In order for pavlovian learning to occur, with what must the conditioned stimulus have been paired?

1. Jonathan C. Gewirtz1,3 and
2. Michael Davis2

1. 1University of Minnesota, Minneapolis, Minnesota 55455, USA; 2Emory University, Atlanta, Georgia 30322, USA

In first-order Pavlovian conditioning, learning is acquired by pairing a conditioned stimulus (CS) with an intrinsically motivating unconditioned stimulus (US; e.g., food or shock). In higher-order Pavlovian conditioning (sensory preconditioning and second-order conditioning), the CS is paired with a stimulus that has motivational value that is acquired rather than intrinsic. This review describes some of the ways higher-order conditioning paradigms can be used to elucidate substrates of learning and memory, primarily focusing on fear conditioning. First-order conditioning, second-order conditioning, and sensory preconditioning allow for the controlled demonstration of three distinct forms of memory, the neural substrates of which can thus be analyzed. Higher-order conditioning phenomena allow one to distinguish more precisely between processes involved in transmission of sensory or motor information and processes involved in the plasticity underlying learning. Finally, higher-order conditioning paradigms may also allow one to distinguish between processes involved in behavioral expression of memory retrieval versus processes involved in memory retrieval itself.

By reducing learning to its most rudimentary components, the influence of undefined and uncontrollable confounding variables can be minimized. Consequently, much of the progress that has been achieved in searching for the neural substrates of learning and memory has been made using some of the simplest forms of learning. In one such paradigm, first-order Pavlovian conditioning, a conditioned stimulus (CS, such as a tone or light) acquires motivational significance by being paired with an intrinsically aversive or rewarding unconditioned stimulus (US, such as foot shock or food). Learning is evaluated by the ability of the CS to elicit a conditioned response (CR) in anticipation of the occurrence of the US. The use of first-order conditioning has revealed genetic and cellular mechanisms underlying learning and memory in species ranging from the fruit fly and sea snail to the mouse and rat.

Thus far, less attention has been paid by neurobiologists to the potential uses of higher-order Pavlovian conditioning, learning phenomena in which a CS (S2) acquires associative strength by being paired with another CS (S1) rather than with a US. The pairing of S2 with S1 may occur before S1 is paired with the US (sensory preconditioning) or after S1 has been paired with the US (second-order conditioning; see Table 1). The cardinal feature of both sensory preconditioning and second-order conditioning—and that which recommends these paradigms to the service of neurobiologists interested in learning and memory—is that S2 acquires associative strength even though it is never paired directly with a US.

View this table:

Table 1.

Phases of Training in First-Order Conditioning, Second-Order Conditioning, and Sensory Preconditioning Paradigms

This article will describe two promising avenues of research in higher-order conditioning. First, the fact that reinforcing value is acquired makes higher-order conditioning well suited to investigating the neural substrates of different forms of reinforcement. Second, the absence of direct pairings between the CS and US allows one to characterize the roles played by molecular, genetic, pharmacological, and anatomical mechanisms in different stages of learning or memory more precisely than could be achieved using other conditioning paradigms.

There have been countless demonstrations of second-order conditioning since Pavlov (1927) first showed that salivation could be conditioned to a black square by pairing it with the sound of a metronome that had previously been paired with food. However, to demonstrate higher-order conditioning conclusively, two criteria should be met: the ability of S2 to elicit a conditioned response must result directly from the pairing of S2 with S1, and the ability of S1 to support conditioning must result directly from its pairing (previous or subsequent) with the US (Rizley and Rescorla 1972). A large number of experiments conducted since the 1960s that have included control groups appropriate to meet the first criterion (e.g., Kamil 1969; McAllister and McAllister 1964; Prewitt 1967; Tait et al. 1969) and several that have included control groups appropriate to meet the second (e.g.,Pfautz et al. 1978; Rizley and Rescorla 1972) have still revealed evidence of robust second-order conditioning and sensory preconditioning. However, clear demonstrations of higher-order conditioning in some studies do not authenticate demonstrations of higher-order conditioning in other studies conducted under very different circumstances. Thus, it is important when using these procedures to include, at least initially, control groups where S2 and S1 are explicitly unpaired in time to rule out stimulus generalization (i.e., the tendency for a response to generalize from one stimulus to another), the most likely source of experimental artifact in higher-order conditioning paradigms.

Although second-order conditioning and sensory preconditioning are intrinsically weaker than first-order conditioning they can, at least, be enhanced by ensuring that first-order conditioning is particularly strong. This can be achieved by using a US of high value or intensity (Helmstetter and Fanselow 1989; Rescorla and Furrow 1977). With regard to second-order conditioning, “refresher” first-order conditioning trials can be interspersed during the phase of second-order conditioning. This prevents the extinction of first-order conditioning that would otherwise result from the presentation of S1 in the absence of reinforcement.

Other factors identified as being important in determining the strength of higher-order conditioning are the similarity of S1 and S2 and the temporal and physical distance between them. In cases where the conditioned stimuli are localized in space (e.g., differently colored key lights), second-order conditioning is enhanced when S2 and S1 are in close spatial proximity (Rescorla 1979; Rescorla and Cunningham 1979). Second-order conditioning also can be favored when S1 and S2 are stimuli within the same sensory modality (Rescorla and Furrow 1977; but see Holland 1977). Sensory preconditioning is most strongly acquired when S1 and S2 are presented simultaneously, although the superiority of this arrangement can only be detected using a sophisticated set of control procedures (Rescorla 1980b).

However, even with the above conditions in place, one difficulty with the use of second-order conditioning is that learning appears typically to be transient. That is, second-order conditioning usually reaches asymptote after a small number of trials and begins to decline with further training. The most plausible interpretation of this transience is that with greater training, second-order learning is obscured by the development of conditioned inhibition (e.g., Herendeen and Anderson 1968; Rescorla 1973; Holland and Rescorla 1975b; Yin et al. 1994). A conditioned inhibitor signals the nonoccurrence of reinforcement and, thus, inhibits the elicitation of conditioned responses by excitatory stimuli. It is not hard to see why procedures described above that produce second-order conditioning—S1 is reinforced on every occasion it is presented (S1-US trials) except when it is accompanied by S2 (S2-S1-no US trials)—would also produce conditioned inhibition. In fact, this training procedure (also called a feature-negative discrimination) is often the procedure of choice for generating conditioned inhibition. Hence, the transience of second-order conditioning can be explained if one assumes that second-order conditioning develops more rapidly than conditioned inhibition, the two effects are antagonistic at the level of the behavioral response, and conditioned inhibition is ultimately the stronger of the two phenomena so that it obscures second-order conditioning as the amount of training is increased (Fig. 1).

Figure 1.

An explanation in schematic terms of the transience of second-order conditioning, frequently observed in the literature. In such cases, it is assumed that the size of the conditioned response is constrained by the sum of second-order conditioning and conditioned inhibition. It should be noted that negative conditioned responses are not observed in most paradigms.

The interplay between second-order conditioning and conditioned inhibition thus creates an inverted U-shaped function, relating the strength of second-order conditioning to the number of training trials. Potentially, this can lead to interpretive problems. For example, a lesion, drug, or genetic manipulation that decreased second-order conditioning might do so by enhancing conditioned inhibition rather than actually impairing second-order conditioning. Paradoxically, therefore, a treatment that actually enhanced overall learning might appear to impair learning if one looked only at second-order conditioning. Thus, for second-order conditioning to be useful, one has to control for the potential confound of conditioned inhibition.

One solution is to arrange training parameters that favor the development of second-order conditioning over conditioned inhibition.Pavlov's (1927) recommendation of using trace conditioning has not been proven to solve this problem (e.g., Kehoe et al. 1981). A more promising approach is the use of a partial reinforcement procedure during first-order conditioning (i.e., a mixture of S1+ and S1− trials, rather than S1+ trials only). This clearly has a deleterious impact on conditioned inhibition (Rescorla 1999), while producing robust second-order conditioning (Kamil 1969; Gewirtz and Davis 1997a).

