Chapter 4.1 Ethological approaches in behavioral neurogenetic research

w.E. Crusio and R.T. Gerlai (Eds.) Handbook of Molecular-Genetic Techniques for Brain and Behavior Research (Techniques in the Behavioral and Neural Sciences, Vol. 13) 9 1999 Elsevier Science BV. All rights reserved. CHAPTER

4.1

Ethological approaches in behavioral neurogenetic research Robert Gerlai Neuroscience D e p a r t m e n t , G E N E N T E C H

Inc., 1 D N A

Introduction

Advances in recombinant DNA and transgenic technology have led to the dawn of a new era for neuroscience: manipulation of single genes and analysis of gene expression changes make it possible to dissect the complexities of neurobiological phenotypes and to understand many of the intricacies of the molecular and neurobiological mechanisms underlying brain and behavior, even in mammals. Thus the goal of bridging the gap from gene to behavior appears closer to reach. The dazzling technological advances in recombinant DNA methodology, however, should not diminish the fact that both pillars of the bridge, i.e. genetics and behavioral science, should stand on firm ground. In this chapter I focus on behavior and argue that appropriate behavioral experimentation is just as important as the genetic techniques. I critically review part of the behavioral literature and argue that principles of ethology may best guide the behavioral scientist in designing behavioral experiments and selecting behavioral phenotypes to measure in a neurobehavioral genetic study. I will focus my attention on the phenotypical analysis of the effects of mutations, introduced by transgenesis or gene targeting in mice, that affect hippocampal function. These studies have yielded fascinating results and may also be viewed

Way, South San Francisco, CA 94080-4990, U S A

as examples that allow the reader to draw general conclusions. I will attempt to persuade the reader that behavioral approaches that take species-specific characteristics into account and use ethological methods may be most useful for analyzing the genetic and neurobiological mechanisms underlying behavior, and without ecological relevance interpreting the behavioral findings and understanding the biological mechanisms of brain function may be difficult. The role of the hippocampus in learning and memory has fascinated neuroscientists ever since the discovery that HM and other patients with hippocampal damage suffer devastating learning and memory deficits (Scoville and Milner, 1957). Since then the hippocampus has been studied at almost every level of analysis from detailed behavioral studies of hippocampal dysfunction to the physiology and molecular mechanisms of neural plasticity. Several gene targeting and transgenic studies have also been carried out to study the behavioral effects of mutations on hippocampal function. However, behavioral analyses can be complicated, and since the experimenters of these gene targeting studies do not always have extensive training in animal behavior, it is a particularly apt time to reconsider some important principles about behavior, and in particular the ethological relevance of these experiments.

606

As several chapters of this book demonstrate, transgenic and gene targeting techniques are powerful tools for assessing how genes influence brain function in vivo. One can express a gene in elevated amount (Gerlai, 1995), decrease or eliminate gene expression using "knock outs" (Grant et al., 1992), and introduce mutations in a targeted manner (Bach et al., 1995). Much of this technology has been carried out on mice because they live and breed readily in captivity and have a short generation time. A large number of transgenic mice have been generated in the hope that analyzing their phenotype will shed light on functional aspects of the mutated gene and the molecular, biochemical pathways in which that gene may be involved. Unfortunately, it is not always possible to foresee what phenotypical changes the introduced mutation may cause because the disruption of a single gene may lead to a cascade of events, e.g. there may be compensatory effects of other genes, the presence or absence of which may vary depending on the mouse strain used (Gerlai, 1996).

Behavioral analysis may reveal abnormal brain function Of the many approaches used to analyze phenotypical effects of mutations on brain function (Grant et al., 1992; Bach et al., 1995; Gerlai, 1995; 1996), behavioral analysis is perhaps the most tricky: experiments appear easy to execute and quantitative data can be collected quickly, yet there are several difficulties with analyzing the behavioral effects of a mutation on hippocampal function. An obvious problem is that certain basic performance factors may influence behavioral results: A mouse with a motor impairment may finish a test more slowly; and a short-sighted rat may not see the visual cue to be learnt. These problems have been extensively dealt with in the literature and the reader will also find several excellent answers to these questions in the following chapters of this book.

The present chapter is centered around another problem, the ethological relevance of the behavioral task. As Nadel (1995) states, many studies of hippocampal function have been "nature-blind" in the sense that they "bear no obvious relation to what the animal does to survive and flourish in the real world", an approach that "has led the field astray in the past, and will continue to do so in the future". Why is it important not to be nature-blind?

