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Different bodies, different memories






 

Post-training memory modulation is seen in many species, including bees, fish, birds, and mammals and this argues that the basic learning process was developed early in evolution. A comparative approach to understanding learning and memory can provide a broader perspective on how brains encode, maintain and retrieve information. We will consider several striking examples in order to imagine the complexity of the relations between brain and behaviour in different creatures who learn and remember.

We start from invertebrate representatives that differ from vertebrates in evolution and functional organisation of their nervous systems (Sarnat and Netsky, 1981). These differences are so fundamental that many students of animal learning and memory argue that invertebrates lack that autoassociative mechanism, and hence lack episodic event memory. Indeed, for many invertebrates, a teaching input (" unconditioned stimulus, " UCS) must position its synapse on a single stimulus which is to be conditioned (CS). The teaching input conditions just that one stimulus. In the vertebrate, a teaching input (UCS) can spread long distances along a dendrite. As a result, the vertebrate teaching input can condition many stimuli simultaneously. The vertebrate mechanism is the one appropriate for pattern association, storage and retrieval of episodic memories. Invertebrates are believed to be lack episodic event memory.

As a behavioural example of this difference, one can contrast the ways in which vertebrates and invertebrates learn mazes. Both can learn simple T- mazes. But an ant taught a T-maze with one eye covered must relearn the maze when that cover is removed and placed on the other eye. No vertebrate would be hindered by such trickery. The vertebrate's general-purpose associative memory store is isolated from the individual sense organs. A vertebrate's memory of the maze is therefore a memory of a series of events which can be recalled at will, independent of sensory cues. Ants, like all insects, operate without benefit of such an abstract and centralised memory store (Wehner and Muller, 1985).

Despite of some limitations of learning and memory in invertebrates, we should not keep our minds closed on the question of their intelligence. There are several marvellous compensatory mechanisms that allow at least some invertebrate species to memory landmarks, remember and pass complex information, and do many other clever things. It is not without reason that M. Lehrer and R.Wehner accomplished one of their project devoted to social Hymenoptera (10-mg insects with the 0.1-mg brain), under the banner: mini brains - mega tasks - smart solutions (see: Wehner et al., eds., 1996; Lehrer, ed., 1997).

Eight arms and one leg for juggling with memory. This issue is devoted to molluscs, a seemingly “primitive” animal group. But heroes of this issue possess properties tempting for students of learning and memory.

The environment and life style of Cephalopods means that they need to be capable of complex and flexible behaviour. As active predators, they need to explore, understand and remember their environment and the behaviour of other animals. They can solve problems as they remove a plug or unscrew a lid to get prey from a container. They use rocks and jets of water in a way that could be classified as tool use. They have been found to play with “toys” and to have individual responses and individual temperaments (Kuba et al., 2003; Sinn et al., 2001).

Despite huge efforts devoted to and analysis of the nervous system of octopuses and other cephalopods such as squid, cuttlefish and nautilus, their brain and limits to what extend these animals are educable remains a mystery. The brains of cephalopods evolved entirely separately from the brains of the vertebrates, and they have an entirely different design. At the same time, at fundamental level- cells communicating by chemical signals - the brain of the cephalopod is essentially the same as that of any vertebrate. Indeed, research on the squid's " giant axon" has been an instrument in showing how nerves work throughout nature. Cephalopod neurons are closer to vertebrate structure than common invertebrate.

Cephalopods possess keen senses and are capable of extremely complex behaviour that requires a lot of neurones for processing. Their complex eyes, as large as car headlamps in some deep- water species, can distinguish detail as well as mammalian equivalents. Cephalopods have highly developed senses of touch, taste and smell, and can detect gravity, a sense which is used in the coordination of muscles during movement. Division of labour between the central and peripheral nervous system greatly simplifies the movement control of flexible arms in the Octopus. The peripheral nervous system is organised as an axial nerve cord composed of about 300 interconnected ganglia two cerebrobrachial (axonal) tracts. The axons in the tracts carry sensory and motor information to and from highly centralised brain. The brain sends global commands to the arm neuronal network to activate and scale their program variables. At the same time, the neuromuscular system of the arms does not require continuos central control. Movements resembling normal arm extensions could be initiated in amputated arms by electrical stimulation of the nerve cord or by tactile stimulation of the skin of suckers. A major part of voluntary movements of the arms is controlled by a pattern generator that is confined to the arm’s neuromuscular system. The octopus also reduces the complexity of controlling of arms by using highly stereotypical movements. Due to the developed peripheral motor program, the arm can be moved in any direction, with a virtually infinite number of degrees of freedom (Young, 1961; Wells, 1978).

