Computational Neuroscience

Computational Neuroscience

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Understanding the human brain is an incredible challenge. We use simulations with computational models to discover principles that reveal how the brain supports language, cognition, perception, and action.

For example, in the domain of language, we use artificial neural networks that simulate the timecourse of human spoken language processing. Our models develop internal representations that resemble those found in the human brain, and provide new hypotheses about the computations that support language understanding.

We use experiments with human participants to test hypotheses that emerge from simulations with computational models.

By gradually making our models more biologically realistic, we will discover principles that can guide theories of typical development and processing, as well as theories of developmental disorders and impairments due to disease or injury.

Our team



Crinnion, A.M., Luthra, S., Gaston, P., & Magnuson, J.S. (2024). Resolving competing predictions in speech: How qualitatively different cues and cue reliability contribute to phoneme identification. Attention, Perception, and Psychophysics, 86(3), 942-961. Doi:10.3758/s13414-024-02849-y
Kapnoula, E.C., Jevtović, M., & Magnuson, J.S. (2024). Spoken Word Recognition: A Focus on Plasticity. Annual Review of Linguistics, 10, 233-256. Doi:10.1146/annurev-linguistics-031422-113507
Magnuson, J.S., Crinnion, A.M., Luthra, S., Gaston, P., & Grubb, S. (2024). Contra assertions, feedback improves word recognition: How feedback and lateral inhibition sharpen signals over noise. Cognition, 242. Doi:10.1016/j.cognition.2023.105661
Magnuson, J.S., You, H., & Hannagan, T. (2024). Lexical Feedback in the Time-Invariant String Kernel (TISK) Model of Spoken Word Recognition. Journal of Cognition, 7(1). Doi:10.5334/JOC.362

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Take a look at our current job offers to join our team and participate in international research projects.

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