Recording from behaving animals: Virtual reality in neuroscience 

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The zebrafish as a model for systems neuroscience

The brain complexity arises from the variety of levels of organization: from synaptic transmission to neuronal circuits and behavior. Each level of organization is attached to a specific discipline, from genetics to ecology. In this context, it is worthwhile to focus technological and scientific efforts on a restricted number of animal models, illustrated in Table 1.1.
Zebrafish (Danio rerio) is a small gregarious teleost fish (∼ 4 cm) originating from the south of Asia. They are easy to breed and have a fast reproduction cycle. Developmental and genetic studies have taken advantages of the transparency of their embryo since the late 1950s. Nowadays, a large library of transgenic and mutant fish is available, enabling us to target specific cell types or provide vertebrate models of human neurodevelopmental, neurological and neurodegenerative diseases (Deo and MacRae, 2011).
With the development of new optical methods and optogenetics, the zebrafish larva has recently become an appealing vertebrate model for systems neuroscience. Due to its small size and transparency, its brain activity is ideally accessible. State of the art optical methods including, two-Photon Scanning Microscopy, Selective Plane Illumination Microscopy, and Light-Field Microscopy have been successfully applied to simultaneously monitor the activity dynamics of large brain regions (Figure 1.6).
Optogenetic sensors, such as GCaMP, a genetically encoded calcium indicator, can be expressed in selected populations of neurons. GCaMP changes its fluorescence properties in response to the binding of Ca2+. The firing of neurons causes an increase in the intracellular calcium concentration resulting in rapid rises and decay in the fluorescence of GCaMP sensors.
Additionally, optogenetic actuators such as halorhodopsin or channelrhodopsin, can also be expressed. These light activated ion channels can induce or suppress neuronal activity. This perturbation of neuronal activity can be useful to probe the causal role of neuronal activity in selected populations of neurons.
All these manipulations are commonly performed in an « all-optical » manner without the need for surgery, or anesthesia and just requires the larva to be head-restrained in agarose leaving the eyes and the tail free to move. In order to understand behavior, it is necessary to understand the total action of the nervous system, as explained by D. Hebb in The Organization of Behavior (1949): One can discover the properties of its various parts more or less in isolation; but it is a truism by now that the part may have properties that are not evident in isolation, and these are to be discovered only by study of the whole intact brain.
The ability to simultaneously monitor sensory and motor areas in a behaving animal make zebrafish an ideal model for the holistic approach on how the brain generates behavior (Ahrens and Engert, 2015).

Goal-driven behavior in the larval zebrafish

Typical habitat of zebrafish consists of shallow and clear water with slow moving streams. Zebrafish are commonly found in ephemeral pools or rice paddles. They are omnivorous, consuming insects, zooplankton and algae (Parichy, 2015). By 6dpf, the vitellus lipids reserves are consumed and the larva needs to catch prey. This vulnerability results in a mortality rate as high as 50% due to starvation at 12 dpf (Bardach et al., 1972). This illustrates the ecological pressure which induced a rapid and early development of functional sensory-motor circuits.
Compared to rodents or primates whose behaviors have been comprehensively evaluated and defined (Shettleworth, 2010), the description of zebrafish behavioral repertoire is still a developing field of research (Kalueff et al., 2013). Field observations of zebrafish larvae behaviors are surprisingly rare. Most of what we know about their behavior has been inferred from experimental studies in laboratory environments. The simplest forms of goal-directed behavior are taxis. During taxis behavior, an animal will try to reach a desired location in the environment. The location can be chosen according to different properties, light in the case of phototaxis, chemical compositions for chemotaxis or prey for telotaxis. I will focus on visually induced taxis in zebrafish larva.

Prediction of the larva’s trajectory from the kinematics of tail movements

Zebrafish larvae navigate by producing discrete stereotypical tail movements called swim bouts. Larvae do not track moving gratings faster than 10 Hz (Rinner et al., 2005), this indicates that a refresh rate of 60 Hz from a video projector is sufficient to accommodate the temporal acuity of zebrafish vision. The typical frequency of oscillations of the tail during a bout in restrained larvae is 20 to 30 Hz (Severi et al., 2014). In order to provide a real-time feedback, the tail kinematics should be filmed at high acquisition rates (above 200 Hz), and the processing of the acquired images must be computed in just a few milliseconds. The Reynold’s number of swimming larvae is between 50 and 900 Re, which puts them in a transitional flow regime (McHenry and Lauder, 2005), thus neither inertial nor viscous forces can be neglected. Approximations in flow regime could enable to compute, in real time, the thrust generated by the tail movements of an adult fish (Bergmann and Iollo, 2011). However, computing the thrust in transitional flow regime is so far unachievable.
To predict trajectories from tail kinematics, I used a data-driven approach to learn the relationship between tail movements and fish kinematics in the horizontal plane. I recorded the displacement and tail kinematics from freely swimming larvae in shallow water to generate a large library of movements. Paramecia were also introduced to induce the larvae to generate prey-capture behaviors (5% of the library). Our library of movements consisted of ∼ 300 tail bouts from 6-8 days post fertilization wild-type larvae. The shape of the tail was quantified by computing the tail deflection using a method developed by Raphael Candelier. Figure 2.2 shows the time series of the tail deflection associated with stereotypical movements. This quantification of tail kinematics was fast (∼ 1ms/frames with 100 px square image in C++), and it resulted in a low-noise, smooth and oscillating times series. To describe the change in orientation and position of the larva in the swimming plane, I have used 3 parameters: axial, lateral and yaw speed (Figure 2.3.A). Figure 2.3.C shows the kinematic parameters for freely swimming larvae associated with 4 different types of movements. Kinematic parameters were chosen to be smooth oscillating time series during swim bouts.

