The hippocampus and the classically conditioned nictitating membrane response: Areal-time attentional-associative model

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1 Psychobiology 1988, Vol. 16 (1), The hippocampus and the classically conditioned nictitating membrane response: real-time attentional-associative model NESTOR. SCHMJUK Boston University, Boston, Massachusetts and JOHN W. MOORE University of Massachusetts, mherst, Massachusetts The present study introduces an attentional-associative model that incorporates (1) a mechanism capable of establishing associations between conditioned stimuli (CSS) and unconditioned stimuli (USs) and between two CSs; (2) a mechanism that, by combining CS-CS and CS-US associations, is capable ofbuilding a "computational cognitive map"; (3) a real-time version ofpearce and Hall's (1980) attentional rule; (4) performance rules that convert leaming variables into a topography ofthe rabbit's nictitating membrane (NM) response; and (5) rules that convert leaming variables into neuronal fring. The present study tested the "aggregate prediction" hypothesis as applied to hippocampal function. This hypothesis assumes that (1) the effect of hippocampallesions (HL) is an impairment in the integration of the aggregate prediction used to compute attentional variables; (2) the effect ofthe induction ofhippocampallong-term potentiation (LTP) is an increase in the value of the aggregate prediction by way of increasing the value of CS-CS associations; and (3) neural activity in the hippocampus is proportional to the instantaneous value of the aggregate prediction. n addition, the present study tested the hypothesis that medial septum activity is proportional to the value of attentional variables. Computer simulations for the HL case were carried out for acquisition under different interstimulus intervals, discrimination reversal, and sensory preconditioning. Computer simulations for the LTP case were carried out for discrimination acquisition. n addition, simulations of hippocampal unit activity during acquisition and extinction, and medial septum unit activity during the acquisition of conditioning, are presented. The aggregate prediction hypothesis proved capable of simulating most, but not all, experimental data regarding hippocampal manipulations in the rabbit's NM response preparation. ttentional theories of hippocampal function emphasize that the hippocampus participates in the control of the level of processing assigned to each stimulus, thereby controlling what information is to be stored in the brain. Schmajuk and Moore (1985) proposed areal-time attentional model of hippocampal function based on Pearce and Hall's (P-H) (1980) model of Pavlovian conditioning. This model was designated the S-P-H model. n the framework of the S-P-H model, Schmajuk and Moore suggested that the effects of hippocampal lesions (HL) could be described as an impairment in the integration of the aggregate prediction used to compute attentional variables. This assumption is called the aggregate prediction hypothesis. Computer simulations (Schmajuk & Moore, 1985) showed that under the aggregated prediction hypothesis, the S-P-H model is capable of describ- The first author was supported in part by NSF Grant ST The second author was supported in part by FORS Grant N.. Schmajuk's mailing address is Center for daptive Systems, Department of Mathernatics, Boston University, Boston, M ing the behavior of HL animals in many classical conditioning paradigms. n its present form, however, the S-P-H model neither encompasses some paradigms in which the effect of HL hall been assessed (e.g., sensory preconditioning) nor provides real-time descriptions of overt behavior in a preparation in which many HL effects have been evaluatednamely, the rabbit's nictitating membrane (NM) response preparation. On the basis ofthe preceding considerations, this paper introduces a rendering ofthe S-P-H model that is capable of sensory preconditioning and that incorporates performance rules that describe the rabbit NM response topography. Using this new version of the S-P-H model, the present study tested the aggregate prediction hypothesis as it relates to hippociunpallesions, hippocampallongterm potentiation (LTP) induction, and hippocampal neuronal activity. n addition, the study explored the correlation between medial septum neuronal activity and attentional variables in the S-P-H model. Copyright 1988 Psychonomic Society, nc. 20

2 HPpoeMPUS ND elsslel eondtonng 21 THE S-P-H MODEL This section describes aversion of the S-P-H model that incorporates second-order associations and performance rules that yield real-time descriptions of NM response topography. The S-P-H model comprises two different types of memory: a trace short-term memory and an associational long-term memory. First-order excitatory and inhibitory associative memories establish predictive relationships between two events (or stimuli): i and k. First-order excitatory and inhibitory associative memories are combined in first-order net associative memories, which express the expectancy that event k is going to follow event i. Secondorder associative values result from the combination of different first-order net predictions: if event i predicts event r, and event r predicts event k, then event i predicts event k. Second-order associative values allow the S-P-H model to build up cognitive maps. n agreement with Pearce and Hall's (1980) view, the S-P-H model assumes that the associability of event i with event k is given by the absolute difference between the aggregate prediction of the intensity of event k and the actual intensity of event k. When this rule is applied, the stimulus that is the best availabe predictor of event k will enter into an associative relationship with event k, to the detriment of all other stimuli. Since stimuli i and k can be separated by a finite interval of time, the S-P-H model incorporates the idea that stimuli give rise to short-term-memory traces in the central nervous system that become associated with other stimuli by simultaneously reaching a critical locus of learning. This section also presents the performance rules that translate learning variables into rabbit NM responses and neural activity. t should be noted that rues that describe changes in trace and associative memories are essential to the model, whereas performance rules are specifc for the type of preparation used and may change accordingly. First-Order Excitatory ssociative Values first-order excitatory associative value, V~, can be interpreted as the prediction that event k will folowes,. Whenever the intensity of event k, }..<, is greater than that of Bk as defned by Equation 5, the excitatory associative value, V~, between es, and event k increases by where () is the rate of change of V~, S, is the salience of es" a~ represents the associability of es, with event k, }..k represents the intensity of event k, and T, represents the trace of es,. ccording to Equation 1, V~ increases whenever event k is presented simultaneously with the trace of es" and the associability of es, with event k is larger than zero. s in the P-H model, V~ never decreases. Note that in the P-H model, V, refers to the association of different es, with the US, whereas in the S-P-H (1) model, V~ refers to the association of different es,s with different events k. First-Order nhibitory ssociative Values First-order inhibitory associative values, N~, can be interpreted as being the prediction that event k will not follow es,. This view agrees with Konorski's (1966) suggestion that a es could be associated with representations both of aus and of no USo Whenever}..k < B<, the inhibitory associative value between es, and event k, N~, increases by where ()' is the rate of change of N~and }.k is the intensity of the inhibitory reinforcer. The intensity of }. k is }. k = Bk - }..k. ccording to Equation 2, N~increases whenever }.k larger than zero is presented simultaneously with the trace of es, and the associability of es, with event k is larger than zero. s in the P-H model, N~ never decreases. First-Order Net ssociative Vaue The net associative value of es, and event k is given by the difference between associative and antiassociative values, and it can be interpreted as the net prediction of event k by es,: (2) li} = n - N~. (3) When i = k, h defmes the net prediction of event i by itself. V~ can both increase and decrease. Second-Order Net ssociative Vaue s presented above, the first-order net prediction ofthe US by es, is represented by the net associative value, V,us, and is given by Equation 3. First-order net predictions, however, are unable to describe classical conditioning phenomena such as sensory preconditioning or higher order conditioning. eonsider now the case in which two ess presented in compound-es, and es,-predict the USo V,us!S the fustorder net prediction of the US by es" and V,us is the first-order net prediction ofthe US by es,. t is assumed that es, predicts the US direct1y by Vps and indirect1y by predicting es" by Vi. n turn, es, predicts the US by V, US. The second-order net prediction of the US by es, is expressed as the product ViV,us. The second-order net prediction of event k by es, is one when Vi and ~are both one, and zero when either Vi or ~is zero. B~, the sum of frst- and second-order net predictions of event k by es" is B~ = (li} + E WiVi~)T,. (4) r Vi is the net associative value of es, with event k. The sum over index r involves all the ess with index r "* k. Vi is the net associative value of es, with all ess with index r "* k. h is the net associative value of all ess

