Sniper-scope: mapping and understanding the visual system of nature’s underwater sniper

By Samantha Decker (Scripps College) [Edited by Lars Schmitz, as part of BIOL 167 “Sensory Evolution”, an upper division class at the W.M. Keck Science Department. Written for educational purposes only].

How animals turn the chaos of environmental stimuli into an accurate, integrated and useful perception is one of the greatest questions of biology, psychology, neuroscience and even philosophy. Careful research can help answer some of the questions that Locke, Socrates, Plato, Descartes, Wundt, Gibson and curious toddlers alike have all had such as “When I see red am I seeing what you call red?” or maybe more advanced “How does light turn into what I see when I open my eyes?” or “How do I know what I’m looking at is real? And what does real even mean anyways?” Before this gets too Matrix-y, let’s bring some scientific understanding into the discussion of vision and perception. But how?

In order to understand what is happening between the time when a light wave hits a retina and the perception of an object, careful investigation of the visual system must be conducted. The birth of neuroscience as a field was pushed by the Nobel-winning discoveries of Hubel and Wiesel with their careful characterization of the cat visual system. Since then, more research on mammal visual systems has occurred and we have a much clearer understanding of how visual stimulus is coded in certain brain regions and integrated to form a perception. Expanding this type of investigation to other species allows for an understanding of how perception evolved and a clearer understanding of what happens between stimulus, perception and behavior.

Archer fish are unique among fish for their sniper-like style of hunting. They shoot a powerful jet of water from their mouth knocking prey off overhanging branches into the water where they snatch it up. Check out this cool video.

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They are able to down targets that move rapidly and adjust the stream of water based on perceived size, speed, and direction of the prey all while dealing with the refraction difference between air and water (Schuster et al, 2006; Gerullis and Schuster, 2014). This remarkable fish is an interesting model organism to study vision due to the complexity of its visual environment and behavior as well as the evolutionary knowledge gained by investigating our not-so-distant (and fishier) relatives in the teleost fish family.

The Archer fish. [commons.wikimedia.org/wiki/File:ArcherFish.jpg]

Figure 2. The Archer fish. [commons.wikimedia.org/wiki/File:ArcherFish.jpg]

Using the archer fish as a model organism to understand vision provides insight into human vision as well as providing information on how this amazing sensory system has evolved and what allows a species to become a visual learner. Recently, Ben-Tov et al. experimentally defined the visual receptive field in this fascinating model organism. Knowing the organization of the visual receptive field can lead to all sorts of interesting research such as evolution of brain regions, developmental biology, characterizing types and networks of neurons all leading to the illumination of the ‘black box’ surrounding how a stimulus turns into perception. The authors investigated the archer fish’s optic tectum and sought to analyze and define the functional organization and receptive field properties of the cells in these areas. To do this, they used extracellular measurements in the optic tectum to record responses from singles cells and develop a retinotectal map. Then, using the same extracellular recording methods, the receptive fields of those cells were determined.

Ben-Tov et al. used the same method to answer both their questions about retinotectal map as well as receptive fields of those cells. They took anesthetized and immobilized archer fish and performed surgery to open the skull above the optic tectum (recording set up in Figure 3b). Then, extracellular recordings of single cells were recorded as various stimulus types were shown to the fish (Figure 3). The various stimulus types were selected to see if the activity of the neuron they were recording from changed when they changed the visual stimuli. These changes were then quantified by counting action potentials recorded (visualized as dots on raster plots like in Figure 6 a and c and insets on figure 7) and using standard index equations used to quantify the cell’s ‘preference’ for certain stimulus.

The archer fish and the experimental setup. A: an archer fish shoots at an insect above water level. B: a schematic view of the experimental setup, which consists of a monitor to present the stimuli, a recording setup of 1 extracellular electrode. C–F: the 4 types of visual stimuli used in the experiment. C: on-off diffuse flash, a repeating loop consisting of 1 s of white screen followed by 1 s of black screen. D: static bar, a stationary bar in 8 different orientations (orientations varied by 22.5°). E: moving bar, a bar moving in 8 different directions (directions varied by 45°). The direction of motion was orthogonal to the bar's orientation. F: sinusoidal gratings, drifting in different spatial and temporal frequencies. Spatial frequency varied between 0.03 and 0.7 cycles/deg (6 logarithmic steps), and temporal frequency varied between 1.5 and 9 cycles/s (6 linear steps). Ben Tov et al, 2013

Figure 3. The archer fish and the experimental setup. A: an archer fish shoots at an insect above water level. B: a schematic view of the experimental setup, which consists of a monitor to present the stimuli, a recording setup of 1 extracellular electrode. C–F: the 4 types of visual stimuli used in the experiment. C: on-off diffuse flash, a repeating loop consisting of 1 s of white screen followed by 1 s of black screen. D: static bar, a stationary bar in 8 different orientations (orientations varied by 22.5°). E: moving bar, a bar moving in 8 different directions (directions varied by 45°). The direction of motion was orthogonal to the bar’s orientation. F: sinusoidal gratings, drifting in different spatial and temporal frequencies. Spatial frequency varied between 0.03 and 0.7 cycles/deg (6 logarithmic steps), and temporal frequency varied between 1.5 and 9 cycles/s (6 linear steps). Taken from Ben Tov et al., 2013 (Fig.1).