Cephalopods use their brains for learning, not only for controlling eight arms. It was demonstrated as early as in 1950s and 1960s that octopuses can learn to distinguish between different shapes, orientations, sizes and degrees of brightness. In one experiment, Young (1960) trained octopuses to select between large and small squares, horizontal and vertical stripes, and black and white circles. He found that the animals could retain all three preferences at once. In other experiments, blinded octopuses learnt to distinguish between differently shaped objects using only their highly sensitive suckers. One octopus remembered the differences for four months (Wells, 1962). Long term memory of associative learning has been revealed in Cephalopods with the use of negative reinforcements such as glass tube for cuttlefishes and electric shock for octopuses (Boycott and Young. 1955; Messenger, 1973; Boal et al., 2000; Agin et al., 2003). Experiments with taste aversion in cuttlefish with the use of learning procedure in which the preferred prey was made distasteful by a bitter taste have clearly demonstrated that cuttlefishes were able to learn that a prey is not acceptable food, even if they usually preyed on it, to recognise it and to avoid it for several days and as a result to eat a usually non preferred prey (Darmaillacq et. al, 2004).

Remarkable memory in Cephalopods can be conditioned upon peculiarities of their brain structure. Cephalopods possess an organ analogous to the vertebrate hippocampus. This organ, the vertical lobe, appears to fashion autoassociation event memories, which cephalopods are known to store long-term in the optic lobes. The octopus appears to use event memories in much the same way that vertebrates use memories recorded by the hippocampus. Field observations suggest that this octopus follows a detailed topographical map when navigating the coral reef near its den. Octopus vulgaris has also demonstrated the ability to retain arbitrary T-maze memories over a long period of time in the laboratory (Hanlon and Messenger, 1996). Recently the functional implication of the vertical lobe complex in learning and memory has been confirmed by the use of a metabolic marker, cytochrome oxidase (Dickel et al., 2000).

The cellular and molecular mechanisms of long term memory (LTM) in seem to be universal in vertebrate and invertebrate, at least, in mollusc species. Insights in developing the theory of formation of LTM have been obtained with the help of a close, but much simpler, relative of cephalopods, the giant marine snail Aplysia, which lends itself to experimental laboratory work as an ideal object for studying memory processes. This mollusc has only one “leg” instead of eight octopuses’ arms. In his Nobel Lecture E.R. Kandel (2001) portrays Aplysia and describes fundamental results which have been obtained in the study of memory processes with the help of this snail. Aplysia has “only” 20, 000 central nerve cells, and the simplest behaviours that can be modified by learning may directly involve less than 100 central nerve cells. In addition to being few in numbers, these cells are the largest nerve cells in the animal kingdom, reaching up to 1000 µm in diameter, large enough to be seen even with the naked eye. The cells can easily be dissected for biochemical studies and can readily be injected with labelled compounds, antibodies, or genetic constructs, procedures which opened up the molecular study of signal transduction within individual nerve cells. Kendal’s research suggests that the cellular and molecular strategies used in Aplysia for storing short- and long-term memory are conserved in mammals and that the same molecular strategies are employed in both implicit and explicit memory storage. With both implicit and explicit memory there are stages in memory that are encoded as changes in synaptic strength and that correlate with the behavioural phases of short- and long-term memory. The short-term synaptic changes involve covalent modification of pre-existing proteins, leading to modification of pre-existing synaptic connections, whereas the long-term synaptic changes involve activation of gene expression, new protein synthesis, and the formation of new connections. Recently clear evidences have been obtained that de novo protein synthesis is an essential and time dependent event for LTM formation of associative learning in the cuttlefish.

Memory sits comfortable in mini bee’s brains. For a long time many explorers of learning and memory argued that only “higher” animals exhibit complex forms of learning and that these forms require neuronal organisations and neuronal mechanisms qualitatively different from those found in “simple” animals. Recent studies on insects, especially on Hymenoptera, such as bees and ants, have demonstrated how excellent learning capabilities may be implemented on mini brains.

