diff --git a/nback_rnn.py b/nback_rnn.py
new file mode 100644
index 0000000..28cf8c4
--- /dev/null
+++ b/nback_rnn.py
@@ -0,0 +1,43 @@
+# %%
+# !pip install pyreadr tensorflow -U
+
+import pyreadr
+import pandas as pd
+import numpy as np
+
+dataset_file = 'data/CL2015.RData'
+
+# read n-back dataset
+datasets = pyreadr.read_r(dataset_file, use_objects="NB")
+dataset = datasets["NB"]
+
+X = dataset.stimulus.cat.codes.values
+X = np.reshape(X, (X.shape[0], 1, 1))
+
+y = pd.get_dummies(dataset.correct).values
+y = np.reshape(y, (y.shape[0], 1, y.shape[1]))
+
+# %%
+from tensorflow import keras
+from tensorflow.keras import layers
+
+model = keras.Sequential()
+
+model.add(layers.SimpleRNN(units=32, input_dim=1, activation="relu"))
+model.add(layers.Dense(8, activation="relu"))
+model.add(layers.Dense(1))
+model.compile(loss='mean_squared_error', optimizer='rmsprop')
+print(model.summary())
+
+model.fit(X, y, batch_size=32)
+
+# model.add(layers.Embedding(input_dim=28, output_dim=64))
+# model.add(layers.LSTM(128))
+# model.add(layers.Dense(10))
+
+# model.summary()
+
+# stimulus -> correct
+
+# %%
+model.summary()