Caltech’s MS in Computation and Neural Systems blends neuroscience, computer science, and engineering to explore how brains compute and how machines can emulate them. These project ideas help students innovate at the interface of biology and technology.
Neural Encoding and Decoding Using Deep Learning Models
Simulating Spiking Neural Networks with Neuromorphic Hardware
Brain-Computer Interface for Assistive Communication
Computational Models of Visual Cortex Processing
EEG Signal Analysis for Cognitive Load Detection
Modeling Synaptic Plasticity in Learning Systems
Reinforcement Learning for Decision-Making Simulations
Real-Time Neural Signal Processing Using Embedded Systems
Neuro-Inspired Robotics: Sensorimotor Integration
Predictive Modeling of fMRI Data Using Graph Neural Networks
Cognitive Modeling of Attention Mechanisms
Exploring Cortical Columns Using Connectomics Data
Computational Psychiatry: Modeling Disorders with Neural Nets
Decoding Motor Intentions from Neural Activity
Designing Closed-Loop Systems for Neurofeedback Therapy
Caltech CNS students can access Collexa’s support for simulation tools, brain data interpretation, algorithm testing, and publication planning.
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