Encoding of movement primitives and body posture through distributed proprioception in walking and climbing insects

Targeted reaching movements and spatial coordination of footfall patterns are prime examples of spatial coordination of animal limbs. To explain this, both physiological and computational studies have suggested the use of movement primitives or the existence of an internal body representation. Since insects lack a dedicated posture-sensing organ or vestibular system (which vertebrates possess), it has been hypothesized that they derive high-level postural information from distributed low-level proprioceptive cues, integrated across their limbs. To test this possibility, we use a multi-layer spiking neural network to extract high-level information about limb movement and whole-body posture from information provided by distributed local proprioceptors. The preceding companion paper introduced the phasic-tonic encoding of joint angles by strictly local proprioceptive afferents, and high-accuracy encoding of joint angles and angular velocities in first-order interneurons. Here, we extend this model by second-order interneurons that encode movement primitives of single legs by coincidence detection from two or three leg-local inputs. By validation against annotated experimental data on whole-body kinematics of unrestrained stick insect locomotion, we show that modelled interneurons can signal particular step cycle phases, but also step cycle transitions such as leg lift-off. To indicate climbing behaviour, third-order interneurons encode body pitch relative to the substrate from position and motion of [Formula: see text] local leg joints. Our results demonstrate that simple combinations of two or three position/velocity inputs from disjunct proprioceptor arrays are sufficient to encode high-order movement information about step cycle phases. The resulting movement primitive encoding may converge to represent particular locomotor states and whole-body posture.