A dedicated visual pathway for prey detection in larval zebrafish
The visual systems of many species have an innate capacity to respond to features that denote either prey or predators ( Olberg et al . , 2000; Ewert et al . , 2001; Simmons et al . , 2010; Yilmaz and Meister , 2013 ) . However , the circuits underlying these responses are mostly unknown . Zebrafish larvae have an instinctive ability to hunt small moving prey objects , such as paramecia , as soon as they start to swim at five days post fertilization ( 5 dpf ) . Before initiating a prey capture swim , a larva must select the target from its surroundings , calculate its location , and make a decision as to whether the target is worth pursuing . It then initiates a multi-step motor routine involving bouts of turning and swimming toward the prey , culminating in a consummatory strike ( Budick and O'Malley , 2000; Borla et al . , 2002; Gahtan et al . , 2005; McElligott and O'Malley , 2005 ) . Precise maneuvers are required , and so prey capture tail movements are quite different from those observed during routine swims or escapes . To orient towards a paramecium on the left or right , larvae perform j-turns—unilateral bends where the tail is held in a J shape . If the prey is directly ahead , they slowly swim toward it , with back and forth undulations of the tail ( Patterson et al . , 2013 ) . These movements appear to be triggered by small moving objects , but it is unclear how or where in the brain these objects are identified as prey . Under the appropriate conditions , larvae can be induced to perform hunting swims when they are partially restrained in agarose . A head-fixed preparation facilitates automated tracking of tail movements , stimulus control , and functional imaging . Two recent studies have shown that a moving dot can evoke eye convergence and approach swims in head-fixed larvae ( Bianco et al . , 2011; Trivedi and Bollmann , 2013 ) . However , these studies did not investigate the specific tail kinematics evoked by prey stimuli , or seek to categorize the tail movements as distinct from other types of swims . Here , we identified the distinctive features of prey capture swims in head fixed larvae , and developed a machine learning algorithm to quantify and distinguish these swims from other types of behavior . This allowed us to quantify the tail movements produced in response to a range of artificial prey stimuli , and determine the ideal stimulus to evoke the prey capture response . To begin to identify the neural circuits for prey identification , we focused on the retinal ganglion cells ( RGCs ) , the output neurons of the retina . There is a precedent for RGCs acting as prey detectors—a classic study in the frog found RGCs that responded to small ( 1–3° ) objects moving through the visual field ( Lettvin et al . , 1959 ) . In the mammalian visual system , certain RGC types act as feature detectors for one aspect of the visual scene , and the axons of these RGCs project to visual nuclei that mediate the response to that feature . For example , intrinsically photosensitive RGCs project to nuclei that control circadian rhythms and the pupillary light reflex ( Chen et al . , 2011 ) , and RGCs that respond to whole field motion innervate nuclei that drive compensatory eye movements ( Simpson , 1984; Dhande et al . , 2013 ) . In zebrafish , RGC axons terminate in ten retinorecipient areas called arborization fields ( AFs ) ( Burrill and Easter , 1994 ) , most of which have not been functionally characterized . We hypothesized that there could be a class of RGCs that respond specifically to prey-like visual stimuli and project to one or more of these AFs . By imaging RGC axons in the AFs , we identified one visual area , AF7 , which responds specifically to the optimal artificial prey stimulus , as well as to actual paramecia . Targeted laser ablation showed that this area is important for the behavior . Finally , we found that neurons with arbors in the AF7 neuropil innervate multiple areas known to be involved in prey capture behavior and locomotion in general—the optic tectum , nMLF , and hindbrain . These results identify AF7 as vital part of the prey capture pathway , and link a dedicated retinal output to an ecologically relevant behavior .
To investigate how prey objects are identified , we developed a head-fixed prey capture assay and automated behavioral classification system . Larvae were embedded in agarose at 6 dpf , and their tails were freed so that swimming movements could be recorded with a high-speed camera . Visual stimulation was provided by a small OLED screen in front of the larva . In preliminary experiments , we found that a small ( ∼3° of the visual field ) white dot moving horizontally across a black screen was effective in evoking behavior . Larvae performed two types of behavior in response to this virtual prey: forward swims , which consisted of low amplitude oscillations of the tail , and j-turns ( Figure 1A ) ( McElligott and O'Malley , 2005 ) . These swims were accompanied by eye convergence , which is another kinematic feature of prey capture ( Bianco et al . , 2011; Patterson et al . , 2013 ) . Larvae also performed spontaneous swims in the absence of visual stimuli . To analyze tail movements , we digitized the tail , using a custom algorithm to assign ∼40 points along its length in each frame ( Figure 1B ) . Plotting the position of the tip of the tail during prey capture and spontaneous swims over time revealed several kinematic differences between the behaviors ( Figure 1C ) . Prey capture forward swims and spontaneous swims both consist of back and forth movements of the tail , but the amplitude of the prey capture swim is much lower . In contrast , during a j-turn the tail is deflected to one side , often for hundreds of milliseconds . 10 . 7554/eLife . 04878 . 003Figure 1 . Head-fixed larvae respond to virtual prey with distinctive swimming movements . ( A ) Overlay of 50 frames ( 167 ms ) of high-speed video showing examples of behavior in head fixed larvae . Larvae performed forward swims in response to a 3° dot . j-turns were observed when the same dot was to the right or left . Spontaneous swims were often observed in the absence of any stimulus . ( B ) Example video frame showing points assigned by the digitization algorithm . ( C ) The position of the tip of the tail over time for the videos in ( A ) . ( D ) The distribution of tail beat amplitudes for each bout in expert-classified prey capture and spontaneous swim videos . ( E ) Duration of the longest bend greater than 20° during each bout . ( F ) Overview of support vector machine ( SVM ) based bout classification procedure , displaying only two parameters ( maximum tail bend and mean tail tip deflection ) for clarity . Bouts are extracted using a threshold on the normalized and smoothed first derivative of tail bend angles . Values for each parameter are calculated for all bouts and used to train an SVM . The SVM is then used to classify unlabeled bouts . See Figure 1—figure supplement 1 for plots of each of the five parameters , and accuracy of the SVM vs number of parameters . DOI: http://dx . doi . org/10 . 