Neuro 120 Intro to Computational Neuroscience

This is a class I help TA/TFed in Harvard. It covered the basic computational models and relevant mathematical tools for sensory processing, neuron excitability, memory and learning etc.

• Sensory encoding, decoding, dimensionality reduction;
• Visual system, from Retina to visual hierarchy
• Single neuron models
• Recurrent Neural Networks, dynamic system, for working memory and long term memory
• Synaptic plasticity 
• Reinforcement learning

Here are some tutorials and review slides I made for the class. 

Class Materials:

Section 4 Encoding Model: STA, LNP

Section 5 PCA, Decoding and Intro to Early Vision

Section 11 Reinforcement Learning

Section 10 Synaptic Plasticity and Hebbian Learning Rules

Section 9 Head Direction Cells, Hopfield Network and Learning Rules

Section 7 Dynamic System and Hodgkin-Huxley Model

Section 6 Cortical Vision and Deep Learning

Section 8 Recurrent Neural Networks and Linear Dynamical System