Stephens & Foraging - Behavior and Ecology - Chapter 3 ppt

3 Neuroethology of Foraging David F. Sherry and John B. Mitchell 3.1 Prologue Alive with color, a patch of flowers is also alive with the constant mo- tion of bumblebees, honeybees, syrphid flies, and other pollinators. A bumblebee lands heavily on a flower, making other insects take flight. She turns, plunges her head into the corolla, and remains motionless. After a few seconds, she backs out, rises noisily into theair,andjoins the pollinators shuttling between flowers.Everyone of these insects is mak- ing decisions about which flowers to visit, how long to remain at each flower, and how much nectar or pollen to take on board before flying off. This aerial traffic has a pattern that foraging theorists try to under- stand with models of energy maximization, efficiency maximization, and other currencies that they can build into a model and test. Underneath the rocketing flight from bloom to bloom is another hubbub invisible to us—the flight of electrical and chemical signals through the pollinators’ nervous systems. Each decision, each choice, each arrival and departure emanates from unseen neural chatter taking place on a scale measured in microns and milliseconds. Electrical signals coursing along neurons carry messages about nectar concentration and the odor and color of flowers. Chemical signals jump the gap from one neuron to the next and relay this information to the bumblebee’s brain. Inside neurons, other chemical messengers jot notes on incoming data 62 David F. Sherry and John B. Mitchell while gene transcription records a long-term archive of foraging experience, changing the way the bumblebee’s nervous system responds to incoming in- formation. Her next search for a flower worth stopping at will use this infor- mation, and hernext foraging decisionwill be basedon the neuralrecord of her past experience. 3.2 Introduction The modeling of foraging behavior has been successful because it makes clear assumptions and explicit predictions about behavior. Part of the appeal of foraging models, and a good deal of their power, is due to their indifference to the cognitive and neural processes underlying foraging choices. This is not to say that researchers working with foraging models are indifferent to causal mechanisms or unaware of the mechanistic questions raised by foraging models. Good foraging models are themselves indifferent to whether a patch departure decision, for example, takes place in the nervous system of an insect, a bird, or a human. Behavioral ecologists can fruitfully construct and test foraging models while remaining uncommitted on the question of how the brain and nervous system arrive at a foraging decision. We expect a foraging model to have broad applicability across taxa and therefore not to depend much on the specifics of mechanism. Increasingly, however, foraging theory has attempted to incorporate information about learning, memory, percep- tion, timing, and spatial ability. One reason for this is that models grounded in accurate information about mechanisms are likely to make better predic- tions. Another reason is that researchers who are perfectly satisfied with the predictive power of a strictly functional foraging model may eventually ask themselves, “How does it work?” This chapter explores the relevance of some recent discoveries in the neu- rosciences to the question of how nervous systems implement foraging deci- sions. We begin with two caveats: First, our coverage is far from comprehen- sive. We have selected several recent findings in the neurobiology of animal cognition that seem particularly clear, interesting, and relevant to foraging. Second, there are pitfalls in searching the nervous system for functions that we identify by observing behavior, but which actual nervous systems may not recognize. Researchon foraging, likeall research onbehavior, requires identi- fying basicconceptual unitssuch assearch time,handling time,encounter rate, and intake rate, not to mention memory, variance sensitivity, and state. Most likely, the nervous system does not compartmentalize things in the same way that we conventionally do when observing behavior. This is not to say that the categories of behavior important in foraging models are wrong: they are Neuroethology of Foraging 63 not. They are categories appropriate to modeling the foraging decisions of an- imals. We should not be surprised, however, to find that categories useful for observing behavior donot alwayscorrespond tohow the nervoussystem actu- ally performs its job of integrating incoming sensory information with prior experience to produce adaptive foraging. Insect pollinators provide many illustrations of the cognitive processes crucial to foraging. Recent studies reveal how the honeybee brain forms associations at the neuronal and molecular levels among stimuli that are im- portant