Courses

CNEC faculty have developed a number of undergraduate and graduate neural engineering and computation courses. CNEC is also facilitating Master's Degree concentrations in Neural Engineering, and Computation that span all SEAS departments.

Concentration in Systems Biology & Neuroengineering in the Dept. of Electrical Engineering

1. Satisfy M.S. degree requirements in Electrical Engineering.
2. Take both
   • BMEB W4020: Computational neuroscience: circuits in the brain;
   • ECBM E4060: Introduction to genomic information science and technology;
3. Take at least one course from:
   • BMEE E4030: Neural control engineering;
   • ECBM E4040: Neural Networks and deep learning;
   • ECBM E4070 Computing with Brain Circuits of Model Organisms;
   • ECBM E4090: Brain computer interfaces (BCI) laboratory;
   • CBMF W4761: Computational genomics;
   • ELEN E6010: Systems biology: design principles for biological circuits;
   • EEBM E6020: Methods in computational neuroscience;
   • BMEE E6030: Neural modeling and neuroengineering;
4.  Take at least one course from:
   • ECBM E6040: Neural networks and deep learning research;
   • ECBM E607x: Topics in neuroscience and deep learning:
     • ECBM E6070: Computing with Brain Circuits of Model Organism;
   • ELEN E608x: Topics in systems biology;
   • EEBM E609x:  Topics in Computational Neuroscience and Neuroengineering:
     • EEBM E6090: Topic: Brain Computer Interfaces;
     • EEBM E6091: Topic: Devices and Analysis for Neural Circuits;
     • EEBM E6095: Topic: Computing with Brain Circuits;
   • ELEN E6261: Computational methods of circuit analysis;
   • ELEN E6717: Information theory;
   • ELEN E6860: Advanced digital signal processing;

Concentration in Data-Driven Analysis & Computation in the Dept. of Electrical Engineering

1. Satisfy M.S. degree requirements in Electrical Engineering.
2. Take at least two courses from:
   • ECBM E4040: Neural networks and deep learning;
   • EECS E4764: Internet of things – intelligent and connected systems;
   • ELEN E4810: Digital Signal Processing;
   • ELEN E490x: Topics in Electrical and Computer Engineering:
     • ELEN E4903: Topic: Machine learning (or equivalent);
   • EEOR E6616: Convex optimization;
   • ELEN E688x: Topics in Signal Processing:
     • ELEN E6880: Topic: Random Matrix Theory with Eng. and Data Science Applications;
     • ELEN E6885: Topic: Reinforcement Learning;
   • EECS E689x Topics in Information Processing:
     • EECS E6893: Topic: Big Data Analytics;
     • EECS E6894: Topic: Deep Learning for Computer Vision, Speech and Language;
3. Take at least one course from:
   • ECBM E6040: Neural networks and deep learning research;
   • EECS E6720: Bayesian models for machine learning;
   • EECS E6765: Internet of things - systems and physical data analytics;
   • EECS E689x: Topics in Information Processing:
     • EECS E6895: Topic: Advanced big data analytics;
4. Take a second course from #3, or one course from:
   • ECBM E4060: Introduction to Genomic Information Science and Technology;
   • ECBM E607x: Topics in Neuroscience and Deep Learning:
     • ECBM E6070: Topic: Fruit Fly Brain as NeuroInformation Processor;
   • ELEN E669x: Topics in Data-driven Analysis and Computation:
     •  ELEN E6690: Topic: Statistical Learning in Biological & Information Systems;
     •  EECS E6699: Topic: Mathematics of Deep Learning;
   • ELEN E6886: Sparse representation and high-dimensional geometry;
   • ELEN E9601: Seminar in data-driven analysis and computation;