7554/eLife . 04878 . 00310 . 7554/eLife . 04878 . 004Figure 1—figure supplement 1 . Prey capture and spontaneous behavior can be classified using five parameters . ( A ) Each parameter used in the virtual prey SVM is plotted vs the other four . ( B ) Accuracy of the virtual prey SVM as more parameters are used to classify the bouts as prey capture or spontaneous swims . The dotted line represents the number of parameters used in the final version . The parameters plotted were: 1 . Maximum tail curvature , 2 . Number of peaks in tail angle , 3 . Mean tip angle , 4 . Maximum tail angle , 5 . Mean tip position . ( C ) . Accuracy of the paramecium SVM plotted vs number of parameters . The parameters shown here were 1 . Mean tip angle , 2 . Maximum tail curvature , 3 . Number of peaks in tail angle , 4 . Mean tip position , 5 . Mean number of frames between peaks , 6 . Maximum tail angle . DOI: http://dx . doi . org/10 . 7554/eLife . 04878 . 004 The apparent differences between spontaneous swims and prey capture were confirmed when we analyzed several hundred expert-classified prey capture and spontaneous swim videos . The average amplitude of the tail movements for prey capture bouts was 17% of tail length , while for spontaneous swims it was 48% of tail length ( Figure 1D ) . We also observed the sustained tail deflections characteristic of j-turns . Plotting the duration of the longest tail deflection for each bout reveals a single peak at 27 ms for the spontaneous swims ( Figure 1E ) . For prey capture swims , we also see a peak at this duration , representing the back and forth motion during forward swims , but in addition we see a long shoulder of turns of much longer duration , which consists of j-turns ( Figure 1E ) . These stark differences suggest that prey detection triggers a specialized motor program . In order to characterize the stimuli that evoke prey capture , we needed an objective method to quantify the behavior and distinguish it from spontaneous swims . We used a support vector machine ( SVM ) , a supervised learning algorithm that allows for multi-dimensional classification , to train a classifier , which could then categorize new data . For this classification , prey capture forward swims and j-turns were not distinguished; both were included within the category of prey capture . We trained the SVM on a set of 369 expert-labeled and digitized videos ( Figure 1F ) . Bouts of swimming within the videos were identified using a thresholding operation on the smoothed derivative of the tail bend angle . For the dimensions of the SVM , we choose relatively simple parameters that allowed us to collapse a feature of the bout ( e . g . tail curvature ) into a single number . During training , each bout was assigned a position in 5-dimensional space corresponding to its values for the 5 parameters ( Figure 1—figure supplement 1 ) , and decision boundaries were drawn in multidimensional space to separate the two types of behavior . The trained SVM could then be used to classify new videos . Using this approach , we were able to achieve a cross-validated accuracy of 96% ( Figure 1—figure supplement 1 ) . We next used the virtual prey assay and the SVM classifier to determine the ideal stimulus for evoking prey capture behavior . We presented head-fixed larvae with white dots ranging in size from 0 . 5–30° in diameter , while recording tail movements . To quantify the behavior , we used the trained SVM to identify swim bouts as prey capture or spontaneous swims . We calculated a prey capture score based on the percentage of time during each trial that the larva performed prey capture bouts . We found that a 3° dot was the optimal size to trigger prey capture ( Video 1 ) , and that the behavior was strikingly reduced when we increased the size to 10° ( Figure 2A ) , which is consistent with data from free swimming and head-fixed larvae ( Bianco et al . , 2011; Trivedi and Bollmann , 2013 ) . We also tested the response to dots traveling at a wide range of speeds , and found that 90°/s is the ideal speed for prey stimuli , and that larvae respond minimally to stimuli moving slower than 12°/s or faster than 360°/s ( Figure 2B ) . 10 . 7554/eLife . 04878 . 005Video 1 . Prey capture in response to virtual prey . A head-fixed larva responds to a 3° dot moving at 90°/s . DOI: http://dx . doi . org/10 . 7554/eLife . 04878 . 00510 . 7554/eLife . 04878 . 006Figure 2 . Prey capture behavior is triggered by dots of a particular size and speed . ( A ) Larvae were shown white dots of various sizes moving at 90°/s . Trials were scored by using the SVM to classify each bout and calculating the percentage of the trial that consisted of prey capture bouts . Scores are expressed as a percentage of the maximum for that larva . n = 16 larvae . ( B ) Prey capture behavior in response to a 3° dot moving at 6 to 480°/s . n = 9 larvae . Error bars = ±SEM . DOI: http://dx . doi . org/10 . 7554/eLife . 04878 . 006 Given the highly selective nature of this innate behavioral response , we hypothesized that the preference for prey stimuli of a certain size and speed might arise in the retina . In that case , there should be a population of RGCs that responds specifically to prey . A recent functional imaging study reported responses to prey in the largest AF , the optic tectum ( Muto et al . , 2013 ) . However , the tectum receives input from many different types of RGCs , and responds to a wide variety of stimuli other than prey ( Gabriel et al . , 2012; Nikolaou et al . , 2012 ) , which makes it difficult to determine whether the tectal RGCs are responding selectively to prey . We thus focused our imaging experiments on AFs 1–9 . We performed two-photon imaging of RGC axons in larvae with the calcium indicator GCaMP6 driven by an RGC-specific promoter . In these larvae , the ten AFs can be identified as distinct regions of fluorescent neuropil ( Figure 3A , C ) . We asked whether the axons in any of these regions would respond to the ideal prey stimulus identified in our behavioral assay ( 3° , 90°/s ) . In our preparation , we could image all but the most ventral arborization field , AF1 . We stimulated larvae with the prey stimulus while imaging RGC axons in AFs 2–9 , and found one area , AF7 , whose RGC axons responded robustly to the stimulus ( Figure 