The course will introduce students to theoretical models and computational approaches in neuroscience. Topics to be covered include neuronal encoding and decoding, analysis of dynamical neural systems, biophysical modeling of single neurons and networks, learning paradigms in neural network theory and contemporary applications in brain research.
Part A: The Neural Code:
- Introduction to the neural code
- Neuronal encoding and decoding
- Noise, information, and population of neurons
- Stimulus statistics, neural adaptation, and optimal coding
Part B: From single Neurons to Network models
- Integrate and fire models, neural excitability, and conductance-based models
- Basic concepts in dynamical systems and single neuron dynamics
- Network models
Part C: Learning and Memory in Simplified Neural Models
- The linear perceptron and basic concepts in learning theory
- The Hopfield model for associative memory
- Multilayered networks, back-propagation, Boltzmann machines, and beyond
- Classical conditioning and reinforcement learning
Part D: Modeling Brain Function
- Sensory perception and decision theory
- Examples of neuronal network models in brain research (e.g., the ring model for orientation selectivity, attractor neural network models of place cells)