A second solution is to find a measure of second-order conditioning that is not affected by the presence of conditioned inhibition. Holland and Rescorla (1975b) observed both conditioned inhibition and second-order conditioning to the same CS in an appetitive conditioning paradigm relative to an unpaired control group. After substantial training, S2 still elicited conditioned responding itself (i.e., second-order conditioning), but suppressed conditioned responding to S1 when the two stimuli were presented in compound (i.e., S2 also passed the summation test of conditioned inhibition.) We have made a similar observation in fear conditioning, using the fear-potentiated startle paradigm. In this paradigm, fear is measured as the enhancement in the amplitude of the startle reflex in the presence versus the absence of a CS. Thus, the time course of the fear response can be measured by presenting the startle-eliciting stimulus at different time points relative to the onset and offset of the CS (Davis et al. 1989). Using a serial S2-S1 training procedure, Falls and Davis (1997) found evidence of second-order fear-potentiated startle when the startle reflex was elicited in test during S2 but of conditioned inhibition of fear-potentiated startle when the reflex was elicited after the offset of S2. Therefore, using this procedure, second-order fear is not masked by conditioned inhibition, even after very extensive second-order training. In fact, rather than an inverted-U function, we found that second-order conditioning increased monotonically as a function of the degree of second-order training (Fig. 2).

Figure 2.

Second-order fear conditioning can still be observed after very extensive training when the fear-potentiated startle paradigm is used (Gewirtz 1998). There were 16 S2-S1 trials presented on each day of second-order training. Fear-potentiated startle is defined as the difference (±SEM) in startle amplitude measured in the presence versus absence of the CS.

Associative learning involves the development of associations between neural representations of events (e.g., Konorski 1967). Debate about the nature of these associations preoccupied traditional learning theorists perhaps more than any other issue. Much of the argument concerned whether the organism learned about the relationship between one stimulus and another (S-S learning), between a stimulus and a response (S-R learning), or between two responses (R-R learning). Some theorists attempted to account for all learning through a single mechanism (e.g., Hull 1943), but it is now more generally appreciated that a conditioned stimulus can enter into an association with a variety of elements in a given learning situation (Rescorla 1980a;Rashotte 1981). As will be seen below, more than one form of association can be demonstrated in higher-order conditioning, depending on the arrangement of S2 and S1. Thus, higher-order conditioning offers a means of analyzing the structure of associations formed in learning, thereby providing an important base from which to elucidate biological mechanisms of associative learning.

As outlined by Rizley and Rescorla (1972), higher-order learning might be based primarily on associations between representations of S2 and S1 (S-S learning), representations of S2 and the US (S-US learning), or representations of S2 and the conditioned response elicited by the US (S-R learning; Fig. 3). In sensory preconditioning, the two conditioned stimuli are paired before the US is presented and, therefore, before the development of a strong conditioned response to S1. Hence, despite an appeal by some S-R theorists to the existence of covert and unmeasurable behavioral responses (Miller 1951), sensory preconditioning would seem to represent as clear a case as one could obtain of S-S learning.

Figure 3.

A schematic representation of hypothetical associations that could be formed in higher-order fear conditioning. Dashed arrows indicate probable representations activated during retrieval of first-order conditioning (S1-US), second-order conditioning (S2-Fear), and sensory preconditioning (S2-S1), based on the existing literature. Dotted arrows indicate other possible representations that may be activated during retrieval of second-order conditioning (see text for explanation).

However, it is less easy to predict the nature of the associations formed in second-order conditioning. These alternatives began to be evaluated in a systematic manner through a series of elegant experiments (Rizley and Rescorla 1972; see also Konorski 1948; Rozeboom 1958). The general approach was to assess the effects of various posttraining manipulations on the strength of second-order conditioning. For example, if the conditioned response to S1 were extinguished, what would be the effect on the response to S2? If S2 is associated with S1, then extinction of responding to S1 should also cause extinction of responding to S2. In fact, in sensory preconditioning, the prototypic measure of S-S learning, repeated nonreinforcement of S1 leads to extinction of responding to S1 as well as extinction of responding to S2 (e.g., Archer and Sjoden 1982; Rizley and Rescorla 1972). In contrast, most evidence suggests that when S2 and S1 are paired in a second-order conditioning procedure, nonreinforcement of S1 does not lead to extinction of responding to S2 (e.g., Rizley and Rescorla 1972). This suggests that S-S associations are not, in general, the basis of second-order conditioning.

How can one assess the possibility that second-order retrieval involves direct activation of a representation of the US (S2-US learning)? Interestingly, this appears to be the basis of retrieval of first-order associations, as the first-order conditioned response is sensitive to posttraining alterations in the value of the US. For example, a CS is paired with food so that a conditioned response can be measured. The US can then be devalued by pairing it with sickness. Following such treatment, animals show a reduction in the conditioned response to the CS compared to animals that did not experience US devaluation. This suggests that during conditioning, animals form a representation of the value of the US with which the CS has been paired. When that representation is changed (either devalued or inflated), then the behavior elicited by the cue also is changed in the same direction. However, second-order conditioning is generally not affected by these treatments, suggesting that an association between S2 and the US does not constitute the primary form of learning in second-order conditioning (e.g., Holland and Rescorla 1975a; Helmstetter and Fanselow 1989).

Further evidence that first- but not second-order conditioning involves the encoding of a detailed representation of the US was obtained in an elegant study that measured the topography of pigeons' keypecks in response to CSs that had been paired with food or water (Stanhope 1992). While pigeons pecked more forcefully for an S1 that had been paired with food than for an S1 that had been paired with water, the force of keypecks to S2 was not similarly dependent on the nature of the reinforcer with which S1 previously had been paired.

On the basis of the devaluation and inflation data, Rescorla (1973;1980a) reasoned that only an association between S2 and the conditioned response to S1 (S-R) learning would be immune to changes in the values of S1 and the US. However, this conclusion was arrived at by default and not on the basis of any direct evidence. In fact, other evidence suggests that S2 is not associated with an overt behavioral response. This is indicated by the finding that the specific form of the conditioned response produced by S2 may be different from the conditioned response produced by S1, when the two stimuli are in different sensory modalities (Holland 1977; Kim et al. 1996). For example, whereas a light paired with food produces a rearing response, a tone paired with the light produces a quick, startle-like response but little evidence of rearing (Holland 1977).

Evidence of second-order fear-potentiated startle (see above) provides further demonstration of this point. The behavior used to measure fear (the startle reflex) is not a CR evoked by S1 (S1 does not elicit startle but instead increases the amplitude of the startle reflex elicited by some other stimulus), and the startle reflex is not elicited at all during second-order training. Thus, the very existence of second-order fear-potentiated startle demonstrates that second-order conditioning must involve more than simply an association between S2 and one or more overt behavioral responses produced by S1.

Therefore, although still often referred to in this way, S-R learning does not really provide an adequate description of second-order conditioning because S2 is not associated with a specific overt behavior. The interpretation most consistent with the data set as a whole is that S2 is associated with a central motivational state (Holland 1977). Thus, in the case of aversive learning, S2 anticipates a state of fear, rather than either a representation of the specific US with which S1 was paired or a specific CR elicited by S1. In the case of appetitive conditioning, S2 anticipates the delivery of reward, activating the hedonic system (Robinson and Berridge 1993) and producing, for wont of a better term, a state of hope (Mowrer 1960).

