Zebrafish and relational memory: Could a simple fish be useful for the analysis of biological mechanisms of complex vertebrate learning?

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Zebrafish and relational memory: Could a simple fish be useful for the analysis of biological mechanisms of complex vertebrate learning? Robert Gerlai Department of Psychology, University of Toronto Mississauga, 3359 Mississauga Road North, Rm CCT4004 Mississauga, Ontario L5L 1C6, Canada

a r t i c l e

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Article history: Received 26 August 2016 Received in revised form 6 January 2017 Accepted 24 January 2017 Available online xxx Keywords: Episodic memory Relational learning Spatial learning Context learning Zebrafish

a b s t r a c t Analysis of the zebrafish allows one to combine two distinct scientific approaches, comparative ethology and neurobehavioral genetics. Furthermore, this species arguably represents an optimal compromise between system complexity and practical simplicity. This mini-review focuses on a complex form of learning, relational learning and memory, in zebrafish. It argues that zebrafish are capable of this type of learning, and it attempts to show how this species may be useful in the analysis of the mechanisms and the evolution of this complex brain function. The review is not intended to be comprehensive. It is a short opinion piece that reflects the author’s own biases, and it draws some of its examples from the work coming from his own laboratory. Nevertheless, it is written in the hope that it will persuade those who have not utilized zebrafish and who may be interested in opening their research horizon to this relatively novel but powerful vertebrate research tool. © 2017 Elsevier B.V. All rights reserved.

Contents 1. 2. 3. 4. 5. 6.

Comparative psychology versus neurobehavioral genetics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 00 Why zebrafish? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 00 Why learning and memory? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 00 Relational learning and episodic memory: are they unique human phenomena? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 00 Relational learning in zebrafish? The answer is a likely yes! . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 00 Mechanisms: the question of construct validity of the zebrafish spatial learning model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 00 Declaration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 00 Conflict of interest . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 00 Acknowledgement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 00 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 00

1. Comparative psychology versus neurobehavioral genetics Comparative ethologists and animal psychologists have long been arguing that the analysis of multiple species and the comparison of these species will help us answer questions about both the evolution (McLennan et al., 1988) and the biological mechanisms (Barton and Dean, 1993) of a wide range of behavioral characteristics. On the other hand, neuroscientists and geneticists often prefer focusing on a single laboratory animal species because this focus allows the accumulation of multidisciplinary scientific techniques

E-mail address: robert [email protected]

and data on their preferred single model organism, which then form the foundation of future research and facilitate cross-laboratory comparison of results (Oliverio, 1975; Crabbe et al., 1999). 2. Why zebrafish? I propose that the zebrafish is useful from both of these above perspectives. Due to concerted efforts, mainly originating from the laboratories of developmental biologists, the zebrafish has become one of the favored model organisms of geneticists (Grunwald and Eisen, 2002). A myriad of forward (Patton and Zon, 2001) and reverse genetic (Huang et al., 2012) methods have been developed specifically for this species or adapted to it from other model organ-

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Please cite this article in press as: Gerlai, R., Zebrafish and relational memory: Could a simple fish be useful for the analysis of biological mechanisms of complex vertebrate learning? Behav. Process. (2017), http://dx.doi.org/10.1016/j.beproc.2017.01.016

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Fig. 1. A Venn diagram to exemplify the utility of the comparative approach. Each circle represents a set of mechanisms underlying a particular phenotypical characteristic, say relational memory, in a given species (human, mouse, rat or zebrafish in the current example). Note that evolutionarily more closely related species are expected to have a larger overlapping area, i.e. higher degree of similarity among mechanisms underlying relational memory. An example for this is the rat and the mouse. Also note that there may be a subset of mechanisms underlying relational memory that is common to all four species studied (indicated by the checkered area, the overlap among all four circles). Arguably, this set of mechanisms may represent the evolutionarily most conserved and perhaps therefore most fundamental biological features of relational memory. Discovery of such mechanisms requires the comparison of multiple species with human. Given that research on humans is often limited by practical, technical and ethical constraints, the use of multiple animal species and the identification of overlapping mechanisms among these animal species is argued to enhance translational relevance. Adapted from Gerlai (2014).

isms. Similarly, the neurobiology tool set designed for this species is becoming increasingly sophisticated (Wyatt et al., 2015). Although it is somewhat behind of more traditional laboratory organisms, the zebrafish is becoming the focus of behavioral studies too (Kalueff et al., 2014). In summary, sophisticated multidisciplinary analysis of the zebrafish is now a reality. But why would one want to study this species when one could work with the laboratory mouse? The number of studies conducted with the mouse in practically any subdiscipline of biology, including in psychology, is orders of magnitude larger compared to those done on the zebrafish (Kalueff et al., 2014). Thus, the amount of information on any features of the house mouse is incomparably more sophisticated. Despite all these facts, there are many reasons why one would prefer the zebrafish. One is the power of the comparative approach. The simplest way to unpack this argument is to consider a Venn diagram (Fig. 1). Each circle on this diagram represents a set of mechanisms (protein expression, biochemical interactions, synaptic processes, neuronal function, circuitry features, etc.) that support or underlie a particular behavioral feature in a given species. The other circle represents the set of mechanisms underlying the same behavioral phenomenon but in another species. Assuming some degree of evolutionary relatedness between two species (an assumption that is always met), one may expect some level of overlap between the two circles. The overlapping area is what we call evolutionary homology, i.e. shared origin leading to mechanistic similarity supporting the same function. The non-overlapping areas represent mechanisms underlying the given behavioral trait that are distinct, i.e. unique to each species. Now, consider that even if one studies a single species very thoroughly, it may not be easy to predict what subset of mechanisms is fundamentally crucial for the given behavioral phenomenon, a question to which we will return later. Another important observation we may make is that even if a behavioral phenomenon is very well

