Neural mechanisms of selective attention in the somatosensory system.
Manuel Gomez-RamirezKristjana HysajErnst NieburPublished in: Journal of neurophysiology (2016)
Selective attention allows organisms to extract behaviorally relevant information while ignoring distracting stimuli that compete for the limited resources of their central nervous systems. Attention is highly flexible, and it can be harnessed to select information based on sensory modality, within-modality feature(s), spatial location, object identity, and/or temporal properties. In this review, we discuss the body of work devoted to understanding mechanisms of selective attention in the somatosensory system. In particular, we describe the effects of attention on tactile behavior and corresponding neural activity in somatosensory cortex. Our focus is on neural mechanisms that select tactile stimuli based on their location on the body (somatotopic-based attention) or their sensory feature (feature-based attention). We highlight parallels between selection mechanisms in touch and other sensory systems and discuss several putative neural coding schemes employed by cortical populations to signal the behavioral relevance of sensory inputs. Specifically, we contrast the advantages and disadvantages of using a gain vs. spike-spike correlation code for representing attended sensory stimuli. We favor a neural network model of tactile attention that is composed of frontal, parietal, and subcortical areas that controls somatosensory cells encoding the relevant stimulus features to enable preferential processing throughout the somatosensory hierarchy. Our review is based on data from noninvasive electrophysiological and imaging data in humans as well as single-unit recordings in nonhuman primates.
Keyphrases
- working memory
- transcranial direct current stimulation
- neural network
- machine learning
- deep learning
- healthcare
- magnetic resonance
- high resolution
- induced apoptosis
- electronic health record
- magnetic resonance imaging
- big data
- multidrug resistant
- multiple sclerosis
- cell cycle arrest
- computed tomography
- data analysis