Ziwei Huang

Eyewire II provides a large-scale EM dataset of the mouse retina, enabling cell type annotation and connectivity mapping through human-in-the-loop deep learning and functional alignment.

The paper introduces JO_SCPLOWAXLEYC_SCPLOW, a framework for differentiable simulation of biophysical neuron models, enabling efficient parameter optimization and task-driven training of large-scale models.

The paper develops a biophysical model of the mouse outer retina to study contrast invariant coding, finding that nonlinearities in the retina enable robust classification despite contrast variations.

The paper shows that modulatory inputs in the dLGN affect neurons based on their visual responsiveness and stimulus type.

The paper introduces a spline-based method for estimating neural receptive fields, showing improved performance over non-spline methods in both simulated and experimental data.

This study shows how four types of mouse retinal ganglion cells use different dendritic integration rules, influenced by both morphology and ion channel distribution.

The paper shows grid cell field firing rates vary, explaining why inhibitory interneurons have aperiodic firing patterns and causing hippocampal cell remapping.