It should be noted that the general expectation of reward or punishment does not underlie second-order conditioning under all circumstances. For example, there have been isolated reports suggesting the presence of S2-US associations in second-order conditioning (Ross 1986; Barnet and Miller 1996). In addition, in one conditioning situation—autoshaping in pigeons—extinction of S1 substantially reduces the conditioned response to S2, suggesting that S2-S1 associations are the principle basis of second-order conditioning in this paradigm (Leyland 1977; Rashotte et al. 1977). This does not reflect simply an idiosyncratic difference between pigeons and other species because rodents may also acquire S2-S1 second-order conditioning when two conditions are applied that normally promote strong sensory preconditioning: when S2 and S1 are presented simultaneously (as opposed to serially) and when very few first-order (S1-US) training trials are included (Rescorla 1982). These conditions minimize the incidence of S2 in the absence of S1 and the incidence of S1 in the absence of S2. Because the stimuli are not experienced independent of one another, these arrangements may thus encourage S2-S1 learning through the formation of a unitary representation of the two stimuli (Rescorla and Durlach 1981).

In sum, sensory preconditioning involves the association between representations of S2 and S1. In contrast, in second-order conditioning, S2 comes to evoke a general expectation of reward or punishment, although S2-S1 learning can be encouraged under specific conditions. In this respect, it is interesting to note the asymmetry between second-order conditioning and first-order conditioning, in which S1 appears to evoke a memory of specific characteristics of the US. This means that one can use sensory preconditioning, second-order conditioning, and first-order conditioning to compare the neural substrates of S2-US, S2-S1, and S2-Fear or S2-Hope learning.

The form of association that predominates in second-order conditioning may also help in understanding the etiology of certain psychiatric disorders. For example, second-order conditioning may provide a suitable nonhuman model for panic disorder, in which symptoms of agoraphobia develop as aversions to places in which panic attacks have occurred. That is, specific contextual cues may become associated with the fear of having a panic attack rather than with whatever triggered the attack in the first place.

The foregoing discussion suggests that comparisons of first-order conditioning, sensory preconditioning, and second-order conditioning might help us elucidate the neural substrates of three distinct forms of associative learning. The problem, however, in translating a model of associations based on behavioral data into a biological model is in reformulating the abstract concepts of S-S, S-US, and S-Fear or S-Hope learning into neural terms. We have suggested that these terms be defined with reference to the point or points of convergence in the brain of pathways conveying CS and US information (Gewirtz and Davis 1997b). Based on this idea, we have developed a schematic model of the pathways that may be involved in first- and higher-order fear conditioning (Fig. 4). This model assumes that pathways conveying S2, S1, and US information converge on one structure, the amygdala, that constitutes a site of plasticity of first-order conditioning. The second-order association is defined as S2-S1 if the S2 pathway inserts into the pathway mediating first-order conditioning before the point of convergence between S1 and US information. It is defined as S2-US if it inserts into the first-order pathway at the point of S1-US convergence; as S2-Fear if it inserts after the point of S1-US convergence but before the point of divergence to different response systems; and as S2-R if it inserts after the point of response system divergence.

We assumed that S1-US convergence required for the acquisition of first-order fear conditioning occurs in the basolateral complex of the amygdala (BLA), probably in the lateral nucleus of the amygdala. This assumption is based on a variety of findings such as that CS and US sensory inputs converge onto the same cells of the lateral nucleus (Romanski et al. 1993) and that lesions (Campeau and Davis 1995b) and local infusion of N-methyl-D-aspartate (NMDA) and non-NMDA-type glutamate receptor antagonists (Miserendino et al. 1990;Campeau et al. 1992; Kim et al. 1993; Lee and Kim 1998) into the BLA block first-order fear conditioning.

On the basis of the devaluation and inflation data, the model assumes that expression of first-order conditioning involves retrieval of a specific memory of the US (i.e., activation of a representation of the US). We postulated that this representation is stored outside the amygdala, in the form of a particular pattern of activity in regions of neocortex activated by the US itself. Retrieval of the memory for the US involves the production of a similar pattern of activation inthese structures, initiated by the CS via the amygdala. Although lacking direct support, there are some data consistent with this view of retrieval of first-order fearful memories. Lesions made after training of posterior portions of insular cortex, particularly below the rhinal sulcus (formerly defined as part of perirhinal cortex;Rosen et al. 1992), block expression of first-order conditioning (Rosen et al. 1992; Campeau and Davis 1995a; Corodimas and LeDoux 1995; Falls et al. 1997). In contrast, acquisition is not blocked when lesions are made before training (Romanski and LeDoux 1992). Interestingly, the critical locus affected by these lesions contains sparse projections from visual and auditory areas but substantial projections from somatosensory cortex. Hence, it is unlikely that this region is required for transmission of CS information (auditory or visual) to the amygdala (Rosen et al. 1992; Campeau and Davis 1995a) or for retrieval of contextual information (Corodimas and LeDoux 1995) as has been suggested. Rather, this region may be part of a network of cortical structures that, orchestrated by the BLA, stores a representation of a somatosensory US. According to this view, when lesions are made after training, the US representation is degraded to the point where expression of conditioned fear is blocked; whereas, when lesions are made before training, a US representation can be acquired adequately using other cortical areas. Thus, the anatomical evidence and behavioral data are consistent with the view that lesions disrupt storage and retrieval of the US representation (Gewirtz and Davis 1997b; Shi 1995). Like first-order conditioning, second-order fear conditioning is dependent on NMDA-type glutamate receptor activation in the BLA (Gewirtz and Davis 1997a). Assuming, then, that second-order conditioning is similar to NMDA-dependent associative LTP, S2 would represent the weak stimulus and S1 the strong, depolarizing stimulus. If so, then one can ask where in this circuitry would S1 produce strong depolarization that would lead to S-Fear learning? Presumably this would be somewhere downstream of the site of first-order (i.e., S-US) plasticity. For example, within the BLA the lateral nucleus may be the site of first-order plasticity and the basal nucleus the site of second-order plasticity. The latter structure receives heavy projections from both the lateral nucleus (Pitkänen et al. 1995) and from perirhinal cortex (Shi 1995), both of which would be activated by S1 during second-order training. Recent evidence that might support this view comes from a study of secondary reinforcement (Amorapanth et al. 2000). Lesions of the basal nucleus of the amygdala prevented a fearful CS (i.e., S1) from supporting acquisition of an instrumental escape response but did not disrupt freezing to the CS itself. In contrast, lesions of the lateral nucleus blocked both conditioned freezing and the ability of the CS to support instrumental learning. This might suggest that the plasticity underlying higher-order learning (in this case, CS-signaled escape learning) occurs in the amygdala downstream of the site of plasticity of first-order conditioning. Alternatively, plasticity underlying higher-order learning may occur in the lateral nucleus, with the basal nucleus forming a conduit for expression of escape behavior.

Mechanisms of Reinforcement II: Other Higher-Order Conditioning Paradigms

Although the neural substrates of higher-order conditioning are still relatively uncharted, involvement of the BLA is emerging as critical in both Pavlovian second-order conditioning and related learning phenomena. First, the BLA is critically involved in second-order appetitive learning. Excitotoxic lesions of the BLA block acquisition of a second-order appetitive conditioned response (approach toward a food cup) but not of the same response conditioned to a first-order CS (Hatfield et al. 1996). The sparing of the first-order response rules out an account in terms of performance deficits and suggests instead that the BLA is involved in the acquisition of second-order appetitive conditioning.

In contrast to lesions of the BLA, lesions of the central nucleus of the amygdala (CeA) block both first- and second-order conditioned orienting responses but not conditioned approach to the food (Hatfield et al. 1996). Based on these and other data, Hatfield et. al. (1996)suggest the CeA “regulates attentional processing of cues during associative conditioning” (Hatfield et al. 1996, p. 5265), whereas the basolateral nucleus of the amygdala is critically involved in “associative learning processes that give CSs access to the motivational value of their associated USs” (Hatfield et al. 1996, p. 5264).

