Asim Iqbal

The paper implements biologically realistic neocortical circuit motifs on neuromorphic hardware to improve Vision Transformer performance in digit classification.

Segment AnyNeuron is an active learning framework for neuron segmentation using minimal annotations, achieving high precision on diverse neuron imaging datasets.

The paper identifies enhancer-AAV tools for specific neuronal subtypes and validates them across species.

MortX provides a benchmark dataset for mouse cortex segmentation and registration, enabling domain generalization across postnatal ages.

NeuroAtlas provides an AI framework for brain atlas generation and segmentation, demonstrating robust performance on human and mouse brains across imaging modalities and age groups.

This paper introduces an AI-driven 3D brain image registration method that improves alignment across scanners and modalities, enhancing atrophy measurement accuracy in neuroimaging studies.

CellSeg3D introduces self-supervised 3D cell segmentation models for fluorescence microscopy, achieving performance comparable to supervised methods without requiring ground truth labels.

StARQ automates high-granularity segmentation and quantification of brainstem nuclei using deep learning, enhancing reliable computational analysis.

The paper identifies a developmental transition in VIP+ and SST+ interneurons in the mouse barrel cortex around P14, where their responses to multi-whisker stimuli diverge and their thalamic inputs shift.