Discrete-Attractor-like Tracking in Continuous Attractor Neural Networks.
Phys Rev Lett
; 122(1): 018102, 2019 Jan 11.
Article
en En
| MEDLINE
| ID: mdl-31012700
ABSTRACT
Continuous attractor neural networks generate a set of smoothly connected attractor states. In memory systems of the brain, these attractor states may represent continuous pieces of information such as spatial locations and head directions of animals. However, during the replay of previous experiences, hippocampal neurons show a discontinuous sequence in which discrete transitions of the neural state are phase locked with the slow-gamma (â¼30-50 Hz) oscillation. Here, we explore the underlying mechanisms of the discontinuous sequence generation. We find that a continuous attractor neural network has several phases depending on the interactions between external input and local inhibitory feedback. The discrete-attractor-like behavior naturally emerges in one of these phases without any discreteness assumption. We propose that the dynamics of continuous attractor neural networks is the key to generate discontinuous state changes phase locked to the brain rhythm.
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1
Colección:
01-internacional
Banco de datos:
MEDLINE
Idioma:
En
Revista:
Phys Rev Lett
Año:
2019
Tipo del documento:
Article
País de afiliación:
Japón