Very bluntly put here, just show me you can come up with an assignment yourself basically. Choose one of these papers or a paper of your own choosing and replicate a figure or multiple figures (if you're trying to work in groups -- undergrads are allowed to do so without multiple figures). Within the replication and writeup I just want to see that you can implement the math covered in class in code or a different analytical technique and replicate a figure with whatever data you'd like! Describe the data like each assignment does for you. Describe your methods and explain the math briefly in a writeup. Does not have to be formal (b/c let's be honest I don't want to read a lot or grade...). Finally, show the replicated figure and describe what I'm looking at and why it's cool. Overall, simply have you mastered the one of the objectives in the syllabus in the class and can you show me that?

Please note that I had a chat with the previous instructor of this course about the final project and have come to the conclusion that the requirements were very arbitrary in my opinion. He seemed to value originality of dataset moreso than implementation of the paper. When I took this class I implemented the Santhanam et al. 2009 Factor Analysis paper and the most relevant figure on that and the feedback I received was more was expected even though I explained everything I did including the Bayesian Decoding from HW3 in this year's rendition and implementing Factor Analysis that was discussed in that paper. On the otherhand, running K-Means clustering and GMMs on fantasy football players to identify best players to start as flex starters was enough even though the code didn't have to be implemented?¯\_(ツ)_/¯

As a result of ¯\_(ツ)_/¯, my overall expectation for things are simply what is listed above in the previous paragraph. This may come off as vague and potentially simplistic but it is not necessarily meant to be difficult. The final problem in HW4 is a paper that's listed on here (so obviously don't use that or do that b/c it's been done) and that is apparently enough for a final project in this class. If that's not clear please reach out to me.

Lastly, as mentioned in class, please email me or discuss a project abstract with me by Thanksgiving! Doesn't have to be anything formal! Thanks!

• Brown et al 2002 “The time-rescaling theorem and its application to neural spike train data analysis.”

• Kemere et al 2004 “Model-based decoding of reaching movements for prosthetic systems.”
• Yu et al 2004 “Improving neural prosthetic system performance by combining plan and peri-movement activity.”
• Santhanam et al 2009 “Factor-analysis methods for higher-performance neural prostheses.”

• Wild et al 2012 “Performance comparison of extracellular spike sorting algorithms for single-channel recordings.”
• Calabrese and Paninski 2011 “Kalman filter mixture model for spike sorting of non-stationary data.”
• The Zhang et al. eLife paper was listed here and that was the final question in homework 4 so that should give you an idea of what a final project would look like!

• Yu et al 2009 “Gaussian-process factor analysis for low-dimensional single-trial analysis of neural population activity.”
• Churland et al 2012 “Neural population dynamics during reaching” (specifically jPCA, e.g., Figure 3)
• Cunningham and Yu “Dimensionality reduction for large-scale neural recordings”
• Wu et al 2017 “Gaussian process based nonlinear latent structurediscovery in multivariate spike train data.”
• Pandarinath et al 2018 “Inferring single-trial neural population dynamics using sequential auto-encoders.”

• Kemere and Meng 2005 “Optimal estimation of feed-foward-controlled linear systems”
• Wu et al 2004 “Modeling and decoding motor cortical activity using a switching Kalman filter.”
• Brown et al 1998 “A statistical paradigm for neural spike train decoding applied to position prediction from ensemble firing patterns of rat hippocampal place cells.”
• Quang et al 2015 “The Kalman Laplace filter: A new deterministic algorithm for nonlinear Bayesian filtering”

• Maboudi at al “Uncovering temporal structure in hippocampal output patterns”
• Kemere et al 2008 “Detecting Neural-State Transitions Using Hidden Markov Models for Motor Cortical Prostheses”
• Ackermann et al 2019 biorxiv "Unsupervised clusterless decoding using a switching poisson hidden markov model"