3 accepted papers
Modeling the nonlinear dynamics of neuronal populations represents a key pursuit in computational neuroscience. Recent research has increasingly focused on jointly modeling neural activity and behavior to unravel their interconnections. Despite significant efforts, these approaches often necessitate…
In computational neuroscience, models representing single-neuron in-vivo activity have become essential for understanding the functional identities of individual neurons. These models, such as implicit representation methods based on Transformer architectures, contrastive learning frameworks, and va…
There exist some challenges in accurately capturing temporal change information and efficiently aggregating multi-level information in the field of remote sensing change detection. In order to expand the detection’s receptive field and fully fuse complementary information across different hierarchic…