NeurosciencePLoS ONEPeer-reviewed
Probabilistic associative learning suffices for learning the temporal structure of multiple sequences
Ramon H. Martinez, Anders Lansner, Pawel Andrzej Herman
The paper investigates how a probabilistic associative learning model can encode and reproduce temporal structures of multiple sequences, showing its robustness to noise and ability to handle overlapping sequences through modularity.
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