Feedforward Inhibition Conveys Time-Varying Stimulus Information in a Collision Detection Circuit

Abstract

Feedforward inhibition is ubiquitous as a motif in the organization of neuronal circuits. During sensory information processing, it is traditionally thought to sharpen the responses and temporal tuning of feedforward excitation onto principal neurons. As it often exhibits complex time-varying activation properties, feedforward inhibition could also convey information used by single neurons to implement dendritic computations on sensory stimulus variables. We investigated this possibility in a collision-detecting neuron of the locust optic lobe that receives both feedforward excitation and inhibition. We identified a small population of neurons mediating feedforward inhibition, with wide visual receptive fields and whose responses depend both on the size and speed of moving stimuli. By studying responses to simulated objects approaching on a collision course, we determined that they jointly encode the angular size of expansion of the stimulus. Feedforward excitation, on the other hand, encodes a function of the angular velocity of expansion and the targeted collision-detecting neuron combines these two variables non-linearly in its firing output. Thus, feedforward inhibition actively contributes to the detailed firing-rate time course of this collision-detecting neuron, a feature critical to the appropriate execution of escape behaviors. These results suggest that feedforward inhibition could similarly convey time-varying stimulus information in other neuronal circuits.

Three-dimensional shape and velocity changes affect responses of a locust visual interneuron to approaching objects

“…The range of l/|v| values used here (13, 23, 47, 93 and 140) was similar to those used previously Dick and Gray, 2014;Gabbiani et al, 2002;Wang et al, 2018), with the lowest value representative of stimuli produced by approaching predators (Santer et al, 2012). For our stimuli that emulated an approaching disc, peak DCMD firing occurred 0.8 ms after the disc reached θ thresh of 41 deg, which is comparable to previous findings (Dick and Gray, 2014;Gabbiani et al, 2001) and relates to timing of behavioural responses to looming (Fotowat et al, 2011).…”

“…4). Given that the LGMD/DCMD firing rate during a loom is controlled by coordination of excitatory and inhibitory presynaptic circuits (Rind et al, 2016;Wang et al, 2018;Zhu et al, 2018), expansion of a sphere would activate the pathway along a different time course. Specifically, feedforward inhibition that defines the time of peak firing inhibition when θ passes ∼23 deg (Gabbiani et al, 2005) would occur later in response to a sphere.…”

“…Animals living in complex, spatiotemporally dynamic visual environments require robust collision-detection systems to successfully orient amongst stationary objects and conspecifics as well as to avoid threats, such as an approaching predator. Behavioural and neural mechanisms underlying collision detection and avoidance have been well studied in taxonomically diverse animals, including humans (Gray and Regan, 1998;Poljac et al, 2006;Vallis and McFadyen, 2005) and other primates (Cléry et al, 2017), cats (Liu et al, 2011b), mice (De Franceschi et al, 2016;Shang et al, 2015;Zhao et al, 2014), birds (Cao et al, 2004;Sun and Frost, 1998), frogs (Yamamoto et al, 2003), fish (Dunn et al, 2016;Preuss et al, 2006;Temizer et al, 2015), crustaceans (Carbone et al, 2018;Oliva et al, 2007;Scarano et al, 2018), insects (Gabbiani et al, 1999;von Reyn et al, 2017;Robertson and Johnson, 1993;Santer et al, 2012;Sato and Yamawaki, 2014;Thyselius et al, 2018;Wang et al, 2018) and sea urchins (Kirwan et al, 2018). Findings suggest that common neural coding strategies exist across these groups and demonstrate the utility of a tractable system to address questions of how complex visual stimuli are detected and how the information is used to drive downstream motor elements to produce adaptive behavioural responses.…”

Neural Basis for Looming Size and Velocity Encoding in the Drosophila Giant Fiber Escape Pathway

“…The tonic hyperpolarization inputs should also be identified. These have size tuning similar to that of inhibitory visual projection neuron inputs to a looming-sensitive neuron in the locust [37], but in Drosophila, the inhibitory inputs likely arise from interneurons receiving input from visual projection neurons, since we find few direct non-LC4/LPLC2 visual projection neuron synapses onto the GF. Interneurons may also provide additional excitatory input to the GF that provides visual information for non-looming stimuli not tested here.…”

Inhibitory Interactions and Columnar Inputs to an Object Motion Detector in Drosophila

“…In mice, cortical V1 center-surround receptive fields reveal stronger inhibitory currents than excitatory currents in both the surround and center, while inhibitory currents are spread more laterally than excitatory currents (Haider et al, 2013). In a visual collision detection circuit in the locust, feedforward inhibitory neurons actively encode dynamical variables such as object angular size (Wang et al, 2018). The inhibitory GABA-A receptor subunit Rdl is expressed by nearly all neurons of the fly visual system so far tested (Davis et al, 2018), highlighting the ubiquity and importance of inhibition for spatial vision.…”