Clever experimenters and "dumb" rats: it all boils down to genetic predisposition In the laboratory, animals may behave in ways that are difficult to interpret, in part because they are genetically predisposed to selectively attend to, process, and recall certain specific stimuli that may be altered under artificial conditions. Early examples demonstrating the importance of designing ethologically-relevant tasks came from studies of learning in rats. For example, rats learned classical conditioning avoidance tasks quickly when food was associated with nausea-inducing substances, and when light was associated with foot shock, but found it much harder to learn the reverse associations (Garcia and Koelling, 1966), i.e. when food was paired with shock and light with nausea, demonstrating the salience of associating appropriate stimuli. Why cannot all stimuli be associated equally well? Consider that animal behavior has been shaped by natural selection over a long evolutionary past of the species and as a result genetic predispositions may facilitate or inhibit making associations between certain stimuli. Returning to the above example, it is clear that encountering a predator can rarely cause nausea in rats and food substances with unusual taste can hardly perform a painful attack, whereas the reverse situation (sight of predator ~ pain; food ~ nausea) is much more plausible, i.e. ecologically. A skeptic might argue that the ethological relevance is not important as long as all animals

607

are exposed to the same task in a controlled way. This argument ignores the fact that artificial tasks may be less sensitive to differences between mutant and wild type animals. The problem is threefold. First, animals may respond to stimuli that seem irrelevant to the experimenter, or may not attend to those judged crucial for solving the task. Second, irrespective of genotype, experimental animals may find ethologically-inappropriate tasks difficult to solve. If performance is poor in all subjects then mutation-related impairments will go undetected. Third, a learning paradigm which ignores the behavioral ecology of the species may be swamped by environmental error variation (phenotypical behavioral variance arising from random variations in the environment during rearing or during the experiment) (Gerlai, 1996), and will be less sensitive to detecting behavioral differences that arise as a result of genetic manipulation (Van Abeelen, 1979; Gerlai, 1996). Finally, as the goal of any neurobehavioral genetic study is to understand the biological mechanisms underlying brain and behavior, it makes sense to focus on behaviors that are not artificially construed but represent ecologically relevant phenes. Why should the analysis of ecologically relevant behaviors facilitate understanding underlying genetic / biological mechanisms? Consider again the effects of natural selection on brain and behavior. Natural selection operates on the genetic level, i.e. it changes allele frequencies in the population. In the case of brain function, the basis of favoring or disfavoring a genotype is the behavioral performance of the given organism. It is the adaptive nature of behavioral traits which makes an animal fit and not the underlying neurobiological or molecular machinery. Clearly, genes that affect brain function have been under selection pressure on the basis of how they influenced the behavioral performance of the animals harboring them. Thus in order to study such genes and their role in brain function, it may be advantageous to analyze appropriate behaviors that correspond to ecologically relevant traits favored by natural

selection. But how can one define ecological relevance and design appropriate behavioral tests?

Know your subject: behavior is species-specific! Species differences in learning are inevitable because each species faces different problems in nature. As Kamil and Maudlin point out "The effects of a learning procedure upon a species will depend on how the learning paradigm makes contact with the adaptations and response repertoire of the animal" (Kamil and Maudlin, 1988). Consider the performance of various rodent species which have been tested on the Morris watermaze, a spatial and relational learning task, that was originally designed for rats (Morris, 1984) which inhabit wetlands (Nowak and Paradiso, 1983). Transgenic studies of the molecular mechanisms of learning use the house mouse (Mus musculus domesticus) rather than the rat because a wealth of information is available on the genetics of mice and most transgenic and gene targeting procedures have been worked out on this species. Although the mouse is closely related to the rat, its natural history and habitat is different. For instance, mice burrow in dry fields such as savannas, grasslands and forests as opposed to wetlands (Sage, 1981; Nowak and Paradiso, 1983; Bonhomme, 1992). In the laboratory, mice have been found to perform below the level of rats in the watermaze (Whishaw et al., 1995), an impairment that cannot be attributed to body size alone. Under similar training conditions and performance requirements mice needed approximately five times more trials (Grant et al., 1992; Gerlai, 1995) than rats (Eichenbaum et al., 1988) to reach a plateau in their learning curves. Given their differences in habitat, mice may not be so well adapted to water as rats, and so the difference in performance may reflect species differences in task suitability rather than memory ability. Support for this claim comes from comparative studies of how other rodents perform on the watermaze. For example, meadow voles (Microtus pennsylvanicus) which

608 inhabit wetlands (Kavaliers and Galea, 1994) perform well, and island populations of deer mice (Peromyscus maniculatus) that frequent marshes in the wild perform better than those that originated from an arid mainland population (Galea et al., 1994).