In addition, BLA lesions block the effect of US devaluation (Hatfield et al. 1996). Similarly, in instrumental conditioning, lesions of the BLA impair the ability of rats to detect a decrease in reward magnitude (the “Crespi” or “negative contrast” effect; Becker et al. 1984; Salinas et al. 1996). Thus, BLA lesions may block acquisition of second-order conditioning by blocking access to a memory of the current motivational value of the reinforcer, stored in the posterior insular cortex. Importantly, because acquisition and expression of first-order appetitive conditioning (in contrast to fear conditioning, see above) are not disrupted by the same lesions, a memory of the original value of the appetitive reinforcer must be stored independent of this amygdalo-cortical network.

As described earlier, lesions of the basal nucleus of the BLA block the secondary reinforcing effects of a fearful CS on acquisition of an avoidance response but do not block freezing (Amorapanth et al. 2000). A similar involvement of the BLA is indicated in appetitive secondary reinforcement as well. Lesions of the BLA markedly reduced the ability of a CS that had been paired with an estrous female to support instrumental acquisition of a lever response. The animal's tendency to respond to the primary reinforcer (the estrous female) was not impaired (Everitt et al. 1989). Finally, in another paradigm related to higher-order conditioning, local infusion of the NMDA antagonist AP5 into the BLA disrupted acquisition of a taste-potentiated odor aversion but not acquisition of a first-order taste aversion (Hatfield and Gallagher 1995).

In summary, several findings now suggest a critical role for the BLA in a variety of higher-order conditioning situations, both aversive and appetitive.

Although applying a treatment (e.g., drug, lesion) before training or testing is typically used to discriminate effects on learning versus performance, this approach is often still problematic. Some of the problems can be overcome by including tests of higher-order conditioning. For example, consider a treatment, such as a drug infused locally into the amygdala, that blocks expression of first-order conditioning when applied before testing, but not acquisition when applied before training, tested at some later time when the treatment no longer is in effect (Table 2). Does the treatment act by blocking memory activation or does it act further downstream by blocking elicitation of a behavioral response? To distinguish between these possibilities one can test the ability of the drug to block acquisition of second-order conditioning. Recall that S2 becomes associated with a central state of anticipation of reinforcement rather than with a specific peripheral response. Therefore, if acquisition of second-order conditioning also were blocked, it would strongly suggest that the treatment interfered with retrieval of the first-order memory and not with performance of an overt behavioral response.

View this table:

Table 2.

How Second-Order Conditioning Can Help in More Precisely Determining the Effects of a Treatment on Learning and Memory

This rationale has recently been applied to exploring effects of dopamine on fear conditioning. Systemic application of the D2 agonist quinpirol blocks acquisition of second-order conditioning (Nader and LeDoux 1999a). Quinpirol does not, however, disrupt acquisition of sensory preconditioning. (The study did not measure the effects of quinpirol on first-order conditioning, but a sparing of sensory preconditioning effectively provides for the same sorts of controls.) This suggests that D2 receptor stimulation does not interfere in a general way with processes involved in synaptic plasticity or transmission of CS information. The inability to acquire second-order conditioning suggests that D2 receptor stimulation (probably in the ventral tegmental area; Nader and LeDoux 1999b) inhibits the retrieval of a central fear state, rather than the performance of a fear-related behavior (freezing).

Second, consider a situation where a drug enhances acquisition of a first-order association but not its expression (see Table 2). The most intriguing conclusion would be that the drug facilitated processes involved in the cellular plasticity that underlies associative learning. Alternatively, the drug might have acted by enhancing transmission along central US pathways, perhaps not reflected by lower brain stem or spinal cord measures of US activation. However, if the drug also increased the rate of acquisition of second-order conditioning this could not be interpreted as reflecting alterations in US processing. This is because the drug would only be given before second-order training sessions, in which the US was not presented (i.e., no refresher trials would be included during second-order training). Using this approach, Cicala et al. (1990) found that the opiate antagonist naloxone enhanced second-order conditioning when given before second-order training. This effect could not be attributed to a change in shock sensitivity because no shocks were given after the drug was administered.

We have also used this rationale to further investigate the role of NMDA-type glutamate receptors in the amygdala in acquisition of conditioned fear. Local infusion of an NMDA receptor antagonist into the amygdala at the time of training disrupts first-order fear conditioning (Miserendino et al. 1990; Campeau et al. 1992; Fanselow and Kim 1994), odor-aversion learning (Staubli et al. 1989), appetitive conditioning (Burns et al. 1994; Baldwin et al. 2000), and inhibitory avoidance learning (Izquierdo et al. 1992; Kim and McGaugh 1992; Liang et al. 1994). Expression of first-order fear conditioning is not disrupted (Miserendino et al. 1990; Campeau et al. 1992; Liang et al. 1994), indicating that transmission of CS information is intact (but see also Maren et al. 1996; Lee and Kim 1998). Furthermore, intra-amygdala AP5 does not affect reactivity to footshock during fear conditioning (Miserendino et al. 1990; Kim and McGaugh 1992; Liang et al. 1994). However, this measure may reflect only a spinally mediated withdrawal reflex. Thus, one cannot rule out with any confidence the possibility that NMDA antagonists interfered with local transmission of US information in the amygdala during first-order conditioning. To test whether blockade of NMDA receptors in the amygdala interferes with associative learning independent of any effect it might have on US processing, we infused AP5 into the amygdala immediately before second-order conditioning (Gewirtz and Davis 1997a). Importantly, not only did AP5 completely block second-order conditioning but the same dose actually enhanced expression of first-order conditioning (Fig.5). Because first-order fear provides the reinforcement signal for second-order conditioning, this strongly suggests that AP5 did not block second-order conditioning by disrupting transmission of the reinforcement signal to the amygdala.

Figure 5.

Acquisition of second-order fear-potentiated startle is blocked by microinfusion of the NMDA antagonist AP5 into the BLA. The drug was injected immediately before each session of second-order conditioning. The control group was injected with artificial cerebrospinal fluid (ACSF) vehicle. The three tests were conducted before second-order training (pretraining) and after two (posttraining 1) and three (posttraining 2) sessions of second-order training. No first-order pairings were given during second-order training. Fear-potentiated startle is defined as the difference (±SEM) in startle amplitude measured in the presence versus absence of the CS. Figure first published in Gewirtz and Davis (1997a) and reproduced with permission of the publisher.

The foregoing discussion has illustrated several ways in which higher-order conditioning can be used to analyze neural substrates of learning and memory. The value of higher-order conditioning stems from the fact that the reinforcer has motivational value that is acquired, as opposed to intrinsic motivational value. As a result, higher-order conditioning can be used to investigate the neural substrates of different forms of associative memory and to identify processes involved in learning and memory versus processes involved in transmission of sensory or motor information.

Beyond this, however, the value of higher-order conditioning to the neurobiologist or learning theorist may be greater than its usefulness as a laboratory instrument. It is likely that most sources of reinforcement in ethological settings have acquired motivational significance rather than any particular intrinsic value. This point is easy to overlook. For example, the sight of a banana can clearly serve as a powerful reinforcing stimulus for a monkey, but the monkey first has to learn to associate its appearance with its odor and taste (Gaffan 1992). Similarly, little human learning presumably involves the direct pairing of objects or events with powerful, unconditioned reinforcers. Hence, higher-order conditioning offers a bridge between Pavlovian conditioning experiments conducted inside the laboratory and the types of learning that perhaps predominate in the outside world.

This work was supported by NIH Grants MH47840, MH57250, MH58922, MH52384, MH59906, MH11370, and the Woodruff Foundation.

