California sea lions employ task-specific strategies for active touch sensing

Active sensing is the process of moving sensors to extract task-specific information. Whisker touch is often referred to as an active sensory system since whiskers are moved with purposeful control. Even though whisker movements are found in many species, it is unknown if any animal can make task-specific movements with their whiskers. California sea lions (Zalophus californianus) make large, purposeful whisker movements and are capable of performing many whisker-related discrimination tasks. Therefore, California sea lions are an ideal species to explore the active nature of whisker touch sensing. Here, we show that California sea lions can make task-specific whisker movements. California sea lions move their whiskers with large amplitudes around object edges to judge size, make smaller, lateral stroking movements to judge texture and make very small whisker movements during a visual task. These findings, combined with the ease of training mammals and measuring whisker movements, makes whiskers an ideal system for studying mammalian perception, cognition and motor control.

Idiosyncratic Functions of Active Touch Strategies in Shape Perception.

Hand movements are essential for tactile perception of objects. However, the specific functions served by active touch strategies, and their dependence on physiological parameters, is unclear and understudied. Focusing on planar shape perception, we tracked at high resolution the hands of eleven participants during shape recognition task. Two dominant hand movements strategies were identified: Contour-following movements, either tangential to the contour or oscillating perpendicular to it, and exploration by scanning movements, crossing between distant parts of the shapes’ contour. Both strategies exhibited non-uniform coverage of the shapes’ contours. Idiosyncratic movement patterns were specific to the sensed object and could be explained in part by spatial and temporal tactile thresholds of the participant. Using simulations, we show how some strategy choices may affect receptors activation. These results suggest that motion strategies of active touch adapt to both the sensed object and to the perceiver’s physiological parameters.

Circuit organization of the excitatory sensorimotor loop through hand/forelimb S1 and M1

Sensory-guided limb control relies on communication across sensorimotor loops. For active touch with the hand, the longest loop is the transcortical continuation of ascending pathways, particularly the lemnisco-cortical and corticocortical pathways carrying tactile signals via the cuneate nucleus, ventral posterior lateral (VPL) thalamus, and primary somatosensory (S1) and motor (M1) cortices to reach corticospinal neurons and influence descending activity. We characterized excitatory connectivity along this pathway in the mouse. In the lemnisco-cortical leg, disynaptic cuneate→VPL→S1 connections excited mainly layer (L) 4 neurons. In the corticocortical leg, S1→M1 connections from L2/3 and L5A neurons mainly excited downstream L2/3 neurons, which excite corticospinal neurons. The findings provide a detailed new wiring diagram for the hand/forelimb-related transcortical circuit, delineating a basic but complex set of cell-type-specific feedforward excitatory connections that selectively and extensively engage diverse intratelencephalic projection neurons, thereby polysynaptically linking subcortical somatosensory input to cortical motor output to spinal cord.

Experience-driven remodeling of S1 digit representation in awake monkeys: the challenge of comparing active and passive touch

Many studies have compared active and passive touch to understand how motor action shapes touch perception. Current views emphasize the difficulties in making such a comparison and promote investigating how motor strategies enable the filtering out of sensory inputs to reshape touch perception. Cybulska-Klosowicz et al. (2020) suggest that primary somatosensory (S1) cortical remodeling of digit representation occurs during active touch. Here, alternative interpretations are proposed and the relevance of studying multidigit scanning is emphasized.

Circuit organization of the excitatory sensorimotor loop through hand/forelimb S1 and M1

Sensory-guided limb control relies on communication across sensorimotor loops. For active touch with the hand, the longest loop is the transcortical continuation of ascending pathways, particularly the lemnisco-cortical and corticocortical pathways carrying tactile signals via the cuneate nucleus, ventral posterior lateral (VPL) thalamus, and primary somatosensory (S1) and motor (M1) cortices to reach corticospinal neurons and influence descending activity. We characterized excitatory connectivity along this pathway in the mouse. In the lemnisco-cortical leg, disynaptic cuneate→VPL→S1 connections excited mainly layer (L) 4 neurons. In the corticocortical leg, S1→M1 connections from L2/3 and L5A neurons mainly excited downstream L2/3 neurons, which excite corticospinal neurons. The findings provide a detailed new wiring diagram for the hand/forelimb-related transcortical circuit, delineating a basic but complex set of cell-type-specific feedforward excitatory connections that selectively and extensively engage diverse intratelencephalic projection neurons, thereby polysynaptically linking subcortical somatosensory input to cortical motor output to spinal cord.

EEG-based trial-by-trial texture classification during active touch

Trial-by-trial texture classification analysis and identifying salient texture related EEG features during active touch that are minimally influenced by movement type and frequency conditions are the main contributions of this work. A total of twelve healthy subjects were recruited. Each subject was instructed to use the fingertip of their dominant hand’s index finger to rub or tap three textured surfaces (smooth flat, medium rough, and rough) with three levels of movement frequency (approximately 2, 1 and 0.5 Hz). EEG and force data were collected synchronously during each touch condition. A systematic feature selection process was performed to select temporal and spectral EEG features that contribute to texture classification but have low contribution towards movement type and frequency classification. A tenfold cross validation was used to train two 3-class (each for texture and movement frequency classification) and a 2-class (movement type) Support Vector Machine classifiers. Our results showed that the total power in the mu (8–15 Hz) and beta (16–30 Hz) frequency bands showed high accuracy in discriminating among textures with different levels of roughness (average accuracy > 84%) but lower contribution towards movement type (average accuracy < 65%) and frequency (average accuracy < 58%) classification.