3 22 SeHMJUK ND MOORE with event k. 7, is the trace of es,. The mathematical expression for 7, is given below. To avoid redundant es, - esk and es, - es, - es k associations, wr = 0 when i = r, and wr > 0 when i ::;:. r. eoefficient wr also serves to adjust the relative weights of first- and second-order predictions in paradigms such as conditioned inhibition, in which firstorder predictions are inhibitory, whereas second-order predictions are excitatory (Rescorla, 1982). Bk, the aggregate prediction of event k made upon all ess (including the context) with 7, > 0 at a given moment, is given by The sum over index i involves an the ess acting at a given moment. The integration of different predictions into a larger and new prediction is sirnilar to the process Tolman (1932) called inference. ccording to Tolman, expectancies can be combined to form new expectancies and then organized in a "cognitive map. " Up to the present, models for classical conditioning did not have a mechanism to account for "inference" processes. The introduction of secondorder associations allows for the building of "computational cognitive maps" (Moore, 1979) in which es-es predictions can be combined with es-us predictions. Figure 1 shows a network that, by computing secondorder net associative values, is capable of describing sensory preconditioning and secondary reinforcement. Stimuli have dual roles as predictors and events. es, es B, and the context (denoted as ex) are predictors of events es, esb, ex, and the USo Each node represents the net prediction of an event by a es. For example, node Vus represents the prediction ofthe US by es, and node V~ represents the prediction of es by esb Kohonen (1977) called this type of network a heteroassociative network. Sensory preconditioning is predicted by allowing esb to be associated with es in a first phase (denoted by the us CSB CX Figure 1. Diagram of a network!hat computes second-order net associative values., B, X, and R are "neural-ike" elements. CSB can generate a CR indirectly by activating element. n turn, element activates element R. Nodes represent CS-CS and CS-US rll'st-order net associative values. Open nodes: zero values. Solid nodes: positive values. (5) solid circle, V~, in Figure 1), and by allowing es to be associated with the US in a second phase (denoted by the solid circle, V US, in Figure 1). When es B is presented alone in a test trial, it activates element through node V~, and element activates node V US, generating a conditioned response (er). Rescorla (1980) found that simultaneous presentations of es() and es(b) produced higher levels of sensory preconditioning than successive presentations. n agreement with this result, the S-P-H model predicts optimal es-es conditioning for simultaneous presentations of two 300-msec ess. Secondary reinforcement is predicted by allowing es to be associated with the US in a first phase, and a esb to be associated with es in a second phase. Because esb is never associated with the US, the model predicts that extinction of the es - US association entails extinction of responding to esb n agreement with this prediction, Rashotte, Griffin, and Sisk (1977) and Rescorla (1978) found that extinction ofthe esrus association led to a substantial reduction in responding to esb Opposite results, however, were obtained by Rizley and Rescorla (1972) and Holland and Rescorla (1975). ssociability Pearce, Kaye, and Hall (1984) suggested that es associability is given by ai(n) = ')' >.(n-l) - Ej V;(n-l) + (1-,),)a,(n-1), where a,(n) is the es, associability on trial n, (n -1) is the US intensity on trial n -1, E j V; (n - 1) is the aggregate prediction of all stimuli present on trial n - 1, a, (n - 1) is the es associability on trial n-l, and ')' is used to compute a,(n) as a weighted average. The computation of a,(n) as a weighted average implies a memory for the values of a,(n-l). By computing ai(n) as a weighted average, the P-H model correct1y predicts that the intensity of latent inhibition is determined by the number of nonreinforced presentations ofthe es, but the model presents problems for describing partial reinforcement (Pearce et al., 1984). To avoid these problems, the present version ofthe S-P-H model assumes that associability is proportional to the absoiute difference between the instantaneous value of the aggregate prediction of the intensity of a given event and the instantaneous value of its actual intensity. Since, in our model, the aggregate prediction of event k is given by Bk, the associability ofes, with event kat time step t is given by ccording to Equation 6, the associability of es, with event k is proportional to the absolute value of the rnismatch between the aggregate prediction of event k and its actual intensity. ccording to Equations 1 and 2, when ans zero, neither V~ nor Nnncrease; that is, when ongoing events are perfect1y predicted, there are no further changes in stored predictions. Therefore, according to the S-P-H model, the total amount of information to be stored in the brain is proportional to the total degree of uncertainty about ongoing events, as given by Ek E,a7. (6)