Footnotes

REFERENCES

Page 2

1. Joseph R. Manns1,
2. Robert E. Clark2, and
3. Larry R. Squire3,4,5

1. 1Department of Psychology, University of California, San Diego, California 92039, USA; 2Department of Psychiatry, University of California, San Diego, California 92039, USA; 3Veterans Affairs Healthcare System, San Diego, California 92161, USA; 4Departments of Psychiatry, Neurosciences, and Psychology, University of California, San Diego, California 92039, USA

Trace eyeblink conditioning (with a trace interval ≥500 msec) depends on the integrity of the hippocampus and requires that participants develop awareness of the stimulus contingencies (i.e., awareness that the conditioned stimulus [CS] predicts the unconditioned stimulus [US]). Previous investigations of the relationship between trace eyeblink conditioning and awareness of the stimulus contingencies have manipulated awareness or have assessed awareness at fixed intervals during and after the conditioning session. In this study, we tracked the development of knowledge about the stimulus contingencies trial by trial by asking participants to try to predict either the onset of the US or the onset of their eyeblinks during differential trace eyeblink conditioning. Asking participants to predict their eyeblinks inhibited both the acquisition of awareness and eyeblink conditioning. In contrast, asking participants to predict the onset of the US promoted awareness and facilitated conditioning. Acquisition of knowledge about the stimulus contingencies and acquisition of differential trace eyeblink conditioning developed approximately in parallel (i.e., concurrently).

Footnotes

• ↵5 Corresponding author.

• E-MAIL lsquire{at}ucsd.edu; FAX (858) 552-7457.

• Article and publication are atwww.learnmem.org/cgi/doi/10.1101/lm.33400.

• • Received May 16, 2000.
  • Accepted July 20, 2000.
• Cold Spring Harbor Laboratory Press

Page 3

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2. Articles by Stark, C. E.
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Page 4

1. Seth A. Balogh1,
2. Yong Tae Kwon3, and
3. Victor H. Denenberg1,2,4,5

1. 1Biobehavioral Sciences Graduate Degree Program and 2Department of Psychology, University of Connecticut, Storrs, Connecticut 06269, USA; 3Division of Biology, California Institute of Technology, Pasadena, California 91125, USA

The N-end rule is one ubiquitin-proteolytic pathway that relates the in vivo half-life of a protein to the identity of its N-terminal residue. NTAN1 deamidates N-terminal asparagine to aspartate, which is conjugated to arginine by ATE1. An N-terminal arginine-bearing substrate protein is recognized, ubiquitylated by UBR1/E3α, and subsequently degraded by 26S proteasomes. Previous research showed that NTAN1-deficient mice exhibited impaired long-term memory in the Lashley III maze. Therefore, a series of studies, designed to assess the role of NTAN1 in short- and intermediate-term memory processes, was undertaken. Two hundred sixty mice (126 −/−; 134 +/ +) received Lashley III maze training with intertrial intervals ranging from 2–180 min. Results indicated that inactivation of NTAN1 amidase differentially affects short-, intermediate-, and long-term memory.

Footnotes

• ↵4 Present address: University of Connecticut, Biobehavioral Sciences, Graduate Degree Program, 3107 Horsebarn Hill Road, U-154, Storrs, CT 06269-4154, USA.

• ↵5 Corresponding author.

• E-MAIL dberg{at}uconnvm.uconn.edu; FAX (860) 486-3827.

• Article and publication are atwww.learnmem.org/cgi/doi/10.1101/lm.33500.

• • Received May 16, 2000.
  • Accepted August 2, 2000.
• Cold Spring Harbor Laboratory Press

Page 5

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2. Articles by Fornari, R. V.
3. Articles by Oliveira, M. G. M.

Page 6

1. Aldemar Degroot1 and
2. Marise B. Parent1,2,3,4

1. 1Division of Neuroscience, 2Department of Psychology, and 3Department of Psychiatry, University of Alberta, Edmonton, Alberta T6G 2E9 Canada

Intra-septal infusions of the γ-aminobutyric acid (GABA) agonist muscimol impair learning and memory in a variety of tasks. This experiment determined whether hippocampal or entorhinal infusions of the acetylcholinesterase inhibitor physostigmine would reverse such impairing effects on spontaneous alternation performance, a measure of spatial working memory. Male Sprague-Dawley rats were given intra-septal infusions of vehicle or muscimol (1 nmole/0.5 μL) combined with unilateral intra-hippocampal or intra-entorhinal infusions of vehicle or physostigmine (10 μg/μL for the hippocampus; 7.5 μg/μL or 1.875 μg/0.25 μL for the entorhinal cortex). Fifteen minutes later, spontaneous alternation performance was assessed. The results indicated that intra-septal infusions of muscimol significantly decreased percentage-of-alternation scores, whereas intra-hippocampal or intra-entorhinal infusions of physostigmine had no effect. More importantly, intra-hippocampal or intra-entorhinal infusions of physostigmine, at doses that did not influence performance when administered alone, completely reversed the impairing effects of the muscimol infusions. These findings indicate that increasing cholinergic levels in the hippocampus or entorhinal cortex is sufficient to reverse the impairing effects of septal GABA receptor activation and support the hypothesis that the impairing effects of septal GABAergic activity involve cholinergic processes in the hippocampus and the entorhinal cortex.

Footnotes

• ↵4 Corresponding author.

• Current address: Department of Psychology, Georgia State University, University Plaza, Atlanta, GA 30303-3083.

• E-MAIL psymbp{at}langate.gsu.edu; FAX (404) 651-3929.

• Article and publication are at www.learnmem.org/cgi/doi/10.1101/lm.32200.

• • Received March 20, 2000.
  • Accepted August 9, 2000.
• Cold Spring Harbor Laboratory Press

Page 7

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2. Articles by Roberto, M.
3. Articles by Brunelli, M.

Page 8

1. 1Department of Physiology, Yamagata University School of Medicine, Yamagata 990-9585, Japan; 2Department of Molecular Neurobiology, Institute of Medical Science, University of Tokyo, Tokyo 108-8639, Japan; 3Exploratory Research for Advanced Technology, Japan Science and Technology Cooperation, Tokyo 113-0012, Japan

In hippocampal CA1 neurons of wild-type mice, delivery of a standard tetanus (100 pulses at 100 Hz) or a train of low-frequency stimuli (LFS; 1000 pulses at 1 Hz) to a naive input pathway induces, respectively, long-term potentiation (LTP) or long-term depression (LTD) of responses, and delivery of LFS 60 min after tetanus results in reversal of LTP (depotentiation, DP), while LFS applied 60 min before tetanus suppresses LTP induction (LTP suppression). To evaluate the role of the type 1 inositol-1,4,5-trisphosphate receptor (IP3R1) in hippocampal synaptic plasticity, we studied LTP, LTD, DP, and LTP suppression of the field excitatory postsynaptic potentials (EPSPs) in the CA1 neurons of mice lacking the IP3R1. No differences were seen between mutant and wild-type mice in terms of the mean magnitude of the LTP or LTD induced by a standard tetanus or LFS. However, the mean magnitude of the LTP induced by a short tetanus (10 pulses at 100 Hz) was significantly greater in mutant mice than in wild-type mice. In addition, DP or LTP suppression was attenuated in the mutant mice, the mean magnitude of the responses after delivery of LFS or tetanus being significantly greater than in wild-type mice. These results suggest that, in hippocampal CA1 neurons, the IP3R1 is involved in LTP, DP, and LTP suppression but is not essential for LTD. The facilitation of LTP induction and attenuation of DP and LTP suppression seen in mice lacking the IP3R1 indicates that this receptor plays an important role in blocking synaptic potentiation in hippocampal CA1 neurons.

Footnotes

• ↵4 Corresponding author.

• E-MAIL sfujii{at}med.id.yamagata-u.ac.jp; FAX 81 236 28 5221.

• Article and publication are at www.learnmem.org/cgi/doi/10.1101/lm.34100.

