Research
Exploring the intersection of neuroscience and artificial intelligence through computational modeling and machine learning
Current Research Areas
Computational Models of Neural Circuits
Developing mathematical and computational models to understand how neural circuits process information, focusing on the dynamics of neural networks and their role in computation.
Current Projects:
- •Mathematical modeling of cortical microcircuits
- •Simulation of neural network dynamics
- •Analysis of information flow in neural circuits
Related Publications:
- Coming soon...
Machine Learning for Neuroscience
Applying advanced machine learning techniques to analyze neural data, decode neural signals, and understand brain function.
Current Projects:
- •Deep learning for neural signal decoding
- •Dimensionality reduction in neural data
- •Unsupervised learning of neural representations
Related Publications:
- Coming soon...
Neural Network Dynamics
Investigating the temporal dynamics of neural networks and how they give rise to learning, memory, and cognition.
Current Projects:
- •Dynamical systems analysis of neural networks
- •Temporal coding in neural circuits
- •Learning dynamics in artificial neural networks
Related Publications:
- Coming soon...
Brain-Computer Interfaces
Exploring methods to decode neural signals for direct brain-computer communication and assistive technologies.
Current Projects:
- •Real-time neural signal processing
- •Motor cortex decoding for prosthetics
- •Non-invasive BCI systems
Related Publications:
- Coming soon...
Research Philosophy
I believe that the best insights come from interdisciplinary collaboration and the application of rigorous computational methods to understand complex biological systems. My research aims to bridge the gap between neuroscience and artificial intelligence, using insights from both fields to advance our understanding of intelligence and develop better AI systems.