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Summer research

Summer projects in computational neuroscience

Students are encouraged to contact with me to discuss opportunities for summer research  Typical work days would consist of running simulations (most commonly with Matlab or Python).  In addition to developing computational skills, there will be opportunities to learn relevant mathematical techniques and theories in the areas of differential equations and dynamical systems, primarily.  Students with strong interest in computations may also have opportunities to organize and run large-scale simulations on the Open Science Grid or similar computing resources.  Course work in biology or neuroscience, or other knowledge of these areas is not required.  Students should be interested in gaining some understanding of these areas and, most importantly, be enthusiastic about the process of mathematical modeling.

Summer research will be funded through Swarthmore College.  Students should read through the information on the college's webpage here and understand the application process.

There will not be a strict expectation of regular 9-5 workdays, but you are expected to treat this as a "full time job" for the duration of the summer research period.  I plan to have regular meetings with students (several times a week, at minimum), to discuss progress, answer questions, and plan next steps.  At other times, students will be working independently.  At the end of the summer, students will write up a summary of their findings and organize any simulation code they have developed, and present their research on campus (Math/Stat department summer research talks, Sigma Xi poster session).   Depending on the stage and nature of the research, it may be possible for summer projects may lead to conference participation or published papers, but there is no guarantee that this can happen for all projects.

Summer 2019: Development of a minimal model of MSO axon dynamics

Students: Madison Shoraka '21 and JJ Balisanyuka-Smith '22

Neurons in an auditory brain stem region called the medial superior olive (MSO) help us to determine the locations of sound sources in the environment.  They do this by responding, with submillisecond temporal precision, to the timing of their inputs.  Specifically, the MSO neurons receive inputs that origniate from both ears, and fire maximally when the inputs arrive at nearly the same time.  For this reason, MSO neurons are known as coincidence detector neurons.  Understanding the unique features of MSO neurons that specialize these cells for temporally-precise coincidence detection advances our knowledge of how the auditory systems creates a sense of space and, more generally, how neurons extract timing information from their inputs.

Mads and JJ constructed a model of an MSO neuron to analyze the spiking dynamics of these neurons.  They showed that a biophysically-detailed and computationally intensive model (previously published in the literature) could be approximated by using what we call a two-compartment model.  The two-compartment model describes two regions of the cell: the soma and dendrite regions are grouped into one compartment, and the axon regions are grouped into the second compartment.  Mads and JJ determined how to parameterize this two-compartment model in a principled way to accurately approximate dynamics and spiking of the detailed model. 

Their original research provides a new model we can use use to investigate MSO dynamics (simpler to analyze and simulate than the detailed model).   The parameterization procedure they developed can also be used by other computational neuroscientists to accurately approximate detailed models of axons with the more parsimonious two-compartment framework.