Towards a state-space geometry of neural responses to natural scenes: A steady-state approach

Highlights

• Non-invasive brain imaging procedures, such as fMRI and EEG, form the backbone of our knowledge of human sensory systems.

• In all imaging modalities, research has focused on the features of the response, such as the relative amplitudes of peaks and troughs in the evoked response, rather than the relative organization of entire responses across a variety of stimuli.

• Here, we consider whole responses as points in a high-dimensional response space of possible alternatives and model the geometry of this space.

• This global approach allows us to observe how the early visual system organizes information, to test the fit of biologically plausible computational models, and to estimate the information available in the signal.

Abstract

Our understanding of information processing by the mammalian visual system has come through a variety of techniques ranging from psychophysics and fMRI to single unit recording and EEG. Each technique provides unique insights into the processing framework of the early visual system. Here, we focus on the nature of the information that is carried by steady state visual evoked potentials (SSVEPs). To study the information provided by SSVEPs, we presented human participants with a population of natural scenes and measured the relative SSVEP response. Rather than focus on particular features of this signal, we focused on the full state-space of possible responses and investigated how the evoked responses are mapped onto this space. Our results show that it is possible to map the relatively high-dimensional signal carried by SSVEPs onto a 2-dimensional space with little loss. We also show that a simple biologically plausible model can account for a high proportion of the explainable variance (~73%) in that space. Finally, we describe a technique for measuring the mutual information that is available about images from SSVEPs. The techniques introduced here represent a new approach to understanding the nature of the information carried by SSVEPs. Crucially, this approach is general and can provide a means of comparing results across different neural recording methods. Altogether, our study sheds light on the encoding principles of early vision and provides a much needed reference point for understanding subsequent transformations of the early visual response space to deeper knowledge structures that link different visual environments.