Investigation of cortical population codes for long-term memory

Supervisors: Gulsen SurmeliMatthias Henning

Project Description:

For over a century researchers have been employing a myriad of psychological and behavioural tests on animals and human patients aiming to understand how memories are stored in the brain. Two brain areas play major roles in memory. The neocortex houses multiple neural systems important for cognitive function and is activated during learning and recall. The hippocampal formation, where memories are first seeded, is a smaller structure that makes extensive connections with the neocortex. From work to date, there are three major conclusions. One is that the hippocampal formation is essential for acquisition of new long-term memories. Second, a large neocortical network is responsible for long-term storage of memories. Third, long-term memory storage in the cortex relies on the interactions with the hippocampus. 

Looking beyond this very general picture, we know very little about how neocortical networks contribute to memories. Our group aims to address this problem by focusing on the interactions between the hippocampal complex and neocortex at a cellular level. Specifically we focus on the role of hippocampal input in the evolution of cortical memory traces. We have started to reveal the principles of anatomical connectivity between the hippocampal complex structures and the neocortex (Sürmeli et al. 2015 and unpublished data). The proposed project aims to monitor activity of neuronal populations in the neocortex in mice to reveal the properties of neuronal population codes over the course of learning and how they change when hippocampal input to cortex is manipulated. 

To this end, we will employ a state-of-the-art technology called “miniscope” (Jercog, Rogerson, and Schnitzer 2016) which is a miniaturized head attached fluorescent microscope that allows monitoring activity of neuronal ensembles in freely moving mice. Combined with advanced genetic techniques this method allows tracking activity of thousands of neurons over weeks during the course of memory formation and further recall test.

These experiments will generate very large and rich datasets, which will help unravel the fundamental principles of the neural basis of memory. Since memory engrams are typically sparse and highly distributed over many neurons, tracking the acquisition of new memories will require advanced analysis methods, which will be developed in collaboration with the second supervisor based in the School of Informatics. Moreover, the results will enable constructing new models of short and long-term memory acquisition that can advance artificial neural networks used in machine learning applications. Therefore, this project will provide not only extensive training in functional neuron imaging in vivo, but also the application of computational methods for data analysis, interpretation and modelling, a valuable skill set for research in systems neuroscience.


Jercog, Pablo, Thomas Rogerson, and Mark J. Schnitzer. 2016. “Large-Scale Fluorescence Calcium-Imaging Methods for Studies of Long-Term Memory in Behaving Mammals.” Cold Spring Harbor Perspectives in Biology 8 (5).
Sürmeli, Gülşen, Daniel Cosmin Marcu, Christina McClure, Derek L. F. Garden, Hugh Pastoll, and Matthew F. Nolan. 2015. “Molecularly Defined Circuitry Reveals Input-Output Segregation in Deep Layers of the Medial Entorhinal Cortex.” Neuron 88 (5): 1040–53.

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