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BME 665 / 565 Students - Spring 2004 | Class home page | ||
Basic background for the course | |||
30% | I have taken a basic cellular neurophysiology course recently | ||
90% | I have taken linear algebra, calculus, and ordinary differential equations | ||
100% | I have taken probability and statistics | ||
Interest in the course | |||
70% | I am interested in doing biophysical modeling of neurons | ||
90% | I am interested in information coding in the brain | ||
70% | I want a better understanding of the biological principles underlying neural network modeling | ||
80% | I am generally interested in modeling biological systems | ||
100% | I want to better understand what role computational methods can play in neuroscience | ||
60% | I want to be able to read the computational neuroscience literature more effectively | ||
30% | It is required for my degree | ||
Familiarity with course material | |||
5 - Expert 4 - Comfortable with the concept 3 - Can define it 2 - Have heard of it 1 - Say what? | |||
2.3 | Hodgkin-Huxley model | ||
3.3 | Action potentials | ||
2.9 | Calcium, potassium and sodium channels | ||
2.3 | PSP's, PSC's | ||
2.3 | Integrate-and-fire neurons, spike response model | ||
2.2 | Stochastic firing and rate models | ||
1.6 | Neuronal oscillations, central pattern generators | ||
2.5 | Hebbian learning | ||
2.0 | Orientation selectivity, ocular dominance | ||
2.4 | Temporal coding | ||
2.8 | Synaptic plasticity | ||
1.8 | Patch clamping, intra- and extra-cellular recording | ||
2.0 | Optical imaging | ||
3.6 | MATLAB programming | ||
3.1 | Bayes' theorem | ||
3.2 | Ordinary differential equations | ||
3.5 | Fourier transforms |