studied, we often do not know all the mechanisms that underlie this phenomenon in the given study species. Last, it is also well accepted that some mechanisms are easier to study in one species but less so in another. For example, some of the molecular mechanisms of spatial memory have been well elucidated in the house mouse because numerous genetic, neurobiological, and behavioral tools have been at the disposal of the experimenter for this species (Morris, 2001). However, many of these tools are not available for, or not ethical to use with, humans. For these reasons, establishing what the overlapping areas are between mouse and human, for example, has been difficult. The side effects of this difficulty are numerous, but one is the high failure rate of drugs in phase II human clinical trials (Kola and Landis, 2004). Many of such failed drugs have been thoroughly tested and found efficacious in preclinical studies, i.e. in mice and rats. Why do not they work in humans? One possible answer to this question is likely because they worked through mechanisms in the mouse outside of the overlapping areas of the Venn diagram circles. How can one solve this conundrum and increase translational relevance of preclinical studies? Pharmaceutical research has been struggling with answering this question (Kola and Landis, 2004). There may be numerous ways to reduce the failure rate of drugs in phase II trials. I would argue that perhaps the best among them would be to employ the comparative psychology/ethology approach. For example, consider a third circle on the Venn diagram of Fig. 1. This circle now represents all the mechanisms underlying the given behavioral phenomenon but now in a third species. The area where all three species overlap represents mechanisms that support the same behavioral feature AND are common to all three species. In other words, employing multiple species in preclinical studies increases translational relevance of our discoveries, because it allows us to identify evolutionarily conserved features among non-human animals, which then likely are similar in our own species. I argue that given the biology information-rich aspect of the zebrafish, this species will be an excellent candidate for such comparative approach, and should be added to studies with the house mouse. There may also be another important argument about why the zebrafish, or fish in general, may be useful in neurobehavioral genetics research. It is because these species represent an evolutionarily more ancient design. Which species is more ancient and which species is more modern in “design” may seem like a somewhat contentious question, after all, all species of today exist now. Nevertheless, when one considers that fish arose more than 400 million years ago, and their genes, basic body structure and likely physiology and behavior remained mainly unaltered, it is clear why these species are considered more primitive and more ancient (Ohno et al., 1968). This evolutionary age offers two important and distinct types of advantages. One, being more ancient and primitive in design means simplicity. Although Stephen J Gould may not fully agree (Gould, 1996), complexity does appear to increase, although not inevitably in all, but at least in some species as evolution progresses. Thus, given enough evolutionary time, more modern species may become increasingly complex. Simplicity of more ancient species, like fish, allows a reductionist approach. This is a practical advantage from an experimental viewpoint. Simplicity is an advantage also from another perspective. The study of more ancient species likely allows one to investigate mechanisms that are more fundamental, more crucial, to the given behavioral feature. To illustrate the point consider Fig. 2, the onion model of evolution. Because the process of evolution must build on prior biological/genetic features and cannot redesign biological systems from scratch, with time layers and layers of complexity may build upon one another, like the rings of an onion. In this model, the layers of complexity are represented as circles, moving from the more ancient (inner or center) towards the more modern (outer) circles.

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Fig. 2. Layers of complexity building over time: The onion model of evolution. The concentric circles represent set of processes, mechanisms, genes, proteins, biochemical interaction, any and all biological system related features that add to complexity. Because adding new features (genes, proteins, biochemical interactions, cells, tissues, organs, etc.) to an existing biological system is usually easier than removing some, as time passes, evolution tends to increase complexity in some (but not necessarily in all) species. The inner-most circle represents the most ancient set of features/mechanisms, and the outer circles represent increasingly newer, more modern features adding to the complexity of the organism as it evolves. The model as shown is overly simplistic as it assumes a two dimensional system with symmetrical evolutionary change. In reality, the system is multidimensional, i.e. has several axes along which evolutionary change may occur. Furthermore, the rate of change is likely nonsymmetrical as more change may occur along some axes and less along some other ones. That is, instead of a circle with a smooth perimeter, one may expect a lot of indentations and protrusions making the smooth line of the circle look more like a rugged zig-zagging line. Irrespective of the simplicity of the onion model, however, the essential point of the model is this: evolutionarily simpler, older species may be less complex, and may only posses more ancient mechanisms that are more fundamental to the given phenotypical feature, relational memory in this case. Modified from Gerlai (2014).

Although the model is overly simplified, it illuminates an essential point: the study of simpler, evolutionarily older species likely will allow one to get to the core mechanisms underlying the behavioral feature of focus. Before I illuminate what I mean by this in the context of the analysis of learning and memory in general, and of relational learning and/or episodic memory in particular, let me discuss first why one would study these phenomena at all? 3. Why learning and memory? There are many interesting behavioral phenomena to study. Nevertheless, a disproportionately large number of scientists produce perhaps an even more disproportionately large number of papers on learning and memory. Why is there such a fascination with these behavioral phenomena? There are many reasons. Considering an evolutionary perspective, learning and memory in a way bypasses the slow and rather cumbersome process by which natural selection makes animals adapt to their environment. Metaphorically speaking, the genome of the individual does not have to wait generations before changes occur that would enable the individual carrying such genetic modifications to appropriately respond to a change in the environment. Learning and memory can do the trick within the lifetime of the given individual. Furthermore, although the brain does serve many functions, arguably, one of the most important among these is neuronal plasticity, mechanisms that allow the rapid change of behavior in response to prior experience. From a clinical perspective too, learning and memory are very