Retinotectal Map:

Retinotectal mapping refers to a mapping between the visual field (what the fish can see in the environment) and the surface of the optic tectum (the superficial brain region where visual processing was thought to be). This mapping is shown in the color coding seen in Figure 4, parts a and b. Part (a) shows an image of an archer fish brain with the optic tectum circles and then ‘zoomed in’ below with their map schematically shown. Part (b) shows the location in the archer fish’s visual field that corresponds to each color which is mapped onto the tectum in part a.

Figure 4

Figure 4. Space and size organization of receptive fields in the archer fish optic tectum. A: a photo of the archer fish brain with the left optic tectum delineated (top) and the electrode positions on the left optic tectum (bottom). B: topographical arrangement of receptive fields in the right visual field of the archer fish. The black circle represents the fish eye. Each color corresponds to the electrode position shown in A. Solid rectangles represent the average receptive field location and dimension, and contour rectangles represent the area into which all the receptive fields belonging to the same grid fall. Taken from Ben Tov et al, 2013 (Fig.2)

After they had recorded and located where in the visual field a certain cell responded to and determined the mapping, Ben-Tov et al. continued recording from that cell and determined if it was selective to different types of stimulus (Figure 3c-f). They determined that cells fall on a continuum between highest response to a diffuse light stimulus turning on, off or both (Figure 5). This shows that most cells respond to both on and off stimulus, so they continued on to determine the receptive field of the cells and whether they were selective for stimulus type.

Figure5

Figure 5. Cell responses to ON-OFF stimulus. Distribution of the response ratio of ON and OFF responses shows that the cells fall on a continuum of response patterns between ON and OFF classes. The most abundant pattern is a symmetrical ON-OFF response. Taken from Ben Tov et al., 2013 (Figure 3d)

Receptive Field:

Ben-Tov et al. found cells that responded strongly only when a bar stimulus was in a certain orientation (Figure 6 a and b) as well as some that didn’t seem to care about orientation and responded equally to the bar stimulus regardless of the orientation (Figure 6 c and d). They determined that they could categorize cells in the optic tectum as either orientation-tuned or orientation-agnostic cells.

Figure6

Figure 6. Orientation selectivity profiles assessed using the static bar stimulus. A: raster plots of the responses to the 8 oriented bars for an orientation-tuned cell. Black lines represent the start and end time of the bar display. B: orientation tuning profile for the orientation-tuned cell from A. The dashed black line represents the fitted von Misses distribution function. C: raster plots of the responses to the 8 oriented bars for an orientation-agnostic cell. D: orientation tuning profile for the orientation-agnostic cell from C. Taken from Ben Tov et al., 2013 (Fig.4)

Another type of stimulus Ben-Tov et al. tried was a moving bar. After finding the center of the receptive field of the cell they moved the stimulus in various directions relative to the fish. They found three distinct types of response profiles. Orientation-tuned cells showed a preference for bars moving along a specific axis. The example shown in Figure 7a is a cell that is highly responsive when the bar moves along the nasal-temporal axis (from the fish’s nose to the side of its head). Alternately, the direction-tuned cell in Figure 7b is only responsive when the bar moves from nasal to temporal but not temporal to nasal. And a final cell response profile was directional agnostic where the cell equally responded to the bar moving along any axis (Figure 7c).

Figure 7. Direction selectivity profiles assessed using the moving bar stimulus. A: orientation-tuned cell profile. B: direction-tuned cell profile. C: direction-agnostic cell profile. D: distribution of selectivity (with 95% confidence interval) for agnostic, orientation-tuned, and direction-tuned cells. Taken from Ben Tov et al., 2013 (Fig.5)

Figure 7. Direction selectivity profiles assessed using the moving bar stimulus. A: orientation-tuned cell profile. B: direction-tuned cell profile. C: direction-agnostic cell profile. D: distribution of selectivity (with 95% confidence interval) for agnostic, orientation-tuned, and direction-tuned cells. Taken from Ben Tov et al., 2013 (Fig.5)

Yet another stimulus type Ben-Tov et al. explored was the sinusoidal grating, trying to identify frequencies that elicited maximal responses. They found low pass cells which had a maximum response to low temporal frequencies, and high pass cells which had a maximum response to high temporal frequencies or no preference all pass (left side of bar graph Figure 8 labeled temporal tuning). To clean up this data a bit, they fitted a Gaussian function to look at difference in response to spatial frequencies and found two types. A low pass for spatial tuning which has a maximum response at the lowest spatial frequency, and a band pass for spatial tuning which had a maximum response in the middle of the frequency range (right side of Figure 8 labeled spatial tuning). Overall, the data show that there are cells in the optic tectum that are clearly stimulus-specific.

