Scott Linderman's Thesis
Harvard University, April, 2016
Neuroscience is entering an exciting new age. Modern recording technologies enable us to simultaneously measure the activity of thousands of neurons in organisms performing complex behaviors. Such recordings offer an unprecedented opportunity to glean insight into the mechanistic underpinnings of intelligence, but they also present extraordinary statistical and computational challenges: how do we make sense of these large scale recordings and turn data into understanding? This thesis develops a suite of tools that instantiate hypotheses about neural computation in the form of probabilistic models and a corresponding set of Bayesian inference algorithms that efficiently fit these models to neural spike trains. From the posterior distribution of model parameters and variables, we seek to advance our understanding of how the brain works.
First, install the included fonts. Then run
./scripts/build. You'll need
xelatex in order to compile with the
To compile a single chapter, run
xelatex --jobname=<chapternumber> singlechater.tex bibtex <chapternumber> xelatex --jobname=<chapternumber> singlechater.tex xelatex --jobname=<chapternumber> singlechater.tex
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