Work

Network Mechanism Supporting Long-Distance-Dependencies

Year: 2021

Type: conference-paper

Abstract: 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 reverse-engineering 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.

Source: International Joint Conference on Neural Networks (IJCNN)

DOI: 10.1109/ijcnn52387.2021.9534151

Authors: Alexis Dubreuil, Arthur Leblois

Institutions: Centre National de la Recherche Scientifique, Institut de la Vision, Inserm, Université de Bordeaux, Institut des Maladies Neurodégénératives

Language: English

Topic: Neural dynamics and brain function

Subfield: Cognitive Neuroscience

Field: Neuroscience

Domain: Life Sciences

Open Access status: green

Funder: Agence Nationale de la Recherche

Award: Cerebellar circuits and function in vocal learning