Menoua Keshishian

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Visit my new website: https://menoua.github.io/.
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I am a sixth-year Ph.D. candidate in Electrical Engineering at Columbia University and a member of the Neural Acoustic Processing Lab which is a part of the Zuckerman Mind Brain Behavior Institute. I am interested in the processing of speech and language in the human brain. Specifically, I use machine learning methods to study how the human auditory cortex analyzes the acoustic and linguistic content of speech as it is processed through the auditory pathway.

Publications

Deep neural networks effectively model neural adaptation to changing background noise and suggest nonlinear noise filtering methods in auditory cortex

Gavin Mischler, Menoua Keshishian, Stephan Bickel, Ashesh D Mehta, Nima Mesgarani

NeuroImage, vol. 266, 2023

Joint, distributed and hierarchically organized encoding of linguistic features in the human auditory cortex

Menoua Keshishian, Serdar Akkol, Jose Herrero, Stephan Bickel, Ashesh D Mehta, Nima Mesgarani

Nature Human Behaviour, 2023

Understanding Adaptive, Multiscale Temporal Integration In Deep Speech Recognition Systems

Menoua Keshishian, Samuel Norman-Haignere, Nima Mesgarani

Thirty-Fifth Conference on Neural Information Processing Systems, 2021

Estimating and interpreting nonlinear receptive field of sensory neural responses with deep neural network models

Menoua Keshishian, Hassan Akbari, Bahar Khalighinejad, Jose L Herrero, Ashesh D Mehta, Nima Mesgarani

eLife, 2020;9, 2020

Projects

dSTRF

A python toolbox to analyze feed forward neural networks trained to predict biological neural responses to sound, by computing the locally linear operations they perform on the input.

PyTCI

A python toolbox to analyze the temporal integration windows of neural networks that process time-series input.

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