Abstract
Information in the cortex is encoded in spatiotemporal patterns of neuronal activity, but the exact nature of that code still remains elusive. While onset responses to simple stimuli are associated with specific loci in cortical sensory maps, it is completely unclear how the information about a sustained stimulus is encoded that is perceived for minutes or even longer, when discharge rates have decayed back to spontaneous levels. Using a newly developed statistical approach (multidimensional cluster statistics (MCS)) that allows for a comparison of clusters of data points in n-dimensional space, we here demonstrate that the information about long-lasting stimuli is encoded in the ongoing spatiotemporal activity patterns in sensory cortex. We successfully apply MCS to multichannel local field potential recordings in different rodent models and sensory modalities, as well as to human MEG and EEG data, demonstrating its universal applicability. MCS thus indicates novel ways for the development of powerful read-out algorithms of spatiotemporal brain activity that may be implemented in innovative brain-computer interfaces (BCI).
Highlights
Brains use sensory systems to generate internal representations of the world
A number of studies have described such attractor-like dynamics, e.g. in the auditory cortex, based on electrocorticogram[6,7,8,9] or extracellular spike recordings[10], most of these studies usually analyze stimulus evoked responses within the first hundred milliseconds after stimulus onset in comparison to long-lasting spontaneous activity[10] or during certain “marked states” that correlate with certain behavioral states[8]
The reason why sustained attractor dynamics still haven’t been analyzed yet may be due to the fact that proper statistics that allow for a differentiation between sustained spatiotemporal patterns belonging to the same or different internal representations have not yet been developed
Summary
Brains use sensory systems to generate internal representations of the world These internal representations serve animal organisms as a frame of reference to guide their behavior. A number of studies have described such attractor-like dynamics, e.g. in the auditory cortex, based on electrocorticogram[6,7,8,9] or extracellular spike recordings[10], most of these studies usually analyze stimulus evoked responses within the first hundred milliseconds after stimulus onset in comparison to long-lasting spontaneous activity[10] or during certain “marked states” that correlate with certain behavioral states[8]. The reason why sustained attractor dynamics still haven’t been analyzed yet may be due to the fact that proper statistics that allow for a differentiation between sustained spatiotemporal patterns belonging to the same or different internal representations have not yet been developed. We discuss possibilities for the development of new read-out algorithms of brain activity that may be used for the construction of a novel generation of brain-computer interfaces (BCI)
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