Adaptive Stimulus Representations' 
A Computational Theory of 
Hippocampai-Region Function 
Mark A. Giuck Catherine E. Myers 
Center for Molecular and Behavioral Neuroscience 
Rutgers University, Newark, NJ 07102 
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Abstract 
We present a theory of cortico-hippocmnpal interaction in discrinination learning. The 
hippocmnpal region is presumed to form new stinulus representations which facilitate 
learning by enhancing the discrininability of predictive stimuli and conpressing 
stimulus-stimulus redundancies. The cortical and cerebellar regions, which are the sites 
of long-term metnory, nay acquire these new representations but m'e not asstuned to be 
capable of forming new representations themselves. Instantiated as a connectionist 
model, this theory accounts for a wide range of trial-level classical conditioning 
phenmnena in normal (intact) and hippocampal-lesioned animals. It also nakes several 
novel predictions which renain to be investigated empirically. The theory implies that 
the hippocmnpal region is involved in even the simplest learning tasks; although 
hippocampal-lesioned animals may be able to use other strategies to learn these tasks, the 
theory predicts that they will show consistently different patterns of transfer and 
generalization when the task demands change. 
1 INTRODUCTION 
It has long been known that the hippocmnpal region (including the entorhinal cortex, 
subiculm' complex, hippocampus and dentate gyrus) plays a role in learning and nemory. 
For example, the hippocampus has been inplicated in hmnan declarative nenory 
(Scoville & Millnet, 1957: Squire, 1987) while hippocampal dmnage in aninals impairs 
such seeningly disparate abilities as spatial mapping (O'Keefe & Nadel, 1978), 
contextual sensitivity (Hirsh, 1974; Winocur, Rawlins & Gray, 1987; Nadel & Willher, 
1980), tenporal processing (Busz'aki. 1989; Akase, Alkon & Disterhoft, 1989), configural 
association (Sutherland & Rudy, 1989) and the flexible use of representations in novel 
situations (Eichenbaum & Buckingham, 1991). Several theories have characterized 
hippocampal function in terms of one or more of these abilities. However, a theory 
which can predict the full range of deficits after hippocampal lesion has been elusive. 
This paper attempts to provide a functional interpretation of a hippocmnpal-region role in 
associative learning. We propose that one function of the hippocampal region is to 
construct new representations which facilitate discrimination lem'ning. We m'gue that this 
937 
938 
Gluck and Myers 
representational function is sufficient to derive and unify a wide range of trial-level 
condilioned effects observable in the intact and lesioned animal. 
2 A THEORY OF CORTICO-HIPPOCAMPAL INTERACTION 
Psychological theories have often found it useful to characterize stimuli as occupying 
points in an internal representation space (c.f. Shepard. 1958: Nosofsky. 1984). 
Connectionisl theories can be interpreted in a similar geometric framework. For example. 
in a connectionisl network (see Figure IA) a stimulus input such as a tone is recoded in 
the network's inlernal layer as a pattern of activations. A light input will activate a 
different pattern of activations in the internal layer nodes (Figure lB). These internal 
layer activations can be viewed as a representation of the stimulus inputs. and can be 
plotted in multi-dimensional internal represenlalion space (Figure IC). Learning to 
classify stimulus inputs corresponds to finding an appropriate partition of representation 
space. In the connectionist model. the lower layer of network weights determine the 
representation while the upper layer of network weights determine the classification. 
Our basic premise is that the hippocampal region has the ability to modify stnnulus 
representations to facilitate classification, and that its representations are biased by two 
constraints. The first constraint, predictive differentiation, is a bias to differentiate the 
representations of stimuli which are to be classified differently. Predictive differentiation 
increases the representational resources (i.e., hidden units) devoted to representing 
stimulus features which are especially predictive of how a stimulus is to be classified. 
