NEURAL NETWORKS AND DEEP LEARNING

1. Introduction: computational and mathematical modeling of neural systems; basics of neuroscience; levels of analysis in system neuroscience.
2. Single-neuron modeling: morphology, neuro-electronics, principles of synaptic transmission; integrate-and-fire models; the Hodgkin-Huxley model.
3. Principles of neural encoding: recording neuronal responses; spike trains, firing rates, local field potentials; tuning functions and receptive fields; efficient encoding principles and information compression.
4. Network modeling: neural network architectures; localistic, distributed, and sparse representations; examples from the visual system.
5. Learning, memory and plasticity: synaptic plasticity in biological systems (Hebb rule, LTP, LTD, STDP); synaptic plasticity in artificial neural networks and overview of machine learning basics.
6. Supervised learning: perceptron, delta rule, error backpropagation.
7. Supervised deep learning: advanced optimization methods for training multi-layer networks; convolutional architectures; transfer learning and multi-task learning.
8. Recurrent neural networks: backpropagation through time, long short-term memory networks.
9. Unsupervised learning: competitive networks; self-organizing maps; associative memories and Hopfield networks; autoencoders and Boltzmann machines.
10. Unsupervised deep learning: hierarchical generative models; generative adversarial networks.
11. Reinforcement learning: exploration-exploitation dilemma; temporal-difference learning; conditioning and dopamine circuits; deep reinforcement learning.
12. Case studies from neurocognitive modeling: visual perception; space coding; semantic cognition; complementary learning systems; hippocampus and experience replay.
13. Large-scale brain organization: structural and functional properties of brain networks; neuronal oscillations and spontaneous brain activity; neuromorphic hardware.