  In brain neural networks, Local Field Potential (LFP) signals represent the
dynamic flow of information. Analyzing LFP clinical data plays a critical role
in improving our understanding of brain mechanisms. One way to enhance our
understanding of these mechanisms is to identify a global model to predict
brain signals in different situations. This paper identifies a global
data-driven based on LFP recordings of the Nucleus Accumbens and Hippocampus
regions in freely moving rats. The LFP is recorded from each rat in two
different situations: before and after the process of getting a reward which
can be either a drug (Morphine) or natural food (like popcorn or biscuit). A
comparison of five machine learning methods including Long Short Term Memory
(LSTM), Echo State Network (ESN), Deep Echo State Network (DeepESN), Radial
Basis Function (RBF), and Local Linear Model Tree (LLM) is conducted to develop
this model. LoLiMoT was chosen with the best performance among all methods.
This model can predict the future states of these regions with one pre-trained
model. Identifying this model showed that Morphine and natural rewards do not
change the dynamic features of neurons in these regions.
