My poster presentation for APPM Recruitment 2024.

Python is one of the more popular scripting languages in scientific computing and data science, along with MATLAB, R, and Julia. An interesting difference is that Python was not designed with scientific computing in mind. Rather, it is a general purpose language designed to be simple and flexible. Strangely, I find that it is uncommon for scientists to take advantage of the features that make Python so successful, and instead to write procedural codes with an overreliance on the array data structure. In this talk I will focus on one feature in particular: generators (also called streams). Generators are built into Python syntax, and they are a natural way to represent mathematical sequences. I will explain what generators are, how they are baked into the Python syntax, how I use them, and why you should use them in your scientific Python codes.

We examine traveling wave solutions of a neural field model (integro-differential system) incorporating synaptic depression, allowing for biologically realistic traveling pulse solutions similar to those observed experimentally. We use these pulses as a biologically plausible model for visual motion processing, and develop an asymptotic wave response function characterizing how traveling pulse solutions respond to perturbative stimuli. For some spatially localized moving stimuli, traveling pulse solutions can accelerate to match the speed and location of the stimulus - a phenomenon known as entrainment. Generally speaking, traveling pulse solutions will entrain to spatially localized moving stimuli if the stimuli are sufficiently weak and sufficiently slow. We use our wave response function to identify this speed-magnitude threshold to first order. Finally, we apply our wave response function to a stimulus meant to evoke the apparent motion illusion - a phenomenon where a sequence of stationary stimuli are presented and motion is perceived.

We examine traveling wave solutions of a neural field model (integro-differential system) incorporating synaptic depression, allowing for biologically realistic traveling pulse solutions similar to those observed experimentally. We use these pulses as a biologically plausible model for visual motion processing, and develop an asymptotic wave response function characterizing how traveling pulse solutions respond to perturbative stimuli. For some spatially localized moving stimuli, traveling pulse solutions can accelerate to match the speed and location of the stimulus - a phenomenon known as entrainment. Generally speaking, traveling pulse solutions will entrain to spatially localized moving stimuli if the stimuli are sufficiently weak and sufficiently slow. We use our wave response function to identify this speed-magnitude threshold to first order. Finally, we apply our wave response function to a stimulus meant to evoke the apparent motion illusion - a phenomenon where a sequence of stationary stimuli are presented and motion is perceived.

I show our asymptotic entrainment threshold agrees with simulations for the apparent motion stimulus.

I discuss the new formula for the wave response and the entrainment threshold.

My poster for the SIAD dynamical systems conference in 2023.

My presentation for my comprehenisve exam. Also, my thesis proposal.

My poster (and suplemental material) for the APPM 2023 recruitment poster session.

I discuss my current work in incorporating adaptation into a neural field model. I recap my failed attempt at incorporating hyper-polarizing adaptation current, and discuss my current approach incorporating a synaptic depression variable.

I present a 15 minute summary of Ge and Liu's 2021 paper Foraging behaviours lead to spatiotemporal self-similar dynamics in grazing ecosystems.

The wave response function in an adaptive neural field model

A very trimmed down version of the Math-bio seminar talk.

The wave response function in an adaptive neural field model

Neural field models are integro-differential equations describing the propagation of neural activity through the brain. The simplest of neural field models describes a single spatial dimension and exhibits traveling-front solutions where activity propagates into inactive regions then remains active indefinitely. More realistic models incorporate adaptation, which allows active regions to eventually decay back to base line inactivity, resulting in traveling pulse solutions. In this talk we will examine one such model that incorporates a hyperpolarizing adaptation current, explore some of its properties. In particular we will examine the wave response function: the shift in position of traveling pulses due to small perturbations.

Functional Programming in Python

Functional programming (FP) is a declarative programming paradigm, in which functions are said to be "first-class citizens", and function composition is used to create complex procedures while maintaining modularity and extensibility. Proponents of FP say that it reduces errors, simplifies debugging, and makes programming more mathematical. Python supports programming in the functional paradigm. In this inaugural presentation of the APPM Graduate Student Seminar we explore the potential of the functional paradigm in scientific computing with specific examples implemented in Python.

Update the research group on my current work, including the $\mathcal{O}(\varepsilon)$ approximation to the wave response function for the adaptive model.