Skip to main content
Kent Academic Repository

Functional Brain Oscillations: How Oscillations Facilitate Information Representation and Code Memories

Parish, George Michael (2019) Functional Brain Oscillations: How Oscillations Facilitate Information Representation and Code Memories. Doctor of Philosophy (PhD) thesis, University of Kent,. (KAR id:83107)

Language: English

Download this file
[thumbnail of 16THESIS_GPARISH_CORRECTED.pdf]


The overall aim of the modelling works within this thesis is to lend theoretical evidence to empirical findings from the brain oscillations literature. We therefore hope to solidify and expand the notion that precise spike timing through oscillatory mechanisms facilitates communication, learning, information processing and information representation within the brain. The primary hypothesis of this thesis is that it can be shown computationally that neural de-synchronisations can allow information content to emerge. We do this using two neural network models, the first of which shows how differential rates of neuronal firing can indicate when a single item is being actively represented. The second model expands this notion by creating a complimentary timing mechanism, thus enabling the emergence of qualitive temporal information when a pattern of items is being actively represented. The secondary hypothesis of this thesis is that it can be also be shown computationally that oscillations might play a functional role in learning. Both of the models presented within this thesis propose a sparsely coded and fast learning hippocampal region that engages in the binding of novel episodic information. The first model demonstrates how active cortical representations enable learning to occur in their hippocampal counterparts via a phase-dependent learning rule. The second model expands this notion, creating hierarchical temporal sequences to encode the relative temporal position of cortical representations. We demonstrate in both of these models, how cortical brain oscillations might provide a gating function to the representation of information, whilst complimentary hippocampal oscillations might provide distinct phasic reference points for learning.

Item Type: Thesis (Doctor of Philosophy (PhD))
Thesis advisor: Bowman, Howard
Uncontrolled keywords: computational model neuroscience oscillations information learning plasticity episodic brain neural-network
Subjects: Q Science > Q Science (General)
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Computing
SWORD Depositor: System Moodle
Depositing User: System Moodle
Date Deposited: 24 Sep 2020 13:10 UTC
Last Modified: 10 Dec 2022 05:02 UTC
Resource URI: (The current URI for this page, for reference purposes)

University of Kent Author Information

Parish, George Michael.

Creator's ORCID:
CReDIT Contributor Roles:
  • Depositors only (login required):

Total unique views for this document in KAR since July 2020. For more details click on the image.