High-Dimensional Representation of Texture in the Somatosensory Cortex of Primates

Abstract

In the somatosensory nerves, the tactile perception of texture is driven by spatial and temporal patterns of activation distributed across three populations of afferents. These disparate streams of information must then be integrated centrally to achieve a unified percept of texture. To investigate the representation of texture in somatosensory cortex, we scanned a wide range of natural textures across the fingertips of Rhesus macaques and recorded the responses evoked in Brodmann's areas 3b, 1, and 2. We found that texture identity is reliably encoded in the idiosyncratic responses of populations of cortical neurons, giving rise to a high-dimensional representation of texture. Cortical neurons fall along a continuum in their sensitivity to fine vs. coarse texture, and neurons at the extrema of this continuum seem to receive their major input from different afferent populations. Finally, we show that cortical responses can account for several aspects of texture perception in humans.

In vivo microstimulation with cathodic and anodic asymmetric waveforms modulates spatiotemporal calcium dynamics in cortical neuropil and pyramidal neurons of male mice

“…Neuronal representation of sensory information is highly complex (Lieber & Bensmaia, 2019), and the ability to selectively recruit neuronal populations proximal to the electrode by varying pulse amplitude, frequency, or polarity could provide the ability to stimulate neural activity that matches physiological response patterns (Anderson, Duffley, Vorwerk, Dorval, & Butson, 2018; Cai et al., 2017; Delhaye, Saal, & Bensmaia, 2016; Grill, 2018; Histed, Bonin, & Reid, 2009; Hofmann, Ebert, Tass, & Hauptmann, 2011; Koivuniemi & Otto, 2011; Kuncel & Grill, 2004; Macherey, van Wieringen, Carlyon, Deeks, & Wouters, 2006; McIntyre & Grill, 2000, 2002; McIntyre, Richardson, & Grill, 2002; Michelson, Eles, Vazquez, Ludwig, & Kozai, 2019). For example, neuronal adaptation is a common response to sustained stimuli and is believed to allow the brain to better respond to transient stimuli (Wark, Lundstrom, & Fairhall, 2007).…”

Sensory computations in the cuneate nucleus of macaques

“…touch j neural coding j receptive fields j vibration j integration T he coding of tactile information has been extensively studied in the peripheral nerves and in the primary somatosensory cortex (S1, Brodmann's area 3b) of nonhuman primates, leading to the conclusion that sensory representations in S1 differ from those at the periphery in at least two important ways. First, while cutaneous nerve fibers can be divided into a small number of classes each responding to a different aspect of skin deformation (1)(2)(3), individual S1 neurons integrate sensory signals from multiple classes of nerve fibers (4)(5)(6)(7). Indeed, while each class of nerve fibers exhibits stereotyped responses to certain stimulus classes, for example, skin indentations or sinusoidal vibrations, cortical responses to these same stimuli include features of the responses from multiple tactile classes or submodalities.…”

Physiological noise optimizes multiplexed coding of vibrotactile-like signals in somatosensory cortex

“…The rate of those spikes can also carry information; for instance, Jadhav et al (2009) found that firing rate and synchrony both increase with roughness, but synchrony varied more consistently than rate for subtle differences in texture. Lieber and Bensmaia (2019) found that variations in rate were sufficient for near-perfect discrimination of textures, suggesting the phase locked spiking they observed in some neurons is unimportant or relevant only to resolve subtler differences. Whether spike timing in S1 affects perception is contentious.…”

“…Several in vivo studies have already established that at least some S1 neurons -depending on their afferent input (Saal et al, 2015) -respond to vibrotactile stimuli with precisely times spikes (Allitt et al, 2017;Arabzadeh et al, 2005;Ewert et al, 2015;Ewert et al, 2008;Harvey et al, 2013;Jadhav et al, 2009;Lieber and Bensmaia, 2019). The rate of those spikes can also carry information; for instance, Jadhav et al (2009) found that firing rate and synchrony both increase with roughness, but synchrony varied more consistently than rate for subtle differences in texture.…”