Population coding strategies in human tactile afferents

Sensory information is conveyed by populations of neurons, and coding strategies cannot always be deduced when considering individual neurons. Moreover, information coding depends on the number of neurons available and on the composition of the population when multiple classes with different response properties are available. Here, we study population coding in human tactile afferents by employing a recently developed simulator of mechanoreceptor firing activity. First, we highlight the interplay of afferents within each class. We demonstrate that the optimal afferent density to convey maximal information depends on both the tactile feature under consideration and the afferent class. Second, we find that information is spread across different classes for all tactile features and that each class encodes both redundant and complementary information with respect to the other afferent classes. Specifically, combining information from multiple afferent classes improves information transmission and is often more efficient than increasing the density of afferents from the same class. Finally, we examine the importance of temporal and spatial contributions, respectively, to the joint spatiotemporal code. On average, destroying temporal information is more destructive than removing spatial information, but the importance of either depends on the stimulus feature analysed. Overall, our results suggest that both optimal afferent innervation densities and the composition of the population depend in complex ways on the tactile features in question, potentially accounting for the variety in which tactile peripheral populations are assembled in different regions across the body. Author summary Touching an object elicits neural responses from hundreds or thousands of individual tactile receptors of different classes embedded within our hand. Information about the extent of contact, the strength of the touch, and its temporal profile are carried jointly in this population response to be processed further by the central nervous system. However, studying the nature of the population code is empirically challenging, as electrophysiological recordings are typically obtained from single or a small number of neurons at most. Here, we make use of a computer simulation to recreate the population activity of large numbers of tactile neurons and examine how information is spread across different neurons. We find that tactile information increases with afferent density, but the saturation point depends on both the tactile feature and afferent class. Importantly, information is generally spread across multiple afferent classes, such that a combination of afferents from multiple classes yields higher information than the same number of neurons from a single class. These results will be useful to guide future experiments and theoretical work on the processing of tactile information by the central nervous system.

Automated summary

Research shows that the encoding of sensory information relies on populations of neurons, with different neuronal populations encoding redundant and complementary information for various tactile features. Combining multiple classes of neurons improves information transmission efficiency. Additionally, temporal information plays a more important role in joint encoding compared to spatial information, although this depends on the analyzed stimulus feature.