Bishal Upadhyaya.

Structured pruning asserts that while large networks enable us to find solutions to complex computer vision problems, a smaller, computationally efficient sub-network can be extracted. We propose a generalization of continuous relaxation in structured pruning to effectively identify these optimal sub-networks, ensuring performance on resource-constrained hardware.

Investigating neural circuit plasticity, this study demonstrates how genetically engineered electrical synapses can reroute information flow to circumvent damaged neurons. By expressing connexin gap junctions, we successfully restored chemotaxis behavior in C. elegans after critical interneuron loss, highlighting the potential of artificial synapses in neural repair.

This research compares the efficacy of FDG PET (metabolic) and Amyloid PET (pathological) biomarkers in training Deep Learning models for Alzheimer's prediction. The study focuses on applying these models to data from underprivileged communities, evaluating accessibility versus predictive power for early diagnosis.

Electrical synapses are critical for fast neural communication. This paper elucidates the distinct functional roles of innexin proteins INX-18 and INX-19 in forming gap junctions that modulate aversive behavioral responses in Caenorhabditis elegans, contributing to our understanding of the genetic basis of behavior.