Department or Program
Mutual information, Natural scenes, Neural state-space, SSVEP, Steady-state visual evoked potentials
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.
Hansen, B. C., Field, D. J., Greene, M. R., Olson, C., & Miskovic, V. (2019). Towards a state-space geometry of neural responses to natural scenes: A steady-state approach. NeuroImage (Orlando, Fla.), 201, 116027-116027. https://doi.org/10.1016/j.neuroimage.2019.116027
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Original version is available from the publisher at: https://doi.org/10.1016/j.neuroimage.2019.116027