The honey bee Apis melifera is a particularly useful animal for the study of learning and memory formation, because this insect exhibits easily manipulated feeding behaviour coupled with extremely high mnemonic fidelity. The size of the honeybee brain has allowed for electrophysiological analysis of the neural correlates of behaviour, sometimes with the single cell resolution, as well as identification of critical brain regions.

Experimental findings concerning how honey bees find a target go back to Romanes (1885). He put a hive in the basement window of a house with a large flower garden on one side and a lawn leading to a beach on the other. When he released foragers from this hive anywhere in the garden, they soon appeared back at the hive. When he released them on the beach, they did not return to the hive, even though many of the sites were closer to the hive than release sites in the garden. The contrast between homing performances from familiar versus unfamiliar territory implies that the homing was not mediated by a random search; rather, it was based on knowledge of the terrain. At the beginning of Twentieth Century, American scientist Charles Turner (1910, 1911; see Abramson, 2003) revealed that honey bees have “ideas” about time and can readily distinguish colours and geometric patterns. Indeed, Turner’s work on colour-vision of bees and their recognition of patterns and shapes predated von Frisch’s work and probably influenced the Nobel winner's investigations.

The honeybee brain contains a pair of organs called " mushroom bodies, " due to their mushroom-like shape. They are the bee's primary organs for the acquisition of complex memories (Mezel, 1985).

The mushroom bodies (MBs) or corpora pedunculata were first described in detail by Dujardin (1850). Forel (1874) developed a hypothesis about close connection between brain structure and complexity of behaviour in different casts in Hymenoptera. Studying ants, he revealed that MBs are much larger in workers which perform different kinds of job in the family, than in queens whose behaviour is more stereotypic and all the more than in males which can only fly and copulate. Workers are also not equal in their learning capability and development of MBs. It was demonstrated on one of the “cleverest” Hymenopterans, red wood ants, that individual worker’s abilities to solve Schneirla’s maze correlate with MBs sizes (Bernstein and Bernstein, 1969).

MBs account about a half of the whole volume of the brain in red wood ants and about a quarter in honey bee whereas in such active hunter as water-tiger possessing complex behaviour, MBs account only about one twentieth of the whole volume of the brain. The honey bee’s brain is as small as 1 µL containing 950, 000 neurons (Witthö ft, 1967). But the point is that the quantity of neurons does not make the cleverest organism. Neuronal activity and brain mechanisms in insects differ from that in vertebrates and this provides “multiple memories” being implemented in mini-brains (Menzel, 2001). Possible regions of long-term memory storage in the mushroom bodies are labelled as median calyx (mC) and lateral calyx (lC). The microstructure of these regions bears a resemblance to that of the vertebrate cerebellum.

The brain of the honeybee has been studied using a standard technique of ablation for the last six decades established by Lashley (1950). Early experiments with surgical removal of MBs after classical conditioning to odours resulted in complete loss of conditioned responses for these stimuli whereas other vital functions remained (Voskresenskaya, 1957). Panov (1957) with the use of histological examinations demonstrated that MBs develop later than other brain structures in honey bees and possess the most complex structure. Recent studies based on the same technique have revealed more and more intriguing details concerning the role of different brain structures in olfactory and tactile learning in honeybees (Scheiner et al., 2001).

Brain mechanisms of learning and memory is better studied in bees than in ants thanks to such a robust phenomenon in honeybees as reward learning based on olfactory and tactile stimuli. What is especially important for elaboration of the appropriate technique is that some peculiarities of bee’s reaction give a possibility to study this insect fixed in a tube like a Pavlov’s dog fixed in a stall.

The preparation which is used to study reward learning in honey bees was introduced by Kuwabara (1957), who first studied colour learning in such a way, and then by his student Takeda (1961), who discovered that bees restrained in tubes form an association between an olfactory stimulus and a sucrose reward. Each bee is harnessed in such a way that it can move only its antennae and mouth parts (mandibles and proboscis) freely. The antennae are the main chemosensory organs. When the antennae of a hungry bee are touched with sucrose solution, the animal reflexively extends its proboscis to reach out toward the sucrose and lick it. Odours or other stimuli to the antennae do not release such a reflex in naive animals. If an odour is presented immediately before sucrose solution (forward pairing), an association is formed which enables the odour to trigger the proboscis extension response in a successive test. This effect is clearly associative and involves classical, but not operant conditioning (Bitterman et al. 1983). This redoubles similarity of appearance of this preparation with Pavlov’s experiments. The odour can be viewed as the conditioned stimulus (CS) and the sucrose solution as the reinforcing, unconditioned stimulus (US). Using this preparation, Hammer (1993) identified a single neuron that serves reinforcement during olfactory conditioning.

