diff --git a/emodel_generalisation/adaptation.py b/emodel_generalisation/adaptation.py
index 58f5f60..0d032cc 100644
--- a/emodel_generalisation/adaptation.py
+++ b/emodel_generalisation/adaptation.py
@@ -201,7 +201,7 @@ def __adapt_single_soma_ais(
 
 
 def _adapt_single_soma_ais(*args, **kwargs):
-    timeout = kwargs.pop("timeout", 5 * 60)
+    timeout = kwargs.pop("timeout", 30 * 60)
     res = isolate(__adapt_single_soma_ais, timeout=timeout)(*args, **kwargs)
     if res is None:
         print("timeout", args, kwargs)
diff --git a/emodel_generalisation/bluecellulab_evaluator.py b/emodel_generalisation/bluecellulab_evaluator.py
index 24aac2b..2f118c2 100644
--- a/emodel_generalisation/bluecellulab_evaluator.py
+++ b/emodel_generalisation/bluecellulab_evaluator.py
@@ -57,7 +57,7 @@ def calculate_threshold_current(cell, config, holding_current):
 def binsearch_threshold_current(cell, config, holding_current, min_current, max_current):
     """Binary search for threshold currents"""
     mid_current = (min_current + max_current) / 2
-    if abs(max_current - min_current) < config["threshold_current_precision"]:
+    if abs(max_current - min_current) < config.get("threshold_current_precision", 0.001):
         spike_count = run_spike_sim(
             cell,
             config,
diff --git a/emodel_generalisation/cli.py b/emodel_generalisation/cli.py
index e65167a..5f1584d 100644
--- a/emodel_generalisation/cli.py
+++ b/emodel_generalisation/cli.py
@@ -206,12 +206,15 @@ def compute_currents(
         )
 
         failed_cells = unique_cells_df[
-            unique_cells_df["input_resistance"].isna() | (unique_cells_df["input_resistance"] < 0)
+            unique_cells_df["input_resistance"].isna() | (unique_cells_df["input_resistance"] <= 0)
         ].index
         if len(failed_cells) > 0:
-            L.info("still %s failed cells (we drop):", len(failed_cells))
+            L.info("still %s failed cells (we set default values):", len(failed_cells))
             L.info(unique_cells_df.loc[failed_cells])
-            unique_cells_df.loc[failed_cells, "mtype"] = None
+            unique_cells_df.loc[failed_cells, "@dynamics:holding_current"] = 0.0
+            unique_cells_df.loc[failed_cells, "@dynamics:threshold_current"] = 0.0
+            unique_cells_df.loc[failed_cells, "@dynamics:input_resistance"] = 0.0
+            unique_cells_df.loc[failed_cells, "@dynamics:resting_potential"] = -80.0
 
     cols = ["resting_potential", "input_resistance", "exception"]
     if not only_rin: