TY - JOUR
T1 - Discrete-Attractor-like Tracking in Continuous Attractor Neural Networks
AU - Fung, Chi Chung Alan
AU - Fukai, Tomoki
N1 - Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected].
PY - 2019/1/8
Y1 - 2019/1/8
N2 - 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 (∼30-50 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.
AB - 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 (∼30-50 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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U2 - 10.1103/PhysRevLett.122.018102
DO - 10.1103/PhysRevLett.122.018102
M3 - RGC 21 - Publication in refereed journal
C2 - 31012700
SN - 0031-9007
VL - 122
JO - Physical Review Letters
JF - Physical Review Letters
IS - 1
M1 - 018102
ER -