Akshay Gautam
PhD Student in Computational Neuroscience & Machine Learning
Exploring the intersection of artificial intelligence and neuroscience to understand how neural networks process information and learn from experience.
About Me
I'm a first-year PhD student passionate about understanding the computational principles underlying neural information processing. My research sits at the intersection of neuroscience and artificial intelligence, where I explore how biological neural networks inspire better machine learning algorithms.
My current focus involves developing computational models that can bridge the gap between biological neural circuits and artificial neural networks. I'm particularly interested in how the brain processes complex information in real-time and how we can apply these insights to create more efficient AI systems.
Beyond research, I enjoy contributing to open-source projects, attending conferences, and collaborating with researchers across different disciplines. I believe that the best scientific discoveries happen at the intersection of multiple fields.
Profile photo coming soon
Research Interests
My research spans multiple areas at the intersection of neuroscience and artificial intelligence
Computational Models of Neural Circuits
Developing mathematical models to understand how neural circuits process and transmit information.
Machine Learning for Neuroscience
Applying advanced ML techniques to analyze neural data and uncover patterns in brain activity.
Neural Network Dynamics
Studying the temporal dynamics of neural networks and their role in learning and memory.
Brain-Computer Interfaces
Exploring methods to decode neural signals for direct brain-computer communication.
Deep Learning Theory
Investigating the theoretical foundations of deep learning and its connections to neuroscience.
Featured Work
A selection of projects showcasing my work in computational neuroscience and machine learning
Neural Network Visualization Tool
In ProgressInteractive web application for visualizing neural network architectures and training dynamics.
Spike Train Analysis Pipeline
CompletedComprehensive toolkit for analyzing neural spike train data with modern statistical methods.
Brain-Computer Interface Demo
In ProgressReal-time EEG signal processing and classification for basic brain-computer interface applications.