Load Non-Linearly Modulates Movement Reflex Gain in an Insect Leg via a Distributed Network of Identified Nonspiking Interneurons

Abstract

Producing context-specific motor acts requires sensorimotor neural networks to integrate multiple sensory modalities. Some of this integration occurs via presynaptic interactions between proprioceptive afferent neurons themselves,1,2 other by afferents of different modalities targeting appropriate motor neurons (MNs).3-5 How the interneuronal network typically interposed between sensory afferents and MNs contributes to this integration, particularly at single-neuron resolution, is much less understood. In stick insects, this network contains nonspiking interneurons (NSIs) converging onto the posture-controlling slow extensor tibiae motor neuron (SETi). We analyzed how load altered movement signal processing by tracing the interaction of movement (femoral chordotonal organ, fCO) and load (tibial campaniform sensilla, tiCS) signals from the afferents through the NSI network to the motor output. On the afferent level, load reduced movement signal gain by presynaptic inhibition; tiCS stimulation elicited primary afferent depolarization and reduced fCO afferent action potential amplitude. In the NSI network, graded responses to movement and load inputs summed nonlinearly and increased the gain of NSIs opposing movement-induced reflexes. The gain of SETi and muscle movement reflex responses consequently decreased. Gain modulation was movement parameter-specific and required presynaptic inhibition; pharmacologically blocking presynaptic inhibition abolished load-dependent tuning of SETi responses. These data describe sensorimotor gain control at the sensory, premotor, and motor levels. Presynaptic inhibition-mediated nonlinear integration allowed the NSI network to respond to movement sensory input in a context (load)-dependent manner. These findings show how gain changes can allow premotor networks to integrate multiple sensory modalities and thus generate context-appropriate motor activity.