Cargando…
Estimating and interpreting nonlinear receptive field of sensory neural responses with deep neural network models
Our understanding of nonlinear stimulus transformations by neural circuits is hindered by the lack of comprehensive yet interpretable computational modeling frameworks. Here, we propose a data-driven approach based on deep neural networks to directly model arbitrarily nonlinear stimulus-response map...
Autores principales: | Keshishian, Menoua, Akbari, Hassan, Khalighinejad, Bahar, Herrero, Jose L, Mehta, Ashesh D, Mesgarani, Nima |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
eLife Sciences Publications, Ltd
2020
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7347387/ https://www.ncbi.nlm.nih.gov/pubmed/32589140 http://dx.doi.org/10.7554/eLife.53445 |
Ejemplares similares
-
Deep neural networks effectively model neural adaptation to changing background noise and suggest nonlinear noise filtering methods in auditory cortex
por: Mischler, Gavin, et al.
Publicado: (2023) -
naplib-python: Neural Acoustic Data Processing and Analysis Tools in Python
por: Mischler, Gavin, et al.
Publicado: (2023) -
naplib-python: Neural acoustic data processing and analysis tools in python
por: Mischler, Gavin, et al.
Publicado: (2023) -
Towards reconstructing intelligible speech from the human auditory cortex
por: Akbari, Hassan, et al.
Publicado: (2019) -
Adaptation of the human auditory cortex to changing background noise
por: Khalighinejad, Bahar, et al.
Publicado: (2019)