For example, if red stimuli alone should evoke a response. then many representational 
(A) 
(B) 
Response 
(c) 
8 Internal Representation Space 
,.0. Tone Classification 
Tone Light Context 0.8- 
R 0.6- 
Response '/' '  Light 
2 ..... :'  ' 
0.4' 
'o) 0;2 0:4 0.% 0'.8 :0 
Tone Light Context 
Figure 1' Stimulus representations. The activations of the internal layer nodes in a 
connectionist network constitute a represenlation of the network's stimulus inputs. (A) 
Internal representation for an example tone stimulus. (B) Internal representation for an 
example light stimulus. (C) Translation of these representations into points in an internal 
represenlation space. with one dimension encoding the aclivation level of each internal 
node. Classifying stimuli corresponds to partitioning representation space so that 
representations of stimuli which ought to be classified together lie in the stone partition. 
Classification is easier if the representations of stimuli to be classified together m'e 
clustered while representations of slimuli to be classified differently are widely separated 
in this space. 
Adaptive Stimulus Representations: Computational Theory of Hippocampal-Region Function 
resources should be devoted to encoding color. The second constraint, redundancy 
c9mpression, reduces the resources allocated to represent lealures which are redundmt or 
irrelevant in predicting the desired response. These lwo constraints are bv nature 
complementary. given a finite amount of representational resources. Compressing 
redundant features frees resources lo encode more predictive features. Conversely, 
increasing the resources allocated to predictive features forces compression of the 
remaining (less predictive) features. 
This proposed hippocampal-region function may be modelled by a predictive autoencoder 
(on the right in Figure 2). An autoencoder (Hinton, 1989) learns to map from stimulus 
inputs, through an internal layer. to an output which is a reproduction of those inputs. 
This is also known as stimulus-stimulus learning. To do this, the network must have 
access to some multi-layer teaming algorithm such as error backpropagation (Rumelhart, 
Hinton & Williams, 1986). When the internal layer is narrower than the input and output 
layers. the system develops a recoding in the internal layer which takes advantage of 
redundancies in the inputs. A predictive autoencoder has the further constraint that it 
must also output a classification response to the inputs. This is also known as stimulus- 
response learning. The internal layer recoding must therefore also emphasize stimulus 
features which are especially predictive of this classification. Therefore. a predictive 
autoencoder learns to form internal representations constrained by both predictive 
differentiation and redundancy compression, and is thus an example of a mechanism for 
implementing the two representational biases described above. 
The cerebral and cerebellar cortices form the sites of long term memory in this theory, but 
are not themselves directly able to ibrm new representations. They can, however, acquire 
new representations formed in the hippocampal region. A simplified model of one such 
cerebellar region is shown on the left in Figure 2. This network does not have access to 
multi-layer learning which would allow it to independently form new internal 
representations by itself. Instead. the two layers of weights in this network evolve 
independently. The bottom layer of weights is trained so that the current input pattern 
generates an internal representation equivalent to that developed in the hippocampal 
model. Independently and simultaneously. weights in the cortical network top layer are 
trained to map from this evolving representation to the classification response. Because 
the cortical networks are not creating new representations, but only learning two 
independent single-layer mappings, they can use a much simpler learning rule than the 
hippocampal model. One such algorithm is the LMS learning rule (Widrow & Hoff, 
1960), which can instantiate the Rescorla-Wagner (1972) model of classical conditioning. 
Cortical (Cerebellar) Network Hippocampal-System Model 
Classification 
Response, 
External Sensory Input 
tra. ininl (training signal) 
sic ha/ 
Single-layer 
learning 
Single-layer 
learning 
Multi-layer 
learning 
939 
Sensory Input 
Figure 2. The cortico-hippocampal model: new representations developed in the 
hippocampal model can be acquired by cortical networks which are incapable. of 
developing such representations by themselves. 
