期刊
PLOS COMPUTATIONAL BIOLOGY
卷 12, 期 11, 页码 -出版社
PUBLIC LIBRARY SCIENCE
DOI: 10.1371/journal.pcbi.1005113
关键词
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资金
- Sir Henry Wellcome Postdoctoral Fellowship [WT082692]
- Wellcome Trust [WT076508AIA, WT108369/Z/ 2015/Z, WT108369/Z/2015/Z]
- Department of Physiology, Anatomy and Genetics at the University of Oxford
- Action on Hearing Loss grant [PA07]
- Biotechnology and Biological Sciences Research Council [BB/H008608/1]
- German Academic Scholarship Foundation
- Biotechnology and Biological Sciences Research Council [BB/H008608/1] Funding Source: researchfish
- RNID [PA07] Funding Source: researchfish
- BBSRC [BB/H008608/1] Funding Source: UKRI
Cortical sensory neurons are commonly characterized using the receptive field, the linear dependence of their response on the stimulus. In primary auditory cortex neurons can be characterized by their spectrotemporal receptive fields, the spectral and temporal features of a sound that linearly drive a neuron. However, receptive fields do not capture the fact that the response of a cortical neuron results from the complex nonlinear network in which it is embedded. By fitting a nonlinear feedforward network model (a network receptive field) to cortical responses to natural sounds, we reveal that primary auditory cortical neurons are sensitive over a substantially larger spectrotemporal domain than is seen in their standard spectrotemporal receptive fields. Furthermore, the network receptive field, a parsimonious network consisting of 1-7 sub-receptive fields that interact nonlinearly, consistently better predicts neural responses to auditory stimuli than the standard receptive fields. The network receptive field reveals separate excitatory and inhibitory sub-fields with different nonlinear properties, and interaction of the sub-fields gives rise to important operations such as gain control and conjunctive feature detection. The conjunctive effects, where neurons respond only if several specific features are present together, enable increased selectivity for particular complex spectrotemporal structures, and may constitute an important stage in sound recognition. In conclusion, we demonstrate that fitting auditory cortical neural responses with feedforward network models expands on simple linear receptive field models in a manner that yields substantially improved predictive power and reveals key nonlinear aspects of cortical processing, while remaining easy to interpret in a physiological context.
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