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important. A large number of neurodegenerative, age-related, neuropsychiatric and drug abuse related diseases are associated with impaired learning and memory processes, and for many of them the impairment is the defining feature of the disease (e.g., Mayes, 1986). Given the importance of learning and memory from these many perspectives, one could assume that its mechanisms must have been very well understood by now. However, this is not the case. Although one of the most comprehensive accounts of the mechanisms of memory (David Sweatt’s book titled the same) does discuss about 400 molecular players (proteins and/or genes) underlying learning and/or memory processes (Sweatt, 2010), this number may be orders of magnitude below of how many molecular players are actually involved. Admittedly, no one really knows how far we are from understanding the biochemistry and molecular biology of these processes. Nevertheless, given that over 60% of the entire genome of an individual is expressed in the brain of this individual at any given time, and given that large brain areas are dedicated to one or another form of learning and/or phase of memory, we likely are talking about tens of thousands of proteins involved in these processes. Briefly, there is a lot we need to discover before we can understand the biological mechanisms of learning and memory.

4. Relational learning and episodic memory: are they unique human phenomena? When faced with such complexity, scientists tend to categorize and organize, an approach that is essentially a model building process. How do observed phenomena relate to one another, what are distinct and what are redundant aspects of learning and memory? Many of these questions were initially attempted to be answered using purely behavioral methods. These behavioral studies yielded surprising results. They showed, for example, that learning (the process that allows the acquisition of experience dependent change in behavior) has distinct forms, that memory (the process that allows the consolidation, maintenance and recollection of the experience dependent change) is not a unitary process, and also that memory has distinct phases (for a review see Squire et al., 1993; also see Tulving, 1987). While generally useful, some of the behavioral categories were based upon experimental procedures alone, and later turned out not to reflect biological reality. Nevertheless, the majority of behavioral categories, the “slices of the learning and memory pie” as cut by earlier behavioral scientists, were vindicated by neurobiological studies (for a comprehensive account see Sweatt, 2010). One of these categories was the distinction between relational learning/memory and simple CS-US associative learning/memory, a distinction that has clear biological bases (Cohen et al., 1997). All animal species that have been tested in this context have been found to be able to associate a neutral stimulus (conditioned stimulus, CS) with a reinforcer (reward or punishment also known as the unconditioned stimulus or US). Thus, this simple form of elemental associative learning appears universal in the animal kingdom. However, humans can perform a more complex form of associative learning too, relational learning. In relational learning, there are multiple CSs, and it is the relationship (temporal order, spatial arrangement or the combination of these) among the CSs that predicts the US. A debate has been raging about whether a form of relational memory, episodic memory, is indeed unique to humans or whether it can be found in some animal species too (Clayton et al., 2001; Tulving, 2005; Scarf et al., 2014; Allen and Fortin, 2013). Episodic memory is essentially a type of relational memory that allows the recollection of what, when and where happened in one’s life (Tulving, 1987; Squire et al., 1993). It is also called declarative memory, as it can be consciously recalled and ver-

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bally declared (unlike procedural memory which although may be clearly demonstrated by improved performance, may not be consciously remembered and verbally declared) (Squire et al., 1993; Sweatt, 2010). The question of whether animals possess episodic memory similar to that of humans has been heavily debated. For example, Roberts et al. (2008) discuss these questions and consider the difference between information learned about “when” and “how long ago”. These authors show evidence for rats being able to remember “how long ago” but not “when” type of information. Zhou and Crystal (2009), on the other hand, did find evidence for rats being able to encode “when” type information. In fact, when “how long ago” versus “when” type information was conflicted, rats used the former, making Zhou and Crystal (2009) conclude that the temporal features of the rat episodic-like memory may not be fundamentally different from that of humans. Although still debated, the question of how unique episodic memory may be in humans appears irrelevant to me. Clearly, one will always be able to find a highly sophisticated behavioral (or neurobiological) method that will distinguish relational memory between humans and other animals. Nevertheless, overwhelming and multidisciplinary evidence from comparative studies does confirm that species are related to each other according to their functions, features and mechanisms. The question is how much, not whether such relationship or overlap exists. For example, although rodents cannot speak, there is evidence that they remember relational information, including what, when and where happened to them (Dere et al., 2005; Allen and Fortin, 2013). There is also evidence for many other forms of relational memory in a variety of vertebrate species (Allen and Fortin, 2013; Rodríguez et al., 2002). Furthermore, there is also plenty of evidence for mechanistic similarities among human and other nonhuman mammalian species in this context (Eichenbaum and Fortin, 2005). Do fish show signs of relational learning too?

5. Relational learning in zebrafish? The answer is a likely yes! An increasing number of studies have been published in the past few years investigating the cognitive (e.g. learning) and mnemonic (memory) characteristics of the zebrafish. Although this minireview is not intended to provide a comprehensive account of this research field, the following illustrative examples demonstrated a recent rapid accumulation of knowledge. Aoki et al. (2015) developed an automated Y-maze avoidance task to measure associative learning in zebrafish. Engert (2013) designed a virtual reality system to investigate motor learning in zebrafish. Ahrens et al. (2012) using two-photon calcium imaging, have analyzed the dynamic neuronal activity changes that occur during a fictive motor interaction in a virtual environment. The larval zebrafish is particularly conducive to such microscopy analyses but it has also been successfully utilized in the analysis of associative learning and memory (Roberts et al., 2013). Learning and memory characteristics of the adult zebrafish have been even more frequently studied (for a detailed review see Gerlai, 2016). A form of relational learning, spatial learning, has also been studied in fish (e.g. Rodríguez et al., 2002). Spatial learning entails the acquisition of dynamic relationships of spatial cues, the establishment of mental representation of a spatial map (O’Keefe and Nadel, 1978). Spatial learning manifests in the laboratory as the subject’s ability to move (in this case swim) to a location where a reinforcer was previously positioned. Usually, spatial learning performance is evaluated during probe trials, administered after a multi-trial training process. During the probe trial, all spatial cues previously present during training remain in their position, but the reinforcer, e.g. food reward, is absent and thus cannot guide the locomotory response of the subject. Numerous fish species, including the gold

