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In TensorFlow Keras, a self-looped neuron can be incorporated by adding a Recurrent layer with the 'return_sequences' parameter set to True. This will allow the output of each time step to be fed back as input to the next time step, creating a self-loop.

Here is an example code snippet for adding a self-looped neuron in a Sequential model:

from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, LSTM

model = Sequential()
model.add(LSTM(units=32, return_sequences=True, input_shape=(10, 1))) # Self-looped LSTM layer
model.add(Dense(units=1))

# Compile the model
model.compile(loss='mean_squared_error', optimizer='adam')

In this example, we have added an LSTM layer with the 'returnsequences' parameter set to True to create a self-looped neuron. The model expects input sequences of length 10 and a single feature, represented by the inputshape parameter. The model is then compiled with a mean squared error loss and the Adam optimizer.

Note that the output of a self-looped neuron will depend on its previous outputs at each time step, which may increase the computational complexity of the model. Care must be taken to properly tune the parameter values to avoid overfitting or underfitting.