A Computational Model of a Descending Mechanosensory Pathway Involved in Active Tactile Sensing

Abstract

Many animals, including humans, rely on active tactile sensing to explore the environment and negotiate obstacles, especially in the dark. Here, we model a descending neural pathway that mediates short-latency proprioceptive information from a tactile sensor on the head to thoracic neural networks. We studied the nocturnal stick insect Carausius morosus, a model organism for the study of adaptive locomotion, including tactually mediated reaching movements. Like mammals, insects need to move their tactile sensors for probing the environment. Cues about sensor position and motion are therefore crucial for the spatial localization of tactile contacts and the coordination of fast, adaptive motor responses. Our model explains how proprioceptive information about motion and position of the antennae, the main tactile sensors in insects, can be encoded by a single type of mechanosensory afferents. Moreover, it explains how this information is integrated and mediated to thoracic neural networks by a diverse population of descending interneurons (DINs). First, we quantified responses of a DIN population to changes in antennal position, motion and direction of movement. Using principal component (PC) analysis, we find that only two PCs account for a large fraction of the variance in the DIN response properties. We call the two-dimensional space spanned by these PCs ‘coding-space’ because it captures essential features of the entire DIN population. Second, we model the mechanoreceptive input elements of this descending pathway, a population of proprioceptive mechanosensory hairs monitoring deflection of the antennal joints. Finally, we propose a computational framework that can model the response properties of all important DIN types, using the hair field model as its only input. This DIN model is validated by comparison of tuning characteristics, and by mapping the modelled neurons into the two-dimensional coding-space of the real DIN population. This reveals the versatility of the framework for modelling a complete descending neural pathway.

Descending interneurons of the stick insect connecting brain neuropiles with the prothoracic ganglion

“…and corroborate findings by [26] regarding potential synaptic contacts in the GNG. Given the behavioural relevance of the antennal tactile sense for adaptive locomotion in nocturnal, canopy-dwelling, obligatory walking stick insects (they can neither jump nor fly), and in conjunction with electrophysiological characterisation [34,26], ablation studies [37] and computational modelling [61] of stick insect DNs, our neuroanatomical data support the view of a fast cephalothoracic pathway conveying antennal postural information. This could be part of the anatomical substrate underlying fast, tactually elicited, aimed reach-to-grasp movements of the front leg in climbing stick insects [7].…”

Stable phase-shift despite quasi-rhythmic movements: a CPG-driven dynamic model of active tactile exploration in an insect

“…Future studies will need to include context-dependent changes in antennal movement pattern, for example during object sampling (Krause and Dürr, 2012 ) or during turning (Dürr and Ebeling, 2005 ), both of which will require two kinds of sensory feedback: proprioceptors, such as hair fields, influencing the joint angle working ranges, and exteroceptors, such as touch/contact sensors, inducing object sampling (Krause and Dürr, 2012 ), negotiation or avoidance (Harley et al, 2009 ; Baba et al, 2010 ). A computational model of antennal hair field function has recently been proposed (Ache and Dürr, 2015 ), and can be easily incorporated into the present model, e.g., by turning the offset and amplitude parameters into functions of the corresponding hair field output. Similarly, a recent model of insect-inspired tactile contour-tracing uses touch events to induce discrete shifts of the oscillator phase (Krause et al, 2014 ).…”

Flow sensing on dragonfly wings

“…50,51 While the singlecell characterization of the dragonfly wing mechanosensors are yet to be done, a generic activation model allows us to predict possible ways the system could encode information given the sensor distribution. [52][53][54] Neural-inspired encoding, using nonlinear activation models, has been successfully implemented to optimize sparse strain sensor placement on flapping wings. 54 While activation models only approximate the neural behavior of a biological system, they are powerful tools to evaluate the readability of a system.…”

“…Spiking sensory neurons can be broadly categorized into phasic and tonic cells, based on their firing responses 50,51 . While the single‐cell characterization of the dragonfly wing mechanosensors are yet to be done, a generic activation model allows us to predict possible ways the system could encode information given the sensor distribution 52–54 . Neural‐inspired encoding, using nonlinear activation models, has been successfully implemented to optimize sparse strain sensor placement on flapping wings 54 .…”