Teaching
Neuro 120 - Introduction to Computational Neuroscience

Neuro 120 - Introduction to Computational Neuroscience

Course Information

Course: Neuro 120 - Introduction to Computational Neuroscience
Institution: Harvard Semester: Fall 2021
Instructor: Eleanor Batty Teaching Fellow: Binxu Wang (Kempner Research Fellow)

Course Description

This course provides a comprehensive mathematical and computational approach to understanding neural systems and brain function. Students learn to build and analyze computational models that explain how the brain processes information.

Topics Covered

Core Areas

  1. Sensory Encoding and Decoding

    • Neural representation of sensory information
    • Population coding principles
    • Decoding algorithms

  2. Dimensionality Reduction

    • Principal Component Analysis (PCA)
    • Neural data analysis techniques
    • High-dimensional neural representations

  3. Visual System Processing

    • Cortical vision models
    • Hierarchical processing
    • Feature extraction mechanisms

  4. Single Neuron Models

    • Neuron excitability models
    • Integrate-and-fire models
    • Computational properties of neurons

  5. Recurrent Neural Networks

    • Network dynamics
    • Attractor networks
    • Continuous-time RNNs

  6. Dynamic Systems

    • Mathematical frameworks for neural dynamics
    • Stability analysis
    • Phase space analysis

  7. Memory Systems

    • Working memory models
    • Long-term memory formation
    • Computational theories of memory

  8. Synaptic Plasticity

    • Learning rules (Hebbian, STDP)
    • Computational theories of plasticity
    • Memory consolidation

  9. Reinforcement Learning

    • Reward prediction error
    • Temporal difference learning
    • Neural implementations of RL

Course Materials

Section Reviews

  • Encoding Models - Mathematical frameworks for neural encoding
  • PCA and Decoding - Dimensionality reduction and decoding methods
  • Cortical Vision - Computational models of visual processing
  • Dynamical Systems - Mathematical analysis of neural dynamics
  • Reinforcement Learning - Computational theories of learning
  • Synaptic Plasticity - Mechanisms of neural adaptation