Designing the right task for the right species An understanding of the species' natural history allows the experimenter to design an ethologically appropriate task by predicting what sort of motor responses may occur, what kind of stimuli the experimental subjects are more sensitive to, and what kind of cognitive constraints characterize their learning abilities. There are numerous examples of species-specificity in motor responses, and the salience of associating the appropriate stimulus and response, e.g. rats learn to bar-press for food and to jump out of a box to avoid shock, but learn the opposite associations with extreme difficulty (Timberlake and Lucas, 1989). Species also differ in the type of sensory stimuli they attend to. Many studies show human-bias. For example, much of the behavioral research on rodents has been concerned with the development of tasks which depend upon the ability to process visuospatial information during daylight (Lee et al., 1998) yet rodents are nocturnal, having poorer visual acuity than humans but possessing excellent olfactory capabilities. It is only relatively recently that researchers have begun to consider olfactory cues in their spatial learning tasks (Eichenbaum et al., 1988; Reid and Morris, 1992; 1993; Lavenex and Schenk, 1995). Such studies now show that nocturnal rodents do not necessarily rely on olfactory cues for solving spatial tasks (Reid and Morris, 1992; Reid and Morris, 1993), but the degree to which they rely on memory of olfactory versus visual cues depends on the test conditions (Lavenex and Schenk, 1995). It is therefore important to test which cues the subjects use for each particular paradigm. Animals may also depend on different sensory modalities at different times of day or season, e.g.

yellow pine chipmunks (Tamia amoenus) rely on spatial memory to locate hidden seed caches during the dry season but use olfactory cues instead of memory during rainy season (Vanderwall, 1991). The degree of hippocampal involvement in learning and memory can depend upon the types of cues used and when, as well as task difficulty and level of motivation, so performance on conventional learning tasks may reveal only part of the story unless they are designed to tap into the animal's natural learning abilities. It is also important to realize that every time a new mutant is tested, novel behavioral responses may be expected. These responses may not necessarily be detected in "standard behavioral assays". Behavior is difficult to assay because it consists of a conglomeration of different but interrelated actions and also because it is affected by a potentially large number of environmental factors that may influence it in a genotype dependent manner. To determine how the mutation alters behavior, pre-existing tests may need to be modified or new ones designed. I suggest that an experimenter armored with knowledge of species-specific characteristics of the animals will be better able to design such tests. Nevertheless, it is tempting to suggest a test that one may consider the best among those available. For hippocampal function, the Morris water maze has generally become the standard (Fig. 1), although some laboratories prefer to use other tasks (Bach et al., 1995), e.g. the Barnes maze, a dry equivalent of the water maze spatial learning task, or the context dependent fear conditioning paradigm (see below). Which task is better? There is no single answer to this question. Although both the Morris water maze and the Barnes dry maze are intended to tap into the animals' spatial abilities, these two tasks are associated with different motivational factors and motor requirements, and they probably evoke different adaptive behavioral responses in the mice being tested. For example, mouse strains that usually exhibit high levels of anxiety are almost impossible to

609 train in the Barnes dry maze because they simply freeze in the large open arena (personal observation), a response probably adaptive in the wild in the avoidance of predators. By contrast, in the Morris water maze, the mouse will be more motivated to actively move and escape from the cold water. It is important to realize, however, that the latter may not always be better. For instance, the Barnes maze has been successfully used in the analysis of the CaMKII-Asp-286 point mutant transgenic mice and has also proven to be superior in investigating alternative strategies of exploratory behavior during spatial learning (Bach et al., 1995). The radial maze may also have some advantages over the above mazes and could be suggested as an appropriate task ecologically relevant under certain circumstances (see Chapter 4.4). The above behavioral tasks are based on decades of experimentation and provide an excellent means with which to study behavior. It is important to remember, however, that there is no one way to run these behavioral tasks, and that behavior is influenced by numerous factors. The question becomes one of not what test to choose, but how to run it.