4 t has been suggested that Ol, could be used as a measure ofthe strength ofthe orienting response (OR) (Kaye & Pearce, 1984) and ofthe "controlled" attention toward CS, (Pearce & Hall, 1980) (hut see Hall & Schachtman, 1987). The intensity of "automatic" attention to CS, is proportional to B,us. n both cases, the intensity of the responses to CS, is proportional to B,us. Whereas Ol, in the P-H model is computed on a trialto-trial basis and always refers to the associability of CS, with the US, Olnn the S-P-H model is computed in real time and refers to the associability of CS, with any event k. These changes do not affect the explanations provided by the S-P-H model for most classical conditioning paradigms. Salience n both the P-H and the previous version ofthe S-P-H model, salience S, is a constant. However, in the present rendering of the S-P-H model, S, is defined by S, = 0, + Oll, (7) where 0, is a constant and Oll is the associability of CS,. Replacing Oll by its value in Equation 6 results in S, = 0, + 1 ' - Bi. Equation 7 implies that when Oll equals zero, salience Si equals Oi. ccording to Equation 8, Si equals zero when the intensity of CSi is perfectly predicted by all acting CSs, including itself, at a given time step. Conceptually, this means that salience Si decreases as CSi becomes increasingly associated with the context and with itself (i.e., is more "familiar" to the animal). When CSi is presented in a novel environment, Si increases again. ccording to Equations 1 and 2, larger increments in V~ and N~ are obtained with novel rather than with familiar CSs. Equation 7 is used to yield latent inhibition-that is, the effect of CS preexposure in the absence of the US on the subsequent acquisition ofthe CS-US association. Wagner (1979) proposed a similar mechanism for latent inhibition in the context ofthe Rescorla-Wagner (1972) model. n both the P-H model and the previous version of the S-P-H model, a mechanism for latent inhibition was provided by computing Oli as a weighted average. Trace Function Conditioning of the NM is typically more efficacious when the CS precedes the US than when the two are presented together. Theorists have proposed that stimuli give rise to traces in the central nervous system that somehow impinge simultaneously on criticalloci of learning, despite the nonsimultaneous arrangement, as observed at the periphery (Gormezano, Kehoe, & Marshali, 1983). The S-P-H model assumes that after a 50-msec delay, CSi generates a trace, Ti' fter the delay, this trace increases over time to a maximum, stays at this level for aperiod oftime, and then gradually decays back to zero. HPPOCMPUS ND CLSSCL CONDTONNG 23 Formally, and specifically for the rabbit NM preparation, increments in the trace for 50 < t ::s 200 msec are defined by.:1 TJt) = kl[cs,max - T,(t)], (9) where CSimax is the maximum intensity of the trace recmited by CS,' and kl is a constant, 0 < kl ::s 1. Parameter kl is selected so that when applying Equations 1, 2, 9, and 10, the interstimulus interval (ls) for NM optimal conditioning is 200 msec. For any CS duration, the amplitude of the trace rises during the first 200 msec after CS, onset. ndependently of the CS duration, Ti(t) remains equal to CS,max for 200 msec. f CSi is present at 200 msec after its onset, Ti(t) does not decay until CSi offset. f CS, is not present at 200 msec after its onset, decrements in Ti(t) are given by.:1 Ti(t) = kl [-T,(t)]. (10) Performance Rules Performance mies were selected to translate variables in the model into the topography of the NM response and simulated unit activity. Response topography. cquisition of the NM condi (8) tioned response proceeds with an orderly sequence of changes: the percentage of NM responses generated in each session increases, the CR latency decreases, and the CR amplitude increases. The CR latency moves progressively forward in the CS-US interval with training (Smith, 1968). t the beginning oftraining, the first CRs are initiated just before the unconditioned response (UR), but CR onset moves to progressively earlier portions of the CS-US interval, with an asymptotic latency occurring at about the midpoint of the CS-US S. s CR onset latency decreases, the maximal response amplitude (CR peak) tends to be located around the time of the US occurrence. n summary, the NM response topography is characterized by (1) the latency to CR ooset, (2) the shape during the CS period, (3) the shape during the US period, and (4) the decay to baseline. Latency to er onset. f t, denotes the time step at which CS ooset occurs, then the time of CR onset, denoted tcr, is the earliest time t such that E E BjUS(t) ~ L. (11) t~t, j The sum over index j involves BjuS of all CSs with Tj > 0, excluding the context. The sum over index t involves all time steps on which Tj > 0, starting at the time step when the amplitude of the NM response, as defined by Equations 12 and 13, equals zero (see below). L is a threshold greater than zero. Equation 11 implies that as BjUS increases over trials, L is reached at earlier times, tcr, and therefore CR onset latency moves progressively toward an asymptote determined by L. n general, er onset latency decreases as Bps increases. For t < ter, NM response amplitude is zero.

5 24 SCHMJUK ND MOORE es period. For time steps t > ter (i.e., after the time ofcr onset), the amplitude ofthe NM response, NMR(t), is changed by anmr(t) = k2[ebjus(t) - NMR(t)]. (12) j The sum over the indexj involves BjuS of all CSs with Tj > 0, including the context. k2 is a constant (0 < k2 :s ) that reflects the mechanical properties of the NM system. Equation 12 implies that as Bpss increase over trials, the amplitude of the NM response also increases. US period. During the US period, when EßPS(t) > US(t), NMR(t) still increases according to Equation 12. However, when EjBjus(t) < US(t), NMR(t) increases by anmr(t) = k2[us(t) - NMR(t)]. (13) Equation 13 implies that when the intensity of the US is greater than EjBpS(t), the US intensity determines the amplitude of the NM response. Decay to baseline. When EßPS(t) and US(t) equal zero, NMR(t) decays to baseline by anmr(t) = k2[ -NMR(t)]. (14) With the addition of the described performance roles, the S-P-H model provides real-time descriptions ofnm topography, defining CR onset latency, CR amplitude during CS and US periods, CR peak amplitude, and latency to CR peak amplitude. With these performance rules, the S-P-H model also describes between-trial changes in NM CR amplitude as a realistic S-shaped acquisition function. n alternative approach to real-time descriptions of NM topography during classical conditioning was proposed by Moore et al. (1986). Performance rules are accurate when they are used to define the time of the CR peak for Ss longer than 200 msec. For Ss longer than 200 msec, Equation 11 generates NM CRs with peaks that appear at approximately the time when the US was presented. Because the trace decreases after 200 msec and the CS is terminated, the model predicts that B j us will decrease when S is increased. Because longer Ss imply smaller Bpss, longer Ss also imply a longer latency before E,EßpS(t), according to Equation 11, exceeds the threshold L. Therefore, the CR latency is longer and the CR peak appears at approximately the time of US presentation. Performance roles are inaccurate when they are used to defme the time of the CR peak for Ss shorter than 200 msec. For these S values, Equation 11 generates NM CR peaks after the US presentation. This is so because the trace increases from 0 to 200 msec, and therefore Bps is direct1y proportional to S in the msec temporal range. Because shorter Ss imply smaller Bpss, they imply a longer latency, according to Equation 11, for E,EjBpS(t) to exceed thresholdll; therefore, CR begins later in the S and the CR peak appears later than the time of the US presentation. Because most simulations use Ss equal to or greater than 200 msec, time inaccuracies are not present in most of the results presented in this paper. Neuronal activity. Neural activity was simulated 00- der the assumption that neurons code the instantaneous magnitude of different model variables. For example, the instantaneous value of BUS(t) was translated into neuronal firing by application of the following rules. f at time t, E,BUS(t) < L2, no spike is generated. f at time t, E,BUS(t) ~ L2, aspike is generated and the sum is reset to zero. Table 1 summarizes the intervening variables and parameters used in the S-P-H model. The S-P-H model has nine learning variables and eight parameters. HYPOTHESES n this section, we introduce the aggregate prediction hypothesis as applied to HL, LTP induction, and hippocampal neuronal activity, as well as the "associability" hypothesis as applied to medial septum neuronal activity. These hypotheses are independent of the description of the S-H-P model as a model for classical conditioning. The ggregate Prediction Hypothesis pplied to HL, L TP nduction, and Hippocampal Neuronal ctivity Schmajuk (1984; Schmajuk & Moore, 1985, p. 281) suggested that the effect of HL can be described as an impairment in the computation of CS-US associability values. This impairment results from the lack of integration of new and old predictions about an event arising from all CSs present at a given time. n mathematical terms, instead of computing CS; associability by a;(t) = 'Y us(t) - EjVPS(t-l) + (1-'Y)a;(t-l), a;(t) forthehlcase was given by a/(t) = US(t) - Vps(t-l). Because individual predictions replace the aggregate prediction in the computation of a;(t) for the HL case, this assumpticln is called the aggregate prediction hypothesis. s in SChmajuk (1984) and SChmajuk and Moore (1985), this paper also tests the aggregate prediction hypothesis for HL. n the present version of the S-P-H model, BUS, the aggregate prediction ofthe US, can be derived from Equations 4 and 5: BUS = E (VPS + E wrvrvrus)t;. (15) i r Since the integration of predictions, according to Equation 15, s achieved through the sum ofthe individual net predictions (E;) and through second-order associations (VrVrUS), the lack of integration of predictions implies that both computations are abse.nt in the HL case. When E; is not computed and Vr becomes zero in Equation 15, associability for the HL case becomes a;us(t) = US(t) - Vps(t-l)T;(t-l)l. (16)