3B ) , while seven other AFs did not respond to virtual prey . Some RGC axons in the optic tectum responded to the 3° stimulus , but the tectum response was much larger for stimuli of >10° ( Figure 3—figure supplement 1 ) . We next varied the size of the stimulus while imaging in the AF7 plane , and found that AF7 RGC axons responded maximally to dots 2–3° in diameter , and much less to stimuli larger than 6° in diameter ( Figure 3C–I ) . Interestingly , AF9 , another AF in the same plane , did not respond to small dots but began to respond as the stimulus size was increased above 6° in diameter ( Figure 3C–F ) . We also measured the response of AF7 to stimuli of varying speeds , and found that the optimal speed was 90°/s , and stimuli slower than 12°/s or faster than 360°/s evoked a much smaller response in these axons ( Figure 3G ) . These tuning curves are strikingly similar to the behavioral tuning curves for size and speed ( Figure 2A , B , grey trace in Figure 3F , G ) . At speeds above 360°/s the strong correlation between behavioral tuning and AF7 response degrades , since the behavioral response to extremely fast speeds is quite low . This could be caused by a threshold or non-linearity in the neural circuit , such as the weak activation of the AF7 RGC axons being insufficient to drive the downstream neurons . We next tested the direction selectivity of the axons in AF7 , and found that the area as a whole responded to prey stimuli moving in all four cardinal directions ( Figure 3I ) , although the response was more robust to stimuli moving horizontally ( p < 0 . 001 , t-test ) . 10 . 7554/eLife . 04878 . 007Figure 3 . Prey stimuli activate RGC axons that project to AF7 . ( A ) Schematic frontal view of the brain showing AFs . Red line indicates imaging plane for C–G . ( B ) Response to the ideal prey stimulus ( 3° dot , 90°/s ) in other AFs in an Isl2b:Gal4 , UAS:GCaMP6s transgenic larva . n = 9 larvae . ( C ) Baseline fluorescence of RGCs in an Isl2b:Gal4 , UAS:GCaMP3 larva . ( D ) Peak frame in the response to a 2° dot . ( E ) Peak frame in the response to a 10° dot . ( F ) Response of all larvae ( n = 9 ) to stimuli 0 . 5–14° in diameter . ROIs were defined anatomically as in ( C ) . The ΔF/F is plotted as a percentage of the maximum response for that larva . Grey lines represent the behavioral tuning curve from Figure 2A . ( G ) Response of AF7 RGC axons to a 3° dot travelling at a speed of 6–800°/s . Grey lines represent the behavioral tuning curve from Figure 2B . ( H ) Responses of AF7 to a range of different size stimuli . Blue box represents the one second interval when the stimulus was onscreen . ( I ) AF7 response to 3° dot moving in various directions . n = 10 larvae . Error bars = ±SEM . See Figure 3—figure supplement 1 for tuning properties of tectal RGC axons . DOI: http://dx . doi . org/10 . 7554/eLife . 04878 . 00710 . 7554/eLife . 04878 . 008Figure 3—figure supplement 1 . Response of RGC axons in the tectum to stimuli of varying sizes . ( A ) Baseline fluorescence of RGC axons in the anterior half of the tectum , 50 µM below the dorsal surface of the tectum . Dotted line shows ROI , which includes the SO , SFGS , and SGC . ( B ) Traces showing responses to a range of different size stimuli in RGC axons in the anterior tectum of one larva . Ath5:Gal4 , UAS:GCaMP6s larvae were used for these experiments . ( C ) Average response of five larvae to stimuli 1–20° in diameter . DOI: http://dx . doi . org/10 . 7554/eLife . 04878 . 008 If AF7-projecting RGCs are mediating behavioral responses to virtual prey , we reasoned that they should also respond to natural prey , such as paramecia . We found that head-fixed larvae will perform j-turns and forward swims when paramecia are added to the surrounding medium ( Figure 4A ) . In the absence of paramecia , head-fixed larvae perform spontaneous swims but rarely prey capture ( Figure 4B ) , while after paramecia were added to the dish we observed frequent prey capture bouts ( Figure 4C ) . Like the behavior evoked by virtual prey , these swims could be reliably classified by an SVM . We next asked whether the paramecia were activating AF7 RGC axons . We recorded baseline activity for 30 s and then added a drop of a dense paramecia culture to the petri dish . We found that AF7 RGC axons were strongly activated during the paramecium trials , whereas AF9 axons were not ( Figure 4D , Video 2 ) . When we quantified the ΔF/F in AF7 and AF9 for nine fish , we found that the peak response was significantly higher in AF7 during trials with paramecia ( Figure 4E ) . The AF9 peak response in the same larvae was not significantly different in the presence of paramecia . The finding that AF7 axons respond to actual prey supports a role for this area in generating prey capture behavior . 10 . 7554/eLife . 04878 . 009Figure 4 . Paramecia evoke prey capture behavior and a response in AF7 . ( A ) Overlay of 50 frames ( 167 ms ) of high-speed video showing a head fixed larva responding to paramecia . ( B ) The position of the tip of the tail of the larva in A without paramecia , showing spontaneous swims . ( C ) The same larva after paramecia were added to the dish . Bouts that were identified as prey capture by the SVM are colored blue . ( D ) Ath5:Gal4 , UAS:GCaMP6s larvae were imaged before and after addition of paramecia . Pseudocolor represents the ratio of the ΔF/F with paramecia to without . ( E ) AF7 and AF9 responses to paramecia in nine larvae . Maximum ΔF/F is plotted for each trial . The AF7 response was significantly higher in trials with paramecia ( p = 9 . 6 × 10−5 , Wilcoxon rank sum test ) , whereas the AF9 response was not significantly different ( p = 0 . 083 ) . n = 10 larvae . Error bars = ±SEM . See also Video 2 . DOI: http://dx . doi . org/10 . 7554/eLife . 04878 . 00910 . 7554/eLife . 04878 . 010Video 2 . AF7 RGC axons respond to paramecia . Ath5:Gal4 , UAS:GCaMP6s axons are activated in the presence of paramecia . Pseudocolor represents the ratio of the ΔF/F with paramecia to without . Scale is the same as in Figure 4D . Imaging frame rate was 3 . 3 Hz . DOI: http://dx . doi . org/10 . 7554/eLife . 04878 . 