Exploring novelty, exploring space: conflicting motivational forces The common feature of spatial learning tests applied in recent molecular neurobiology studies, apart from the fact that they were intended to reveal hippocampal dysfunction, is that they represent novel situations in which exploratory behavior is evoked. Exploratory behavior, however, is a complex response to novelty resulting from a compromise between the motivation to gather information about the surroundings, and avoiding predation (Suarez and Gallup, 1981; Crusio and Van Abeelen, 1986). So laboratory conditions which influence hunger, sexual drive, fear and chronic stress could all alter the behavioral performance of the subjects and should be controlled in a learning paradigm in which animals are exposed to novelty. For instance,

increased sex drive may increase active exploration and lead to better learning performance. Dominant males typically exhibit elevated sexual drive compared to subordinates. If mutant males are subordinate to their wild type cage mates (Gerlai, 1996), a significant "spatial learning impairment" might be recorded not because the mutant animals are necessarily less able to learn but simply because of their suppressed sexual drive. Indirect support for this comes from studies which show that changes in circulating levels of sex hormones can have a dramatic effect on spatial learning (Sherry and Hampson, 1998). In meadow voles and deer mice, for example, changes in reproductive status have been shown to influence spatial learning in the Morris watermaze: male deer mice learned significantly faster during the breeding season than during the non-breeding season, whereas the reverse was true for females (Galea et al., 1996). Changes in steroid hormones arise not only as a consequence of changes in reproductive status, but also as a result of changes in various environmental stressors such as competition for food or dominance status, and these changes in hormone levels can have a dramatic impact on hippocampal structure and function (Sapolsky, 1996; McEwan, 1997). Since the position in the dominance hierarchy may determine how well the animal responds to such stress and how much neurological damage is incurred as a result of steroid hormone levels (Sapolsky, 1996), these factors should not be ignored. Furthermore, stress does not only influence the organism's behavior while under test, but also has long term effects on how the animal performs in subsequent tests. Every attempt should be made to minimize the amount of stress to the animal, and to test for differences between mutant and control mice in the way they respond to stress. In the absence of careful control, differences in behavioral performance may reflect differences in the way that animals respond to stress and how these differences affect hippocampal structure and function rather than genetic differences in learning and

610

memory. It is also useful to know what stimuli may elicit stress or what stimuli the experimental species are sensitive to under stressful conditions. For example, a pair of horizontal eye-like spots has been found to elicit aversive behaviors in a

number of species from fish through birds to rodents (Topfil and Cs/myi, 1994), responses that resemble predator avoidance. Dark bird like patterns above a maze of a rodent is also not a good idea, neither is high level of illumination, etc.

D 9OO~ 700 [

\

6oo~. \

-

500

400, 3OO "~ 200 loo 0 0 1 2 3 4 5 6 7 8 9 1 0 1 1

Three-trial sessions Fig. 1. In the Morris water maze rodents are trained to locate and climb onto a safe platform from water. In the spatial version of the task, the platform is hidden just beneath the surface of the water and the experimental subjects need to learn its position in relation to external visual cues around the maze. (A) a mouse has just found the hidden platform and is climbing onto it. Note that the water maze is black (Chen et al., 1997) and that a low illumination level is used in order to reduce stress. The water maze is 2 m in diameter, an unusually large maze for mice compared to those typically used, and this increases the spatial feature of the task. (B) An appropriately trained mouse often rears on the platform. (C) Mice that are not habituated to handling or are improperly handled during training (Gerlai et al., 1995), e.g. picked up from the platform by the tail, exhibit predator avoidance behaviour and actually escape from the platform. (D) An example of the swim path of a mouse at the beginning of training. The platform is indicated by the shaded circle, the starting location of the mouse by the arrow. (E) An example of the swim path of the same mouse 33 trials later. Note the change in the size of the target platform: in order to facilitate learning, at the beginning of training, larger platforms are used and as training proceeds their size is gradually decreased (Eichenbaum et al., 1990). (F) Learning performance in the hidden platform task (Gerlai unpublished data) of C57BL/6 x 129/SV F1 hybrid mice (n -- 10) as expressed in cumulative distance from target. Note that this measure was found to reflect spatial accuracy and spatial learning performance better (Gallagher et al., 1993) than the frequently used escape latency measure. (From Gerlai and Clayton, 1999, with permission.)

