This course introduces mathematical and computational modelling approaches to brain information processing. The objective is to initiate students to computational neuroscience and to teach key quantitative concepts. The course is organized in three modules:

  1. Modeling of cognition and behavior (classical and operant conditioning, reinforcement learning, decision-making) 
  2. Information processing (neural decoding, population encoding, sensory processing, linear receptive fields,)
  3. Dynamics and mechanisms (biophysics of neurons, feedforward and recurrent neural networks, synaptic plasticity, associative memories)

Learning outcomes
Students will be introduced to and familiarized with concepts of computational modelling in neuroscience.
Students will be able to:

  • understand models of reinforcement learning
  • understand models of neural decoding using signal detection theory
  • understand models of population coding and decision making based on single neuron activity
  • understand  models of neuronal biophysics and network dynamics
  • read and analyse papers in computational neuroscience

Prerequisites:
Mathematical skills are prerequisite to the course (linear algebra, vector arithmetic, notions of probability and statistics, calculus and  differential equations). Previous training in quantitative disciplines is strongly recommended.

ECTS: 4