Context Matters: The Illusive Simplicity of Macaque V1 Receptive Fields

Robert Haslinger,Bruss Lima,Li I Zhang,Gordon Pipa,Emery N Brown,Wolf Singer,Sergio Neuenschwander

doi:10.1371/journal.pone.0039699

Abstract

Even in V1, where neurons have well characterized classical receptive fields (CRFs), it has been difficult to deduce which features of natural scenes stimuli they actually respond to. Forward models based upon CRF stimuli have had limited success in predicting the response of V1 neurons to natural scenes. As natural scenes exhibit complex spatial and temporal correlations, this could be due to surround effects that modulate the sensitivity of the CRF. Here, instead of attempting a forward model, we quantify the importance of the natural scenes surround for awake macaque monkeys by modeling it non-parametrically. We also quantify the influence of two forms of trial to trial variability. The first is related to the neuron’s own spike history. The second is related to ongoing mean field population activity reflected by the local field potential (LFP). We find that the surround produces strong temporal modulations in the firing rate that can be both suppressive and facilitative. Further, the LFP is found to induce a precise timing in spikes, which tend to be temporally localized on sharp LFP transients in the gamma frequency range. Using the pseudo R2 as a measure of model fit, we find that during natural scene viewing the CRF dominates, accounting for 60% of the fit, but that taken collectively the surround, spike history and LFP are almost as important, accounting for 40%. However, overall only a small proportion of V1 spiking statistics could be explained (R2∼5%), even when the full stimulus, spike history and LFP were taken into account. This suggests that under natural scene conditions, the dominant influence on V1 neurons is not the stimulus, nor the mean field dynamics of the LFP, but the complex, incoherent dynamics of the network in which neurons are embedded.

Highlights

Cortical processing of visual stimuli takes place in neuronal networks that are both complex and dynamic
Upon inclusion of the local field potential (LFP) in the Generalized Linear Models (GLMs), we further found that spikes tended to be localized on fast transients in gamma band LFPs
The monkey had to release the lever within a window of 200 to 500 ms after the fixation point color change

Summary

Introduction

Cortical processing of visual stimuli takes place in neuronal networks that are both complex and dynamic. Most of the synaptic activity represents network interactions, both locally recurrent and long range [1,2,3,4,5,6] Despite this fact, the canonical approach for understanding vision has been to ignore the network and to assume that neurons signal by increasing their discharge rate in the presence of features to which their ‘‘classical receptive fields’’ (CRF) are tuned. The canonical approach for understanding vision has been to ignore the network and to assume that neurons signal by increasing their discharge rate in the presence of features to which their ‘‘classical receptive fields’’ (CRF) are tuned For simplified stimuli such as moving bars or gratings the receptive field model has been extremely successful at explaining the spiking of V1 neurons [7,8,9]. Extending this approach towards more complex stimuli, such as natural scenes, has proven difficult [10,11,12,13,14,15,16,17]

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