fish, demonstrate significant preference for the prior location of the reinforcer during probe trials, a result that is taken as evidence for the acquisition, consolidation, retention and recall of spatial memory (Rodríguez et al., 2002; Vargas et al., 2004; Schluessel and Bleckmann, 2005; Delicio and Barreto, 2008). Do these results represent unequivocal proof for spatial learning and memory in fish? I argue they may not. Finding the previous location of a reinforcer in the experimental tank may be achieved by the subject using non-spatial information. For example, if the experimental subjects were started during training from the same location, egocentric cues may be used (Schluessel and Bleckmann, 2005). But even if this confound is addressed by randomly varying the starting location of the subject, the possibility remains that the subject can associate a single cue with the reinforcer, and essentially turn the complex spatial/relational learning task into a simple elemental CS-US associative learning task (Gerlai, 1998). The problem is clearly demonstrated in the rodent literature by what has become known as the issue of background versus foreground cues. Phillips and LeDoux (1992) noticed that rats with a hippocampal lesion could freeze during a probe trial in the test chamber where they previously received an electric shock. Similar findings were obtained by Kim and Fanselow (1992) and Gerlai (1998). Freezing is a natural response in rodents to fear or pain, and the experimental subjects showed this clear fear response to the chamber during a probe trial even though they did not receive the shock during this trial. This was a surprising finding given that the hippocampus was known to be crucial for spatial learning (O’Keefe and Nadel, 1978). How could the rats learn and remember spatial cues characterizing the shock chamber and respond to this chamber when their hippocampus was dysfunctional? To answer this question, consider that the hippocampally lesioned rats could freeze to the chamber only if their hippocampal damage was induced prior to training, not after the training. Similarly, the hippocampally lesioned rats could respond to the chamber only if during training no salient associative cue (CS) was experimentally paired with the shock. There is a parsimonious explanation for all these findings, which is as follows. Phillips and LeDoux (1992) and Kim and Fanselow (1992) theorized that rats whose hippocampal function was impaired could pick out a single cue from the background and thus could turn the complex spatial/relational learning task into a simple elemental CS-US associative learning task. Rats whose hippocampus was lesioned after training and before the probe trial performed badly during the probe trial because the spatial map they acquired during training could not be consolidated and/or accessed due to the hippocampal damage (the hippocampus has been shown to be involved both in the consolidation and in the recall of spatial memory). It is also notable that rats that had normal hippocampal function could learn both the associative cue (CS) paired with the shock, as well as the spatial cues characterizing the training chamber during training. However, rats with hippocampal lesion could not learn relational information, and thus when provided with a clear CS-US pairing, they could only learn this association and not the relationship between the US and spatial cues which were less contingent and less contiguous with the US. They also could not pick out a single spatial cue because a salient CS has already been experimentally provided. Thus, the hypothesis was proposed that the hippocampus supports learning of the loose relationship between US and background cues (relational or configural learning), but it is not necessary for learning one or very few foreground cues that temporally coincide with and causally predict the US (elemental learning). Similarly, mice that were trained in the context and cue dependent fear conditioning task could learn both the CS-US as well as the spatial features-US association when their hippocampus was intact. But when it was lesioned before training, they could freeze to the shock chamber during probe only if the shock was not paired with a CS during training (Gerlai, 1998).