for successful foraging, such as floral odor and nectar. We begin with a look at the cognitive processes that control honeybee foraging, followed by a more detailed examination of how neurons in the honeybee brain form associations. Similar molecular processes of associative learning turn up in many invertebrates and vertebrates. Next, we look at some more complex aspects of cognition, beyond basic association of stimuli and events. Although associative learning forms an important building block of animal cognition, we can examine many cognitive processes more easily at a level of abstraction once removed from the formation of associations. The hippocampus, a tanta- lizing and perplexing structure in the vertebrate brain, participates in many cognitive operations relevant to foraging, including spatial memory, episodic memory, declarative memory, and the formation of complex associations. We examine the involvement of the hippocampus in two of these opera- tions, spatial memory and declarative memory. Finally, we discuss the role of the mammalian prefrontal cortex in working memory. Working memory is memory for the ongoing performance of a task and is of central importance in many foraging decisions. The prefrontal cortex and its involvement in working memory illustrate the large-scale integration of neural information processing. We will begin, then, with a description of how foraging animals learn that two stimuli go together, describe some more complex cognitive op- erations that involve the hippocampus, and end with the role of the prefrontal cortex in keeping track of foraging as it occurs. 3.3 Honeybee Foraging The Patch Departure Decision Honeybees leave their hive and travel to nectar sources that may be anywhere fromafewmetersto2kmaway.Abeevisitsaseriesofflowers,drawsnectar into its honeycrop, and then begins the journey home, often with only a par- tially filled crop (Schmid-Hempel et al. 1985). As floral density decreases and travel time to the next flower becomes longer, bees visit fewer flowers before returning home. This correlation between floral density and the number of 64 David F. Sherry and John B. Mitchell flowers visited before returning to the hive supports the assumption that hon- eybees maximize efficiency (net energy gain/energy expenditure) rather than the more conventional currency of net energy gain (Schmid-Hempel et al. 1985; see also section 8.3). In order to respond to the travel time between flowers, foraging honeybees must monitor this variable in some way and then base their decision to cease foraging on their current estimate of travel time, stored in working memory. Memory for travel times between flowers is an important part of honeybee foraging. Flower Constancy Honeybees, like other pollinators, can show remarkable constancy within patches of flowers, often specializing on only one of many available species of flowering plants (Chittka et al. 1999). Students of foraging have explained the phenomenon of flower constancy in several ways, including pollinators’ limited memory for rewarding flower types, limited memory for flower han- dling techniques (Gegear and Laverty 1998), and reduced efficiency caused by switching among flower types (Darwin 1876). Chittka and Thomson (1997) found, for example, that bumblebees could learn two flower handling tech- niques if trained appropriately, but made substantially more errors and wasted more time than bees that learned only a single flower handling technique at a time. The way memory for flowers works in the honeybee brain may make flower constancy advantageous. Memory can have pervasive and unexpected effects on foraging. Learning Flowers Honeybees must learn to identify floral nectar sources. Although bees have shape, color, and odor preferences, they do not recognize specific flowers innately and certainly do not know the locations of flowers before they begin foraging. They learn the location, shape, color, and olfactorycharacteristics of flowers by associating thesefeatures with the nectar thata flower provides. As Collett (1996) and others have shown, honeybees learn the locations of nectar sources by remembering a retinotopic representation of the local landmark array around a nectar source. “Retinotopic” means that the bee retains in memory a representation that preserves the relations among objects in the visual world as they impingeon the retina. Beesreturn to flowersby traveling in a manner that produces a match between their current retinal image of landmarks and their remembered representation of landmarks viewed during the departure flight from the flower. We have known since the work of von Frisch that honeybees learn the color of rewarding food sources (von Frisch Neuroethology of Foraging 65 1950). The ways bees learn about the shape and olfactory characteristics of flowers has also been studied extensively (Greggers and Menzel 1993). Learn- ing to recognize sources of food is an essential component of foraging. 