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RECURRENT NETWORK MODEL OF FAMILIARITY-EVOKED THETA OSCILLATIONS IN MOUSE VISUAL CORTEX

thesis
posted on 2024-10-14, 12:16 authored by Varun Machhale KumarVarun Machhale Kumar

Brain oscillations are crucial for several cognitive functions, such as memory, attention, perception, and communication between brain regions. In particular, visual familiarity is known to induce oscillations in the theta frequency band (4-8 Hz). Today, most machine learning models lack this biologically inspired component. In the first aim of this study, we build a deep learning model that exhibits oscillations in the theta band using predictive coding theory. Predictive coding theory suggests that higher-order brain regions predict the activity of lower-order brain regions. The prediction errors are then propagated up the hierarchy and the predictions are updated. We were able to replicate the neural activities seen in a mouse training paradigm and observed that units of the neural network also exhibit oscillations in the theta band for a familiar stimulus. This offers the potential to bridge the gap between artificial neural networks and biological systems to build more biologically plausible neural networks. In the second objective of this study, we investigate the learning impairment of FragileX (FX) mice compared to Wild-Type (WT) mice when trained to discriminate Go and No-Go visual stimuli. Using recorded neural data, we build a classifier to perform stimuli and genotype classification. Both FX and WT mice achieved significantly higher accuracies in the theta oscillatory activity window compared to spontaneous activity in the V1 region. This suggests that the theta oscillations store essential information learned from visual discrimination. However, we observe that FX mice require more neuronal units to achieve performance similar to that of WT mice. In the hippocampus, the overall accuracy was lower for the classification of stimuli, indicating a more complex nature of information processing in the hippocampus. In addition, high classification accuracies for genotype decoding indicate that the neural responses to stimuli in V1 region are influenced by genetic variations. These differences are discernible even in individual trials when averaged across a few hundred neurons.

Classification using neural activities from Go trials yielded a higher accuracy than No-Go trials suggesting that these differences could be task-dependent and enhanced during certain tasks. We also observed that these variations are more notable during the theta oscillatory activity window demonstrating that genetic factors substantially influence memory encoding via theta oscillations.

History

Degree Type

  • Master of Science

Department

  • Electrical and Computer Engineering

Campus location

  • West Lafayette

Advisor/Supervisor/Committee Chair

Dr. Alexander Chubykin

Advisor/Supervisor/Committee co-chair

Dr. Joseph Makin

Additional Committee Member 2

Dr. David Inouye

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