Jeremy R. Manning

Harrison F. Stropkay, Jiayi Chen, Mohammad J. Latifi et al.

We show that large language models (LLMs) can be used to distinguish the writings of different authors. Specifically, an individual GPT-2 model, trained from scratch on the works of one author, will predict held-out text from that author more accurately than held-out text from other authors. We suggest that, in this way, a model trained on one author's works embodies the unique writing style of that author. We first demonstrate our approach on books written by eight different (known) authors. We also use this approach to confirm R. P. Thompson's authorship of the well-studied 15th book of the Oz series, originally attributed to F. L. Baum.

Jeremy R. Manning

Identifying stimulus-driven neural activity patterns is critical for studying the neural basis of cognition. This can be particularly challenging in intracranial datasets, where electrode locations typically vary across patients. This chapter first presents an overview of the major challenges to identifying stimulus-driven neural activity patterns in the general case. Next, we will review several modality-specific considerations and approaches, along with a discussion of several issues that are particular to intracranial recordings. Against this backdrop, we will consider a variety of within-subject and across-subject approaches to identifying and modeling stimulus-driven neural activity patterns in multi-patient intracranial recordings. These approaches include generalized linear models, multivariate pattern analysis, representational similarity analysis, joint stimulus-activity models, hierarchical matrix factorization models, Gaussian process models, geometric alignment models, inter-subject correlations, and inter-subject functional correlations. Examples from the recent literature serve to illustrate the major concepts and provide the conceptual intuitions for each approach.

Michael J. Anderson, Mihai Capotă, Javier S. Turek et al.

The scale of functional magnetic resonance image data is rapidly increasing as large multi-subject datasets are becoming widely available and high-resolution scanners are adopted. The inherent low-dimensionality of the information in this data has led neuroscientists to consider factor analysis methods to extract and analyze the underlying brain activity. In this work, we consider two recent multi-subject factor analysis methods: the Shared Response Model and Hierarchical Topographic Factor Analysis. We perform analytical, algorithmic, and code optimization to enable multi-node parallel implementations to scale. Single-node improvements result in 99x and 1812x speedups on these two methods, and enables the processing of larger datasets. Our distributed implementations show strong scaling of 3.3x and 5.5x respectively with 20 nodes on real datasets. We also demonstrate weak scaling on a synthetic dataset with 1024 subjects, on up to 1024 nodes and 32,768 cores.