Antennal Lobe Dynamics And The Generation Of Diverse Response Patterns To Mechanosensory Stimulation

Sensory integration within antennal lobe (AL) is thought to play a role in the guidance of odor tracking, though while responses to olfactory input within the AL have been well-studied, responses to mechanosensory stimuli, in the form of wind speed, have received less scrutiny. Recent experimental work has systematically characterized mechanosensory responses of individual neurons within the AL, showing four distinct response patterns, labeled as sustained, transient, biphasic, and offset types; furthermore, these experiments have demonstrated that the distribution of response patterns, as well as the response type of a fixed neuron, can vary with stimulus intensity (wind speed). In this work, we develop a realistic biophysical model of the AL and, using this model, we show that internal AL dynamics are capable of generating the response patterns observed experimentally - namely, we find that response type is determined by the interplay of slow synaptic inhibition, an intrinsic calcium-dependent potassium (SK) current, and stimulus strength, and hence that a heterogeneous distribution of slow inhibition and SK current strength across the AL can lead to the emergence of all four types at a fixed wind speed. Moreover, similar to experiment, we find that the distribution of response patterns across the AL changes with wind speed. Finally, we examine the distribution of response types in the presence of a simulated odor stimulus as well as odor separation by the AL in the presence versus absence of a strong mechanosensory signal.

Odor tracking is a critical for insects, requiring integration of chemosensory (odor) with mechanosensory (wind speed) input. The antennal lobe (AL), the first structure in the insect olfactory pathway, has been extensively studied within the context of odor encoding, but less so in terms of mechanosensory responses. Recent experiments, reported in a companion paper, have systematically studied AL responses to mechanosensory input. In this work, we develop a computational model of the AL to study the network mechanisms that give rise to these empirically observed response patterns. Characterizing the responses of AL neurons to mechanosensory input, and elucidating the network dynamics that give rise to these response patterns, is crucial to understanding sensory integration within the AL.