Figure 8. Distribution of tuning profiles (with 95% confidence interval). Light gray, temporal frequency tuning; dark gray, spatial frequency tuning. We calculated the confidence interval for the possibility of the existence of an additional tuning profile group that we have not observed in our data (see MATERIALS AND METHODS). For both the spatial frequency and temporal frequency tuning, we found that with 95% of certainty, the proportion of a possible missing group is between 0 and 0.12. Taken from Ben Tov et al., 2013 (Fig.6H)

Figure 8. Distribution of tuning profiles (with 95% confidence interval). Light gray, temporal frequency tuning; dark gray, spatial frequency tuning. We calculated the confidence interval for the possibility of the existence of an additional tuning profile group that we have not observed in our data (see MATERIALS AND METHODS). For both the spatial frequency and temporal frequency tuning, we found that with 95% of certainty, the proportion of a possible missing group is between 0 and 0.12. Taken from Ben Tov et al., 2013 (Fig.6H)

Other research labs had previously focused on the archer fish’s special behavior enabled by their remarkable terrestrial/aquatic vision. If you’ve ever tried to look underwater or looked at your straw sitting in a glass you’ve noticed the distortion that occurs when looking at an object in water versus in air. The archer fish’s behavior that has been the focus of research centers on their ability to learn how to compensate for this distortion to accurately determine the size of prey and adjust hunting behavior as fit (Gerullis and Schuster, 2014). Archer fish also learn to alter hunting strategy based off of speed of the target as well as learning this activity as juveniles by observing peers and successful adults (Schuster et al, 2006). To understand this behavior more fully, knowledge of the visual system must be expanded. It had been determined previously determined that there are directionally selective retinal ganglion cells in archer fish (Tsvilling et al, 2012). Ben-Tov et al. expand this investigation and show that there are directionally selective (as well as orientation selective) cells in the tectum (in addition to the retina). This substantially advances the field by providing valuable information about organization of visual processing in the archer fish. This ‘zooming out’ within the visual processing system provides valuable information and allows others to leverage this knowledge to gain a deeper understanding of this valuable model organism.

Characterization of the intricate details of the teleost visual system makes it possible to investigate how visual processing evolved within teleosts. Such studies allow for comparisons to well characterized visual processing systems and could result in improved understanding of how the evolution of organized sensory systems and brain regions results in perception that is accurate and useful for the organism.

Ben-Tov et al also made an attempt to make a comparison to other species’ functional units for visual processing. It is possible that the optic tectum (the dorsal-ventral axis) of the archer fish was reversed compared to some other fish species, which might be a result of the archer fish’s unique visual behavior of having a visual environment above and below water. This is an intriguing hypothesis, which clearly should be investigated in more detail. One could do this by comparisons between archer fish and close relatives that do not rely on terrestrial vision for hunting.

Ben-Tov et al. also speculated as to the analogue of their findings in mammals, an interesting developmental and evolutionary comparison. There may be functional similarities between the archer fish’s optic tectum and the mammalian superior colliculus, yet little data analysis or statistics are in place to back this up at this point. Nevertheless, understanding the evolution of optic tectum structures may allow for further understanding of disorders and development in humans. It would also be interesting to investigate some of the behaviors in archer fish while using the map and recording from cell types characterized in this paper to more deeply understand the coding process that orchestrate the complex behaviors of Archer fish. Another possible future study would be to see where the cells characterized in this paper project (using lentiviral, GFP or CLEAR brain techniques) to further ‘zoom out’ within the organization of visual processing.

References

Ben-Tov, M., Kopilevich. I., Donchin, O., Ben-Shahar, O., Giladi, C., Segev, R. 2013 Visual receptive field properties of cells in the optic tectum of the archer fish. Journal of Neurophysiology 110(3), 748-759. (DOI: 10.1152/jn.00094.2013)

Schuster, S., Wohl, S., Griebsch, M., Klostermeier, I. 2006 Animal Cognition: How Archer Fish Learn to Down Rapidly Moving Targets. Current Biology 16(4), 378-383. (doi:10.1016/j.cub.2005.12.037)

Tsvilling, V., Donchin, O., Shamir, M., Segev, R. 2012 Archer fish fast hunting maneuver may be guided by directionally selective retinal ganglion cells. European Journal of Neuroscience 35(3), 436-44 (DOI: 10.1111/j.1460-9568.2011.07971.x)

Gerullis, P., and Schuster, S. 2014 Archerfish Actively Control the Hyrdrodynamics of their Jets. Current Biology. 24(18), 2156-2160. (doi:10.1016/j.cub.2014.07.059)

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