• • Received May 29, 2000.
  • Accepted August 10, 2000.
• Cold Spring Harbor Laboratory Press

Page 9

1. 1Division of Neuroscience, 2Departments of Pediatrics and Neurology, 3Department of Pathology, Baylor College of Medicine, Houston, Texas 77030, USA

Voltage-gated A-type potassium channels such as Kv4.2 regulate generation of action potentials and are localized abundantly in the hippocampus and striatum. Phosphorylation consensus sites for various kinases exist within the sequence of the potassium channel subunit Kv4.2, including consensus sites for extracellular signal-regulated kinase/mitogen activated protein kinase (ERK/MAPK), protein kinase A (PKA), protein kinase C (PKC), and calcium/calmodulin-dependent kinase II (CaMKII), and kinase assays have shown that particular amino acids of the consensus sites are bonafide phosphorylation sites in vitro. We have developed antibodies recognizing Kv4.2 triply phosphorylated at the three ERK sites as well as two antibodies recognizing singly phosphorylated Kv4.2 channels at the PKA sites (one amino-terminal and one carboxy-terminal). In the present study, we report the development of reliable immunohistochemistry protocols to study the localization of these phosphorylated versions of Kv4.2, as well as total Kv4.2 in the mouse brain. A general description of the areas highlighted by these antibodies includes the hippocampus, amygdala, cortex, and cerebellum. Such areas display robust synaptic plasticity and have been implicated in spatial, associative, and motor learning. Interestingly, in the hippocampus, the antibodies to differentially phosphorylated Kv4.2 channels localize to specific afferent pathways, indicating that the Kv4.2 phosphorylation state may be input specific. For example, the stratum lacunosum moleculare, which receives inputs from the entorhinal cortex via the perforant pathway, displays relatively little ERK-phosphorylated Kv4.2 or PKA carboxy-terminal-phosphorylated Kv4.2. However, this same layer is highlighted by antibodies that recognize Kv4.2 that has been phosphorylated by PKA at the amino terminus. Similarly, of the three antibodies tested, the soma of CA3 neurons are primarily recognized by the ERK triply phosphorylated Kv4.2 antibody, and the mossy fiber inputs to CA3 are primarily recognized by the carboxy-terminal PKA-phosphorylated Kv4.2. This differential phosphorylation is particularly interesting in two contexts. First, phosphorylation may be serving as a mechanism for targeting. For example, the amino-terminal PKA phosphorylation may be acting as a tag for a discrete pool of Kv4.2 to enter stratum lacunosum moleculare. Second, as phosphorylation may regulate channel biophysical properties, differential phosphorylation of Kv4.2 in the dendrites of pyramidal neurons may confer unique biophysical properties upon particular dendritic input layers.

Footnotes

• ↵4 Corresponding author.

• E-MAIL jsweatt{at}bcm.tmc.edu; FAX (713) 798-3946.

• Article and publication are at www.learnmem.org/cgi/doi/10.1101/lm.35300.

• • Received June 26, 2000.
  • Accepted July 26, 2000.
• Cold Spring Harbor Laboratory Press

Page 10

1. 1Centro de Memoria, Departamento de Bioquimica, Instituto de Ciencias Basicas da Saude, UFRGS, 90035–003 Porto Alegre, RS, Brazil; 2 Instituto de Biologia Celular y Neurociencias, Facultad de Medicina, Universidad de Buenos Aires, Buenos Aires, Argentina

Long-term habituation to a novel environment is one of the most elementary forms of nonassociative learning. Here we studied the effect of pre- or posttraining intrahippocampal administration of drugs acting on specific molecular targets on the retention of habituation to a 5-min exposure to an open field measured 24 h later. We also determined whether the exposure to a novel environment resulted in the activation of the same intracellular signaling cascades previously shown to be activated during hippocampal-dependent associative learning. The immediate posttraining bilateral infusion of CNQX (1 μg/side), an AMPA/kainate glutamate receptor antagonist, or of muscimol (0.03 μg/side), a GABAA receptor agonist, into the CA1 region of the dorsal hippocampus impaired long-term memory of habituation. The NMDA receptor antagonist AP5 (5 μg/side) impaired habituation when infused 15 min before, but not when infused immediately after, the 5-min training session. In addition, KN-62 (3.6 ng/side), an inhibitor of calcium calmodulin-dependent protein kinase II (CaMKII), was amnesic when infused 15 min before or immediately and 3 h after training. In contrast, the cAMP-dependent protein kinase (PKA) inhibitor Rp-cAMPS, the mitogen-activated protein kinase kinase (MAPKK) inhibitor PD098059, and the protein synthesis inhibitor anisomycin, at doses that fully block memory formation of inhibitory avoidance learning, did not affect habituation to a novel environment. The detection of spatial novelty is associated with a sequential activation of PKA, ERKs (p44 and p42 MAPKs) and CaMKII and the phosphorylation of c-AMP responsive element-binding protein (CREB) in the hippocampus. These findings suggest that memory formation of spatial habituation depends on the functional integrity of NMDA and AMPA/kainate receptors and CaMKII activity in the CA1 region of the hippocampus and that the detection of spatial novelty is accompanied by the activation of at least three different hippocampal protein kinase signaling cascades.

Footnotes

• ↵3 Corresponding author.

• E-MAIL mrmvianna{at}yahoo.com; FAX 55-51-316-55-40.

• Article and publication are at www.learnmem.org/cgi/doi/10.1101/lm.34600.

• • Received June 14, 2000.
  • Accepted August 16, 2000.
• Cold Spring Harbor Laboratory Press

Page 11

1. Jeff E. Engel1,3,
2. Xian-Jin Xie1,
3. Marla B. Sokolowski2, and
4. Chun-Fang Wu1

1. 1Department of Biological Sciences, University of Iowa, Iowa City, Iowa 52242-1324, USA; 2Department of Zoology, University of Toronto, Missisauga, Ontario L5L1C6, Canada

The Drosophila giant fiber jump-and-flight escape response is a model for genetic analysis of both the physiology and the plasticity of a sensorimotor behavioral pathway. We previously established the electrically induced giant fiber response in intact tethered flies as a model for habituation, a form of nonassociative learning. Here, we show that the rate of stimulus-dependent response decrement of this neural pathway in a habituation protocol is correlated with PKG (cGMP-Dependent Protein Kinase) activity and foraging behavior. We assayed response decrement for natural and mutant rover and sitter alleles of the foraging (for) gene that encodes a Drosophila PKG. Rover larvae and adults, which have higher PKG activities, travel significantly farther while foraging than sitters with lower PKG activities. Response decrement was most rapid in genotypes previously shown to have low PKG activities and sitter-like foraging behavior. We also found differences in spontaneous recovery (the reversal of response decrement during a rest from stimulation) and a dishabituation-like phenomenon (the reversal of response decrement evoked by a novel stimulus). This electrophysiological study in an intact animal preparation provides one of the first direct demonstrations that PKG can affect plasticity in a simple learning paradigm. It increases our understanding of the complex interplay of factors that can modulate the sensitivity of the giant fiber escape response, and it defines a new adult-stage phenotype of the foraging locus. Finally, these results show that behaviorally relevant neural plasticity in an identified circuit can be influenced by a single-locus genetic polymorphism existing in a natural population of Drosophila.

Footnotes

• ↵3 Corresponding author.

• Present address: Department of Biological Sciences, Western Illinois University, 1 University Circle, Macomb, IL 61455.

• E-MAIL je-engel{at}wiu.edu; FAX (309) 298-2270.

• Article and publication are at www.learnmem.org/cgi/doi/10.1101/lm.31600.