611 In summary, knowledge of the natural habitat and behavior of the species under study may be helpful. Stress induced fear responses may complicate the interpretation of spatial learning test results. Mutant and control mice may differ in their fear response, or in ethological terms, their predator avoidance behavior (Topfil and Csfinyi, 1994). Stimuli that are involved in predator recognition and avoidance, including the sight of a human observer, may evoke active or passive defense in novel situations, and if mutant and control mice differ in their responses then this may represent a confounding factor. For example, Silva et al. (1992) claimed that aCaMKII null mutant mice showed spatial learning deficits. However, the apparent "learning" deficit seen in these mutants was later attributed to differences in their fear response (Deutsch, 1993) because the mutants were more anxious when handled (Silva et al., 1992). Thus impaired "learning" performance of aCaMKII mutants might reflect an increase in sensitivity to human handling. Variation in response to human handling is rarely mentioned in the literature and yet it should not be overlooked: in the Morris watermaze, mice may learn to avoid the target platform from which the experimenter picked them up if they are not habituated to the experimenter or not handled appropriately during training (Fig. 1). Depending on the level of fear or pain, the types of stimuli encountered during the experiment, and previous handling procedures, different forms of predator avoidance behavior may be evoked. These responses may range from species-specific passive avoidance (freezing) to vocalization, defensive attack posture, biting, or active avoidance (Blanchard et al., 1989). Given this variation in avoidance response, results could easily be misinterpreted especially if only one behavioral response is measured like in the context-dependent fear conditioning task (Bach et al., 1995), in which freezing is used as a measure of memory of a feared place. A decrease in freezing response is usually interpreted as a loss of memory of the place where the rodent received the shock, but it could also

reflect the fact that the animal responds with behavioral reactions other than passive avoidance. Therefore, recording multiple behavioral responses, e.g. several elements of the natural behavioral repertoire of the species (ethogram) may be advisable. The marriage of field and lab studies

Throughout this paper, I have emphasized the importance of considering the natural behavior of the animal when designing behavioral tests. In order to design ethologically appropriate tasks for the species in question, we need information on how these animals perform under naturalistic conditions. Surprisingly, it is only very recently that such an approach has been adopted to study hippocampal function in rodents in the field (Lavenex et al., 1998; Shiftlett and Jacobs, in press). Ethologists seem to have reserved the right to study more exotic species and neurobiologists appear uninterested in the natural behavior of their laboratory rodents. It would be illuminating to evaluate the spatial memory capabilities of free ranging mice, to determine which cues mice rely on naturally in the wild and how similar their responses are to those of laboratory-raised mice. Observing natural behavior in the wild would possibly also allow experimenters to design novel behavioral tasks in the laboratory that are ecologically relevant. In naturalistic studies spontaneous behavior is observed. There is no artificially controlled punishment or reinforcement to shape the behavior of the animals under study, and no pretraining is enforced by human experimenters (Lavenex et al., 1998). In contrast, laboratory animals are usually confined to the apparatus during the test, and require numerous pretraining trials to teach them which cues to attend to, which motor responses to make, and which stimuli they should learn to associate. By allowing variables to be manipulated in a carefully constrained and controlled way, laboratory tests provide valuable information about what animals are capable of

612

learning. However, they do not tell us how untrained animals would naturally solve these problems. As I argued above, naturalistic studies can inform us about the cognitive processes that are sensitive to natural selection, ones that might have been responsible for the evolution of spatial learning, and such studies may be used in designing behavioral experiments more appropriate for the analysis of neurobiological and genetic mechanisms underlying the behavior in question. The food-caching paradigm serves as a striking example of how an understanding of the species and its natural history can be employed to develop novel approaches to the study of animal learning and memory in the laboratory (Clayton and Dickinson, 1998). Using this paradigm Clayton and Dickinson were able to test, and prove the existence of, episodic-like memory in birds, a type of memory that was previously attributed to our own species only. The advantage of naturalistic/semi-naturalistic tests is that they can be conducted in the laboratory but capitalize on species-specific behavioral capabilities whose ethological relevance is known. A closer synergism between laboratory and field studies holds great promise for the future: The goal is to apply the findings from naturalistic studies to laboratory tests of behavior, and thus achieve rigorous laboratory control while maintaining ethological validity.

