We apply brain dissection across the visual cortex to uncover functional differences between regions.

While results presented in the main text of the paper show scaling by averaging across cortex, we can also examine scaling on a per-voxel basis. Model size increases in semantic models seem to be most beneficial for predicting amodal, post-auditory cognitive areas such as prefrontal cortex. Figure B.1: Performance of audio encoding models, averaged across all voxels in auditory cortex. Figure B.2: Performance of HuBERT models, averaged across voxels in cortex. Figure D.1: Long Context Artifact - An example of a long context artifact effect as measured on an Figure E.2: Histogram showing the slopes of voxelwise scaling laws for two OPT model sizes, shown Flatmaps presented in the main text only used one subject, S3 .

Figure 1: Timescales estimated in MT model (revised after bug fix). Colormap follows Figure 1 in main text. We thank the reviewers for their insights and suggestions. All references follow the main paper. As noted in supplementary section 1.3, there was a Encoding model fits rely on cross-validation.

We apply brain dissection across the visual cortex to uncover functional differences between regions.

Ontologies are essential for developing standardized vocabularies and defining relationships that help describe and interpret data from diverse sources. They are crucial for achieving semantic interoperability in many domains, allowing different systems to exchange data with a consistent and shared meaning. Ontologies are extensively used in biological and biomedical research Hoehndorf et al. (2015); Antezana et al. (2009), due to their ability to: provide standard identifiers for classes and relationships representing complex phenomena; include metadata to clarify the intended meaning of classes and relationships; include machine-readable definitions that allow computational access to class properties and relationships; standardize vocabulary across multiple data sources. Ontology-based data integration plays a vital role in neuroscience, where researchers synthesize knowledge across physiology, anatomy, molecular and developmental biology, cytology, and mathematical modeling to support accurate data representation, analysis, and simulation. A common challenge for many large neuroscience projects is the integration of data across a wide diversity of species, spatial resolutions, and temporal scales.

Figure 1: Timescales estimated in MT model (revised after bug fix). Colormap follows Figure 1 in main text. We thank the reviewers for their insights and suggestions. All references follow the main paper. As noted in supplementary section 1.3, there was a Encoding model fits rely on cross-validation.

Representations from transformer-based unidirectional language models are known to be effective at predicting brain responses to natural language. However, most studies comparing language models to brains have used GPT-2 or similarly sized language models. Here we tested whether larger open-source models such as those from the OPT and LLaMA families are better at predicting brain responses recorded using fMRI. Mirroring scaling results from other contexts, we found that brain prediction performance scales logarithmically with model size from 125M to 30B parameter models, with ~15% increased encoding performance as measured by correlation with a held-out test set across 3 subjects. Similar logarithmic behavior was observed when scaling the size of the fMRI training set. We also characterized scaling for acoustic encoding models that use HuBERT, WavLM, and Whisper, and we found comparable improvements with model size. A noise ceiling analysis of these large, high-performance encoding models showed that performance is nearing the theoretical maximum for brain areas such as the precuneus and higher auditory cortex. These results suggest that increasing scale in both models and data will yield incredibly effective models of language processing in the brain, enabling better scientific understanding as well as applications such as decoding.

This team has decades of practical experience in working with Java and with billions of rows of data. If you are an analyst or a data scientist, you're used to having multiple systems for working with data. With Spark, you have a single engine where you can explore and play with large amounts of data, run machine learning algorithms and then use the same system to productionize your code. Scala: Scala is a general purpose programming language - like Java or C . It's functional programming nature and the availability of a REPL environment make it particularly suited for a distributed computing framework like Spark. Analytics: Using Spark and Scala you can analyze and explore your data in an interactive environment with fast feedback.

In my previous article, I introduced you to the basics of Apache Spark, different data representations (RDD / DataFrame / Dataset) and basics of operations (Transformation and Action). We even solved a machine learning problem from one of our past hackathons. In this article, I will continue from the place I left in my previous article. I will focus on manipulating RDD in PySpark by applying operations (Transformation and Actions). As you would remember, a RDD (Resilient Distributed Database) is a collection of elements, that can be divided across multiple nodes in a cluster to run parallel processing. It is also a fault tolerant collection of elements, which means it can automatically recover from failures.