Integrated neural dynamics of sensorimotor decisions and actions
Recent theoretical models suggest that deciding about actions and executing them are not
implemented by completely distinct neural mechanisms but are instead two modes of an …
implemented by completely distinct neural mechanisms but are instead two modes of an …
Adaptation accelerating sampling-based bayesian inference in attractor neural networks
The brain performs probabilistic Bayesian inference to interpret the external world. The
sampling-based view assumes that the brain represents the stimulus posterior distribution …
sampling-based view assumes that the brain represents the stimulus posterior distribution …
BrainPy: a flexible, integrative, efficient, and extensible framework towards general-purpose brain dynamics programming
The neural mechanisms underlying brain functions are extremely complicated. Brain
dynamics modeling is an indispensable tool for elucidating these mechanisms by modeling …
dynamics modeling is an indispensable tool for elucidating these mechanisms by modeling …
Translation-equivariant representation in recurrent networks with a continuous manifold of attractors
Equivariant representation is necessary for the brain and artificial perceptual systems to
faithfully represent the stimulus under some (Lie) group transformations. However, it remains …
faithfully represent the stimulus under some (Lie) group transformations. However, it remains …
Oscillatory tracking of continuous attractor neural networks account for phase precession and procession of hippocampal place cells
Hippocampal place cells of freely moving rodents display an intriguing temporal
organization in their responses known astheta phase precession', in which individual …
organization in their responses known astheta phase precession', in which individual …
Brain-inspired multiple-target tracking using Dynamic Neural Fields
Despite considerable progress in the field of automatic multi-target tracking, several
problems such as data association remained challenging. On the other hand, cognitive …
problems such as data association remained challenging. On the other hand, cognitive …
Continuous Recurrent Neural Networks Based on Function Satlins: Coexistence of Multiple Continuous Attractors
Y Huang, J Yu, J Leng, B Liu, Z Yi - Neural Processing Letters, 2022 - Springer
The brief investigates the coexistence of multiple continuous attractors in a recurrent neural
network, ie, the symmetric saturated Satlins linear neural networks, based on a …
network, ie, the symmetric saturated Satlins linear neural networks, based on a …
Multiple bumps can enhance robustness to noise in continuous attractor networks
R Wang, L Kang - PLOS Computational Biology, 2022 - journals.plos.org
A central function of continuous attractor networks is encoding coordinates and accurately
updating their values through path integration. To do so, these networks produce localized …
updating their values through path integration. To do so, these networks produce localized …
Firing rate adaptation in continuous attractor neural networks accounts for theta phase shift of hippocampal place cells
Hippocampal place cells of freely moving animals display 'theta phase precession', whereby
spikes are fired at successively earlier phases of the 6-10 Hz local field potential (LFP) theta …
spikes are fired at successively earlier phases of the 6-10 Hz local field potential (LFP) theta …
The Brain-Inspired Cooperative Shared Control for Brain-Machine Interface
S Zheng, L Liu, J Yang, L Qian, G Gao, X Chen… - arXiv preprint arXiv …, 2022 - arxiv.org
In the practical application of brain-machine interface technology, the problem often faced is
the low information content and high noise of the neural signals collected by the electrode …
the low information content and high noise of the neural signals collected by the electrode …