6 HPPOCMPUS ND CLSSCL CONDTONNG 25 Symbol Vk N~ Bk T, NMR Table 1 S-P-H Model: ntervening Variables and Parameters Variable First-order excitatory association of es, with event k First-order inhibitory association of es, with event k Net first-order association of es, with event k First-order plus second-order net association of es, with event k ggregate net association of all ess present with event k ssociability of es, with event k Salience of es, ntensity of event k Trace of es, mplitude of the NMR Psychological nterpretation The long-term memory of the direct prediction that event k will folowes,. The long-term memory of the direct prediction that event k will not folowes,. The net direct prediction that event k will or will not folowes,. The sum of direct and indirect net predictions that event k will or will not folowes,. The prediction of event k based on all ess present at a given moment. The mismatch between the actual intensity of event k and its predicted intensity based on all ess present at a given moment. The mismatch between the actua1 intensity of es, and its predicted intensity based on all ess present at a given moment, plus a constant. The perceived intensity of event k. The short-term memory of es,. The prediction of the intensity of the US based on all ess present at a given moment. Symbol Parameter 8 Rate of change of V~ 8' Rate of change of N'; w~ Weight of second-order associations 0, eonstant part of S, ki Trace constant k2 NMR mechanical constant L Threshold for er onset Value L2 Threshold for neural activity 0.5 Equation 16 is equivalent to that used by Schmajuk and Moore (1985) for the HL case. n the S-P-H model, blocking and overshadowing are achieved when the associability of the to-be-blocked CS is reduced due to the association of the blocker with the USo Since, according to Equation 16, the associability ofthe to-be-blocked CS is independent of the association accrued by the blocker CS, the use ofequation 16 implies impairments in blocking and overshadowing. n the S-P-H model, inhibitory association is achieved when the associability of CS- is increased due to the excitatory association of the CS+. Since, according to Equation 16, the associability of CSis independent of the association accrued by CS+, the associability of CS- remains zero, and CS- cannot gain inhibitory association. Consequently, Equation 16 implies impairments in inhibitory conditioning paradigms, such as conditioned inhibition and differential conditioning. Because Equation 16 allows the context to accrue more association with the US than when Equation 6 is applied, responding in HL animals depends relatively more on the association gained by the context and relatively less on the association gained by the CS. Consequently, changes in the characteristics of the nominal CS do not affect responding in HL animals as much as they do in normal

7 26 SCHMJUK ND MOORE animals. Therefore, the generalization gradient is sharper in normal animals than in HL animals. s mentioned above, Kaye and Pearce (1984) suggested that the strength of the OR was proportional to (Xi. Under this assumption, since (X i us for the HL case (Equation 16) is greater than (XiUS for the normal case (Equation 6), the use of Equation 16 also implies that HL animals display stronger ORs tl}an do normal animals. When Ei is not computed and Vrbecomes zero in E.guation 15, the intensity ofthe inhibitory reinforcer, Us, becomes (17) Equation 17 is equivalent to that used by Schmajuk and Moore (1985) for the HL case. When Ei is not computed and vr becomes zero in Equation 15, salience Si is given by Si = Oi + i. (18) Equation 18 means that salience does not decrease over trials, or, equivalently, that the CS does not become increasingly "familiar" over time. The use ofequation 18 imp1ies impairments in latent inhibition. Because all vrs equal zero, Bius, whieh was defined in Equation 4, is given by (19) n general, the use of Equation 19 implies impairments in cognitive mapping. Specifical1y, in the case of classical conditioning, the use of Equation 19 implies impairments in sensory preconditioning, secondary reinforcement, compound conditioning, and serial compound conditioning. Performance rules translate B,us values into NM responses, thereby accounting for changes in NM response topography after HL. Since, according to Equation 16, overshadowing is absent after HL, B,us is generally greater in the HL than in the normal case. Because a larger BiuS implies a shorter CR onset latency according to Equation 11, the use of Equation 19 implies shorter onset latencies after HL. Whereas the aggregate prediction hypothesis considers the effect of HL to be an impairment in the integration of multiple predietions, the present paper assurnes that this integration increases when LTP is induced. Since this integration is achieved through the sum of the individual n<?t eredictions (Ei) and through second-order associations (VWrUS ) in Equation 15, the aggregate prediction hypothesis proposes that all vrs increase whenever LTP is induced. Since the aggregate prediction hypothesis accounts for HL and LTP effects by affecting the computation of B US, for consistency we assurne that some hippocampal neurons code this aggregate prediction, that is, that the activity of some hippocampal neurons is proportional to the instantaneous value of BUs. The "ssociability" Hypothesis Regarding Medial Septum Neuronal ctivity s mentioned before, (X i can be used as a measure of the strength of both the OR and the "voluntary" attention toward CSi From a different perspective, Vanderwolf, Kramis, Gillespie, and Bland (1975) suggested that, 'voluntary" behavior was correlated with hippocampal theta. n addition, nchel and Lindsley (1972) found that the strength of the OR was correlated with hippocampal theta. Therefore, voluntary behavior, theta activity, and strong ORs would be correlated with large values of (X~, whereas nontheta activity, automatie behavior, and weak ORs wou1d be corre1ated with small values of (X~. Berger and Thompson (1978b) suggested that neural activity in the medial septum represented an arousal signal that controlled hippocampal theta. s discussed in the previous paragraph, theta activity is corre1ated with (X~. Therefore, the present study assurnes that the frequency of medial septum neuronal firing is proportional to the instantaneous magnitude of EkEi(X~-that is, the sum of CS-CS and CS-US associabilities of all CSs present at a given time. s mentioned above, EkEi(X~ is proportional to the total degree of uncertainty about ongoing events in the external world, and it deterrnines the total amount of information to be stored in the brain. SMULTONS Method The simulations that described NM topography for normal, HL, and LTP cases were carried out with the S-P-H model. This section defines and justifies the values of the different parameters adopted for the simulations. Parameter values were kept constant for alt simulations. For the simulations, the model was converted to discrete time, which allowed for the generation of values of the relevant descriptive variables at discrete time instants, which we denote as t = 1, 2, 3,... For convenience, we assume that the basic time step is one abstract unit, which can be related to various intervals of real time as required. n our simulations, we assumed that one time step or bin was equivalent to a duration of 10 msec. Each trial consisted of.120 bins, which was equivalent to 1,200 rnsec. Only 600 msec are represented in the figures that display NM response topography. ll simulations assumed 200-rnsec ess, the last 50 rnsec of which overlapped the USo es onset was at 200 msec. Since asymptotic conditioned NM responding is reached in approximately 200 trials (Gormezano et al., 1983), 1 simulated trial is approximately equivalent to 20 experimental trials. The right upper panel of all figures that display simulation results shows the net associative values at the end of each trial. The right lower panel shows the product of associabilities multiplied by salience at the moment the US was presented on reinforced trials, as a function oftrials. The left panel shows NM response topography as a function of time and of trials. The initial values of Vs and Ns were normally zero. n the case of LTP, all v2~s were set to be equal to one. The initial values of associability were always set to be equal to zero. The parameter values for the variations of associative values were selected (J =.03 and (J' =.015. For computations of m, w~ = 2 when i * r. The value of w; was selected so as to allow the model todisplay secondary reinforcement before conditioned inhibition in a secondary reinforcement paradigm. The constant part of Si, Oi, was set equal to 0.5 for every es.