• • Received January 24, 2000.
  • Accepted August 17, 2000.
• Cold Spring Harbor Laboratory Press

Page 12

1. Bill P. Godsil1,3,
2. Jennifer J. Quinn1, and
3. Michael S. Fanselow1,2

1. 1Department of Psychology, University of California, Los Angeles, California 90095–1563, USA; 2 Brain Research Institute, University of California, Los Angeles, California 90095–1761, USA; 3Interdepartmental Ph.D. Program in Neuroscience, University of California, Los Angeles, California 90095–1761, USA

On six days rats were exposed to each of two contexts. They received an electric shock in one context and nothing in the other. Rats were tested later in each environment without shock. The rats froze and defecated more often in the shock-paired environment; they also exhibited a significantly larger elevation in rectal temperature in that environment. The rats discriminated between each context, and we suggest that the elevation in temperature is the consequence of associative learning. Thus, body temperature can be used as a conditional response measure in Pavlovian fear conditioning experiments that use footshock as the unconditional stimulus.

Footnotes

• ↵3 Corresponding author.

• EMAIL godsil{at}lifesci.ucla.edu; FAX (310) 206-5895.

• Article and publication are at www.learnmem.org/cgi/doi/10.1101/lm.32800.

• • Received April 25, 2000.
  • Accepted July 26, 2000.
• Cold Spring Harbor Laboratory Press

Page 13

1. 1Institut des Neurosciences, Département de Neurochimie-Anatomie and 2Laboratoire, UniversitéPierre et Marie Curie, F-75005 Paris, France

Striatal output neurons (SONs) integrate glutamatergic synaptic inputs originating from the cerebral cortex. In vivo electrophysiological data have shown that a prior depolarization of SONs induced a short-term (≤1 sec) increase in their membrane excitability, which facilitated the ability of corticostriatal synaptic potentials to induce firing. Here we propose, using a computational model of SONs, that the use-dependent, short-term increase in the responsiveness of SONs mainly results from the slow kinetics of a voltage-dependent, slowly inactivating potassium A-current. This mechanism confers on SONs a form of intrinsic short-term memory that optimizes the synaptic input–output relationship as a function of their past activation.

Footnotes

• ↵3 Corresponding author.

• E-MAIL severine.mahon{at}snv.jussieu.fr; FAX 33-1-44-27-26-69.

• Article and publication are at www.learnmem.org/cgi/doi/10.1101/lm.34800.

• • Received June 19, 2000.
  • Accepted August 10, 2000.
• Cold Spring Harbor Laboratory Press

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2. Articles by Zhang, S.
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Page 15

1. 1Veterans Affairs San Diego Healthcare System, San Diego, California 92161 USA; Departments of 2Neurosciences and 3Psychiatry, UCSD, La Jolla, California 92093, USA; 4Department of Psychology, Boston University, Boston, Massachusetts 02134, USA.

Monkeys with lesions of perirhinal cortex (PR group) and monkeys with lesions of inferotemporal cortical area TE (TE group) were tested on a modified version of the delayed nonmatching to sample (DNMS) task that included very short delay intervals (0.5 sec) as well as longer delay intervals (1 min and 10 min). Lesions of the perirhinal cortex and lesions of area TE produced different patterns of impairment. The PR group learned the DNMS task as quickly as normal monkeys (N) when the delay between sample and choice was very short (0.5 sec). However, performance of the PR group, unlike that of the N group, fell to chance levels when the delay between sample and choice was lengthened to 10 min. In contrast to the PR group, the TE group was markedly impaired on the DNMS task even at the 0.5-sec delay, and three of four monkeys with TE lesions failed to acquire the task. The results provide support for the idea that perirhinal cortex is important not for perceptual processing, but for the formation and maintenance of long-term memory. Area TE is important for the perceptual processing of visual stimuli.

Footnotes

• ↵5 Corresponding author.

• E-MAIL szola{at}ucsd.edu; FAX (858) 534-1569.

• Article and publication are at www.learnmem.org/cgi/doi/10.1101/lm32100.

• • Received May 20, 2000.
  • Accepted September 18, 2000.
• Cold Spring Harbor Laboratory Press

Page 16

1. 1Department of Integrative Physiology, Graduate School of Medical Sciences Kyushu University, Fukuoka 812–8582 Japan; 2Department of Pathology, Osaka University Graduate School of Medicine, Suita 565 Japan

The c-kit receptor tyrosine kinase encoded by the white-spotting (W) gene is highly expressed in rat hippocampal CA1–CA4 regions. We found an impaired spatial learning and memory in homozygous c-kit (Ws/Ws) mutant rats that have a 12-base deletion in the tyrosine kinase domain of thec-kit gene and a very low kinase activity. Electrophysiological studies in hippocampal slices revealed that the long-term potentiation (LTP) induced by the tetanic stimulation (100 Hz, 1 sec) in the mossy fiber (MF)–CA3 pathway, but not in the Schaffer collaterals/commissural–CA1 pathway, was significantly reduced in c-kit mutants compared with wild-type (+/+) rats. The paired-pulse facilitation (PPF) was measured before the tetanus and after the establishment of the LTP in each slice. The initial PPF in the MF–CA3 pathway positively correlated with the amplitude of the LTP in the wild-type rats but not in the c-kit mutant rats. Furthermore, they failed to show the normal characteristics observed in the MF–CA3 pathway of +/+ rats; that is, the negative correlation between the initial PPF and the changes in PPF measured after the LTP. These findings suggest an involvement of SCF/c-kit signaling in hippocampal synaptic potentiation and spatial learning and memory.

Footnotes

• ↵3 Present address: Cell Biology Laboratory, National Institute of Bioscience and Human Technology, 1–1 Higashi, Tsukuba, Ibaraki 305, Japan.

• ↵4 Corresponding author.

• E-MAIL kataf{at}physiol.med.kyushu-u.ac.jp; FAX 81-92-642-6093.

• Article and publication date are at www.learnmem.org/cgi/doi/10.1101/lm.33900.

• • Received May 24, 2000.
  • Accepted September 29, 2000.
• Cold Spring Harbor Laboratory Press

Page 17

1. Cornelia Schauz1 and
2. Michael Koch2,3

1. 1Animal Physiology, University of Tübingen, 72076 Tübingen, Germany; 2Brain Research Institute, University of Bremen, 28334 Bremen, Germany

The association between a conditioned stimulus (CS) and an unconditioned stimulus (US) in fear-conditioning depends onN-methyl-d-aspartate (NMDA) receptors in the basolateral amygdala complex (BLA). Latent inhibition (LI) is the retardation in learning due to nonreinforced presentation of the prospective CS before conditioning. Disruption of LI in rats is an animal model of schizophrenia, reflecting the deficits of schizophrenic patients in neglecting irrelevant information. We investigated whether the BLA is involved in LI of fear-potentiated startle. Infusions of the NMDA receptor antagonistd,l-2-amino-5-phosphonopentanoic acid (AP-5; 12.5 nmoles) into the BLA before preexposure of rats to the neutral stimulus prevent LI of fear-conditioning. We also demonstrated by the same method that a complex of thalamic nuclei, comprising the medial part of the medial geniculate nucleus, the posterior intralaminar nucleus, and the suprageniculate nucleus, is involved in fear-conditioning, but not in LI. This suggests that the presentation of an innocuous stimulus during preexposure leads to an NMDA receptor-dependent change of neurotransmission in the BLA, but not in the thalamus. Our data show that the BLA but not the thalamus regulates in LI of fear-potentiated startle. Furthermore, it supports the hypothesis that the inability of schizophrenic patients to ignore irrelevant stimuli may be caused by hypofunction of the glutamatergic transmission in the brain and suggests an involvement of the amygdala in the neuropathology of schizophrenia.

Footnotes

• ↵3 Corresponding author.

• E-MAIL michael.koch{at}uni-bremen.de; FAX 49 421 218 4932.

• Article and publication are at www.learnmem.org/cgi/doi/10.1101/lm.33800.