010 The activation of AF7 RGC axons specifically by small moving dots and paramecia suggests that there could be a specialized class of RGCs that project to this area . A subpopulation of RGCs has been previously shown to innervate AF7 and the stratum opticum ( SO ) , the most superficial layer of the tectum ( Xiao and Baier , 2007 ) . However , little is known about their dendritic morphology , and it is unclear if these are the only inputs to AF7 . We set out to characterize the anatomy of the AF7-targeting RGCs using BGUG ( Xiao and Baier , 2007 ) , a highly variegated UAS:mGFP reporter that allows the visualization of individual neurons within a Gal4 line . By expressing mCherry and BGUG in RGCs , we labeled most RGCs red and <1% of them green . We could thus identify single RGCs with arbors in AF7 ( Figure 5A ) and trace the axon back to the retina to observe the cell’s dendritic morphology . We identified 19 larvae with an arbor in AF7 . All 19 RGCs exhibited the same axonal morphology , projecting to AF7 and the SO , but not to any other AFs ( Figure 5A , B ) . Thus , a prey-specific information channel is routed to two distinct visual areas by each axon . Examining the dendritic morphology of these AF7-targeting RGCs revealed that AF7 receives inputs from two distinct RGC types . Thirteen cells ( out of 19 ) had bistratified arbors with branches in ON and OFF layers of the inner plexiform layer ( IPL ) ( Figure 5C ) , and the other six formed diffuse arbors spanning the entire IPL ( Figure 5D ) . These dendritic morphologies correspond to two of the 14 RGC classes previously described in the zebrafish retina ( Robles et al . , 2014 ) , known as B2 and D1 These two types also project to other AFs , in much smaller numbers . However , the B2 and D1 neurons that project to AF7 only arborize in AF7 and the tectum . The RGC axon responses to prey stimuli in AF7 could be due to either B2 or D1 neurons , or both , as our Gal4 line labels both types . 10 . 7554/eLife . 04878 . 011Figure 5 . Two types of RGCs project to AF7 and also arborize in the tectum . ( A ) A single GFP-expressing RGC axon ( cyan ) arborizes in AF7 in an Ath5:Gal4 , UAS:mCherry , BGUG larva . ( B ) The same axon also innervates the SO layer of the tectum . ( C ) Section of the retina showing dendritic morphology of a bistratified AF7-projecting RGC . Bracket indicates borders of the IPL ( 16 µM ) . ( D ) Dendritic morphology of a diffuse bistratified AF7-projecting RGC . DOI: http://dx . doi . org/10 . 7554/eLife . 04878 . 011 To test whether the AF7-projecting RGCs play a role in prey capture behavior , we performed two-photon laser ablations targeting the AF7 neuropil . Ablations were performed bilaterally in larvae expressing the fluorescent protein Dendra in RGCs ( Figure 6A ) . Within 3 hr , degeneration of axons that project to AF7 could be observed ( Figure 6B ) . Ablation of AF7 did not detectably change the projection of nearby RGC axons on their course to the tectum , nor did it decrease the intensity of RGC axon labeling in the SO ( Figure 6—figure supplement 1A–D ) . We also observed that tectal neuron responses to small stimuli remained intact after AF7 ablation ( Figure 6—figure supplement 1E–H ) . These findings suggest that the ablation was restricted to the AF7 neuropil . 10 . 7554/eLife . 04878 . 012Figure 6 . Ablation of AF7 markedly reduces prey capture behavior . ( A ) Intact Ath5:Gal4 , UAS:Dendra larva at 8dpf . ( B ) Ath5:Gal4 , UAS:Dendra larva after ablation of AF7 neuropil . ( C ) Pre-ablation response to paramecia in one example larva . Behavior was recorded for 5 min and the videos were edited to display all swim bouts . Each bout was classified by the same SVM as in Figure 4C . ( D ) The same larva after 2P laser ablation of AF7 . ( E ) Total duration of prey capture bouts during the five 1 min trials before and after AF7 ablations . n = 14 larvae . p = 2 . 59 × 10−4 , Wilcoxon rank sum test . ( F ) Prey capture time before and after AF9 ablations . n = 15 larvae . ( G ) Duration of prey capture bouts after ablation as a percent of initial prey capture time . p = 2 . 68 × 10−4 , Wilcoxon rank sum test . ( H ) Optomotor response to moving gratings after AF7 and AF9 ablation . AF7ablation n = 7 larvae , AF9 ablation n = 9 larvae . p = 5 . 2 × 10−3 , Wilcoxon rank sum test . Error bars = ±SEM . See Figure 6—figure supplement 1 for AF7 and SO axon anatomy and periventricular neuron activity before and after ablation . DOI: http://dx . doi . org/10 . 7554/eLife . 04878 . 01210 . 7554/eLife . 04878 . 013Figure 6—figure supplement 1 . RGC axons in AF7 and the tectum after AF7 ablation . ( A ) Ath5:Gal , UAS:Dendra transgenes label RGC axons in AF7 and 9 . ( B ) The same larva 3 hr after laser ablation of AF7 . ( C ) RGC axons in the tectum in the same larva as above , before ablation . ( D ) SO axons 3 hr after AF7 ablation . ( E ) Responses of tectal neurons in the same larva as in A-D . Periventricular neurons of the tectum labeled by elavl3:GCaMP5G responded to a prey stimulus ( 3° dot moving at 90°/s ) . ( F ) The same field of view after AF7 ablation . ( G ) Traces showing the responses of the individual cell bodies . ( H ) Responses in the same six cells after AF7 ablation . DOI: http://dx . doi . org/10 . 7554/eLife . 04878 . 013 We recorded responses to swimming paramecia for 5 min before the ablation and again several hours afterwards , and used our SVM to categorize the bouts ( Figure 6C , D ) . As a control , we ablated a similarly sized region of AF9 . We found that larvae with AF9 ablations spent as much time performing prey capture bouts as before ablation , whereas in AF7-ablated larvae prey capture time was significantly reduced ( Figure 6E , F ) . On average , the AF7-ablated larvae spent 40% as much time performing prey capture bouts as they did before the ablation ( Figure 6G ) . Although the effect was robust , ablated larvae did perform some prey capture bouts , which could reflect a failure to ablate all AF7 RGC axons , or a contribution from other retinal inputs to the behavior . To confirm that the AF7 ablation did not generally impair visual function , we tested the optomotor response ( OMR ) before and after ablations in AF7 or AF9 . We found that , consistent with previous work ( Roeser and Baier , 2003 ) the OMR was not impaired in AF7-ablated larvae , but was reduced in AF9-ablated larvae ( Figure 6H ) . These data suggest that AF7-projecting RGC axons are specifically required for prey capture behavior . We used single cell electroporation to identify AF7's putative postsynaptic partners , by targeting cell bodies near AF7 . We identified two classes of neurons with a proximal arbor located in the AF7 neuropil and distal arbors in other brain areas . The first class of neurons with processes in AF7 , as well as the neighboring non-retinorecipient neuropil , formed a distinct projection to the tectum ( Figure 7A , B ) . In all cases ( n = 12 ) the tectal branch was located in a region between the SO and SFGS layers that does not receive RGC axons . We also observed that all of these projections terminated in the anterior fifth of the tectum ( Figure 7B , C , and Figure 7—figure supplement 1 ) . 10 . 7554/eLife . 04878 . 