8 HPPOCMPUS ND CLSSCL CONDTONNG 27 The S-P-H is very robust with respect to changes in its parameters. Previous simulations with the model (Schmajuk, 1986; Schmajuk & Moore, 1985) yielded qualitative1y the same results, even in a completely different range of parameters (6 =.1 and 6' =.01), a different trace constant (k =.067), a different number of simulated bins (12 or 60), and slightly different mes for computing o:~. NM Response Topography For the computation of T, k 1 was set to k 1 =.1, which was the appropriate value for an optimal S of 200 msec. For computations of the NM er onset, L was set equal to.5 so the er onset latencies would reach asymptote at approximately the midpoint of the S. For the NM response topography, k2 =.5, which ensured that the NM response reached the value of ;ßjuS within the es period. Neuronal ctivity To obtain rea1istic neural frequencies, threshold L2 was set equal to.5. Results This section presents relevant experimental data and contrasts the data with the results of computer simulations that describe the NM topography of normal and HL rabbits. The following procedures were simulated for the normal and the HL case: delay conditioning, conditioning under different Ss, discrimination reversal, and sensory preconditioning. n addition, hippocampal neuronal unit activity during acquisition and extinction, medial septum unit activity during acquisition, and discrimination acquisition after L TP induction were simulated for the normal case. cquisition of Delay Classical Conditioning Experimental data. Several studies have described the effect of HL on acquisition rates. Using a delay conditioning paradigm, Schmaltz and Theios (1972) found that the acquisition of the conditioned NM response in HL rabbits was faster than normal with a 250-msec CS, a 50- msec shock US, and a 250-msec S. n contrast with these data, Solomon and Moore (1975) and Solomon (1977) found no difference in the rate of acquisition between normal and HL rabbits in forward-delay conditioning of the NM response with a 450-msec CS, a 50-msec shock US, and a 450-msec S. n summary, acquisition rates become accelerated or remain unaffected by HL. Several studies have described the effect of HL on NM topography during acquisition. Solomon and Moore (1975) and Solomon (1977) found that CR topography did not differ in normal and HL rabbits in forward-delay conditioning of the NM response with a 450-msec CS, a 50- msec shock US, and a 450-msec S. Port and Patterson (1984), using a 500-msec CS, a 50-msec shock US, and a 450-msec S, found that CR latency was shorter in rabbits with fimbriallesions (i.e., hippocampal output) than in rabbits with cortical or sham lesions, mainly during the first day of acquisition. n summary, er onset lateneies in a delay conditioning paradigm become shorter or remain unaffected after HL. Powell and Buchanan (1980) reported an increased OR (measured as an increased bradycardia) over conditioning trials in HL rabbits relative to controls. Simulation results. Figure 2 shows simulations of 10 trials in a delay conditioning paradigm. The model describes an S-shaped acquisition function, and shows the abrupt shifts in responsiveness that occurred during acquisition. s Figure 2 illustrates, CR amplitude for the normal case shows a small increment during the first two trials, followed by a sudden increment on Trial 3, and a relatively slow increment between Trials 3 and 10. n agreement with Schmaltz and Theios (1972), the simulation results show a faster than normal acquisition rate in the HL case. Simulations for the normal Case show that both context and CS associabilities decreased over trials, with the CS overshadowing the context. n the HL case, both the CSs and the contextual associabilities were larger than in the normal case; therefore, both the CS and the context were able to acquire larger associative values at a faster rate than in the normal case. N RSPOSS: JßlL- TRLS nlls TRLS Figure 2. Delay conditioning. L: HL case. N: normal case. : CS(). X: context. Left panels: Simulated NM response topography in 10 reinforced trials. Upper-right panels: Net associative values (VT) at the end of each trial as a function of trials. Lowerright panels: ssociability (LPH) at 350 msec as a function of trials. x x x

