Network Receptive Field Modeling Reveals Extensive Integration and Multi-feature Selectivity in Auditory Cortical Neurons.
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.
Dept. of Physiology, Anatomy and Genetics (DPAG), Sherrington Building, University of Oxford, United Kingdom.|Institute of Biomedical Engineering, Department of Engineering Science, Old Road Campus Research Building, University of Oxford, Headington, United Kingdom.
Nicol S
NS
Harper
Dept. of Physiology, Anatomy and Genetics (DPAG), Sherrington Building, University of Oxford, United Kingdom.|Bio-Inspired Information Processing, Technische Universität München, Germany.
Oliver
http://orcid.org/0000-0002-7182-2429
O
Schoppe
Dept. of Physiology, Anatomy and Genetics (DPAG), Sherrington Building, University of Oxford, United Kingdom.
Ben D B
BD
Willmore
Institute of Biomedical Engineering, Department of Engineering Science, Old Road Campus Research Building, University of Oxford, Headington, United Kingdom.
Zhanfeng
Z
Cui
Department of Biomedical Science, City University of Hong Kong, Kowloon Tong, Hong Kong.|Dept. of Physiology, Anatomy and Genetics (DPAG), Sherrington Building, University of Oxford, United Kingdom.
Jan W H
JW
Schnupp
Dept. of Physiology, Anatomy and Genetics (DPAG), Sherrington Building, University of Oxford, United Kingdom.
Andrew J
AJ
King
D000161:Acoustic Stimulation / Q000379:methods; D000818:Animals; D001303:Auditory Cortex / Q000502:physiology*; D001307:Auditory Perception / Q000502:physiology*; D003198:Computer Simulation; D006801:Humans; D008959:Models, Neurological; D009415:Nerve Net / Q000502:physiology*; D009433:Neural Inhibition / Q000502:physiology*; D017711:Nonlinear Dynamics; D011984:Sensory Receptor Cells / Q000502:physiology*; D018511:Systems Integration