Aravendra Kumar Sharma
Papers
1
Total Citations
11
H-Index
1
About
Aravendra Kumar Sharma is a computational neuroscientist whose research focuses on the neural mechanisms underlying memory and information processing in the hippocampus. His key contributions center on understanding how excitation/inhibition balance and network architecture enable efficient data storage and retrieval. In his most cited work, "Design Partition in an Active to Imbalanced Excitation/Inhibition Hippocampus Neural Arrange" (2023, 11 citations), Sharma integrates recent anatomical and functional data from the entorhinal cortex, dentate gyrus, and hippocampus to demonstrate how a three-layer feed-forward spiking neural network achieves effective design partitioning. This work provides critical insights into how the dentate gyrus manages the transition from balanced to imbalanced neural activity, a fundamental process for memory encoding. Sharma's research bridges theoretical modeling with empirical neuroscience, offering a framework for understanding hippocampal function in health and disease. With his innovative approach to neural network design, he is contributing to the development of more biologically plausible models of memory, with potential applications in neuromorphic computing and treatments for memory disorders.
Research Focus
Key Achievements
Top Papers
- 1