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Zebrafish have been shown to be able to perform well in a variety of learning tasks including spatial alternation (Williams et al., 2002), visual discrimination (Colwill et al., 2005; Fernandes et al., 2016), active avoidance conditioning (Xu et al., 2007), elemental associative learning (Al-Imari and Gerlai, 2008; Sison and Gerlai, 2010; Levin et al., 2006), shuttle box learning (Pather and Gerlai, 2009), and spatial and non-spatial learning in the plus maze (Sison and Gerlai, 2011a,b, 2010), to mention but a few methods. From our current perspective, however, the most important of these is a spatial learning task conducted in a large open tank (Karnik and Gerlai, 2012). In this task, zebrafish received multiple training trials during which the location of a reward (the presence of conspecific stimulus fish) was kept constant relative to external visual cues, but not relative to intra-maze cues (as the maze was rotated randomly from trial to trial). Because the fish were started from different locations relative to the target (reward location), an egocentric strategy was unlikely. Yet, the experimental subjects showed significant spatial bias towards the prior location of the reward during a probe trial, even though the reward was absent during this trial. Thus, one may conclude that zebrafish are capable of spatial learning. However, as we discussed above, finding significant spatial bias, i.e. good spatial learning performance, does not necessarily prove the actual existence of spatial memory. One has to show that the subject did not use an elemental learning strategy, i.e. did not pick out a single cue with which it associated the reward. Given that we do not know what spatial cues zebrafish actually perceived and learned, we cannot be certain whether the experimental zebrafish learned the dynamic spatial map, or just used a simple elemental learning strategy. Nevertheless, we can still use the reasoning and experimental procedures employed with rodents, and study whether zebrafish can learn the location of a reinforcer and simultaneously the association between the reinforcer and an experimentally controlled CS. Interestingly, zebrafish trained this way demonstrated a learning ability that was equivalent to how rodents with intact hippocampus behaved (Gerlai, 1998; Phillips and LeDoux, 1992; Karnik and Gerlai, 2012). In the above described spatial learning study (Fig. 3), fifty percent of the experimental zebrafish received a training procedure during which the spatial location of the stimulus fish was constant relative to extra-maze cues and this location was also marked by a visual cue (paired group). The other fifty percent of the experimental fish (unpaired group) was exposed to the same experimental procedures as fish of the paired group. But these fish received the stimulus fish at random locations (relative to external visual cues), and the single visual cue (CS) that was used to mark the reward location for the paired group, was also presented in random places in the experimental tank. Two probe trials were administered during which no stimulus fish were present. Fifty percent of fish of the paired and the unpaired groups received a probe trial during which the single visual cue (CS) was presented at a location other than where the stimulus fish used to be during training (probe 1). Probe 1 aimed to test whether the fish learned the CS-US association, an elemental learning task. The other fifty percent of the fish of the paired and the unpaired groups were given a probe trial during which neither the stimulus fish nor the visual cue (CS) was present (probe 2). Probe 2 was conducted to test whether the experimental fish learned the spatial location of the stimulus fish. Not surprisingly, the fish of the unpaired group showed chance level performance both in probe 1 and probe 2. They showed no preference towards the visual cue (CS) nor did they show any spatial bias towards the prior location of the stimulus fish. Interestingly, however, fish of the paired group showed excellent learning performance in both probe trials. These fish stayed in close proximity of the visual cue when it was presented, and they stayed in close proximity of the prior location of the stimulus fish when this single associative cue was absent. In fact, fish of the paired group showed a somewhat stronger

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preference for the spatial location in probe 2 than for the location of the visual cue in probe 1, perhaps because during probe 1 the location of the visual cue conflicted with the prior spatial location of the stimulus fish (Fig. 3). In summary, zebrafish performed similarly to rats and mice with normal hippocampal function, and could learn simultaneously both a salient associative cue and another piece of information that allowed the fish to identify the prior location of the reinforcer. Does this unequivocally prove the existence of spatial learning ability in zebrafish? No, not unequivocally, because it is still possible that fish of the paired group learned two salient cues, one that the experimenter controlled (the CS) and another that the fish picked out from the background. Nevertheless, decades of research on associative learning suggests that a non-hippocampal elemental learning strategy would not allow the simultaneous acquisition of association between the reinforcer and a salient foreground and a more diffuse background cue. Likely, the correct interpretation of the results of the above described zebrafish experiment is similar to that of the cited rodent studies: the good spatial learning performance likely reflects actual establishment of spatial memory, i.e. the zebrafish is capable of relational learning. A similar conclusion was reached by Rodríguez et al. (2002) who were able to dissociate geometry, featural or ego-centric cue based learning in the gold fish, and concluded that this species (a close relative of the zebrafish) is capable of spatial learning. Similarly, Hamilton et al. (2016) concluded that zebrafish are capable of the acquisition of episodic-like memory. These authors based this conclusion on their results obtained with a novel object/context configuration discrimination task that followed the procedures of a task designed previously for mammals (Eacott and Norman, 2004). Hamilton et al. (2016) found that zebrafish preferred an object that was placed at a new location considering the context in which the test was performed. In order for the experimental fish to show such preference, they had to remember the object (what), the difference between its previous and current locations (where), and the also remember the context, The background color of the test chamber (which was time dependent, past vs present, i.e. when). Given that the definition of episodic information is the relationship between what when and where type information, the authors concluded that zebrafish can solve episodic memory tasks. Although suggestive, the interpretation of their results is equivocal. The contextual information employed in their study, for example, is overly simplistic, it is merely a single color, as opposed to a complex set of spatial cues. Furthermore, and perhaps most importantly, the visual features of the objects used may be perceived differently depending on the background used. That is, the objects may have appeared different depending on the background color (“context”) employed. Although cones in the retina are known to have species-specific light frequency peak sensitivities that remain stable (the zebrafish, for example, is tetrachromatic, i.e. has four distinct cones), central processing of visual cues and interpreting their color depends upon relative comparisons between the color of the cue and the color of the background. In other words, the task as administered by Hamilton et al. (2016) may have been a simple object discrimination task in which only the location of the object had to be learned and remembered. This task is still a relational learning task as it requires the acquisition of spatial information based upon geometric cues, but it does not satisfy the requirements of a true episodic memory task. Irrespective of these alternative possibilities, the Hamilton et al. (2016) and the Rodríguez et al. (2002) studies together with the Karnik and Gerlai (2012) study reinforce the notion that cyprinids, and specifically the zebrafish, may be able to solve relational learning tasks. But how is this possible? Fish do not posses a structure that would resemble the mammalian hippocampus, the structure