• • Received May 16, 2000.
  • Accepted September 18, 2000.
• Cold Spring Harbor Laboratory Press

Page 18

1. Joanna L. Jankowsky1,3,
2. Brian E. Derrick2, and
3. Paul H. Patterson1,4

1. 1Division of Biology, California Institute of Technology, Pasadena, California 91125, USA; 2Cajal Neuroscience Research Center, Division of Life Sciences, University of Texas, San Antonio, Texas 78249, USA

Because exogenous application of a number of cytokines and growth factors can alter synaptic properties, we sought to determine if endogenous cytokine expression is affected by neuronal activity. In addition, we examined whether cytokine expression is altered by the techniques used to stimulate and record from hippocampal neurons. Using semi-quantitative RNase protection and RT-PCR assays, we studied the expression of 18 cytokine, growth factor, and receptor genes in the hippocampus following the induction of Schaffer collateral-CA1 long-term potentiation (LTP). We found that various cytokines are dramatically induced following preparation of slices for in vitro recording and as a result of injury following acute electrode placement in vivo. These increases can be overcome in vivo, however, using permanent electrodes implanted three weeks prior to testing. Using this chronic preparation, we found that interleukin-6 (IL-6) mRNA was upregulated nearly 20-fold by LTP induction in vivo, marking the first demonstration of endogenous regulation of this cytokine in response to LTP. In situ hybridization for IL-6 revealed that upregulation is tightly localized near the site of stimulation and is detected only in non-neuronal cells, identified as GFAP+ astrocytes and GFAP− cells within proximal blood vessels. Coupled with previous results showing that exogenously applied IL-6 can prevent the induction of LTP, this finding suggests a mechanism by which the local release of a cytokine could regulate LTP at nearby sites.

Footnotes

• ↵3 Present address: Division of Neuropathology, Johns Hopkins University School of Medicine, Baltimore, MD 21205

• ↵4 Corresponding author.

• E-MAIL php{at}caltech.edu; FAX 626-585-8743.

• Article and publication are at www.learnmem.org/cgi/doi/10.1101/lm.32600.

• • Received April 17, 2000.
  • Accepted August 25, 2000.
• Cold Spring Harbor Laboratory Press

Page 19

1. 1Division of Basic Medical Sciences, 2Department of Psychology, Memorial University of Newfoundland, St. John's, Newfoundland A1B 3V6, Canada

Norepinephrine (NE) and serotonin (5-HT) are important modulators of early odor preference learning. NE can act as an unconditioned stimulus (UCS), whereas 5-HT facilitates noradrenergic actions. In this study, we examined the phosphorylation of an important transcription factor, cAMP response element binding protein (CREB), which has been implicated in long-term-memory formation (McLean et al. 1999) during NE-induced odor preference learning in normal and olfactory bulb 5-HT-depleted rat pups. We also examined NE modulation of olfactory nerve–evoked field potentials (ON-EFPs) in anesthetized normal and bulbar 5-HT depleted pups. Systemic injection of 2 mg/kg isoproterenol (β-adrenoceptor agonist) induced odor preference learning, enhanced pCREB expression in the olfactory bulbs at 10 min after odor pairing, and increased ON-EFPs in normal rat pups but not in bulbar 5-HT-depleted rat pups. A dose of 6 mg/kg isoproterenol, which was ineffective in modulating these measures in normal rat pups, induced odor preference learning, enhanced phosphorylated CREB (pCREB) expression, and increased ON-EFPs in bulbar 5-HT-depleted pups. These outcomes suggest that NE and 5-HT promote specific biochemical and electrophysiological changes that may critically underlie odor preference learning.

Footnotes

• ↵3 Corresponding author.

• E-MAIL charley{at}play.psych.mun.ca; FAX (709) 737-4000.

• Article and publication are at www.learnmem.org/cgi/doi/10.1101/lm.35900.

• • Received July 19, 2000.
  • Accepted October 5, 2000.
• Cold Spring Harbor Laboratory Press

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2. Articles by Nikitin, E. S.
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1. 1Howard Hughes Medical Institute, Cold Spring Harbor Laboratory, Cold Spring Harbor, New York 11724, USA; 2Institute of Experimental Medicine, Hungarian Academy of Sciences, 1083 Budapest, Hungary; 3Cardiff School of Biosciences, Cardiff University, Cardiff CF1 3US, Wales, United Kingdom

To detect subtle changes in neuronal morphology in response to changes in experience, one must image neurons at high resolution in vivo over time scales of minutes to days. We accomplished this by infecting postmitotic neurons in rat and mouse barrel cortex with a Sindbis virus carrying the gene for enhanced green fluorescent protein. Visualized with 2-photon excitation laser scanning microscopy, infected neurons showed bright fluorescence that was distributed homogeneously throughout the cell, including axonal and dendritic arbors. Single dendritic spines could routinely be resolved and their morphological dynamics visualized. Viral infection and imaging were achieved throughout postnatal development up to early adulthood (P 8–30), although the viral efficiency of infection decreased with age. This relatively noninvasive method for fluorescent labeling and imaging of neurons allows the study of morphological dynamics of neocortical neurons and their circuits in vivo.

Footnotes

• ↵4 Corresponding author.

• E-MAIL svoboda{at}cshl.org; FAX (516) 367-8866.

• Article and publication are at www.learnmem.org/cgi/doi/10.1101/lm.32700.

• • Received April 18, 2000.
  • Accepted September 21, 2000.
• Cold Spring Harbor Laboratory Press

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2. Articles by Tate, D. F.
3. Articles by Bigler, E. D.

Page 23

This extract was created in the absence of an abstract.

Learning & Memory 7: 244–256 (2000)

Revisiting the Maturation of Medial Temporal Lobe Memory Functions in Primates

Maria C. Alvarado and Jocelyn Bachevalier

The introductory paragraph of the text of this paper was inadvertently set as an abstract by the publisher.

In Table 1 (p. 247) the publication date of Clark et al. is 1996, not 1997; the publication date of Buffalo et al. is 1999, not 1998.

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1. 1Division of Neuroscience and 2Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas 77030, USA; 3Alzheimer Research Laboratory, Case Western Reserve University School of Medicine, Cleveland, Ohio 44106, USA

The extracellular signal-regulated kinases (ERKs) are members of the mitogen-activated protein kinase (MAPK) superfamily of enzymes and have recently garnered considerable attention in the field of learning and memory. ERK activation has been shown to be required for the induction of long-term potentiation (LTP) in the rat hippocampus and for the formation of associative and spatial memories in both the rat and the mouse. However, the individual roles for the two isoforms of ERK have yet to be deciphered. To investigate the specific contribution of the ERK1 (p44) isoform of MAPK to mammalian learning, we performed a general behavioral and physiological characterization of mice lacking the ERK1 gene. The ERK1-null animals demonstrated significantly higher levels of activity in the open field test. However, we observed no other discernible deficits in the ERK1 knockout mice in our behavioral testing. Specifically, no differences were observed in the acquisition or retention (24 h and 2 wk after training) of either contextual or cue fear conditioning between the ERK1−/− and their wild-type littermate controls. In addition, no learning phenotype was observed in the passive avoidance test. When hippocampal slices were analyzed, we found no deficits in baseline synaptic transmission or in tetanus-induced LTP in hippocampal area CA1. We found no apparent compensatory changes in the expression of ERK2 (p42 MAPK). We conclude that hippocampus- and amygdala-dependent emotional learning does not depend critically on the activity of ERK1.

Footnotes

• ↵4 These authors contributed equally to this work.

• ↵5 Corresponding author.

• E-MAIL jsweatt{at}bcm.tmc.edu; FAX (713) 798-3946.

• Article and publication are at www.learnmem.org/cgi/doi/10.1101/lm.37001.

• • Received September 29, 2000.
  • Accepted December 2, 2000.
• Cold Spring Harbor Laboratory Press

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