Network Mechanism Supporting Long-Distance-Dependencies
| Autor: | Arthur Leblois, Alexis Dubreuil |
|---|---|
| Přispěvatelé: | Institut de la Vision, Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Institut des Maladies Neurodégénératives [Bordeaux] (IMN), Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS), Leblois, Arthur |
| Rok vydání: | 2021 |
| Předmět: |
Structure (mathematical logic)
Neural correlates of consciousness Quantitative Biology::Neurons and Cognition Artificial neural network Computer science Mechanism (biology) business.industry [SDV]Life Sciences [q-bio] Computer Science::Neural and Evolutionary Computation [SDV] Life Sciences [q-bio] Encoding (memory) Identity (object-oriented programming) Production (computer science) Artificial intelligence Set (psychology) business |
| Zdroj: | IJCNN 2021 International Joint Conference on Neural Networks (IJCNN) 2021 International Joint Conference on Neural Networks (IJCNN), Jul 2021, Shenzhen, France. pp.1-6, ⟨10.1109/IJCNN52387.2021.9534151⟩ |
| DOI: | 10.1109/ijcnn52387.2021.9534151 |
| Popis: | International audience; Sequential behaviors such as language or bird songs are structured in time. This structure relies on the notion of long-distance-dependencies: transitions between words depend on the identity of words produced in the past. Here we propose a network mechanism supporting such dependencies. To do so we trained artificial neural networks to produce a minimal set of sequences exhibiting long-distance-dependencies. By reverseengineering the trained networks we found this to rely on two superposing neural sequences, one responsible for the production of the motor sequence and another one encoding a contextual memory. We show how these two sequences are supported by neural activity and network connectivity and how they interact with each other to decide on transitions between words. We discuss similarities between the neural activity of our artificial neural networks and neural correlates of long-distance-dependencies that have recently been exposed in songbirds. |
| Databáze: | OpenAIRE |
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