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Fig. 3. Spatial learning in the open tank. Panel A: the tank contained four stimulus tanks placed adjacent to the walls (made of transparent glass). One of the stimulus tanks contained five conspecific stimulus fish during training. Behind one of the stimulus tanks a red plastic cue card could be placed. The dimensions of the experimental tank along with the dimensions of certain areas of this tank are indicated in cm. Experimental zebrafish were divided randomly into two groups: paired (for which the stimulus fish were presented in a single spatial location which was also marked with a red cue card, indicated as a grey rectangle behind the stimulus fish in this figure) and not-paired (for which the stimulus fish were presented at random locations, and for which the red cue card was given also at random locations). Fish in the “paired” group received the cue card always behind the tank that contained the conspecifics, and both this cue card and the stimulus tank that contained the conspecifics were placed in a constant location relative to extra-tank visual cues. Fish in the “not-paired” group received the cue card at random order behind the 4 stimulus tanks and the stimulus tank that contained the stimulus fish was also placed at the four possible locations in a random order. Each day, four 5-min long training trials were administered for a total of five consecutive days. For each trial, the experimental fish was removed individually from the holding tank and transferred to the center start box of the experimental tank (not shown), where it was allowed to acclimatize for 10 s. The box was then lifted remotely using a metal rod and the trial commenced. The fish was allowed to explore the experimental tank while the experimenter waited outside of the testing room. The room in which the experiment was conducted contained numerous visual cues including large pieces of equipment, shelf units, fluorescent light fixtures and several complex three-dimensional Styrofoam blocks attached to the walls of the room and painted with different colors. Panel B: shows the results obtained during probe trials. The probe trials took place one day after the last training trial. During the probe trials, no stimulus fish were presented and only a single probe trial of 5 min was run for each fish. From each of the two training groups, experimental fish were randomly assigned to one of two probe groups: probe 1 with cue card and probe 2 with no cue card (a 2 × 2 between subject design with training as a factor with two levels [paired and not-paired], and probe with two levels [cue card present, cue card absent]). In probe 1, experimental fish received the cue card at any one of the four locations behind one of the stimulus tanks. The location of presentation across multiple experimental fish followed a random order. Probe 1 was expected to test whether experimental fish learned the association between the conditioned stimulus (cue card) and the reward (stimulus fish). Probe 2 was expected to reveal whether the past spatial location of the stimulus fish was learned. Zebrafish that received the paired training showed significant preference towards the red cue card during probe 1, and also towards the prior location of the stimulus fish during probe 2. The data are expressed as percent of time spent per unit of area of the tank. Mean ± S.E.M. are shown, sample sizes (n) equal 10 in each group. Random chance is indicated by the straight horizontal line. Note that fish of the not-paired training group are statistically indistinguishable from chance. Also note that performance of the fish of the paired training group is significantly above chance in both probe 1 and 2, and these fish are also significantly above the performance of the fish of the not-paired group. Modified from Karnik and Gerlai (2012).

known to be crucial for relational, including spatial, learning and memory in mammals. 6. Mechanisms: the question of construct validity of the zebrafish spatial learning model Although fish indeed do not have a hippocampus, there is a homologous area in their brain, the lateral pallium, that is considered the evolutionary precursor of the mammalian hippocampus (Portavella et al., 2002; López et al., 2000). The reason why this is notable is that the mammalian hippocampus is a complex structure with anatomical, circuitry cellular and synaptic level features that each contributes slightly differently to learning and memory. For example, each component of the hippocampal tri-synaptic circuit (CA1-CA3-dentate gyrus) may serve different functions, and most recently even the CA2 region is considered as having a unique role (Oh et al., 2016; Dudek et al., 2016; Kitamura et al., 2015; Tannenholz et al., 2014; Kesner, 2013). In addition, major anatomical parts of the hippocampus, e.g. the dorsal versus the ventral region may play different roles in mammalian learning and memory (Fanselow and Dong, 2010). In summary, teasing apart the roles this mammalian brain structure plays in learning and memory processes has been a rather daunting task. Beyond the questions concerning the idiosyncratic features of the hippocampus, some argue that spatial learning mechanisms may be better understood at the level of synaptic function (Emes et al., 2008; Nithianantharajah et al., 2013). However, synaptic mechanisms may be more difficult to study in vivo in an organism as complex

as mammals, i.e., in which the hippocampus and many different parts of it may all complicate the picture. Fish may offer a simpler alternative, the possibility of a reductionist approach in which the investigator may be able to dissociate hippocampal complexity from the more essential and functionally and evoluntionarily more fundamental synaptic level mechanistic details (Gerlai, 2016, 2014). Do we have any evidence that synaptic mechanisms similar to mammalian processes may underlie learning and memory in fish? This is an important question, one which is often referred to as the question of construct validity of the fish model. Although zebrafish, and fish in general, are quite new in neuroscience in general and learning and memory research in particular, some evidence already does exist demonstrating mechanistic similarities between mammalian and fish learning and memory processes. Without trying to be comprehensive, I highlight a few important examples. Fish possess neurotransmitter systems highly similar to mammalian systems, with neurotransmitter receptors whose amino acid sequence, and thus structure and function are similar to those of mammalian counterparts (Rico et al., 2011; Ryu et al., 2006; Lillesaar, 2011; Chatterjee et al., 2014). The reason why this is noteworthy from an experimental viewpoint is that it allows drugs and other small molecules that have been previously developed for mammalian receptors to be used in fish research with excellent efficacy (Rico et al., 2011; Kalueff et al., 2014). The mechanistic similarities also suggest that genes discovered as serving a particular function in the zebrafish will likely have homologues in mammals. Such genes can easily be identified due to the high