014Figure 7 . Morphologies of pretectal AF7 neurons . ( A ) Single cells were electroporated with tetramethylrhodamine ( TMR ) dextran ( cyan ) in an Ath5:Gal4 , UAS:GCaMP6s larva ( red ) . These neurons innervate AF7 , as well as the adjacent non-retinorecipient neuropil in the same plane . ( B ) The cell imaged in ( A ) projects to the rostral tectum . ( C ) Tracing of the same cell , showing pretectal and tectal arbors . ( D–E ) Confocal images of a single electroporated cell in an Ath5:Gal4 , Gal4s1171t UAS:GCaMP6s larva . ( F ) Tracing of the same cell as in ( D ) and ( E ) overlaid on a confocal image showing the Gal4s1171t labeling to identify the nMLF . See Figure 7—figure supplement 1 for more examples . DOI: http://dx . doi . org/10 . 7554/eLife . 04878 . 01410 . 7554/eLife . 04878 . 015Figure 7—figure supplement 1 . Examples of AF7 neurons . ( A–C ) . Tracings of example neurons with projections in AF7 and the tectum . ( D ) Overlay of tracings from four neurons with arbors in AF7 and the nMLF . In two cases , the projections toward the hindbrain became too faint to follow . DOI: http://dx . doi . org/10 . 7554/eLife . 04878 . 015 A second type of neuron with dendrites in AF7 formed a projection to the nucleus of the medial longitudinal fasciculus ( nMLF ) and hindbrain ( Figure 7D–F , and Figure 7—figure supplement 1 ) . The line Gal4s1171t , which labels neurons in the midbrain tegumentum , including the nMLF ( Thiele et al . , 2014 ) , was used as a landmark to identify the nMLF . These neurons ( n = 4 ) projected to the ipsi- and contralateral nMLF before terminating in the contralateral hindbrain . Thus , these AF7-innervating neurons could be directly involved in generating prey capture swims and j-turns .
While it was previously established that larvae respond to small moving stimuli ( Bianco et al . , 2011; Trivedi and Bollmann , 2013 ) , little was known about where in the visual system this selectivity is generated . Our finding that a subset of RGC axons is tuned to prey stimuli suggests that the selectivity is generated early in the visual pathway , by circuits in the retina . There are examples of retinal circuits that generate a similar degree of stimulus selectivity . Experiments in the rabbit have identified local edge detectors—RGCs that respond only to slowly moving targets less than 1° in diameter ( Levick , 1967 ) , and a similar type has been found in the mouse retina ( Zhang et al . , 2012 ) . The unusually strong surround suppression in these cells is thought to be a consequence of amacrine cells acting presynaptically to inhibit bipolar cells as well as RGCs ( van Wyk et al . , 2006; Russell and Werblin , 2010 ) . A similar mechanism could account for the size tuning of AF7-projecting RGCs . In many cases , RGCs detect features that have a clear survival value for the animal . For example , fast-OFF RGCs in the salamander anticipate the location of a moving object ( Berry et al . , 1999 ) , a feature that could be used to predict the location of moving prey ( Leonardo and Meister , 2013 ) . A classic study in the frog found RGCs that responded to small ( 1–3° ) objects moving though the visual field; these cells were hypothesized to act as ‘bug detectors’ ( Lettvin et al . , 1959 ) . These examples show that the retina can perform computations to detect ecologically important objects , allowing RGCs to transmit pre-processed information to the brain to coordinate behavior . Our data support a model in which retinal circuitry confers prey selectivity on a subset of RGCs , which transmit this information to two visual areas in the midbrain: AF7 and the SO layer of the optic tectum . Based on the axonal branching pattern of its RGCs , we can identify AF7 as the parvocellular superficial pretectal nucleus ( PSp ) . The PSp is a retinorecipient brain area innervated by RGC axons that also form an arbor in the most superficial retinorecipient layer of the tectum in goldfish ( Springer and Mednick , 1985 ) . PSp was one of the two possible adult brain areas suggested for AF7 based on its location in classical dye tracing work ( Burrill and Easter , 1994 ) . This nucleus was originally identified as the homologue of the lateral geniculate nucleus ( LGN ) in the mammalian thalamus ( Schnitzlein , 1962 ) , although this is controversial ( Northcutt and Butler , 1976 ) . PSp has been anatomically described in several fish species , and is known to receive topographically organized retinal input ( Springer and Mednick , 1985 ) , although little functional information is available . Our group has recently demonstrated that AF7/PSp contains a retinotopic map . Most of the RGCs that project to this area reside in the temporal retina , creating a high-resolution representation of the anterior visual field ( Robles et al . , 2014 ) . This is the part of the visual environment in which the prey is usually located during prey capture behavior ( Patterson et al . , 2013 ) . Why might it be advantageous to route prey-responsive RGCs to both AF7 and the tectum ? One possibility is that tectal neurons are responsible for directing the orienting movements towards a particular point in visual space , while AF7 neurons modulate or gate the tectal output , based on behavioral state ( e . g . hunger , arousal ) . The tectum and its mammalian homologue , the superior colliculus , have been shown to be involved in the transformation of sensory information encoded in terms of visual space into the appropriate motor command ( Bergeron et al . , 2003; du Lac and Knudsen , 1990 ) . The tectum has also been implicated in zebrafish prey capture in several recent studies . Ablation experiments have shown that the tectal neuropil and , more specifically , inhibitory tectal neurons in the most superficial layer play a role in prey capture ( Gahtan et al . , 2005; Del Bene et al . , 2010 ) , and functional imaging experiments have found that tectal neurons are activated by moving paramecia ( Muto et al . , 2013 ) . Thus , the tectal circuits could translate a prey object’s precise location in visual space into a swimming movement that results in pursuit of the prey . We have identified a class of pretectal neurons that have dendrites in the AF7 neuropil and also project to the most rostral fifth of the tectum ( Figure 7A–C , Figure 7—figure supplement 1 ) which corresponds to the anterior visual field . If the motor map is roughly faithful to the visual map , tectal output neurons in this region could trigger prey capture forward swims . Within the pretectum , the AF7/tectum neurons also have processes in the non-retinorecipient neuropil next to AF7 , which most likely corresponds to the magnocellular superficial pretectal nucleus ( PSm ) . We hypothesize that these neurons could be integrating the retinal response to prey with information on behavioral state , and then providing excitatory input to the tectum when appropriate . These AF7 neurons project to a layer of the tectum just ventral to SO , the tectal layer that receives prey-responsive RGC axons ( Figure 5B , Figure 8 ) . Tectal neurons with dendrites in these superficial layers could thus receive positional information from the raw retinal input in the SO , and excitatory input from the AF7 neurons that would release prey capture behavior only under certain conditions . However , at this point we do not know the response properties or type of neurotransmitter released by these neurons , so we can only speculate as to the input they provide to the tectum . 