Guozhang Chen

The paper identifies how sparse cortical activity in mouse V1 is organized into flexible, condition-dependent spike-order codes through a combination of experimental data and a computational model.

The paper presents a deep-learning-assisted simulator of a mouse primary visual cortex model, integrating multimodal data to study circuit function and synaptic organization.

The paper introduces a brain-inspired framework for domain adaptation in AI, using recurrent neural networks to create transferable representations that enable rapid few-shot adaptation with fewer parameters.

This paper identifies a mechanism for reliable neural information processing through neural heterogeneity, showing it disrupts spatiotemporal patterns and aligns network activity with inputs.

This paper develops a spatially extended neural network model with spectral theory to show how local connectivity parameters determine global neural dynamics and explains cortical dynamics shifts in mice.

The paper trains large-scale models of cortical microcircuits to understand their computational organization and finds they match brain performance and reproduce unique experimental traits.

The paper analyzes a large-scale V1 model using anatomical and neurophysiological data to show it can robustly multiplex visual tasks and exhibit brain-like noise resilience.

This paper proposes an evolutionary algorithm model of brain learning using non-local rules and demonstrates its effectiveness in training neural networks with biologically plausible weights.