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Fig. 4. MK801 significantly impairs consolidation and recall of memory in the plus maze. Panel A. The plus maze consisted of four end compartments and one center compartment (linear dimensions of the maze are indicated in cm, the depth of the maze was 10 cm). In the middle of the center compartment, the start box is shown. Each end compartment contained a stimulus tank. During training, one of the stimulus tanks contained stimulus fish (5 zebrafish, indicated) the others were empty (grey fill). The end compartment with the stimulus fish is designated as the target compartment (gray dashed background). Note that the empty stimulus tanks are surrounded on three sides by white cover sheets (CS-, indicated by the black solid line) so as to prevent visual access to their content from all directions except from behind the tank. Also note that the stimulus tank with the stimulus fish also had cover sheets on three sides but the color of the sheets was red (the CS+, indicated by the gray and black patterned line). Other walls of the maze were transparent. Experimental fish were trained in the maze singly. The fish was placed in the release box positioned in the central compartment of the maze, and after a 30 s acclimation period it was released into the maze by lifting the box up using a nylon string from a remote location. The fish was allowed to explore the maze for 5 min. Each fish had four consecutive trials a day with an inter-trial interval of 2 min. Training was conducted for four consecutive days. After the 16 training trials, all experimental zebrafish were tested in a probe trial. The probe trial was identical to the training trials except that no stimulus fish were presented in any tank. Fish that associated the red color cue (CS) with the presence of conspecifics (US) were expected to choose the red colored stimulus tank and spend more time in the target compartment. Panel B shows the results of the probe trial. Percent of time in the target compartment during probe trial was found significantly reduced by post-training trial and pre-probe trial administration of MK-801. Mean + Standard Error are shown. n = 12 for each group. The gray checkered (0 ␮M MK-801) and black (20 ␮M MK-801) bars represent fish that were administered paired CS-US during training. Fish represented by the white bar were administered the CS and the US in a randomized manner (unpaired control) during training and these fish did not receive MK-801. All these groups were treated and tested in a fully randomized manner and blind with regard to their experimental condition. Also note that the performance of fish that received MK-801 after training or before probe is statistically indistinguishable from that of the unpaired control, a performance level considered baseline, but is significantly below of the performance of fish of pared groups that did not receive MK-801. Modified from Sison and Gerlai (2011a).

nucleotide sequence homology between zebrafish and mammalian genes (Howe et al., 2013). For example, in my own laboratory we were able to utilize a mammalian drug, MK801 to manipulate learning and memory because the molecule it is expected to bind to, the NMDA-receptor, is highly similar between fish and mammals (Sison and Gerlai, 2011a,b). (dizocipline or [5R,10S]-[+]-5-methyl-10,11MK801 dihydro-5H-dibenzo[a,d]cyclohepten-5,10-imine) is a selective non-competitive NMDA-R (N-methyl-d-aspartate-receptor) antagonist. It binds inside the ion channel of the NMDA-R and prevents the flow of ions through the channel. NMDA-R is a coincidence detector, a ligand- AND voltage-gated ion channel in the post-synaptic terminal of the synapse. NMDA-R opens and

allows cations (calcium and sodium ions) to flow through only when glutamate released from the pre-synaptic terminal (signal from neuron 1) binds to this receptor, and this binding coincides with depolarization of the post-synaptic terminal (resulting from a back-propagating action potential triggered in the target neuron by another stimulus coming from neuron 2). Without the coincidence of these two stimuli (carried by neurons 1 and 2) converging on the target neuron, the receptor remains closed, and the cascade of events required for neuronal plasticity (strengthening or weakening of the given synaptic connection) does not occur. Thus, the NMDA-R, among other molecular components, is considered a crucial part of synaptic plasticity, a process that is believed to underlie associative learning and memory consolidation (Morris

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et al., 1990; Collingridge et al., 1992; Izquierdo, 1991; Shimizu et al., 2000). Importantly, MK801 has been found to impair learning and memory both in mammals and in zebrafish (Butelman, 1989; Ogura and Aigner, 1993; Ng et al., 2012; Sison and Gerlai, 2011a). I focus here on the latter. Concentrations of MK801 shown not to alter performance characteristics including motor function in zebrafish (Sison and Gerlai, 2011b) have been shown to significantly impair learning performance if the drug is administered within the consolidation window, i.e. within 90 min after training, or right before probe trials (Sison and Gerlai, 2011a) (Fig. 4). These results suggest that MK801 impairs, and thus NMDA-R is involved in, memory consolidation and recall in zebrafish (but see Xu et al., 2007), findings that partially coincide with what we know about these processes in mammals (Izquierdo and Medina, 1997; Abel and Lattal, 2001). The mammalian and our own zebrafish results only partially match because mammalian studies often find NMDA-R to be involved in consolidation but not in recall processes (for review see e.g. Abel and Lattal, 2001). Our understanding of memory retrieval or recall is far less advanced compared to what we know about the mechanisms of memory acquisition and consolidation. Nevertheless, the emerging picture shows that memory recall is also an active synaptic processes which may involve numerous biochemical pathways and neuroanatomical structures previously assigned specifically to acquisition and/or consolidation of memory (Ben-Yakov et al., 2015; Riedel et al., 1999; Holt et al., 1999). Once we better understand the temporal sequence of events and the intricacies of the biochemistry and neuroanatomy of memory recall, the role of NMDA-receptors may be more firmly established. Although less well studied in the zebrafish, there is also direct evidence for synaptic plasticity in this species. Long-term potentiation (LTP), the long lasting strengthening of synaptic efficacy, has been demonstrated in the zebrafish (Nam et al., 2004). LTP, together with the opposite process LTD (long-term depression), is believed to be the basis of memory formation (Martin et al., 2000). LTP (and LTD) is particularly important in the hippocampus, and has been shown to be crucial in relational (including spatial) learning (Martin et al., 2000; Morris et al., 1990). Do these findings prove that zebrafish has episodic memory? No, they do not. Nevertheless, they do demonstrate many similarities between fish and mammals at multiple levels of biological organization in the context of learning and memory. Notably, a learning task that would combine the need to acquire and remember information on what, where and when happened (the basic definition of episodic memory) has not been used in fish research, at least not yet. However, the definition of human episodic memory appears to be like a moving target, it is continually dressed with increasing sophistication and human-specific features, which makes animals, and animal researchers, unable to keep up, hence the twisted phrase we now often see in the literature “episodiclike” memory in animals (Dere et al., 2005; Morris, 2001). But the relevant question may not be whether zebrafish possesses human episodic memory. The relevant question is whether one can identify the rudiments, the basic building blocks of episodic memory in fish. The analysis of place cells (mammalian hippocampal pyramidal neurons) has revealed that these cells do not only encode place (spatial information), they do not only encode time (temporal information), but rather they encode both (Howard and Eichenbaum, 2015), and perhaps much more. These cells are multimodal relational information processing units that calculate relationships among any and all sorts of stimuli and information that is processed by the brain. In the same vein, episodic-memory of what, where and when happened in the life of an individual (human or non-human animal) may be but one of many possible relational type memories. The evolutionarily ancient, fundamental aspects of the mechanisms of relational information processing systems potentially found in