940 Gluck and Myers 
3 MODELLING HIPPOCAMPAL 
CLASSICAL CONDITIONING 
INVOLVEMENT IN 
A popular experimental paradigm for the study of associative learning in animals is 
classical conditioning of the rabbit eyeblink response (see Gormezano, Kehoe & 
Mm'shall. 1983. for review). A puff of air delivered to the eye elicits a blink response m 
the rabbit. If a previously neutral stimulus, such as a tone or light (called the conditioned 
stimulus). is repeatedly presented just before the airpuff. the animal will develop a blink 
response to this stimulus -- and time the response so that the lid is maximally closed just 
when the airpuff is scheduled to arrive. Ignoring the many temporal factors -- such as the 
interval between stimuli or precise timing of the response -- this reduces to a 
classification problem: learning which stimuli accurately predict the airpuff and should 
theretore evoke a response. 
During a training trial. both the hippocampal and cortical networks receive the same 
input pattern. This pattern represents the presence or absence of all stimulus cues -- both 
conditioned stimuli and background contextual cues. Contextual cues are always present. 
but may change slowly over time. The hippocampus is trained incrementally to predict 
the current values of all cues -- including the US. The evolving hippoocampal internal 
layer representation is provided to the cortical network. which concurrently learns to 
reproduce this representation and to associate this evolving internal representation with a 
prediction of the US. This cortical network prediction is interpreted as the system's 
response. 
The complete (intact) cortico-hippocampal model of Figure 2 can be shown to produce 
conditioned behavior comparable to that of normal (intact) animals. Hippocampal lesions 
can be simulated by disabling the hippocampal model. This eliminates the training signal 
which the cortical model would otherwise use to construct internal layer representations. 
As a result, the lower layer of cortical network weights remains fixed. The lesioned 
model's cortical network can still modify its upper layer of weights to learn new 
discriminations for which its current (now fixed) internal representation is sufficient. 
4 BEHAVIORAL RESULTS 
A stimulus discrimination task involves learning that one stimulus A predicts the airpuff 
but a second stimulus B does not. The notation <A+. B-> is used to indicate a series of 
training trials intermixing A+ (A preceeds the airpuff), B- (B does not preceed the 
airpuff) and context-alone presentations. Figure 3A shows the appropriate development 
of responses to A but not to B during this task. Both the intact and lesioned systems can 
acquire this discrimination. In tact, the lesioned system learns somewhat faster: it is only 
learning a classification. since its representation is fixed and (for this simple task) 
generally sufficient. In the rotact system. by contrast. the hippocampal model is 
developing a new representation and transferring it to the cortical network The cortical 
network must then learn classifications based on this changing representation. This will 
be slower than learning based on a fixed representation. This paradox of discrimination 
facilitation after hippocampal lesion has often been reported in the animal lilerature 
(Schmaltz & Thelos, 1972: Eichenbaum. Fagan. Mathews & Cohen. 1988): one previous 
interpretation has been to suggest that the hippocampal region is somehow "unneccessm'y 
for" or even "inhibitory to" simple discrimination learning. Our model suggests a 
different interpretation: the intact system learns more slowly because it is actually 
learning more than the lesioned system. The rotact system is learning not only how to 
map from stimuli to responses, it is also developing new stimulus representations which 
enhance the differentiation among representations of predictive stimulus latures while 
compressing the representations of redundant and irrelevant stimulus tatures. 
Adaptive Stimulus Representations: Computational Theory of Hippocampal-Region Function 941 
The benefit of this re-representation can most readily be seen when the task demands 
suddenly change. For example, suppose the task valences shift from <A+, B-> to <A-, 
B+>. The representation developed during the first training phase, which maximally 
differentiated features distinguishing stimulus A from B, will still be useful in the second 
training phase. Onlv the classification needs to be relearned. Figure 3B shows that the 
intact system can learn the reversed task slightly more quickly than it learned the original 
task. Successive reversals are expected to be even more facilitated, as the representations 
of A and B grow ever more distinct (see Sutherland & Mackintosh, 1971, for a review of 
the relevant empirical data). In contrast, the lesioned system is severely impaired in the 
reversal task (Figure 3B). In the lesioned system, with a fixed representation, all the 
information is contained in the upper classificatory layer of weights. This information 
must be unlearned before the reversal task can be learned. Consistent with the model's 
behavior, empirical studies of hippocampal-lesioned animals show strong impairment at 
reversal learning (Berger & Orr, 1983). 
