This repository is my workspace for NESC5330 Neural Network Models of Cognition and Brain Computation at the University of Virginia during Spring of 2016.
In the near future, the findings of experimental neuroscience will be used to cure mental diseases and drug addiction. The bridge between neurobiological experiments and cognition is constructed from quantitative computer simulations built from neurons and synapses. The central focus of this course is computer-simulated neurobiology that reproduces cognitive paradigms. The goal is to produce students with an intuitive grasp of such a simulation-based approach.
Lab 1: Introduction to MATLAB and familiarization with the notation used to describe neural networks
Lab 2: The McCulloch-Pitts neuron as a linear decision-maker (binary pattern recognition)
Lab 3: Exponentially-weighted moving averages as a memory encoding process
Lab 4: Three kinds of associative memory storage via synaptic modification: self-supervised, category supervised, and error-corrected (with application to pavlovian delay conditioning)
Lab 5: Using synaptogenesis to construct feedforward networks with performance evaluation using information and cost calculations
Lab 6: Controlling the activity of a feedback neural network and simple sequence prediction
Lab 7: A model that learns pavlovian trace conditioning