10 . 7554/eLife . 04878 . 016Figure 8 . Model for prey capture circuitry . A prey stimulus on the left activates RGCs in the left eye ( blue ) which project to AF7 and the SO layer of the right tectum . Pretectal neurons ( red ) arborize in AF7 and send projections to the tectum or the nMLF and hindbrain . Activation of this circuitry produces a j-turn to the left , turning the larva in the direction of the prey . Previous studies have demonstrated connections between the tectum and nMLF ( Gahtan et al . , 2005 ) and between the tectum and hindbrain ( Robles et al . , 2011 ) ( gray arrows ) . DOI: http://dx . doi . org/10 . 7554/eLife . 04878 . 016 Modulation of the tectum by other visual areas is a common motif in vertebrate systems , and in many cases suppression or facilitation of tectal activity determines whether a behavioral response will occur . For example , a GABAergic projection from the basal ganglia to the superior colliculus is thought to act as a gate for the saccade-generating neurons of the superior colliculus , providing tonic inhibition whose cessation releases saccade movements ( Shires et al . , 2010 ) . Another example of tectal modulation is the local cholinergic and global GABAergic feedback to the tectum provided by the isthmic nuclei in birds ( Knudsen , 2011 ) . The focal enhancement by cholinergic input has been shown to be necessary for the transmission of visual information to the area downstream of the tectum ( Marín et al . , 2007 ) . Finally , studies of prey capture in toads have identified a pretectal area that provides inhibitory input to the tectum , resulting in the preference for worm-like prey stimuli ( Ewert et al . , 2001 ) . The sign of this modulation is opposite to that described in our model , but this might be explained by the different types of stimuli being selected by the two systems ( i . e . , elongated objects moving in the direction of their long axis , vs small moving circles ) . In addition to the class of AF7-innervating neurons that project to the tectum , we found another class which projects bilaterally to the nMLF and contralaterally to the caudal hindbrain ( Figure 7D–F and Figure 7—figure supplement 1 ) . The nMLF is a cluster of reticulospinal neurons that are involved in controlling swim orientation ( Thiele et al . , 2014 ) , as well as speed ( Severi et al . , 2014 ) . Two of the identified nMLF cells , MeLr and MeLc , have also been shown to be necessary for successful prey capture ( Gahtan et al . , 2005 ) . Projections from AF7 to the nMLF could therefore be involved in generating prey capture tail movements . The hindbrain also plays an important role in locomotion . Neurons in the hindbrain project to the spinal cord , and activation of the caudal hindbrain has been shown to drive swimming behavior ( Arrenberg et al . , 2009; Kimura et al . , 2013 ) . Thus far , the tectum has been considered the main output pathway to premotor centers in zebrafish . However , in other systems , multiple areas project to the premotor nuclei and may drive behavior independently or direct different aspects of a behavior; for example , in primates , both the frontal eye fields and the superior colliculus provide input to the brainstem regions controlling eye movements ( Scudder et al . , 2002 ) . It remains to be seen what role the neurons projecting from AF7 to the nMLF and hindbrain might play in the various components of prey capture behavior . Our results identify a specific pretectal , retinorecipient region as a key component of the prey detection circuit . They also point toward a series of parallel and interconnected pathways between multiple areas involved in this elementary form of object recognition . Finally , they identify potential anatomical links of the visual network to midbrain and hindbrain areas that serve to coordinate the locomotor maneuvers involved in capturing prey . Further studies should shed light on what role each area plays in the behavior and how they interact .
Embryos were raised in Danieau's solution ( 17 mM NaCl , 2 mM KCl , 0 . 12 mM MgSO4 , 1 . 8 mM Ca ( NO3 ) 2 , 1 . 5 mM HEPES ) at 27 . 5°C on a 14/10 light/dark cycle . Wild type TL larvae were used for behavioral experiments , and TLN larvae ( nacre mutants ) were used for imaging . Since gonadal differentiation has not occurred at this stage , males and females were used indiscriminately . All animal procedures conformed to the institutional guidelines of the Max Planck Society and the local government ( Regierung von Oberbayern ) . The following transgenic lines were used: Tg ( Atoh7:Gal4-VP16 ) s1992t ( written as Ath5:Gal4 ) , Tg ( UAS:Dendra-Kras ) s1998t , Tg ( UAS:mCherry ) , Tg ( pou4f3:Gal4 , UAS:mGFP ) ( a . k . a . BGUG ) , Gal4s1171t , Tg ( Isl2b . 2:Gal4-VP16 ) and Tg ( elavl3:GCaMP5G ) . To construct the UAS:GCaMP6s line , a fragment encoding GCaMP6s ( Addgene ) was cloned into a 14xUAS , pTol2 plasmid . At 5 dpf , each dish of larvae was fed about 2 ml of a dense culture of paramecia . At 6 dpf , the larvae were embedded in 2 . 5% low melting point agarose ( Invitrogen , Carlsbad , California ) in Danieau's solution and positioned 3 mm from the edge of a 35 mm Petri dish lid . After the agarose had set , the dish was filled with Danieau's solution and some of the agarose was cut away , leaving the tail caudal to the swim bladder free to move . After embedding , larvae were kept at 27 . 5°C for 48 hr . Behavioral experiments were conducted at 8 dpf in a 27 . 