Neural circuits for behavior transform sensory inputs into motor outputs in patterns with strategic value. Determining how neurons along a sensorimotor circuit contribute to this transformation is central to 19 understanding behavior. To do this, a quantitative framework to describe behavioral dynamics is needed. Here, we built a high-throughput optogenetic system for Drosophila larva to quantify the sensorimotor 21 transformations underlying navigational behavior. We express CsChrimson, a red-shifted variant of 22 Channelrhodopsin, in specific chemosensory neurons, and expose large numbers of freely moving 23 animals to random optogenetic activation patterns. We quantify their behavioral responses and use 24 reverse correlation analysis to uncover the linear and static nonlinear components of navigation dynamics 25 as functions of optogenetic activation patterns of specific sensory neurons. We find that linear-nonlinear 26 (LN) models accurately predict navigational decision-making for different optogenetic activation 27 waveforms. We use our method to establish the valence and dynamics of navigation driven by 28 optogenetic activation of different combinations of bitter sensing gustatory neurons. Our method captures 29 the dynamics of optogenetically-induced behavior in compact, quantitative transformations that can be 30 used to characterize circuits for sensorimotor processing and their contribution to navigational decision-31 making. Drosophila larva navigation involves the regulation of transitions between two basic motor states, runs 43 during which the animal moves forward using rhythmic peristaltic waves and turns during which the larva 44 sweeps its head back and forth until it selects the direction of a new run (4-6) ( Fig 1A) . Attractive and 45 repulsive responses can be estimated by the tendency of the larva to aggregate near or avoid an 46 environmental stimulus (7). Attractive and repulsive responses can also be observed in the movement 47 patterns of individual larvae (4, 8, 9). When the larva encounters improving conditions over time, it lowers 48 the likelihood of ending each run with a turn, thereby lengthening runs in favorable directions. When the 49 larva encounters improving conditions during each head sweep of a turn, it increases the likelihood of 50 starting a new run, thereby starting more runs in favorable directions. Thus, subjecting the larva to an 51 attractant tends to suppress transitions from runs to turns and stimulate transitions from turns to runs; 52 subjecting the larva to a repellant has the opposite effects. Much progress has been made in understanding the molecular and cellular organization of the 54 chemosensory system of the Drosophila larva, but how specific chemosensory neurons relay information 55 to guide navigational movements remains poorly understood. (7, 10-12). One challenge of studying 56 chemotaxis is that it is difficult to provide sensory input to behaving animals with the flexibility, receptor-57 specificity, and precision needed to build computational models of chemosensory-guided navigation. The 58 recent development of a red-shifted version of Channelrhodopsin, CsChrimson, which is activated at 59 wavelengths that are invisible to the larva's phototaxis system, now allows us to specifically manipulate 60 the activity of neurons in behaving animals with reliability and reproducibility (13). Here, we sought a mathematical characterization of the navigation dynamics evoked by optogenetic 62 activation of different sets of neurons. We focus on the navigation driven by chemosensory inputs. Although the organization of the chemosensory periphery is well defined, the quantitative mapping from 64 sensory activity to behavioral dynamics has not yet been determined. To do this, we engineered a high 65 throughput experimental setup capable of recording the run and turn movements of freely moving larvae 66 subjected to defined optogenetic activation of selected chemosensory neurons. By measuring large 105 the freely moving larvae. We used the simplest white process for reverse-correlation, a Bernoulli process 106 where we assigned -1 for lights OFF and +1 for lights ON, and calculated the mean stimulus history that 107 preceded each run to turn or turn to run transition (Fig 1E, F) . These event-triggered stimulus histories 108 represent how the animal uses optogenetic activation patterns of specific neurons to regulate each motor 109 state transition and are proportional to the linear filter components of f r→t and f t→r (see Materials and 110 Methods and Supplemental Methods). 111 112 113 558 Odors. Neuron 61(4):570-586. 559 16. Brand AH, Perrimon N (1993) Targeted gene expression as a means of altering cell fates and 560 generating dominant phenotypes. Development 118(2):401-415. 561 17. Keene AC, Sprecher SG (2012) Seeing the light: photobehavior in fruit fly larvae. Trends Neurosci 562 35(2):104-110. 563 18. Asahina K, Louis M, Piccinotti S, Vosshall LB (2009) A circuit supporting concentration-invariant 564 odor perception in Drosophila. J Biol 8(1):9.
For example, an optogenetic system has been introduced recently to examine how neurons contribute to transform sensory inputs into motor outputs REF .
Reverse-correlation analysis of navigation dynamics in Drosophila larva using optogenetics
bioRxiv
Biology