9 28 SCHMJUK ND MOORE lso, in agreement with Port and Patterson (1984), the simulated er latency was shorter for the HL than for the normal ease. s mentioned above, Kaye and Pearce (1984) suggested that the strength of the OR was proportional to X i. Figure 2 shows greater X ius for the HL than for the normal ease, a result in agreement with Powell and Buehanan's (1980) data. UJ " ~ J C. : ~ oe: u 6 S Effects Experimental data. Systematie manipulations of the S affeet CR topography in normal animals. t Ss of zero, CRs are negligible; at Ss of around 200 msec, CRs inerease dramatieally; and for longer Ss, CRs gradually decrease (Sehneiderman, 1966; Smith, 1968). Port, Mikhail, and Patterson (1985) examined the effeets of HL on the aequisition rates of the NM response in groups of rabbits trained with short (150 msec), optimal (300 msec), or long (600 msec) Ss. For all groups, CS and shock US durations were 850 and 50 msec, respeetively. The aequisition rates beeome aeeelerated, and the HL animals produeed more CRs than the normal animals in the short- and long-s HL groups, but not in the optimal-s HL group. However, as mentioned before, Sehmaltz and Theios (1972) reported faster aequisition rates after HL with an optimal (250 msee) S. n summary, CR aequisition rates beeome aeeelerated with short, optimal, and long Ss, but they sometimes remain unaffected with optimal Ss. Port et al. (1985) also reported that HL rabbits showed a shorter CR onset lateney with a short S than did the eontrols, but normal CR onset lateney with optimal and long Ss. More recently, however, James, Hardiman, and Yeo (1987) reported that HL rabbits trained with a 250- msee white noise CS, a 50-msee shock US, and a long (750-msee) S showed shorter onset latencies than eontrols. s mentioned before, Port and Patterson (1984), using a 5OO-msee CS, a 50-msec shock US, and an optimal 450-msee S, found that CR lateney was shorter in rabbits with fimbrial lesions. n summary, CR onset lateneies may beeome shorter with short, optimal, and long Ss, but they sometimes remain unaffected with optimal and long Ss. Different results have been reported regarding NM response topography in HL animals when different types of US were used. Solomon, Vander Sehaaf, Thompson, and Weisz (1986) reported that CR onset latencies were shorter in HL rabbits than in normal rabbits trained in a traee-eonditioning paradigm with a 250-msec tone CS, a 5OO-msec S, and a loo-msee airpuff USo Port, Romano, Steinmetz, Mikhail, and Patterson (1986) observed similar effects in HL rabbits trained in a traeeeonditioning paradigm with a 250-msee tone CS, a 750- msee S, and a loo-msec airpuff USo n summary, CR onset latencies with long Ss become eonsistently shorter with airpuff USo Simulation results. The aequisition of classical eonditioning was simulated with a 50-msec CS, and 0-,200-, 0 ~ 200 '.l UJ C) E: v )- U Z UJ... ~.J... UJ C) Z 0 oe: u :: S (MSEC) S (MSEC) NORML +HL FiguR 3. Simulated er amplitude and er ooset latency as a function of S for normal and L cases after 10 simulated trials with a SO-msec es, a SO-msec US, and 0-, 100-, 200-, 300-, and 400-msec Ss. 300-, and 4oo-msec Ss. The shock US was 50 msec in duration. Figure 3 (upper panel) summarizes the simulated results for CR amplitude for the normal and HL eases with Ss of 0, 100, 200, 300, and 400 msec. The simulated data resemble the "inverted-u" S funetion obtained by Smith (1968), Sehneiderman (1966), and Sehneiderman and Gormezano (1964) in normal rabbits, in whieh a peak oceurred at the 150-msee S. The simulated data show aeeelerated aequisition rates and larger CR amplitudes for all groups. The simulation results agree with data showing aeeelerated aequisition rates in short-, optimal-, and long-s HL groups. The S-P-H model deseribes some eonditioning with simultaneous CS-US presentations, a prediction that awaits experimental testing. Figure 3 (lower panel) summarizes the results after 10 simulated trials for CR onset lateney for the normal and HL eases with Ss of 100,200,300, and 400 msee. The CR onset latencies are shorter for the HL ease than for the normal ease for short-, optimal-, and long-s groups. The results agree with HL experimental data obtained with shock and airpuff USo Discrimination Reversal Experimental data. The effeet of HL on diserimination reversal has been studied in rabbit NM and eyelid preparations. Buebanan and Powell (1980) examined the effeet of HL on aequisition and reversal of eyeblink diserimination in rabbits. HL slightly impaired the aequisi-

10 tion of discrimination and severely disrupted its reversal by increasing responding to es-. Berger and Orr (1983) contrasted HL and control rabbits in two-tone differential conditioning and reversal of the rabbit NM response using an 850-msec es, a loo-msec airpuff US, a 750- msec S, and an average 30-sec T. lthough HL did not affect initial differential conditioning, these animals were incapable of suppressing CRs to the original es+ after the es+ assumed the role of es-. This was true even after extended training. Similar results were recently reported by Weikart and Berger (1986) in a tone-light discrimination reversal-learning paradigm, which suggests that deficits in two-tone reversal learning after HL are not due to increased within-modality generalization to the tone es that serves as es+ and es-. Port, Romano, and Patterson (1986), who used a tone 200-msec es+, a tone 800-msec es-, a 50-msec shock US, al-sec S, and a 30-sec T, found that HL impaired the reversallearning of a stimulus-duration discrimination paradigm. The effect of HL on NM topography in a discriminationreversal paradigm has also been studied. Orr and Berger (1985) used an 850-msec es, a loo-msec airpuff US, a 750-msec S, and an average 30-sec T, and reported that HL affected er topography in a discriminationreversal task, but not during discrimination acquisition. On the last trials of reversal, the HL animals showed a greater peak NM amplitude and a greater area under the NM response curve during the es period for both the es+ and the es-. Simulation results. Figure 4 shows simulations of a discrimination-reversal paradigm. n the differential conditioning phase, five reinforced trials with one es () were alternated with five nonreinforced trials with a second es (B). During reversal, the original nonreinforced es (B) was reinforced for five trials; these trials were alternated with five trials in which es( )-the reinforced es in the first phase-was presented without the l}s. fter differential conditioning in the normal case, V us was higher than VB US, which had become inhibitory. fter reversal in the normal case, VB us increased to approximately the same level achieved by V us during differential conditioning. t the same time, V us decreased as a consequence of the nonreinforced trials, and Vx us became inhibitory. fter differential conditioning in the HL case, V us was higher than VBUS, but VBus did not become inhibitory. fter reversal in the HL case, VB us increased to approximately the same level achieved by V us during differential conditioning. t the same time, V us decreased as a consequence of the nonreinforced trials, but since V x us did not become inhibitory, a higher level of responding was achieved. Given the reported high levels of conditioned responding to both reinforced and nonreinforced ess after extended reversal training in HL animals, it would appear that the S-P-H model renders a realistic portrayal ofthese data. Furthermore, in agreement with Orr and Berger's (1985) results, normal and HL simulations show differences in the NM amplitude and in the area under the NM HPPOeMPUS ND elssel eondtonng 29 L RSPUtS.'S T :1.5 B N RSPUtS.'S B /; '111S1C T :1 TRLS Figure 4. Discrimination reversal. L: HL case. N: normal case. : CS(). B: CS(B). X: context. Left panels: SimuJated NM response topography in - and B - trials after discrimination acquisition with five CS() reinforced trials alternated with five CS(B) nonreinforced trials fouowed discrimination reversal with five CS(B) reinforced trials alternated with five CS() nonreinforced trials. Upper-right panels: Net associative values (V1) at the end of each trial as a function of trials. Lower-right panels: ssociability (L PH) at 350 msec as a function of trials. response curve during the es period on the last trials of reversal, with either the es+ or the es-. Sensory Preconditioning Experimental data. n sensory preconditioning, es() and es(b) are presented together in the absence of the USo Later pairing of es() with the US results in condi. tioned responding to es(b). Port and Patterson (1984) preconditioned control rabbits and rabbits with fimbrial lesions with simutaneous presentations of a tone es and a light es in the absence of the US. Preconditioning was followed by presentations ofthe light es in the presence ofthe USo This procedure yielded excitatory conditioning to the tone in normal animals, but not in rabbits with fimbriallesions. Simulation results. Figure 5 shows simulations of a sensory preconditioning paradigm. n the first phase, there