3.4 Associative Learning All of these components of honeybee foraging—whether they deal with trav- el time, flower handling techniques, or floral features—involve the formation of an association between a food reward and properties of the food source. Whereas nectar in a flower or sucrose solution in a laboratory experiment is the reward, the stimulus properties of the food source are the cues indicating the presence of a reward. The stimulus properties of the food source hold no special significance for the bee until she has experience with the relation between thosestimuli and thepresence of foodand has associatedthose stimuli with a food reward. The bee’s ability to form associations lies at the heart of foraging success. The simplest way of conceptualizing the formationof associations is classi- cal, or Pavlovian,conditioning. Classicalconditioning describes theformation of an association between an unconditioned stimulus (US) that has innate sig- nificance for an animal, as nectar does for a honeybee, and a conditioned stim- ulus (CS) with no such prior significance. As a result of pairing between the CS and US, the CS becomes associated with the US. After repeated pairings, the occurrence of the CS alone produces responses by the animal that the CS did not cause prior to the formation of the association. Over a century of experimental research has shown how such associations form. Many interesting complications and variations on the simple account of classical conditioning given above have been discovered (Rescorla 1988; Shettleworth 1998). For example, co-occurrence in time of a CS and US is not enough to produce learning. Instead, the US must be contingent upon the occurrence of the CS, or, to put it another way, the CS must be a good predictor of the US. Animals can form associations not only to a CS, but also to the context in which the CS occurs. In addition, animals can form inhibitory associations that reduce the probability of a response to a CS that predicts that the US will not occur. The fundamental idea underlying the formation of Pavlovian associations, however, is a simple one. Association of a CS with a US causes animals to respond to the CS in ways that they didnot priorto learning.Discovering how such associations form in the nervous system has become the Holy Grail of the neurobiology of learning. Somewhere in the nervous system—at a synapse, in the soma of a neuron, or in the combined action of many neurons—there 66 David F. Sherry and John B. Mitchell must be a relatively permanent change that is the association. Somewhere, neurally encoded information about the CS and the US has to converge. The temporal properties of their co-occurrence must change the nervous system so that subsequent occurrences of the CS have effects that they did not have previously. Not all learning, even in honeybees, consists of the formation of associations, andnot all associations are formedin the sameway. Nevertheless, much of the neurobiological investigation of learning, as we shall see, has been a search for the mechanisms by which associations form. Honeybees, like many insects, reflexively extend the proboscis upon stim- ulation of sucrose receptors on theantennae, mouthparts, ortarsae. Classical con- ditioning of theproboscis extensionresponse (PER) hasbeen analyzedindetail in honeybees. This unconditioned response is not only of central importance in natural honeybee foraging, but can also be conditioned in restrained honey- bees (Takeda 1961). The conditioned response to olfactory and visual cues can be assessed behaviorally by measuring the probability, latency, or duration of proboscis extension, or electrophysiologically by measuring the latency, du- ration, and frequency of spike potentials in the muscle controlling proboscis extension (Rehder 1989; Smith and Menzel 1989). Olfactory CSs are more readily associated with sucrose than are visual cues (Menzel and M ¨ uller 1996), so classical conditioning of olfactory CSs to a sucrose US will be discussed below. The neuralpathways responsible forclassical conditioning of the PER are well understood and illustrate a general feature of systems that support associative learning: convergence of CS and US inputs at a common neuronal target. The Mushroom Bodies of the Honeybee Brain The mushroom bodies of the honeybee brain are bilateral three-lobed struc- tures located in the protocerebrum. Each mushroom body consists of about 170,000 neurons, called Kenyon cells, and their projections. The cell bodies of the Kenyon cells are located around the mushroom body calyces, and the rest of the mushroom body consists of a dense neuropil of projections from, and afferent inputs to, the Kenyon cells (see box 3.1 for a glossary of itali- cized terms). In honeybees, the mushroom bodies receive olfactory afferents from the