It turned out that in bees a single association of an odour and a sucrose reward will lead to a memory lasting for days. Three pairings of an odour and a reward lead to a lifelong memory. This is much more fast and reliable than in many vertebrates. Reward learning in honeybees initiates a sequence of memory phases that lead to long-lasting memory passing through multiple forms of transient memories. This was called “Multiple Memories “(Menzel, 1999). It is possible to distinguish between two forms of LTM: early LTM characterised by protein synthesis-independent retention, and late LTM characterised by protein synthesis-dependent retention (Menzel, 2001). In general, these experiments revealed that in Hymenoptera, the mushroom bodies control complex behaviour, learning and memory and receive multisensory input.

An avian version of episodic memory. The title of this issue is a paraphrase from Milius’s (1998, Science News) paper about Clayton’s investigations on scrub jays. Food-storing animals, and in particular scrub jays are good candidates for studying animal capability of declarative memory because they remember what they cached where and when based on a single caching episode. In the wild jays cache and recover many different food items during the autumn and winter months, and should therefore be able to remember which sites have been depleted by cache recovery and subsequently return only to those sites where their caches remain

Clayton and her co-authors conducted some series of ingenious experiments on scrub jays Aphelocoma coerulescens basing on the food-caching paradigm as striking examples of how an understanding of the species and its natural history can be employed to develop novel approaches to the study of episodic memory in animals. Indeed, the memory capability of jays fulfils Tulving’s classic behavioural criteria for episodic memory, and is thus referred to as “episodic-like” (Clayton and Dickinson, 1998).

The experiments conducted on the episodic-like memories of food-caching birds were guided by two key features of their natural behaviour. First, birds rely, at least in part, on memory to recover their caches. Second, in the wild some food-storing species including scrub jays cache insects and other perishable items in the wild as well as seeds. It may be useful, therefore, for them to encode and recall information about what has been cached when, as well as where. This enables experimentalists to capitalise on the jays’ natural propensity to cache and recover perishable items in designing experimental test for memory of “what, where and when”.

To test if scrub jays are capable of episodic-like memory recall, birds were allowed to cache and recover perishable " wax worms" (wax moth larvae) and non-perishable peanuts. The logic is as follows. Jays show a strong preference to cache, recover and eat wax fresh worms when given both worms and peanuts. Worms decay rapidly over time, however, so that if worms are left for a period of five days or so they become rotten and unpalatable. If birds can remember when they cached as well as what they cached and where, then they should recover worms when they were cached just a few hours ago. They should avoid the worms, however, if the worms were cached several days ago and have had time to rot.

Sixteen hand-raised scrub jays were given worms and peanuts which they could cache in and recover from sand-filled plastic ice-cube trays, containing an array of ‘cache sites’. Each tray was attached to a wooden board and surrounded by a visio-spatially-distinct structure of Lego bricks that was placed next to one of the long sides of the tray (Clayton and Dickinson, 1998). For each trial, the trays were unique and care was taken to ensure that the trays were not placed in the same location across trials so that the birds could not learn general rules about the contents of caching trays. Instead, they had to remember the ‘what, where and when’ of each individual caching episode.

The birds were divided into two groups, Degrade (D) and Replenish (R), which differed in whether or not birds had the opportunity to learn that worms degrade and become unpalatable over time. In order to learn that worms decay, jays in the D group were given a series of pre-training trials in which they cached peanuts in one tray and worms in another tray, and then recovered their hidden caches from both trays either 4 h or 124 h later. Birds in the R group received the same treatments as those in the D group except that the old wax worms were removed and replaced by fresh ones just before the start of the cache recovery phase so that these birds never had the opportunity to learn that worms decay over time. The birds in the R group serve as an important control to test whether any switch in preference from worms to peanuts after the long retention interval can be explained in terms of either a genetic predisposition to prefer worms in some instances and peanuts in others, or simply that memories for perishable worms are forgotten more quickly than memories for non-perishable nuts. The results of the special test trials showed that birds in the D group preferred to recover worms after the 4 h retention interval, but preferred to recover the peanuts and avoid the worms after the 124 h retention interval.

