Dynamical Decoder Model for High-Dimensional Neural Data

Richards, Maceo

Etd

Dynamical Decoder Model for High-Dimensional Neural Data

Public Deposited

Recent advancements in neural recording technologies both enable deeper insight into the brain through the collection of large datasets, however this introduces novel challenges to analyzing neural data. High-dimensional datasets impose challenges on both accurate and efficient decoding of neural data, which motivates the development of a decoder framework that balances predictive accuracy with efficient data processing. This thesis research explores development of such a framework, called the Direct Discriminative Decoder, and applies it in a neural decoding task. The movement of a rat as it traverses a maze is decoded from the spiking activity of place cells in its hippocampus, which are responsible for encoding spatial information pertaining to location. A decoder framework of this design offers benefits in memory research and more broadly in time-series analysis.

Creator