antennal lobes, visual afferents from the optic lobes, and multimodal input from a variety of other brain areas (Heisenberg 1998; Strausfeld et al. 1998). After examining the firing patterns of individual neurons, Erber et al. (1987) were able to propose several functions for the mushroom bodies, in- cluding detection of stimulus combinations, detection of temporal patterns between events, and detection of stimulus sequences. The mushroom bodies are promising candidates as a site for the integration of sensory information, the formation of associations, and the control of honeybee foraging behavior. BOX 3.1 Glossary Acetylcholine(Ach) Abiogenic aminethat actsas aneurotransmitter in verte- brate and invertebrate nervous systems. Neurons using the transmitter acetylcholine are described as cholinergic.Themuscarinic acetylcholine receptor is a membrane protein in the postsynaptic membrane that contains an ion channel activated by the binding of acetylcholine. The action of acetylcholine at this receptor is mimicked by the plant alkaloid muscarine. The nicotinic acetylcholine receptor is a G protein-coupled membrane protein with no ion channel. Nicotine mimics the action of acetylcholine at this receptor. Antagonist A compound that opposes the action of a neurotransmitter, hor- mone, or drug by acting on its receptor. An agonist, in contrast, acts on a receptor with aneffectsimilar to that ofatransmitter, drug, orhormone. Antisense A strand of DNA or RNA that is complementary to a coding sequence. Because it is complementary to the coding sequence, the anti- sense hybridizes with it and thereby inactivates it. Antisense can beused to precisely target specific proteins and prevent their synthesis. Biogenic amines Compounds that serve communication functions in both plants and animals. Serotonin (5-hydroxytryptamine), acetylcholine, histamine, octopamine, and the catecholamines adrenaline, noradrena- line, and dopamine are all biogenic amines. Ca 2+ The calcium ion. Ca 2+ acts as a second messenger in neurons. The intracellular Ca 2+ concentration is maintained at a very low level com- pared with the extracellular concentration by a calcium pump and a Na + /Ca 2+ exchange protein. Calmodulin mediates the effect of Ca 2+ on proteins. Calmodulin A protein that binds Ca 2+ and regulates the activation of other proteins, including the Ca 2+ /calmodulin-dependent (CaM) protein kinases. CRE (cyclic AMP response element) A highly conserved DNA sequence that acts as a promoter of the transcription of many different target genes. The cAMPresponseelement binding protein(CREB) isa transcription factor that isactivated bycAMP via the action of protein kinase A (PKA),binds to the CRE promoter site, and initiates transcription of the target gene. Cyclic AMP (cAMP, 3  ,5  -cyclic adenosine monophosphate) A cyclic nucleotide that acts as a second messenger in neurons and was the first second mes- senger discovered. The enzyme adenylate cyclase (also called adenyl cyclase and adenylyl cyclase) converts ATP to cAMP, while the enzyme cyclic nu- cleotide phosphodiesterase rapidly degrades cAMP to 5  -AMP. Activation 68 David F. Sherry and John B. Mitchell (Box 3.1 continued) of these two enzymes thus regulates the concentration of cAMP within neurons. cAMP activates the cAMP-dependent protein kinase protein kinase A. Glutamate An amino acid that acts as an excitatory neurotransmitter in the mammalian nervous system. There are several different glutamate receptors, named according to the agonist that most effectively mim- ics the effect of glutamate, including the NMDA (N-methyl-D-aspar- tic acid) receptor and the AMPA (α-amino-3-hydroxy-5-methyl-4- isoxazoleproprionate) receptor. Neuropil (neuropile) A dense feltlike matrix of axons, axon terminals, and the dendrites with which these axons form synapses. Octopamine A biogenic amine that acts both as a hormone and as a neuro- transmitter in invertebrate and vertebrate nervous systems. As a neuro- transmitter, it is an adrenergic agonist. Phosphorylation The transfer of a phosphate group from ATP to a protein. Phosphorylation changes the shape, and hence the activity, of many proteins, including ion channels, second messengers, enzymes, and pro- teins that regulate gene transcription. Protein kinase A compound that catalyzes the transfer of phosphate from ATP toa widevariety ofproteins, aprocess calledphosphorylation. Protein kinase A is activated by cAMP, protein kinase C is activated by phospho- lipids and influenced by Ca 2+ . The CS Pathway In honeybees, odors activate chemoreceptors on each antenna, which relay signals to the antennal lobes, where odor characteristics are neurally encoded (Lachnit et al. 2004; Flanagan and Mercer 1989) (fig. 3.1). The projection neu- rons of the antennal lobe form three main tracts, one of which innervates the calyces of the mushroom bodies. This projection from the antennal lobe