2015/02/12 by He Wang, Kin‐Man Lam, Kin Lam +4
Biochemistry, Genetics and Molecular Biology · Computer Science · Engineering · Mathematics · Neuroscience · Physics and Astronomy · Psychology · #Acoustics #Advanced Memory and Neural Computing #Artificial intelligence #Attractor #Biology #Chaotic #Computer science #Dynamics (music) #ENCODE #Gaussian #Geometry #Mathematical analysis #Mathematics #Neural Networks and Applications #Neural dynamics and brain function #Neuroscience #Orientation (vector space) #Physics #Psychology #Statistical physics #Term (time) #Topology (electrical circuits) #cond-mat.dis-nn #q-bio.NC
paper · pdf · doi:10.1103/physreve.92.032908
published as Phys. Rev. E 92, 032908 (2015)
arxiv created 2015/02/12 · openalex publication_date 2015/09/14 · arxiv updated 2015/09/23 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
In continuous attractor neural networks (CANNs), spatially continuous information such as orientation, head direction, and spatial location is represented by Gaussian-like tuning curves that can be displaced continuously in the space of the preferred stimuli of the neurons. We investigate how short-term synaptic depression (STD) can reshape the intrinsic dynamics of the CANN model and its responses to a single static input. In particular, CANNs with STD can support various complex firing patterns and chaotic behaviors. These chaotic behaviors have the potential to encode various stimuli in the neuronal system.