file(key):
@@ -124,9 +127,43 @@ class SubjectProperties(dj.Manual):
         return tup
 
 
+@schema
+class Cell(dj.Manual):
+    definition = """
+    # Table that stores information about recorded cells.
+    id : int auto_increment
+    ----
+    -> Subject
+    -> Dataset
+    cell_name : varchar(256)
+    cell_type : varchar(256)
+    firing_rate : float
+    structure : varchar(256)
+    region : varchar(256)
+    subregion : varchar(256)
+    depth : float
+    lateral_pos : float
+    transversal_section : float
+    """
+
+    @staticmethod
+    def get_template_tuple(cell_id=None):
+        tup = dict(id=None, dataset_id=None, subject_id=None, cell_name="", cell_type="", firing_rate=0.0, depth=0.0,
+                   region="", subregion="", structure="", lateral_pos=0.0, transversal_section=0.0)
+        if cell_id is not None:
+            d = dict((Cell() & {"cell_id": cell_id}).fetch1())
+            return d
+        return tup
+
+
 def read_info(info_file):
+    exp = ""
+    quality = ""
+    comment = ""
+    rec_date = None
+    has_nix = False
     if not os.path.exists(info_file):
-        return None, None, False
+        return exp, rec_date, quality, comment, has_nix
     has_nix = len(glob.glob(os.path.sep.join(info_file.split(os.path.sep)[:-1]) + os.path.sep + "*.nix")) > 0
     info = read_info_file(info_file)
     p = []
@@ -137,7 +174,15 @@ def read_info(info_file):
     find_key_recursive(info, "Date", p)
     if len(p) > 0:
         rec_date = dt.date.fromisoformat(deep_get(info, p))
-    return exp, rec_date, has_nix
+    p = []
+    find_key_recursive(info, "Recording quality", p)
+    if len(p) > 0:
+        quality = deep_get(info, p)
+    find_key_recursive(info, "Comment", p)
+    if len(p) > 0:
+        comment = deep_get(info, p, default="")
+
+    return exp, rec_date, quality, comment, has_nix
 
 
 def populate_datasets(data_path):
@@ -145,7 +190,7 @@ def populate_datasets(data_path):
     if not os.path.exists(data_path):
         return
     dset_name = os.path.split(data_path)[-1]
-    experimenter, rec_date, has_nix = read_info(os.path.join(data_path, 'info.dat'))
+    experimenter, rec_date, quality, comment, has_nix = read_info(os.path.join(data_path, 'info.dat'))
     if not experimenter:
         return
 
@@ -154,8 +199,9 @@ def populate_datasets(data_path):
     inserts["data_source"] = data_path
     inserts["experimenter"] = experimenter
     inserts["recording_date"] = rec_date
+    inserts["quality"] = quality
+    inserts["comment"] = comment
     inserts["has_nix"] = has_nix
-
     Dataset().insert1(inserts, skip_duplicates=True)
 
 
@@ -196,12 +242,65 @@ def populate_subjects(data_path):
         SubjectProperties.insert1(props, skip_duplicates=True)
 
 
-if __name__ == "__main__":
-    datasets = glob.glob('/data/apteronotus/2018-05-08*')
-    # Dataset.drop()
-    # Subject.drop()
-    # SubjectProperties.drop()
-    # SubjectDatasetMap.drop()
+def populate_cells(data_path):
+    print("Importing subject(s) of %s" % data_path)
+    dset_name = os.path.split(data_path)[-1]
+    info_file = os.path.join(data_path, 'info.dat')
+    if not os.path.exists(info_file):
+        return None, None, False
+    info = read_info_file(info_file)
+    p = []
+    find_key_recursive(info, "Subject", p)
+    subject_info = deep_get(info, p)
+
+    p = []
+    find_key_recursive(info, "Cell", p)
+    cell_info = deep_get(info, p)
+
+    p = []
+    find_key_recursive(info, "Firing Rate1", p)
+    firing_rate = deep_get(info, p, default=0.0)
+    if isinstance(firing_rate, str):
+        firing_rate = float(firing_rate[:-2])
+
+    dataset = dict((Dataset & {"dataset_id": dset_name}).fetch1())
+    subject = dict((Subject & {"subject_id": subject_info["Identifier"]}).fetch1())
+
+    cell_props = Cell.get_template_tuple()
+    cell_props["subject_id"] = subject["subject_id"]
+    cell_props["dataset_id"] = dataset["dataset_id"]
+    cell_props["cell_name"] = dataset["dataset_id"]
+    cell_props["cell_type"] = cell_info["CellType"]
+    cell_props["firing_rate"] = firing_rate
+    if "Structure" in cell_info.keys():
+        cell_props["structure"] = cell_info["Structure"]
+    if "BrainRegion" in cell_info.keys():
+        cell_props["region"] = cell_info["BrainRegion"]
+    if "BrainSubRegion" in cell_info.keys():
+        cell_props["subregion"] = cell_info["BrainSubRegion"]
+    if "Depth" in cell_info.keys():
+        cell_props["depth"] = float(cell_info["Depth"][:-2])
+    if "Lateral position" in cell_info.keys():
+        cell_props["lateral_pos"] = float(cell_info["Lateral position"][:-2])
+    if "Transverse section" in cell_info.keys():
+        cell_props["transversal_section"] = float(cell_info["Transverse section"])
+
+    Cell.insert1(cell_props, skip_duplicates=True)
+
+
+def drop_tables():
+    Dataset.drop()
+    Subject.drop()
+
+
+def populate(datasets):
     for d in datasets:
         populate_datasets(d)
         populate_subjects(d)
+        populate_cells(d)
+
+
+if __name__ == "__main__":
+    datasets = glob.glob('/data/apteronotus/2018-05-08*')
+    # drop_tables()
+    populate(datasets)