dc.contributor.advisor	Nicola, Wilten
dc.contributor.author	Ibarra Molinas, Josué Simón
dc.contributor.committeemember	Gomes da Rocha, Claudia
dc.contributor.committeemember	Davidsen, Jörn
dc.date	2024-05
dc.date.accessioned	2024-04-12T16:10:10Z
dc.date.available	2024-04-12T16:10:10Z
dc.date.issued	2024-04-11
dc.description.abstract	Hearing is a key function to perceive and interact with the environment. For humans, audition is also of particular relevance because of its role in many functions including not only the perception of speech and music, but their production as well. While we have knowledge about the structure and organization of the auditory system, the computations these substructures perform remain to be properly understood. A description of a neuron's activity in response to sound that is common in auditory research is the so-called frequency response area (FRA) of the neuron, which measures neuronal response as a function of the frequency and intensity of evoking acoustic stimuli. However, these FRAs have not yet been modelled computationally. In this thesis, based on experimental recordings of neurons located in the primary auditory cortices (A1s) of mice, we develop a data-driven model of auditory computation that seeks to reproduce the observed FRAs of the neurons, as well as offer a possible mechanism behind the generation of the particular underlying geometry of the FRAs. Our novel, biologically-plausible model posits that auditory neurons encode sound as quadratic forms on the frequency and intensity of sound, which A1 neurons integrate. Specifically, we utilized spiking neurons and advanced optimization and validation techniques to simulate synthetic leaky integrate-and-fire neurons that reproduce the FRAs of recorded neurons. Further, utilizing a network modelling technique known as the neural engineering framework, we show that these FRAs could be produced by a single-layer neural network. We also argue that it is not possible to reproduce the nonlinearities observed in the FRAs with a linear encoding, regardless of the nonlinearity of the A1 neuronal integration. Our work here might inform future modelling, simulations and algorithms of sound processing, as well as eventual human research on auditory encoding mechanisms.
dc.identifier.citation	Ibarra Molinas, J. S. (2024). Quadratic encoding displayed by primary auditory cortical neurons (Master's thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca.
dc.identifier.uri	https://hdl.handle.net/1880/118402
dc.language.iso	en
dc.publisher.faculty	Graduate Studies
dc.publisher.institution	University of Calgary
dc.rights	University of Calgary graduate students retain copyright ownership and moral rights for their thesis. You may use this material in any way that is permitted by the Copyright Act or through licensing that has been assigned to the document. For uses that are not allowable under copyright legislation or licensing, you are required to seek permission.
dc.subject	Computational neuroscience
dc.subject	Auditory neuroscience
dc.subject	Primary auditory cortex
dc.subject	Auditory tuning curves
dc.subject	Frequency response area
dc.subject	Data-driven modelling
dc.subject	Biologically plausible
dc.subject.classification	Bioinformatics
dc.subject.classification	Biophysics
dc.subject.classification	Computer Science
dc.subject.classification	Physics
dc.subject.classification	Neuroscience
dc.title	Quadratic Encoding Displayed by Primary Auditory Cortical Neurons
dc.type	master thesis
thesis.degree.discipline	Physics & Astronomy
thesis.degree.grantor	University of Calgary
thesis.degree.name	Master of Science (MSc)
ucalgary.thesis.accesssetbystudent	I do not require a thesis withhold – my thesis will have open access and can be viewed and downloaded publicly as soon as possible.

Quadratic Encoding Displayed by Primary Auditory Cortical Neurons

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