Modeling friction force avalanches in molybdenum disulfide and spiking neuronal avalanches in mouse cortex

Salners, Tyler

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https://hdl.handle.net/2142/121522 Copy

Description

Title

Modeling friction force avalanches in molybdenum disulfide and spiking neuronal avalanches in mouse cortex

Author(s)

Salners, Tyler

Issue Date

2023-07-13

Director of Research (if dissertation) or Advisor (if thesis)

Dahmen, Karin A

Doctoral Committee Chair(s)

Chemla, Yann R

Committee Member(s)

• Stone, Michael
• Weaver, Richard L

Department of Study

Physics

Discipline

Physics

Degree Granting Institution

University of Illinois at Urbana-Champaign

Degree Name

Ph.D.

Degree Level

Dissertation

Keyword(s)

Avalanches, Statistical Physics, Critical Phenomena, Friction, Neuronal Dynamics

Abstract

In nature we find widely varying systems can sometimes be described by a single set of simple laws. For example, intermittent jumps of both sliding tectonic plates (during an earthquake) and spontaneous magnetization (the Barkhausen effect in magnets) follow the same set of “crackling” laws. Crackling refers to the quick jumps between metastable configurations that are facilitated by slow external forcing in a disordered system. The cascades of jumps (referred to as ‘avalanches’) follow simple scaling laws for quantities like their size and duration. In this work, we highlight two systems that follow scaling laws and compute predictions about the system using avalanches. Building on a simple slip model we show that friction force fluctuations of molybdenum-disulfide may result from slip-avalanches. We show how experimental conditions can affect the observed scaling behavior, and thus provide a blueprint for the interpretation of data in a range of other systems which might have similar experimental conditions. Importantly, we find that building the experimental conditions into the model yields good agreement with the experimental results. Next, we study fluctuations of spiking neurons in mice cortex. They too can be described as avalanches and follow similar statistics as the slips in the friction experiments. Here we use the same basic avalanche model used for friction, and to capture the essential physics of inhibition and recurrent firing, we add mean field connections to an external inhibitory population and a threshold reduction mechanism, respectively. We find that this simple model can mimic many statistical properties of the data. We show that model simulations with a few biological tweaks can even reproduce empirical signatures of recurrent firing. Finally, we show that though the inhibitory species are the minority population, they make up a majority of the activity in neuronal avalanches. We demonstrate the models ability to reproduce this inhibitory dominance and conclude with future directions.

Graduation Semester

2023-08

Type of Resource

Thesis

Copyright and License Information

Copyright 2023 Tyler Salners

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