fish thus may be a highly important research focus. It may allow us to understand the evolution of such systems, as well as the core, most fundamental mechanistic underpinnings of these biological phenomena. It may allow us to learn about how episodic memory arose in our own species, and how relational memory really works in a variety of vertebrates. Given the increasingly sophisticated multidisciplinary techniques developed for the zebrafish, the zebrafish may be just the right tool for enriching our research to address these questions. Declaration Experiments conducted in the laboratory of the author reviewed in this paper were performed according to the Federal (Canadian Council for Animal care, CCAC, Canadian Food Inspection Agency, CFIA), Provincial and Local (University of Toronto Animal Care Committee and University of Toronto Mississauga Local Animal Care Committee) guidelines for the maintenance and use of animals in research, which meet or exceed the guidelines set forth by National Institutes of Health guide for the care and use of Laboratory animals (NIH Publications No. 8023, revised 2011). The author also believes that studies conducted in other laboratories and reviewed in this paper also followed the latter guidelines. Conflict of interest The author declares no conflict of interest. Acknowledgement The author’s research is supported by NSERC Discovery Grant (#311637). References Abel, T., Lattal, K.M., 2001. Molecular mechanisms of memory acquisition, consolidation and retrieval. Curr. Opin. Neurobiol. 11, 180–187. Ahrens, M.B., Li, J.M., Orger, M.B., Robson, D.N., Schier, A.F., Engert, F., Portugues, R., 2012. Brain-wide neuronal dynamics during motor adaptation in zebrafish. Nature 485, 471–477. Al-Imari, L., Gerlai, R., 2008. Sight of conspecifics as reward in associative learning in zebrafish (Danio rerio). Behav. Brain Res. 189, 216–219. Allen, T.A., Fortin, N.J., 2013. The evolution of episodic memory. Proc. Natl. Acad. Sci. U. S. A. 110 (Suppl. 2), 10379–10386. Aoki, R., Tsuboi, T., Okamoto, H., 2015. Y-maze avoidance: an automated and rapid associative learning paradigm in zebrafish. Neurosci. Res. 91, 69–72. Barton, R.A., Dean, P., 1993. Comparative evidence indicating neural specialization for predatory behaviour in mammals. Proc. Biol. Sci. 254, 63–68. Ben-Yakov, A., Dudai, Y., Mayford, M.R., 2015. Memory retrieval in mice and men. Cold Spring Harb. Perspect. Biol. 7, http://dx.doi.org/10.1101/cshperspect. a021790, pii: a021790. Butelman, E.R., 1989. A novel NMDA antagonist, MK-801, impairs performance in a hippocampal-dependent spatial learning task. Pharmacol. Biochem. Behav. 34, 13–16. Chatterjee, D., Shams, S., Gerlai, R., 2014. Chronic and acute alcohol administration induced neurochemical changes in the brain: comparison of distinct zebrafish populations. Amino Acids 46, 921–930. Clayton, N.S., Griffiths, D.P., Emery, N.J., Dickinson, A., 2001. Elements of episodic – like memory in animals. Phil. Trans. Roy. Soc. B 356, 1483–1491. Cohen, N.J., Poldrack, R.A., Eichenbaum, H., 1997. Memory for items and memory for relations in the procedural/declarative memory framework. Memory 5, 131–178. Collingridge, G.L., Randall, A.D., Davies, C.H., Alford, S., 1992. The synaptic activation of NMDA receptors and Ca2+ signalling in neurons. Ciba Found Symp. 164, 162–171. Colwill, R.M., Raymond, M.P., Ferreira, L., Escudero, H., 2005. Visual discrimination learning in zebrafish (Danio rerio). Behav. Processes. 70, 19–31. Crabbe, J.C., Wahlsten, D., Dudek, B.C., 1999. Genetics of mouse behavior: interactions with laboratory environment. Science 284, 1670–1672. Delicio, H.C., Barreto, R.E., 2008. Time-place learning in food-restricted Nile tilapia. Behav. Processes. 77, 126–130. Dere, E., Huston, J.P., De Souza Silva, M.A., 2005. Integrated memory for objects, places, and temporal order: evidence for episodic-like memory in mice. Neurobiol. Learn. Mem. 84, 214–221.

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Please cite this article in press as: Gerlai, R., Zebrafish and relational memory: Could a simple fish be useful for the analysis of biological mechanisms of complex vertebrate learning? Behav. Process. (2017), http://dx.doi.org/10.1016/j.beproc.2017.01.016