Concluding remarks Tempting though it may be to employ a general "behavioral assay" for investigating phenotypical changes that a single gene manipulation may have caused, the studies cited above demonstrate that we need to consider the ethological relevance of the tasks for the species in question and that we may need to modify existing tests or design new ones, depending on the question asked and the genetic manipulation we employ. Whilst the importance of using ethologically-relevant tasks does not imply that there are no general features

and common molecular mechanisms underlying behavioral phenomena, a knowledge of the species-specific characteristics of behavior is essential for discovering these commonalities and generalizing the findings of animal research to human. The challenge is to design appropriate behavioral tests which tap into the natural memory capabilities of the species in question, and thereby make these tests sensitive enough to detect the genetic effects which we wish to study. In the following chapters of the book the reader will find numerous examples in which the ethological approach is combined with laboratory testing.

Acknowledgments This chapter is based on two previously published papers (Gerlai, 1996; Gerlai and Clayton, 1999). I thank Pam Banta, Nicky Clayton, Wim Crusio, Tony Dickinson, Dan Griffiths, Martin Kavaliers, Sandra Kelly, Pierre Lavenex and Stephen Maxson for their useful comments on an earlier version of this paper and Kevin Ling for video processing.

References Bach, M.E., Hawkins, R.D., Osman, M., Kandel, E.R. and Mayford, M. (1995) Impairment of spatial but not contextual memory in CaMKII mutant mice with a selective loss of hippocampal LTP in the range of the theta frequency. Cell, 81: 905-915. Blanchard, R.J., Blanchard, D.C. and Hori, K. (1989) An ethoexperimental approach to the study of defense. In: R.J. Blanchard, P.F. Brain, D.C. Blanchard and S. Parmigiani (Eds.), Ethoexperimental Approaches to the Study of Behavior, NATO ASI Series. Dordrecht. Kluwer Academic Press, pp. 114-136. Bonhomme, F. (1992) In: D. Goldowiz, D. Wahlsten and R.E. Wimer (Eds.), Techniques for the Genetic Analysis of Brain and Behavior: Focus on the Mouse, Amsterdam, Elsevier, pp. 41-56. Chen, K.S., Nishirnura, M.C., Armanini, M.P., Crowley, C., Spencer, S.D. and Phillips, H.S. (1997) Disruption of a single allele of the nerve growth factor gene results in atrophy of basal forebrain cholinergic neurons and memory deficits. Journal of Neuroscience, 17(19): 7288-7296. Clayton, N.S. and Dickinson, A. (1998) Episodic-like memory during cache recovery by scrub jays. Nature, 395: 272-274.

613

Crusio, W.E. and Van Abeelen, J.H. (1986) The genetic architecture of behavioural responses to novelty in mice. Heredity, 56: 55-63. Deutsch, J.A. (1993) Spatial learning in mutant mice. Science, 262: 760-763. Eichenbaum, H., Fagan, A., Mathews, P. and Cohen, N.J. (1988) Hippocampal system dysfunction and odor discrimination learning in rats: impairment or facilitation depending on representational demands. Behav. Neurosci., 102: 331-339. Eichenbaum, H., Stewart, C. and Morris, R.G. (1990) Hippocampal representation in place learning. Journal of Neuroscience, 10(11): 3531-3542. Galea, L.A., Kavaliers, M. and Ossenkopp, K.P. (1996) Sexually dimorphic spatial learning in meadow voles (Microtus pennsylvanicus) and deer mice (Peromyscus maniculatus). J. Exper. Biol., 199: 195-200. Galea, L.A., Kavaliers, M., Ossenkopp, K.P., Innes, D. and Hargreaves, E.L. (1994) Sexually dimorphic spatial learning varies seasonally in two populations of deer mice. Brain Res., 635:18-26. Gallagher, M., Burwell, R. and Burchinal, M. (1993) Severity of spatial learning impairment in aging: development of a learning index for performance in the Morris water maze. Behavioral Neuroseience, 107(4): 618-626. Garcia, J. and Koelling, R.A. (1966) Relation of cue to consequence in avoidance learning. Psychonom. Sci., 4: 123-124. Gerlai, R. (1995) Over-expression of a calcium binding protein, S100b, in astrocytes alters synaptic plasticity and impairs spatial learning in transgenic mice. Learning and Memory, 2: 26-39. Gerlai, R. (1996) Molecular genetic analysis of mammalian behavior and brain processes: Caveats and perspectives. Sem. Neurosci., 8: 153-161. Gerlai, R. and Clayton, N.S. (1999) Analysing hippocampal function in transgenic mice: an ethological perspective. Trends Neurosci., 22: 47-51. Grant, S.G., O'Dell, T.J., Karl, K.A., Stein, P.L., Soriano, P. and Kandel, E.R. (1992) Impaired long-term potentiation, spatial learning, and hippocampal development in fyn mutant mice. Science, 258: 1903-1910. Kamil, A.C. and Maudlin, J.E. (1988) In: R.C. Bolles and M.D. Beecher (Eds.), Evolution and Learning, Lawrence Erlbaum, pp. 117-134. Kavaliers, M. and Galea, L.A. (1994) Spatial water maze learning using celestial cues by the meadow vole (Microtus pennsylvanicus). Behav. Brain Res., 61: 97-100. Lavenex, P. and Schenk, F. (1995) Influence of local environment olfactory cues on place learning in rats. Physiol. Behav., 58: 1059-1066. Lavenex, P., Shiftlett, M.W., Lee, R.K. and Jacobs, L.F. (1998) Spatial versus nonspatial relational learning in free-ranging fox squirrels. J. Comp. Psyehol., 112:127-136. Lee, D.W., Miyasato, L.E. and Clayton, N.S. (1998) Neurobiological bases of spatial learning in the natural environment: neurogenesis and growth in the avian and mammalian hippocampus. Neuroreport, 9:R15-27.