5°C chamber . For virtual prey experiments , larvae with a prey capture score ( percent of the trial spent in prey capture bouts ) of less than 30% for the 3° stimulus were excluded from the analysis . Visual stimuli were designed using a custom program in the Python-based Vision Egg software ( Straw , 2008 ) . The stimulus was a white dot on a black background , moving horizontally from left to right . Stimuli were presented on a 12 × 9 mm OLED Microdisplay ( Emagin , Bellevue , WA ) covered with three magenta Wratten filters ( Edmund Optics , Barrington , NJ ) and positioned 1 cm from the larva . Larvae were illuminated from below with an IR light source . We used a high-speed camera ( Photonfocus , Switzerland ) to record tail movements at 300 frames/second ( f/s ) , with a resolution of 300 × 300 pixels . For behavioral experiments with paramecia , we embedded 6 dpf larvae in the center of a 35 mm Petri dish and cut away some of the agarose in front of the eyes by making an incision perpendicular to the body axis 1 mm from the head . The tail below the swim bladder was also freed . As we did for the virtual prey experiments , we waited 48 hr after embedding to test behavior . We added a few drops of paramecia ( Paramecium multimicronucleatum , Carolina Biological Supply Company , Burlington , NC ) to the dish and recorded at 300 f/s for 5 min . The procedure was repeated a few hours after laser ablations . For post-ablation behavioral experiments , the experimenter was blinded as to whether the animal was in the AF7 or AF9 ablation group . To test the OMR , each embedded larva was positioned in an arena surrounded by three LED screens ( 5 . 5 × 7 . 5 cm ) . Gratings moving leftward at 20°/s were displayed on all three screens to produce turns . Stimulus presentation was controlled with a custom LabVIEW script , and tail movements were recorded at 250 f/s . The same procedure was repeated a few hours after laser ablations . To measure the number of swims , we digitized the tail as described below and used a thresholding operation on the smoothed derivative of the tail bend angle to identify swim bouts . We developed a custom Python program that quantifies the tail position in each frame as a series of points , with approximately 40 points covering the tail . The program uses the OpenCV library to load videos . At the start of each video , the user is queried to select the base of the tail . From that point , the program iterates through the tail in each frame , taking a slice through the image where the next tail midpoint is expected to be . This slice is smoothed , convolved with a tail-like kernel , and the maximum is taken to determine the midpoint of the tail . This sequence is repeated until the end of the tail , which is detected using a threshold for change in luminance across the slice . To improve accuracy , the width and contrast of the tail in the first frame of video are characterized and used to refine the tracking for the rest of video . The SVM code was written in Python . The SVM and cross-validation procedures were provided by the scikit-learn library ( Pedregosa et al . , 2011 ) , which uses LIBSVM ( Chang and Lin , 2011 ) for the SVM implementation . To avoid testing and training on the same dataset , we used fivefold stratified cross-validation for training ( Kohavi , 1995 ) . For this validation , data are split into five groups of approximately equal mean . One group is reserved for testing while the others are used for training , and this procedure is repeated five times before the results are combined . We quantified 16 parameters for each bout . A radial basis function ( RBF ) kernel shape was used to draw the decision boundaries . A subset of the 16 parameters was selected by plotting the accuracy vs number of parameters . The point that maximized performance while minimizing the number of parameters was selected . For the virtual prey SVM , five parameters were optimal ( Figure 1—figure supplement 1 ) . The virtual prey SVM was trained on 248 prey bouts and 121 spontaneous bouts . The paramecia SVM was trained on 273 prey bouts and 396 spontaneous bouts . For the parameters described below , ‘tail angle’ is the angle of deflection of tail center of mass , ‘tail tip’ is defined as the last eight points of tail , and ‘tip angle’ is the angle of a line dawn through the last eight points of the tail , with respect to vertical . Parameters 1–5 were used in the virtual prey SVM; parameters 1–6 were used in the paramecia SVM . ‘Maximum tail curvature’: maximum over the bout . ‘Number of peaks in tail angle’ . ‘Mean tip angle’: absolute value of tip angle in each frame , averaged across the bout . ‘Maximum tail angle’: maximum of the bout . ‘Mean tip position’: average position of last eight points in the tail ( horizontal deflection as a fraction of the tail length ) . ‘Number of frames between peaks’: mean ( over bout ) number of frames between peaks in the tail angle . ‘Medium frequency power of tail angles’: power of middle frequency band of fourier transform of tail angle . ‘Low frequency power of tail angles’: power of low frequency band of fourier transform of tail angle . ‘Tail angle vs tip angle’: mean ( over bout ) difference in angle of deflection between tail center of mass and tip center of mass . ‘Tail angle vs . tip angle at frame of maximum tail angle’: difference in angle of deflection between tail center of mass and tip center of mass in the frame with the maximum tail angle . ‘Variance in tail angle’: over entire bout . ‘Mean tail curvature’: mean curvature over entire bout . ‘Mean tail tip angle’: mean over entire bout‘Mean tail position’: average position of tail from 12th point to end ( horizontal deflection as a fraction of the tail ) . ‘Mean tail curvature vs . tail angle’: mean ( over bout ) difference between tail curvature and tail angle . ‘Maximum tip horizontal deviation’ . Larvae were embedded as for behavioral experiments . Imaging experiments were performed at 7 or 8 dpf . Stimuli were presented on the same type of OLED screen as for behavior , also covered with three magenta wratten filters . Imaging was performed on a movable objective microscope ( Sutter , Novato , CA ) using a 40× objective ( Olympus , Japan ) . Excitation light was generated by a Ti: Sapphire laser ( Coherent , Santa Clara , CA ) tuned to 920 nm . Time series were recorded at 3 . 6 Hz with a resolution of 128 × 128 pixels . We used a red LED to provide some ambient light to the larva during imaging , and we heated the microscope box to 27°C . In order to record the response to paramecia in RGC axons , we embedded larvae in the center of a dish as for the paramecium behavior experiments and again cut away the agarose in front of the eyes and below the swim bladder . Since bright ambient light was required to allow the larvae to see the paramecia , we performed these imaging experiments on a confocal microscope ( Zeiss 780 ) rather than a two-photon . Time series were acquired at a rate of 3 . 