Investigating the role of thalamic activity in visual cortical plasticity

Experience-dependent plasticity is an integral process by which the brain learns to respond to relevant stimuli in the world. The visual cortex is a well-studied locus of such learning, exemplified by the simple yet robust phenomenon of stimulus-selective response plasticity (SRP). In SRP, neurons in the cortex alter their activity profile to a visual stimulus as it becomes familiar with repeated exposure. It requires the mechanisms of long-term potentiation (LTP), the basis for solidifying memories in the brain, which makes SRP a compelling blueprint for the basic principles of how learning might work in general. Recently, deeper investigations into SRP have revealed that it requires circuit changes that extend beyond a simple model of LTP, including the requirement of inhibitory interneuron activity but not LTP onto the classic input to layer 4 of the visual cortex. Such differences may be clarified by analyzing the dorsal lateral geniculate nucleus (dLGN) of the thalamus, which is the primary source of visual information for the cortex. In the following sections, we test the hypothesis that SRP expression is driven by the mode of activity of dLGN neurons, which are in turn driven by feedback from layer 6 of visual cortex. In this thesis, I describe two complementary approaches for investigating the role of thalamic activity in visual cortical plasticity. In Chapter 1, I provide a broad overview of the dLGN and our current understanding of SRP. In Chapter 2, I describe how we used calcium fluorescence imaging of single dLGN neurons to track the changes that occur in this population over time. In Chapter 3, I summarize an experiment which used extracellular electrophysiology to compare the differences in dLGN spiking activity to familiar vs. novel stimuli. In short, no differences in the activity of dLGN cells were found to familiar vs. novel stimuli using either method, a novel finding that I seek to place into the broader context in Chapter 4. I discuss how these findings update our potential models of SRP circuitry and describe the remaining questions to be answered by future research.

Name thesis:degree_name

Doctoral

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2023

Author dc:creator

• Wang, Joyce

Advisor dc:contributor.advisor

• Bear, Mark F.

Statement dc:rights

• Attribution-ShareAlike 4.0 International (CC BY-SA 4.0)
• Copyright retained by author(s)

Licence dc:rights.uri

• https://creativecommons.org/licenses/by-sa/4.0/

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/152562

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/152562