This course looks into the mechanisms of adaptability of neural connections. From the classic Hebb rule to modern models of Hebbian learning, spike-timing dependent potentiation and depression, as well as long and short term synaptic changes. We review unsupervised learning, the Hopfield model, and reward-based learning.

Course Objectives:

1. Comprehend the Hebb rule and its experimental foundations.
2. Explore and analyze various models of Hebbian learning.
3. Understand the mechanisms behind spike-timing dependent potentiation and depression.
4. Investigate the principles and implications of long and short term synaptic depression and potentiation.
5. Examine the concept and applications of unsupervised learning in neural networks.
6. Gain insights into the Hopfield model and its role in associative memory.
7. Understand the fundamentals and applications of reward-based learning in neural systems.

Bibliography

1. Gerstner W, Kistler WM, Naud R, Paninski L (2014) Neuronal Dynamics: From single neurons to networks and models of cognition. Cambridge University Press.
2. Dayan, P. and Abbott, L.F. (2001) Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems. The MIT Press.
3. Izhikevich E.M. (2007) Dynamical Systems in Neuroscience: The Geometry of Excitability and Bursting. The MIT press