title: Continuous Attractor Neural Networks: Candidate of a Canonical Model for Neural Information Representation.
abstract: Owing to its many computationally desirable properties, the model of continuous attractor neural networks (CANNs) has been successfully applied to describe the encoding of simple continuous features in neural systems, such as orientation, moving direction, head direction, and spatial location of objects. Recent experimental and computational studies revealed that complex features of external inputs may also be encoded by low-dimensional CANNs embedded in the high-dimensional space of neural population activity. The new experimental data also confirmed the existence of the M-shaped correlation between neuronal responses, which is a correlation structure associated with the unique dynamics of CANNs. This body of evidence, which is reviewed in this report, suggests that CANNs may serve as a canonical model for neural information representation.
authors.affiliation: State Key Laboratory of Cognitive Neuroscience & Learning, IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, 100875, China.
authors.forename: Si
authors.initials: S
authors.lastname: Wu
authors.affiliation: Department of Physics, Hong Kong University of Science & Technology, Clear Water Bay Peninsula, Hong Kong.
authors.forename: K Y Michael
authors.initials: KY
authors.lastname: Wong
authors.affiliation: RIKEN Brain Science Institute, Wako-shi, Saitama, Japan.
authors.forename: C C Alan
authors.initials: CC
authors.lastname: Fung
authors.affiliation: State Key Laboratory of Cognitive Neuroscience & Learning, IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, 100875, China.
authors.forename: Yuanyuan
authors.initials: Y
authors.lastname: Mi
authors.affiliation: State Key Laboratory of Cognitive Neuroscience & Learning, IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, 100875, China; Department of Physics, Hong Kong University of Science & Technology, Clear Water Bay Peninsula, Hong Kong.
authors.forename: Wenhao
authors.initials: W
authors.lastname: Zhang
keywords: Continuous Attractor Neural Network; Neural network; anticipative tracking; canonical model; multi-sensory information integration