to the mushroom bodies serves as the CS pathway for conditioning of the proboscis extension response (PER). Menzel and M ¨ uller (1996) suggest that acetylcholine isthe neurotransmitter intheCSpathway from theantennallobesto the mush- room bodies becauseacetylcholine antagonists disrupt conditioningof the PER without disrupting olfactory perception (Cano Lozano et al. 1996; Gauthier et al. 1994). This result indicates that acetylcholine antagonists do not impair PER conditioning simply by eliminating the incoming olfactory CS from the antennal lobe, but instead disrupt the CS signal at a later stage of processing. Neuroethology of Foraging 69 Figure 3.1. Schematic diagram of the CS and US pathways for olfactory conditioning in the honeybee. The olfactory CS detected by the antenna is relayed to the antennal lobe (AL) and then by acetylcholine- containing projections to the lateral protocerebral lobe (LPL) and the calyx (c) of the mushroom body (MB). The sucrose US detected at the proboscis is relayed to the subesophageal ganglion (s) and then by the octopamine-containing VUMmx1 nerve to the antennal lobe, the lateral protocerebral lobe, and the calyx of the mushroom body. The mushroom body, antennal lobe, and lateral protocerebral lobe are all bilateral structures that occur on both sides of the brain. Neural signals triggered by activation of chemoreceptors on the antennae thus deliver information about the odor of a nectar source to Kenyon cells of the mushroom bodies via projections from the antennal lobe (Mobbs 1982). The US Pathway The unconditioned response of extending the proboscis in response to sucrose begins with sucrose receptors on the proboscis that send projections to the sub- esophageal ganglion (Rehder 1989). In the subesophageal ganglion, a group of ventral unpaired median (VUM) neurons receive input from the sucrose receptors. One of these neurons, the VUMmx1, responds to sucrose with a long burst offiring thatoutlasts theactual sucroseUS presentation(Hammer 1993). Axons of the VUMmx1 neuron converge with the CS pathway at three different sites: the antennal lobe, the lateral protocerebral lobe, and the lip and basal ring of the mushroom body calyces (see fig. 3.1). There are thus several sites where information about the odor CS and the sucrose US converge. The VUMmx1 neuron uses the neurotransmitter octopamine (Kreissl et al. 1994).Direct injectionsofoctopamineinto twoofthetargets oftheVUMmx1 70 David F. Sherry and John B. Mitchell neuron, the mushroom body calyces and the antennal lobe, result in classi- cal conditioning of the PER when the odor CS is paired with octopamine (Hammer and Menzel 1998). When octopamine and other biogenic amines are depleted by treatment with the drug reserpine, conditioning of the PER does not occur. Following such depletion, supplements of octopamine can restore conditioning (Menzel et al. 1999). To summarize, the US signal that the honeybee has encountered sucrose is conveyed to the mushroom bodies by the VUMmx1 neuron. Manipulations of the VUMmx1 neurotransmitter, octopamine, confirm this. Depletion of octopamine prevents conditioning, while its application at VUMmx1 terminals is sufficient to produce learning. The Mushroom Bodies as a Locus for Memory Although CS and US information converges at both the antennal lobes and the mushroom body calyces, the mushroom bodies appear to be especially important in conditioning, and direct evidence confirms this (Hammer and Menzel 1995). Cooling thecalyces of the mushroom bodies produces amnesia similar to that produced by cooling the whole animal (Erber et al. 1980). Mutations resulting in abnormal mushroom body structure cause a loss of conditioning to odors (Heisenberg et al. 1985), and so does destruction of the mushroom bodies (de Belle and Heisenberg 1994). Associative learning of any kind requires a point of neural convergence between conditioned and unconditioned stimuli. Neurobiological studies of associative learning have begun to describe what occurs at these points of convergence. An important concept introduced by Donald Hebb (1949, 62) serves as a guide for this research: “When an axon of cell A is near enough to excite a cell B and repeatedly or persistently takes part in firing it, some growth process or metabolic change takes place in one or both cells such that A’s efficiency, as one of the cells firing B, is increased.” In other words, structural changes in the nervous system result from one cell taking part in the firing of another. In the case of the honeybee proboscis extension response, Hebb’s postulate leads us to ask what happens to mushroom body neurons when projections from the antennal lobe cause them to fire, and that firing is rapidly followed by further firing of these cells by octopamine release from the VUMmx1 axons. To find the answer to this question, we must now look inside the neurons that are activated in this way. Cellular Mechanisms Whereas neurotransmitters are the first line of biochemical messengers carry- ing signalsfrom oneneuron toanother, thereare alsointracellular biochemical [...]