McEwan, B. (1997) Ovarian steroids and the brain: Implications for cognition and aging. Neurology, 48: 8-15. Morris, R. (1984) Developments of a water-maze procedure for studying spatial learning in the rat. J. Neurosr Methods, 11: 47-60. Nadel, L. (1995) In: E. Alleva, A. Fasolo, H.-P. Lipp, L. Nadel and L. Ricceri, (Eds.) Behavioral Brain Research in Naturalistic and Semi-Naturalistic Settings, Kluwer Academic Press, pp. 323-342. Nowak, R.M. and Paradiso, J.L. (1983) Walker's Mammals of the World, 4th Edn, The John Hopkins University Press, pp. 569-1362. Reid, E.N. and Morris, R.G.M. (1992) Smells are no surer: rapid improvement in olfactory discrimination is not due to the acquisition of a learning set. Proc. Roy. Soc. Lond. B, 247: 137-143. Reid, E.N. and Morris, R.G.M. (1993) The enigma of olfactory learning. Trends Neurosci., 16: 17-20. Sage, R.D. (1981) In: H.L. Foster, J.D. Small and J.G. Fox, (Eds.), The Mouse in Biomedical Research, Academic Press, pp. 34-57. Sapolsky, R.M. (1996) Why stress is bad for your brain. Science, 273: 749-750. Scoville, W.B. and Milner, B. (1957) Loss of recent memory after bilateral hippocampal lesions. J. Neurol. Neurosurg. Psych., 20:11. Sherry, D.W. and Hampson, E. (1998) Evolution and the hormonal control of sexually-dimporphic spatial abilities in humans. Trends Cogn. Neurosci., 1:17-22 Shiftlett, M. and Jacobs, L.F. Spatial orientation on a vertical maze in free-ranging fox squirrels. J. Comp. Psychol., in press. Silva, A.J., Paylor, R., Wehner, J.M. and Tonegawa, S. (1992) Impaired spatial learning in alpha-calcium-calmodulin kinase II mutant mice. Science, 257:206-211. Suarez, S.D. and Gallup, G.G. (1981) An ethological analysis of open-field behaviour in rats and mice. Learning and Motivation, 12: 342-363. Timberlake, W. and Lucas, G.A. (1989). In: S.B. Klein and R.R. Mowrer (Eds.), Contemporary Learning Theories: Instrumental Conditioning Theory and the Impact of Biological Constraints on Learning, Lawrence Erlbaum, pp. 237-275. Topfil, J. and Csfinyi, V. (1994) The effect of eye-like schema on shuttling activity of wild house mice (Mus musculus domesticus): Context-dependent threatening aspects of the eyespot patterns. Anim. Learn. Behav., 22: 96-102. Van Abeelen, J.H.F. (1979) In: J.R. Royce and L.P. Mos (Eds.), Theoretical Advances in Behavior Genetics, Sijthoff and Noordhoff, pp. 101-119. Vanderwall, S.B. (1991) Mechanisms of cache recovery by yellow pine chipmunks. Animal Behav., 41:851-863. Whishaw, I.Q., Cassel, J.C. and Jarrad, L.E. (1995) Rats with fimbria-fornix lesions display a place response in a swimming pool: a dissociation between getting there and knowing where. J. Neurosci., 15: 5779-5788.