12 citations · 19 across the 9 of their papers we have counts for
10 papers
Diffusion learning reveals viable parameter manifolds and compensation geometry in biological dynamical systems
Ruilin Zhang, Louis Tao, Zhuo-Cheng Xiao
Models of complex systems often have many parameters, yet are constrained by far fewer experimentally accessible observables; consequently, similar activity can emerge from coordin…
Mostly-monocular responses and other visual functions in a multiscale network model of Macaque V1
Zhuo-Cheng Xiao, Kevin K. Lin, Lai-Sang Young
Visual signals from the two eyes merge gradually as they pass through the primary visual cortex (V1). Here we use a computational model of Macaque V1 to study the first stage of th…
Numerical analysis for leaky-integrate-fire networks under Euler-Maruyama
Xu'an Dou, Frank Chen, Kevin K Lin +1
Leaky integrate-and-fire (LIF) networks are standard reduced models for spike-based neural dynamics and a natural substrate for neuromorphic computation. We study time-driven Euler…
Finite-state Markovian surrogates for long-time neuronal state distributions and firing rates
Zhongyi Wang, Louis Tao, Zhuo-Cheng Xiao
Spiking neuronal networks connect cellular and synaptic mechanisms to collective activity, but estimating their long-time statistics often requires costly spike-by-spike simulation…
Minimizing information loss reduces spiking neuronal networks to differential equations
Jie Chang, Zhuoran Li, Zhongyi Wang +2
Spiking neuronal networks (SNNs) are widely used in computational neuroscience, from biologically realistic modeling of local cortical networks to phenomenological modeling of the…
Learning biological neuronal networks with artificial neural networks: neural oscillations
Ruilin Zhang, Zhongyi Wang, Tianyi Wu +4
First-principles-based modelings have been extremely successful in providing crucial insights and predictions for complex biological functions and phenomena. However, they can be h…