<p> </p>
<p>Over the last decade, measurement technology that records neural activity such as ECoG and Utah array has dramatically improved. These advancements have given researchers access to recordings from multiple neurons simultaneously. Efficient computational and statistical methods are required to analyze this data type successfully. The time-series model is one of the most common approaches for analyzing this data type. Unfortunately, even with all the advances made with time-series models, it is not always enough since these models often need massive amounts of data to achieve good results. This is especially true in the field of neuroscience, where the datasets are often limited, therefore imposing constraints on the type and complexity of the models we can use. Not only that, but the Signal-to- noise ratio tends to be lower than in other machine learning datasets. This paper will introduce different architectures and techniques to overcome constraints imposed by these small datasets. There are two major experiments that we will discuss. (1) We will strive to develop models for participants who lost the ability to speak by building upon the previous state-of-the-art model for decoding neural activity (ECoG data) into English text. (2) We will introduce two new models, RNNF and Neural RoBERTa. These new models impute missing neural data from neural recordings (Utah arrays) of monkeys performing kinematic tasks. These new models with the help of novel data augmentation techniques (dynamic masking) outperformed state-of-the-art models such as Neural Data Transformer (NDT) in the Neural Latents Benchmark competition. </p>
| Identifer | oai:union.ndltd.org:purdue.edu/oai:figshare.com:article/21358182 |
| Date | 25 October 2022 |
| Creators | Bryan Jimenez (13979295) |
| Source Sets | Purdue University |
| Detected Language | English |
| Type | Text, Thesis |
| Rights | CC BY 4.0 |
| Relation | https://figshare.com/articles/thesis/Neural_Network_Models_For_Neurophysiology_Data/21358182 |
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