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Nicol S. Harper

Affiliations: 
Redwood Center for Theoretical Neuroscience, UC Berkeley, Berkeley, CA, United States 
Area:
Auditory System
Google:
"Nicol Harper"
Mean distance: 17.62 (cluster 17)
 
Cross-listing: CSD Tree

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Parents

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David McAlpine grad student UCL
Andrew J. King research scientist Oxford

Children

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Yosef Singer grad student 2014- Oxford
Monzilur Rahman grad student 2015- Oxford
Aleksandar Ivanov grad student 2016- Oxford
Freddy Trinh grad student 2016- Oxford
Crystal Y. C. Leung grad student 2018-2018 Oxford

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Publications

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Mishra AP, Peng F, Li K, et al. (2021) Sensitivity of neural responses in the inferior colliculus to statistical features of sound textures. Hearing Research. 412: 108357
Mishra AP, Harper NS, Schnupp JWH. (2021) Exploring the distribution of statistical feature parameters for natural sound textures. Plos One. 16: e0238960
Rahman M, Willmore BDB, King AJ, et al. (2020) Simple transformations capture auditory input to cortex. Proceedings of the National Academy of Sciences of the United States of America
Rahman M, Willmore BDB, King AJ, et al. (2019) A dynamic network model of temporal receptive fields in primary auditory cortex. Plos Computational Biology. 15: e1006618
Sheikh AS, Harper NS, Drefs J, et al. (2019) STRFs in primary auditory cortex emerge from masking-based statistics of natural sounds. Plos Computational Biology. 15: e1006595
Singer Y, Teramoto Y, Willmore BD, et al. (2018) Sensory cortex is optimised for prediction of future input. Elife. 7
Singer Y, Teramoto Y, Willmore BD, et al. (2018) Author response: Sensory cortex is optimized for prediction of future input Elife
Robinson BL, Harper NS, McAlpine D. (2016) Meta-adaptation in the auditory midbrain under cortical influence. Nature Communications. 7: 13442
Harper NS, Schoppe O, Willmore BD, et al. (2016) Network Receptive Field Modeling Reveals Extensive Integration and Multi-feature Selectivity in Auditory Cortical Neurons. Plos Computational Biology. 12: e1005113
Schoppe O, Harper NS, Willmore BD, et al. (2016) Measuring the Performance of Neural Models. Frontiers in Computational Neuroscience. 10: 10
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