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Optomotor response in a two-dimensions visual virtual reality system

The optomotor response, a visual component of rheotaxis, is a highly reproducible behavior in zebrafish larva. Presenting a moving grating below the larva elicits a movement in the same direction. I tested whether larvae were capable of orienting towards and follow a moving grating stimulus in the VR.
The speed of the grating (1 cm/s) and its spatial period (1 cm) were chosen according to previous studies (Portugues and Engert (2011), Ahrens et al. (2013a)). At the beginning of each trial, I randomly chose the angle between the initial orientation of the grid movement and the head direction of the larva (between −180◦ and 180◦).
During the stimulation, the speed and orientation of the grid was updated according to the tail movements of the larva. The stability of the trajectories in the VR was improved by applying a gain of 3 to the axial speed. Each experiment consisted of 120 trials, each trial was split into periods of visual stimulation of 6s and resting periods of 20s.
Using this paradigm, we found that when the whole-field motion was aligned with the larvae, they displayed a shorted response time before the first bout (Figure 2.4.F). During the stimulation, larvae maintain an average speed of 0.15 cm/s in the direction of the grid. They produced on average 3 bouts per trial (3.26, N=549, from 9 larvae) and the average bout duration was ∼ 300ms (0.313ms, N=1783, from 9 larvae), which is consistent with previous report (Severi et al., 2014). As expected, the distribution of the angles between the larva and the grating’s direction deceased with time (Figure 2.4.C,D). Successive bouts brought the head angle of the larva to an average deviation of 20◦ with the grid (Figure 2.4.G). Considering that the larva is aligned with the motion if the difference between the angle of its head and the angle of the grid motion is lower that 30◦ (the deviation observed in free swimming OMR, Ahrens et al. (2013a)). The proportion of aligned larva increased by two folds during the 6 s trial (from 28.2◦ to 51.6◦, N=546, from 9 larvae, Figure 2.4.E,H).

Prey-capture behavior in two-dimension visual virtual reality

Zebrafish larvae begin to hunt paramecia after 5 days post fertilization, just two days after hatching. This visually driven behavior is crucial for their survival. After detecting a prey, the larva orients itself towards the prey and uses forward scoots and J turns. The larva executes a capture maneuver and swallows its prey when the paramecia is closer than 400 μm.
Under head-restrained conditions, the larvae could perform orienting and pursuit maneuvers toward the pseudo-paramecia in a visual virtual environment (Figure 2.5.A,B). Each trial mimicked a situation where a 100 μm paramecia appears 1.5 mm away from the larva. In this configuration, the apparent angle of the paramecia (diameter of 4◦) was presented to optimally elicit a prey-capture behavior (Bianco and Engert (2015), Semmelhack et al. (2015)).
At the beginning of each trial, we projected on the circular screen a 4◦ circular black spot moving on a white background at an angular speed of ±90◦/s along the azimutal plane. The angular velocity of 90◦/s is not consistent with the speed of moving paramecia (∼ 100 μm/s) at a distance of 1.5 mm from the larva, but it has been shown to be optimal to elicit prey capture (Semmelhack et al., 2015). It is possible that this optimal speed results from the relative velocity between the larva and the paramecia when the larva is actively foraging. Right after the onset of the larva’s first tail bout, the angular speed of the prey inj the virtual environment was set to 0◦/s and the change in size and position of the black circle projected on the screen were computed according to the predicted trajectory of the larva. Figure 2.5.B illustrates the experimental design. If the larva oriented itself toward the virtual paramecium,  the circle projected then reached the center of the field of view of the larva and its radius increased as the larva swam in its direction. We considered that a larva captured the virtual prey if its trajectory in the virtual environment was closer than 400 μm from the virtual prey (the corresponding apparent angle of the virtual prey would have a diameter of 15◦).

Table of contents :

List of figures
List of tables
1 Introduction 
1.1 Understanding behavior: the sensory-motor dialogue
1.2 Sensory feedback in the perception-action loop
1.2.1 Recording from behaving animals: Virtual reality in neuroscience
1.2.2 How real is virtual reality?
1.3 Internally driven behaviors
1.3.1 Motivation for action in absence of sensory stimulation
1.3.2 Neural basis of spontaneous behavior
1.4 Large-scale analysis of circuit dynamics underlying behavior in zebrafish larva
1.4.1 The zebrafish as a model for systems neuroscience
1.4.2 Locomotion of zebrafish larva
1.4.3 Goal-driven behavior in the larval zebrafish
1.5 Main aims
2 A visual virtual reality system for the zebrafish larva 
2.1 Introduction
2.2 Results
2.2.1 Prediction of the larva’s trajectory from the kinematics of tail movements
2.2.2 Optomotor response in a two-dimensions visual virtual reality system
2.2.3 Prey-capture behavior in two-dimension visual virtual reality
2.2.4 Integration of visual information during tail bouts
2.3 Materials and methods
3 Internally driven behavior in zebrafish larvae 
3.1 Introduction
3.2 Internally driven behaviors of zebrafish larva
3.2.1 Locomotor repertoire of zebrafish larva
3.2.2 Chaining of spontaneous motor actions
3.2.3 Supplementary Methods
3.3 Neuronal patterns predictive of spontaneous behaviors
3.3.1 Methods
3.4 Results
3.5 Supplementary Methods
4 Conclusions and perspectives 
4.1 A visual virtual reality system for the zebrafish larva
4.2 Internally driven behaviors in zebrafish larva
4.3 Neural basis of internal decisions


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