As predicted, on test trials, birds in the D group reversed their preference from warms to peanuts at the long interval, but R group preferred the warms at both intervals. The switch in preference from warms to peanuts after the long interval required the birds to recognise a particular cash site in terms of both its contents and the relative time that had elapsed between cashing and recovery. This result can only be explained by recall of information about “what” items (peanuts and warms) were cashed, “where” each type of items was stored and “when” (4 or 124 h) the warms were cached. Furthermore, the information was acquired as a result of single, trial-unique experience.

In further experiments researchers tested the jays’ ability to recall specific past experiences during a cache recovery episode (Clayton et al., 2003). They concluded that scrub jays encode information about the type of food they store in cache sites. Besides, the birds can update their memory of whether or not a caching location currently contains a food item. They also can integrate information of the content of a cache at recovery with information about the specific location of the cache site. Taken together, these results fulfil the behavioural criteria for episodic memory in its classic definition: the jay remembers a series of facts about an object (the food item), a place (where they stored it), a time (how long it was since they stored the item) and an action (caching versus cache recovery) that allow the bird to subsequently recall that information and execute the appropriate behaviour. Each item could be considered as a semantic fact but when all the facts are integrated, the jay has sufficient information to isolate what was cached and what was recovered, where and how long ago: functionally, the animal has enough information to recall the episode of caching a specific item (Griffiths et al. 1999).

One could call Scrub Jay a member of Cashing Club. This species shares with other animal cashiers (such as several mice, squirrels and bird species) specific brain properties that could have evolved when there was pressure on memory and supporting brain structures. There is a speculation that spatial memory in birds and humans is hippocampus-dependent to a great extend (Pravosudov, 2003). There are some parallels which, of course, should not obscure many differences between the avian and mammalian hippocampus (see Macphail, 2002 for detailed analysis).

Food storing species have, relatively to telencephalon size, larger hippocampal volumes than their non food – storing counterparts in a wide variety of species. Volumetric differences are not accompanied by differences in cell density but rather by a greater number of cells as well quantitatively different cells such as larger immunopositive neurones. Comparative studies on song-birds revealed that seasonal peaks in food storing correlate with seasonal changes in brain morphology. These seasonal changes in hippocampus do not occur in non food-storing species and are probably specific to food-storing birds (Krebs et al., 1989). A striking example of difference in hippocampus sizes between storing and non-storing species is provided by two parid species, the storing marsh tit Parus palustris, and the non-storing great tit, P. major. The marsh tit, at 11 gm, is much smaller bird than the great tit (20 gm), and possesses a telencephalon that is about 20 percent smaller than the great tit telencephalon. But the hippocampus of the marsh tit is some 30 percent larger than that of a great tit. Further support for a hippocampal role in food storing is provided by studies that find disruption of cache recovery by hippocampal lesions (Sherry and Vaccarino, 1989). It is important to note that hippocampal damage does not disrupt the tendency to cache food: it is the memory for the locations of the caches that appears to be disrupted.

A reasonable interpretation of the hippocampal enlargement seen in storing birds is that it is an adaptive specialisation that enhances the spatial memory that is served by the hippocampus. The experimental data have recently been reviewed by Macphail and Bulhuis (2001), who concluded, perhaps surprisingly, that the tendency to store food does not appear to correlate with spatial memory capacity in other contexts (see also Chapter 22). In other words, it seems clear that the hippocampus is in some way involved in food storing and recovery, but it is not clear that this association strengthens the case for supposing a role for the avian hippocampus in spatial learning and memory.

9. CHICKS DO NOT SUFFER FROM SCHIZOPHRENIA: BRIEFLY ABOUT BRAIN MECHANISMS FOR PROCESSING AND STORING MEMORY

 

Processes of learning and memory storage in some sense look like “editing brains” and there seem to be a variety of ways of doing this: killing whole cells, disconnecting some interconnections between selected cells, creating additional synapses, and increasing and decreasing synaptic strengths. Memory thus may work not only by adding some new materials but also, in part, by eliminating some neural connections (Young, 1970; Calvin, 1989). Unlike other mental processes such as thought, language, and consciousness, learning and memory storage seemed from the outset to be readily accessible to cellular and molecular analysis (Kandel, 2001). There is a huge literature devoted to modern approaches and results obtained in this field. This chapter does not pretend to give a whole picture of how learning occurs as a function of the activities of the brain and its constituent neurones. I place only a brief review here leaving this theme with experts who professionally study these fascinating problems.

 






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