11 30 SCHMJUK NO MOORE L mpolll's B N USPOlll'S B -,!- -,~ -'151<- T :1 T :1.5 Ll S : TRLS TRLS TRLS Figure 5. Sensory preconditioning. L: HL case. N: normal case. : es(). B: es(b). X: context. Left panels: Simulated NM response topography in - and B- trias after 10 es() and es(b) nonreinforced trias and 10 CS() reinforced trias. Upper-right panels: Net associative values (VT) at the end of each trial as a function of trias. Lower-right panels: ssociability (LPH) at 350 msec as a funetion of trials. were 10 nonreinforced trials with a compound CS( and B). Ouring the second phase, one of the nonreinforced CSs () was reinforced for 10 trials. test trial assessed the CR to CS(B), which was never paired with the USo The simulations show that context associability decreased during preconditioning in the normal case, but not in the HL case. n the nonreinforced test trial in the normal case, CS(B) acquired inhibitory associative value because it was presented in a context with excitatory associative value. CS(B) generated a CR only in the normal case. Port and Patterson (1984) found that fimbriallesions eliminated the responses to CS(B), a result in accordance with the simulations. LTP Effects Experimental data. Berger (1984) found that entorhinal cortex stimulation that produced LTP increased the rate of acquisition of a two-tone dassical discrimination x ofthe rabbit NM response. LTP increased responding to CS+ and, to a lesser degree, to CS-. Berger suggested that LTP might have enhanced the rate of conditioning by enhancing the rate at which hippocampal unit activity increased during acquisition. Simulation results. Figure 6 shows CR amplitude for CS+ and CS- after 10 simulated trials in a discrimination paradigm for LTP and control cases. The simulations show faster acquisition and larger CR amplitude in the treated group than in the control group because of the increased vg~ associations. These increased associations increased the values of Bgt and Bg~+, resulting in stronger responding to CS+ and to CS- in the LTP group and, therefore, in little change in the discrimination between CS+ and CS-. These results are in disagreement with experimental data obtained by Berger (1984). Hippocampal Neuronal ctivity During cquisition and Extinction of Classical Conditioning Experimental data. Hippocampal activity during dassical conditioning of the NM response is positively correlated with the topography of the NM response. Ouring acquisition, increments in hippocampal unit activity precede the acquisition ofnm CR by over 100 trials (Berger, lger, & Thompson, 1976). More specifically, Berger and Thompson (1978a) and Berger, Rinaldi, Weisz, and Thompson (1983) found that pyramidal and dentate granule cells in the dorsal hippocampus increased their frequency of firing over conditioning trials with a pattern that correlates with the amplitude-time course ofthe rabbit NM response. Lesions of the dentate and interpositus cerebellar nudei ipsilateral to the trained eye caused abolition of both the NM CR and the conditioned increases in hippocampal C neural activity evoked by the CS (Clark, McCormick, Lavond, & Thompson, 1984). Berger and Thompson (1982) found that, during extinction, pyramidal and granule cells in the dorsal hippocampus decreased their frequency of firing in correlation with behavioral extinction during the US period, but in lulvance ofbehavioral extinction during the CS period. cs+ ~ cs- Figure 6. Effect of long-term potentiation on discrimination aequhiition. Simulated er amplitude after live es+ and live es-- trials for experimental and eontrol groups.

12 HPPOCMPUS ND CLSSCL CONDTONNG 31 Simulation results. Figure 7 shows simulations of single-unit recordings obtained from the hippocampus after 10 trials of classical conditioning. t was assumed that the frequency of pyramidal firing was proportional to BUs. n agreement with experimental data, the simulated unit activity shows a pattern that correlates with the amplitude-time course of the rabbit NM response. The upper panel of Figure 8 shows that the predicted unit activity preceded the behavioral response during acquisition. This was so because a sufficiently large value of EjB JUS is necessary before the behavioral threshold (see Equation 11) is surpassed and behavior is elicited. The lower panel of Figure 8 shows that simulated unit activity decreased simultaneously with CR amplitude during extinction. Therefore, the model rendered realistic simulations of single-unit recordings obtained from pyramidal cells during acquisition (Berger et al., 1983) but not during extinction of classical conditioning (Berger & Thompson, 1982). Medial Septum Neuronal ctivity During cquisition of Classical Conditioning Experimental data. Berger and Thompson (l978b) recorded neuronal unit activity from the medial septum during classical conditioning of the rabbit NM. They found that neuronal activity in the medial septum during the acquisition of classical conditioning decreased over trials during both the CS and the US periods. Unpaired controls showed the same pattern of activity, and no statisticahy significant difference was found between groups. Simulation results. simulated paired group received 10 CS-US presentations, and a simulated unpaired group received 20 alternated CS-alone and US-alone trials. Figure 9 shows simulated medial septum unit recordings made during the acquisition of classical conditioning. Simulated unit activity decreased during both the CS and the US period within every trial, and across trials during acquisition. On Trial 10, activity during the US period CQU ~3 T O~ 1 L345678'?1ü TRLS E:-;TtKT Oll 0L-~~2~~3--74~5~~6--~7~8~~9~170J TRL ~3 UNT CTHJTY er f'lpl TUDE Figure 8. Simulated er amplitude and hippocampal unit activity at the time of US presentation during acquisition and extinction of delay conditiouing. was higher than activity during the CS period, and activity was higher in the unpaired group than in the paired group during the US period, but activity was higher in the paired group than in the unpaired group during the CS period. During the CS period, simulated neural activity reflects the sum lx - Bxl + cs - Bcsl + -Busl. s the CS-US association increases over trials, so does the value of 1 _BUS. This increase is compensated, however, by the decreasing values of lx - B X + cs - BCS. During the US period, simulated neural activity reflects the sumof lx - Bxl + cs - Bcsl + lus - Busl. s the CS-US association increases over trials, the value of lus - Busl decreases. ' n summary, simulated medial septum activity during the acquisition of classical conditioning decreased over trials during both the CS and the US periods in both paired and unpaired control groups. These results are in agreement with single-unit recordings obtained from medial septum cells during acquisition by Berger and Thomp SOll (l978b). DSCUSSON The present article shows that the S-P-H model is able to provide real-time descriptions of the rabbit NM response topography in many complex classical condition- ing paradigms. Figure 7. Simulated NM response topography and hippocampal single-unit activity after 10 reinforced trials.