... sight Lesions of the prefrontal cor- Neuroethology of Foraging B A Mean Percent Correct 95 95 85 85 75 75 65 65 55 55 45 45 35 1-2 2 -3 Order 3- 4 35 1 3 2 4 Item Figure 3. 5 Mean percentage of correct responses in tests of (A) order and (B) item memory in rats with lesions of the prefrontal cortex During the study phase, the animal visited four arms of an eight-arm radial-arm maze The experimenter ensured... convergence of CS odor and US sucrose signals in Kenyon cells Protein kinase A then activates CREB CREB, in turn, modulates the activity of particular genes A Ca2+ -dependent mechanism can also increase CREB binding and gene expression CS- and US-induced activity converge at PKA (because Ca2+ Neuroethology of Foraging enhances cAMP activation of PKA) and at CREB (because a Ca2+ -dependent kinase and PKA each... offering the subject multiple levers or a maze with multiple goal boxes (Box 3. 3 continued) In research modeled on foraging, C R Gallistel and his co-workers have studied how the magnitude and rate of reward are combined by selfstimulating rats (Gallistel and Leon 1991; Leon and Gallistel 1998) Two levers are provided, and the rat cannot predict exactly when the stimulation will become available However,... figures 3. 3.1 and 3. 3.2, time is treated as a continuous variable in rate estimation theory Decisions such as patch leaving are under the control of internal stochastic processes and need not be driven by transitions in external sensory input The debate between proponents of associationist and rate estimation theories concerns the neural and psychological bases of evaluation, decision making, and learning... The concept of reward represents an important link between foraging and the neuroscience of behavior 3. 5 The Hippocampus Many of the cognitive processes involved in foraging, including spatial memory, working memory, episodic and declarative memory, the formation of complex associations, and the integration of experience over time, to name BOX 3. 3 Neural Mechanisms of Reward Peter Shizgal Neuroscientists... play the role of a “primary reward signal” (“r” in figures 3. 3.1 and 3. 3.2), which normally reflects the current value of a goal object, such as a piece of food Indeed, the rewarding effect of electrical stimulation has been shown to compete with, sum with, and substitute for the rewarding effects of gustatory stimuli (Conover and Shizgal 1994; Green and Rachlin 1991) It is very difficult to hold the value... Aplysia, Drosophila, and laboratory rats follows a broadly similar pattern It is likely that the estimation of travel time between flowers in a patch, the representation of landmarks, acquisition of flower handling techniques, and many other components of foraging involve similar neurobiological processes It is likely that foraging decisions and the acquisition of information while foraging, though they... produces an action potential and depolarizes a hippocampal neuron, the Mg2+ blockade of the NMDA receptor ceases, and glutamate can then activate the NMDA receptor Such activation leads to an increase in intracellular Ca2+ 73 74 David F Sherry and John B Mitchell levels and recruits mechanisms that cause long-term changes in synaptic function (Bliss and Collingridge 19 93) Here, too, we can observe... David F Sherry and John B Mitchell (Box 3. 3 continued) foragers to allocate their behavior profitably It will be interesting indeed for students of foraging to see how this debate plays out Suggested Readings Dyan and Abbott’s (2001) textbook presents temporal difference learning within an overview of computational approaches to many different topics in neuroscience and psychology Gallistel and Gibbon (2000)... better than anticipated, and the dopamine neurons increase their firing rate After training, delivery of the reward merely confirms the monkey’s (Box 3. 3 continued) expectation, and thus the dopamine neurons are quiescent when the anticipated reward is delivered Omission of the reward constitutes a worsethan-expected outcome, and the firing of the dopamine neurons slows Figure 3. 3.2 provides a simplified . agonist that most effectively mim- ics the effect of glutamate, including the NMDA (N-methyl-D-aspar- tic acid) receptor and the AMPA (α-amino -3 - hydroxy-5-methyl- 4- isoxazoleproprionate) receptor. Neuropil. binding and gene expression. CS- and US-induced activity converge at PKA (because Ca 2+ Neuroethology of Foraging 77 enhances cAMP activation of PKA) and at CREB (because a Ca 2+ -dependent kinase and. link between foraging and the neuroscience of behavior. 3. 5 The Hippocampus Many of the cognitive processes involved in foraging, including spatial mem- ory, working memory, episodic and declarative