High-fidelity tactile transmission and hierarchical neural coding of touch
Year: 2026
Type: article
Abstract: The tactile sensory system encodes mechanical stimuli through a hierarchically organised network of specialised mechanoreceptors and central neural pathways. While the encoding properties of peripheral afferents are well characterised, the mechanisms governing the transmission, transformation and integration of tactile information across successive central relays remain incompletely understood. This review synthesises current evidence on neural transmission and coding throughout the tactile sensory pathway, with particular emphasis on the dorsal column nuclei (DCN) as the first major site of central synaptic integration. Evidence from identified Pacinian afferent pathways demonstrates that tactile signals can be transmitted through the DCN with exceptionally high fidelity. This finding raises the hypothesis that differences in perceptual sensitivity between mechanoreceptor classes may depend substantially on transformations within distributed thalamocortical networks rather than solely on transmission at early central relays. Traditional sensory theories often assume that perception requires the brain to reconstruct incomplete or degraded sensory signals. In contrast, this review argues that the tactile system delivers a remarkably accurate representation of peripheral events to higher centres, and that perception arises primarily from the interpretation of this information rather than its recovery. A unified hierarchical framework is proposed in which faithful early transmission provides the substrate for progressively distributed neural computations that give rise to conscious tactile perception. This perspective has important implications for understanding somatosensory function, refining computational models of neural coding, and guiding the development of biomimetic neuroprosthetic systems.
Author: Richard Tarvin
Institution: University of Sussex
Language: English
Subfield: Cognitive Neuroscience
Field: Neuroscience
Domain: Life Sciences
Open Access status: hybrid
OpenAlex ID: W7204576401