3 Hz and a resolution of 128 × 128 pixels . Experiments were performed with overhead lights on and a white LED illuminating the dish to enhance the visibility of the prey . Temporal series of the same field of view with and without paramecia were joined together . The resulting stack was registered with StackReg in Fiji to reduce motion artifacts and pixel misalignment between the two acquisitions . From the raw data , ΔF/F was computed pixel-wise by a custom Matlab routine . Because we did not know when a paramecium was within the field of view , we calculated the baseline F for each pixel as the value of that pixel during a period of low activity . This was defined as the eighth percentile of the pixel's fluorescence over a sliding window of 25 frames . Maximum intensity projections of ΔF/F , computed independently for the two segments of the stack ( before and after addition of paramecia ) , were divided to give the ΔF/F ratio between the two conditions for the pseudocolor image shown in Figure 4D . From the ΔF/F maximum intensity projections , the average values over regions of interest corresponding to AF7 and AF9 were computed to compare the activity between the two arborization fields . During the 30 s windows we imaged over , occasional responses were observed in AF7 and AF9 in the absence of paramecia added to the bath . This could be the result of spontaneous activity , or small objects , for example bubbles or debris , floating in the solution . UAS:mCherry , BGUG , Islet2b:Gal4 ( or Ath5:Gal4 ) larvae were treated with 0 . 2 mM 1-phenyl-2-thiocarbamide ( PTU ) from 1 dpf to reduce pigmentation of the retina . Larvae with sparse GFP in AF7 were identified , and confocal stacks of the axonal projections were acquired . To visualize dendritic arbors in the retina , vibratome sectioning was performed as previously described ( Robles et al . , 2013 ) . Briefly , 50 µm vibratome sections were cut from larvae embedded in gelatin/albumin/sucrose and stained with a chick anti-GFP primary antibody ( Catalog number GTX13970; GeneTex , Irvine , CA ) and an Alexa 488 anti-chick secondary antibody ( Catalog number A-11039; Invitrogen ) . Ablations were performed in 8 dpf Ath5:Gal4 UAS:Dendra , TLN larvae , using the same two-photon microscope as for the imaging experiments . We found that treating the larvae with 0 . 1 mM PTU starting at 1 dpf was helpful in preventing tissue damage during ablation . Axons were killed by scanning an 850 nm beam for 750 ms over a 3 × 3 µM area . Post-objective laser power was 80 mW/mm2 . 4–5 scans were usually sufficient to ablate the AF7 neuropil . A Z stack of the area was taken 24 hr later to assess completeness of the ablation , and larvae with less than 80% of the volume ablated bilaterally were not used for the analysis . As a control , we ablated a similar volume of AF9 neuropil , with 4–5 scans . 6 or 7 dpf larvae were embedded in agarose , immersed in extracellular solution and anesthetized with 0 . 02% tricane . Larvae with the transgenes Ath5:Gal4 , UAS:GCaMP6s , and for some experiments Gal4s1171t were used . Patch pipettes ( 25–32 MΩ ) were filled with 15% tetramethylrhodamine dextran ( 3000 MW ) in intracellular saline . AF7 and AF9 were used as landmarks to locate AF7 cells . Following contact with a cell , a voltage train was applied ( 1 s duration , 150 Hz , 1 . 4 ms pulse width , 2–6 V ) by an Axon Axoporator ( Molecular Devices , Sunnyvale , CA ) . Labeled cells were imaged on a Zeiss LSM780 confocal microscope . The Jarque–Bera test was used to determine whether data were normally distributed . The t-test was used to evaluate normally distributed data . For non-normally distributed data , we used the Wilcoxon rank sum test . Variance was tested and found to be similar between groups by Levene's test . All custom software is available at https://bitbucket . org/mpinbaierlab/semmelhack-et-al . -2014 .
Zebrafish larvae show characteristic prey capture behavior in response to small moving objects . The neural mechanism used to recognize objects as prey remains largely unknown . We devised a machine learning behavior classification system to quantify hunting kinematics in semi-restrained animals exposed to a range of virtual stimuli . Two-photon calcium imaging revealed a small visual area , AF7 , that was activated specifically by the optimal prey stimulus . This pretectal region is innervated by two types of retinal ganglion cells , which also send collaterals to the optic tectum . Laser ablation of AF7 markedly reduced prey capture behavior . We identified neurons with arbors in AF7 and found that they projected to multiple sensory and premotor areas: the optic tectum , the nucleus of the medial longitudinal fasciculus ( nMLF ) and the hindbrain . These findings indicate that computations in the retina give rise to a visual stream which transforms sensory information into a directed prey capture response .
Our ability to recognize objects , and to respond instinctively to them , is something that is not fully understood . For example , seeing your favorite dessert could trigger an irresistible urge to eat it . Yet precisely how the image of the dessert could trigger an inner desire to indulge is a question that has so far eluded scientists . This compelling question also applies to the animal kingdom . Predators often demonstrate a typical hunting behavior upon seeing their prey from a distance . But just how the image of the prey triggers this hunting behavior is not known . Semmelhack et al . have now investigated this question by looking at the hunting behavior of zebrafish larvae . The larvae's prey is a tiny microbe that resembles a small moving dot . When the larvae encounter something that looks like their prey , they demonstrate a hardwired hunting response towards it . The hunting behavior consists of a series of swimming maneuvers to help the larvae successfully capture their prey . Semmelhack et al . used prey decoys to lure the zebrafish larvae , and video recordings to monitor the larvae's response . During the recordings , the larvae were embedded in a bed of jelly with only their tails free to move . The larvae's tail movements were recorded , and because the larvae are completely transparent , their brain activity could be visually monitored at the same time using calcium dyes . Using this approach , Semmelhack et al . identified a specific area of the brain that is responsible for triggering the larvae's hunting behavior . It turns out that this brain region forms a circuit that directly connects the retina at the back of the eye to nerve centers that control hunting maneuvers . So when the larva sees its prey , this circuit could directly trigger the larva's hunting behavior . When the circuit was specifically destroyed with a laser , this instinctive hunting response was impaired . These findings suggest that predators have a distinct brain circuit that hardwires their hunting response to images of their prey . Future studies would involve understanding precisely how this circuit coordinates the larvae's complex hunting behavior .