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# Models of Higher Brain Function Course: Models of Higher Brain Function Instructor: Prof. Dr. Sprekeler Bernstein Center for Computational Neuroscience This is a private repository for storing the assignments of the programming tutorial of the course Models of Higher Brain Function. ## Brief description of each Assignment: ### 1. Deep Linear Networks: In this assignment, we explore the learning dynamics of a deep linear network and contrast them with those of a shallow network. ### 2. Slow Feature Analysis: We apply Linear Slow Feature Analysis (SFA) transformation on time-dependent signals and explore SFA on a high-dimensional correlated signal. ### 3. Visual Attention: The goal of this assignment is to compute saliency maps for natural images, similar to the work presented in [Itti, Koch & Niebur (1998)](http://pages.cs.wisc.edu/~dyer/cs534/papers/itti98-saliency.pdf). ### 4. Perceptual Bistability: In this assignment, we delve into a model of binocular rivalry presented by [Laing and Chow (2002)](https://link.springer.com/content/pdf/10.1023/A:1014942129705.pdf) and a model of perceptual bistability developed by [Moreno-Bote et al. (2007)](https://journals.physiology.org/doi/pdf/10.1152/jn.00116.2007). ### 5. Decision Making I & II: This assignment involves analyzing the reaction time distribution of the **drift diffusion model** for perceptual decision making. Additionally, we utilize the Chapman-Kolmogorov equation to find the time-dependent distribution of the decision variable in a drift-diffusion model for decision making. The Fokker-Planck equation is also explored. ### 6. Reinforcement Learning I & II: In these assignments, we learn policies for the optimal strategy for a 10-armed bandit and implement and practice SARSA and Q-Learning algorithms in 2D grid games. # Final Project: Decision Making in Low Rank Recurrent Neural Network For the final project, we will study the Low Rank RNN frameworks and replicate the results based on the paper by [Debreuil et al.(2022)](https://pubmed.ncbi.nlm.nih.gov/35668174/). You can find the report of the project available through this [link](https://github.com/Erfan7bt/MHBF/blob/58872380a400a0fdaff3da8305544580aed2cf26/Projects/dm_project.pdf).

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