Network Receptive Field Modeling Reveals Extensive Integration and Multi-feature Selectivity in Auditory Cortical Neurons.

Nicol S Harper,Ben D B Willmore,Zhanfeng Cui,Oliver Schoppe,Jan W H Schnupp,Andrew J King,Israel Nelken

doi:10.1371/journal.pcbi.1005113

Abstract

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.

Highlights

Developing models capable of quantitatively predicting neural responses to sensory stimuli is key to understanding the neural computations underlying perception
To investigate the ability of network receptive field (NRF) to account for cortical sensory responses, we fitted models to neural responses to clips of natural sounds
Given their capacity to predict the responses of auditory cortical neurons to natural sounds, we propose that the NRF models capture important aspects of the general signal processing performed by the neural circuitry driving the recorded neurons, and that this is likely to apply to other areas of the brain too

Summary

Introduction

Developing models capable of quantitatively predicting neural responses to sensory stimuli is key to understanding the neural computations underlying perception. RF models, simple and useful, are only moderately effective in capturing neural responses since processing by networks of neurons includes highly nonlinear operations. They can fail to produce adequate descriptions of neural responses, to natural stimuli [17,18]. More complex and often nonlinear STRF models [20,21,22,23,24,25] of A1 neurons have achieved improved predictions of experimental data, sometimes at the expense of being very computationally intensive These newer models have tended to concentrate on better modeling of features local to the neuron, such as synaptic depression [23] or refractoriness [22]. Other valuable approaches adopted to characterize the feature selectivity of A1 neurons are more phenomenological in nature [20]

Methods

Results

Discussion

Conclusion