The stimuli consisted of embossed dot patterns, varying in their spatial properties, presented passively to the skin using a rotating drum stimulator Johnson and Phillips, The roughness estimates, obtained for a variety of dot patterns, were plotted against predictions derived from each putative neural code. A hypothesis was eliminated if it failed to account for roughness estimates under any single experimental condition.
The putative neural codes for roughness included 1 the mean firing rate elicited in a given population of mechanoreceptive afferents fibers; 2 the temporal variability in the firing of a given population of mechanoreceptive afferent fibers; 3 the spatial variability in the firing of a given population of mechanoreceptive afferent fibers. The spatial variability in the responses of slowly adapting type 1 afferents was found to account for perceived roughness of all the textures tested.
In the aforementioned studies, however, textured surfaces tended to be relatively coarse, ranging in inter-element spacing from 0. When roughness coding was investigated with a wide range of textures, it was found that the spatial code mediated by SA1 fibers could not account for the perceived roughness over the range of tangible textures. Rather, roughness judgments could be accounted for based on a combination of spatial variation in SA1 following Johnson and colleagues and temporal variation in RA and PC fibers Weber et al.
That is, a texture is rough to the extent that the response it evokes in SA1 afferents is spatially inhomogeneous and the response it evokes in RA and PC afferents is temporally inhomogeneous. Information about roughness is encoded in SI as evidenced by the fact that the responses of neurons in this brain area are sensitive to changes in surface properties that determine perceived roughness, namely the spatial period of embossed dot patterns and the groove width of tactile gratings Darian-Smith et al.
Lesions in SI, particularly in areas 3b and 1, lead to severe impairments in roughness discrimination Randolph and Semmes, The second somatosensory cortex SII has also been implicated in the processing of surface roughness as it contains neurons that are sensitive to the relevant surface properties Jiang et al.
Finally, the lateral parietal opercular cortex Roland et al.
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Spatial variability in the peripheral response — which drives the perception of roughness for coarse textures — is likely computed by the subpopulation of neurons, described above, whose receptive fields comprise excitatory and inhibitory sub-regions. In fact, a subset of these neurons exhibit responses to embossed dot patterns that match their perceived roughness: Their response increases for inter-element spacings up to about 2mm, then decreases Arun Sripati, personal communication ; this population of neurons is thus ideally suited to extract roughness information from spatial patterns of SA1 activation.
The roughness signal carried in the responses of RA and PC afferents likely drives responses in another population of SI neurons, also described above, that are highly sensitive to skin vibrations over a wide range of frequencies Harvey et al. As the hand is pressed up against a compliant object, it conforms to the contour of the hand in proportion to the contact force. The compliance and the softness of the object may be signaled by the growth of the area over which the skin contacts the object as the contact force increases, as well as the increase in the force exerted by the object on the skin across the contact area.
Softness perception likely relies on signals from SA1 fibers Srinivasan and LaMotte, : First, PC fibers are too sparse and their RFs too large to play a significant role in softness perception.
Cortically induced thalamic plasticity in the primate somatosensory system
Second, the response of RA fibers to a surface indented into the skin is not modulated by the compliance of the surface whereas the response of SA1 fibers is Srinivasan and LaMotte, Although the evidence suggests that SA1 fibers are implicated in softness perception, the neural code for softness is unclear: the rate of indentation in addition to surface compliance modulates the discharge rate in individual SA1 afferents whereas softness perception is independent of the rate with which a surface is passively indented into the skin.
Thus, the firing rate in a single SA1 fiber does not unambiguously encode the compliance of an object.
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One possibility is that the average pressure exerted across the contact area is predictive of compliance and invariant with respect to indentation velocity; it may then be this quantity — average pressure — that is encoded in the population response of SA1 fibers. Indeed, magnitude estimates of stickiness have been shown to closely match the measured kinetic friction between skin and surface, i.
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Furthermore, when judging stickiness, subjects do not substantially vary the normal forces they apply on the surface, but the applied tangential forces tend to vary across surfaces, suggesting that tangential forces are critical in the perception of stickiness. Lateral interactions in cortical networks. Somatosensory processing: from single neuron to brain imaging Spurious dynamics in somatosensory cortex.
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Somatotopic Representation of Second Pain in the Primary Somatosensory Cortex of Humans and Rodents
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Lee, C. In vitro Studies. Cerebral Cortex 2, — Lee, J.
Experimental Neurobiology 23, 53—64 Journal of Neurophysiology 94, — Columnar distribution of activity dependent gabaergic depolarization in sensorimotor cortical neurons. Molecular Brain 5, 33 Lee, K. The influence of continuous theta-burst stimulation over the primary somatosensory cortex on temporal order judgment perception.
Continuous theta-burst stimulation modulates tactile synchronization. BMC neuroscience 14, 89 Maeda, Y. Functional deficits in carpal tunnel syndrome reflect reorganization of primary somatosensory cortex. Brain: A Journal of Neurology , — Johansson , L. This volume is a compilation of current research on somatosensation and its underlying mechanisms written by international experts from a broad range of disciplines. It is divided into six sections: structural basis of information processing and neocortical neurotransmitters, psychophysics of somatosensation, cortical representation of somatosensation, sensory-motor interface, neuronal population behavior, and cortical neurocomputation and modelling.
It highlights not only important new findings but also novel methods and technologies applied to major unresolved issues in the field of neuroscience. The number of methods for investigating the neural mechanisms of somatosensory perception has grown substantially in the last decade. The book encompasses levels of inquiry from ionic channels, single unit recordings of neural activity, and functional brain imaging of the coordinated activity of large neuronal ensembles to human psychophysics of controlled natural somatic stimulation.
This work is of great value for researchers and students interested in the dynamic neuronal mechanisms involved in the complex processes of sensory perception and provides a picture of our present understanding of the neural representation of the external world relayed through the somatosensory system. Inhibitory circuitry in relation to the functional organization of somatosensory cortex. Pain temperature and the sense of the body. The functional role of a noninactivating sodium current in neocortical neurons.
Psychophysics of somatosensation.
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A novel approach for studying direction discrimination. Tactile directional sensibility theoretical and functional aspects. Experimental assessment of the temporal hypothesis of velocity scaling. Linearity as the basic law of psychophysics Evidence from studies of the neural mechanisms of roughness magnitude estimation.
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