This course describes the nervous system from the perspective of its functionality: information processing. This perspective complements traditional studies based on morphological and physiological descriptions. Models of neurons and neural networks are studied at different description levels: from conductance-based biophysical paradigms to abstract models used in large population networks. In this course we emphasize the role of intrinsic neuronal dynamics, the connectivity, the learning mechanisms and the overall network dynamics. Several theoretical formalisms to describe coding, learning and information execution are explained, including decision making and cognitive activity. The models are used to provide further insight on the nervous system and to study several types of neurodegenerative diseases such as epilepsy, Parkinson’s disease and multiple sclerosis.
1. Introduction to Computational Neuroscience
- Fundamentals of Neuroscience
- Introduction to neural information processing
- Scales and strategies in neural modeling
2. Single neuron modeling
- Conductance based-models
- Simplified dynamical models and Integrate & Fire models
- Rate models
3. Dynamical analysis of neural models
- Phase plane analysis
- Model dimension reduction
- Neuronal excitability
4. Connectivity models. Learning and plasticity
- Chemical synapses.
- Graded synapses
- Gap junctions.
- Synaptic plasticity and learning.
- Subcellular learning
5. Stochastic neural models
- Motivation for stochasticity in neural models
- How to include the stochasticity in neural models
- Examples of stochastic neural models
6. Neural network dynamics and codes
- Motor systems
- Sensory systems
- Central nervous system
- Decision making and cognitive activity
More info on the course official guide (Guía docente)