13 32 SCHMJUK ND MOORE >- - ~ -- 0 ~.J ~ Q:; :::l ll Z US PEROD TRLS es PEROD 3L-~~2~~3~~4~~ ~~8~~9~1~0~ TRLS UHPRED PRED Figure 9. Simulated medial septum unit activity during es and US periods during acquisition of delay conditioning for paired and unpaired groups. One important feature of the present version of the S-P-H model is that it incorporates a heteroassociative network that allows the building of "computational, cognitive maps." n the case of classical conditioning, cognitive maps are applied to sensory preconditioning and secondary reinforcement. n the context of instrumental learning, cognitive maps have been associated with spatiallearning (O'Keefe & Nadel, 1978). Notably, it has been proposed that heteroassociative networks represent the neuronal arrangement ofhippocampal C3 and C1, and that these networks participate in the learning of conjunctions of environmental information, as wel as in spatial mapping (McNaughton & Morris, 1987; Rols, 1987). n the present paper, it was assumed that the hippocampus was involved in the computation of the aggregate prediction of ongoing events. This internal prediction was compared with information from the external world in order to determine the amount of processing to be assigned to external stimuli. t was assumed that the aggregate prediction was disrupted by HL, enhanced by LTP induction, and represented in hippocampal neuronal activity. ctivity in the medial septum was assumed to be proportional to the degree of uncertainty about ongoing events and to the amount of processing and storage of external information. Results obtained through the application ofthese hypotheses to the S-P-H model are discussed below. HL Effects The aggregate prediction hypothesis regards the effect of HL as an impairment in the integration of multiple predictions into the aggregate prediction BUs. Table 2, summarizes the results of the simulation experiments for the HL ~ase obtained in the present paper together with results obtained in a previous study (Schmajuk, 1986). Under the aggregate prediction hypothesis, the model successfully described HL effects on delay conditioning, conditioning with short, optimal, and long Ss with a shock US, conditioning with long Ss and an airpuff US, extinction, latent inhibition, generalization, blocking, discrimination reversal, and sensory preconditioning. n addition, the model predicts that paradigms involving cognitive mapping (such as secondary reinforcement, compound conditioning, and serial compound conditioning), differential conditioning, positive and negative patterning, conditional responding, and overshadowing are impaired by HL, and that partial reinforcement and simultaneous conditioning are facilitated by HL. These predictions await experimental testing in the rabbit's NM response preparation. The S-P-H model has problems in describing conditioned inhibition and in describing mutual overshadowing for the HL case. The failure of the model to explain HL effects in an overshadowing paradigm was limited to the case in which both CSs had similar saliences: the S-P-H model correctly described HL effects on overshadowing when CSs of different saliences were used (Schmajuk & Moore, 1985). n summary, simulation results show that the aggregate prediction hypothesis predicts a large number of experimental data, although the hypothesis fails to predict some HL effects. n alternative assumption that yields improved HL descriptions is that Oi g~ equals zero but Oig~ remains unaffected. f Oig~ equals zero, anterograde but not retrograde CS-CS amnesia is predicted; that is, rc~ equals zero for CSs paired after HL, but rc~ remains unchanged for CSs paired before HL. mpairments in Oi~~ imply impajrment in latent inhibition and sensory preconditioning. s a consequence of the CS-CS anterograde associative deficits, Oi ps is also modified, and is given by OiiUS(t) = >.us(t) - EV j us (t-)tt-l). (20) j Because E j V j US Tj is smaller than BUs, Oi ys for the HL case computed with Equation 20 is larger than Oi ys for the normal case given by Equation 6. Therefore, the use of Equation 20 implies impairments in blocking and overshadowing. Schmajuk (1987) showed that the use ofequation 20 yields correct predictions for HL effects on conditioned inhibition. LTP Effects The aggregate prediction hypothesis assumes that L TP induction increases the integration of multiple predictions

14 HlPPOCMPUS ND CLSSCL CONDTONNG 33 Table 2 Simulations of the S-P-H Model Compared With the Experimental Results of Classical Conditioning of the NM Response Paradigm Observed Simulated Delay Conditioning S Effects (Shock US) Zero S Short S Optimal S Long S S Effects (irpuff US) Long S Orienting Response Conditioned nhibition Extinetion Latent nhibition Generalization Blocking Mutual Overshadowing Diserirnination Reversal Sensory Preconditiouing LONG-TERM POTENTTON EFFECT Diserimination equisition + 0* equisition Extinetion HPPOCMPL LESON EFFECT nonnaj/ shorter lateney shorter lateney normallfaster acquisition faster aequisition? + shorter lateney shorter lateney faster acquisition faster aequisition nonnaj/shorter lateney nonnaj lateney nonnaj/faster acquisition faster aequisition nonnaj/ shorter lateney shorter lateney nonnaj/faster acquisition faster aequisition shorter lateney shorter lateney faster acquisition faster aequisition * * greater NM peak: greater NM peak: greater CS+ area greater CS+ area greater CS- area greater CS - area HPPOCMPL NEURL CTNTY inereases precedes behavior models NM response deereases preeedes behavior MEDL SEPTUM NEURL CTMTY equisition decreases deereases inereases precedes behavior models NM response decreases simultaneous with behavior* Note-Symbols: - = defieit, + = facilitation, 0 = no effect,? = no available data. *The model fails to aeeurately describe the experimental result. into the aggregate prediction BUS by way of increasing CS-CS associations. Computer simulations show that under this hypothesis, the S-P-H model does not correctly describe the effect of LTP induction on the acquisition of a classical discrimination. f the effect of LTP induction is not adequately described as an increase in CS-CS associative values, then perhaps CS-CS associations are stored in regions of the brain other than the hippocampus in the form of LTP. Therefore, heteroassociative networks that represent the neuronal arrangement of hippocampal C3 and Cl would store information other than CS-CS associations. Hippocampal Neuronal ctivity The aggregate prediction hypothesis assumes that neural activity in the hippocampus is proportional to the instantaneous value of the aggregate prediction, BUs. We have shown that activity in pyramidal cells in the dorsal hippocampus is correctly described as proportional to BUS during acquisition but not during extinction of classical conditioning. Medial Septum Neuronal ctivity The associability hypothesis proposes that neuronal activity in the medial septum is proportional to EkEia~ (i.e., to the degree of uncertainty about ongoing events in the external world and the amount of information to be stored in the brain). s shown above, the S-P-H model adequately describes medial septal unit activity over acquisition trials. Simulation results suggest that neural activity in the medial septum is proportional to the sum of different a7 = k - Bk -that is, comparisons between actual and predicted events. These results do not imply, however,

15 34 SCHMJUK ND MOORE that actual and predicted events are compared in the medial septum. t is possible that these comparisons are performed in other brain regions and conveyed to the medial septum. For example, Vinogradova (1975) argued that the C3 region is involved in comparing actual and predicted events; when novelty is detected, an orienting response is elicited and cortical activity increases in order to store new information. Similarly, Gray (1982) proposed that actual and predicted events were compared in the subiculum: when there is a match, then behavior is maintained; when there is amismatch, then the current behavior is inhibited, attention is increased, and new information is stored in the temporal lobe. 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