The simplest evidence for redundancy compression likewise occurs during a transfer task. 
During unreinforced pre-exposure to a stimulus cue A, the presence or absence of A is 
irrelevant in terms of predicting US arrival (since a US never comes). Our theory expects 
that the representation of A will therefore become compressed with the representations of 
of the background contextual cues. In a subsequent training phase in which A does 
predict the US, the system must learn to respond to a feature it previously learned to 
ignore. The representation of A must now be re-differentiated from the context. Our 
theory theretore expects that learning to respond to A will be slowed. relative to learning 
(A) (e) 
Response 
l- 
A+ 
o.8- 
0.6- 
0.2-, B- 
0 - 
0 20 40 60 80 100 
Training Trials 
(c) 
Response 
1- 
0.8, 
0.6- 
0.4- 
0.2- 
0 
I I I 
A+ 
A+ only 
-, hA+ 
ii I rll I III II I II 
50 0 100 
Training Trials 
Trials to Learn 
4OO 
300 
200 
100 
0 
i 
A-, B+ 
(D) 
Res lPC'-r se A- 
0.$. 
0.6- 
0.4- 
0.2- 
I I I I I I 
o 5o 
A+ 
I I I I I I I I I I I I 
o lOO 
Training Trials 
Figure 3. Behavioral results. Solid line = intact system, dashed line = lesioned system 
(A) Discrimination learning <A+, B-> in intact and lesioned models: lesioned model 
learils slightly faster. (B) Discrimination reversal (<A+, B-,> then <A-, B+>) Intact 
system shows facilitation on successive reversals, lesioned system is severely impaired. 
(C) Latent inhibition (A- impairs A+) in the intact model; (D) No latent inhibition in the 
lesioned model. All results shown are consistent with empirical data (see text for 
references). 
942 Gluck and Myers 
without pre-exposure to A (Figure 3C). This effect occurs in animals and is known as 
latent inhibition (Lubow. 1973). 
In this theory. latent inhibition arises from hippocampal-dependent recodings. In the 
lesioned system. there is no stimulus-stimulus learning during the pre-exposure phase. 
and no redundancy compression in the (fixed) internal representation. Therefore. 
unreinforced pre-exposure does not slow the learning of a response to A (Figure 3D). 
Empirical studies have shown that hippocampal lesions also eliminate latent inhibition in 
animals (Solomon & Moore. 1975). 
Incidentally. a stm'dard teedforward backpropagation network. with the same architecture 
as the cortical network. but with access to a multi-layer learning algorilhm. fails to show 
latent inhibition. Such a network can form representations in its internal layer. but unlike 
the hippocampal model it does not perform stimulus-stimulus learning. Therefore. there 
is no effect of unreinfored pre-exposure of a stimulus. and no latent inhibition effect 
(sinulations not shown). 
This cortico-hippocampal theory can account tbr many other effects of hippocampal 
lesions (see Gluck & Myers. 1992. 1993 / in press): including increased stimulus 
generalization and elimination of sensory preconditioning. It also provides an 
interpretation of the observation that hippocampal disruption can damage learning more 
than complete hippocampal removal (Solomon. Solomon. van der Schaaf & Perry. 1983): 
if the training signals from the hippocampus are "noisy". the cortical network will acquire 
a distorted and continuously changing internal representation. In general. this will make 
classification learning harder than in the lesioned system where the internal 
representation is simply fixed. 
The theory also makes several novel and testable predictions. For example. in the intact 
animal. training to discriminate two highly similar stimuli is facilitated by pre-training on 
an easier version of the same task -- even if the hard task is a reversal of the easy task 
(Mackintosh & Little. 1970). The theory predicts that this effect arises from predictive 
differentiation during the pre-training phase. and therefore should be eliminated after 
hippocampal lesion. Another effect observed in intact animals is compound 
preconditioning: discrimination of two stimuli A and B is impaired by pre-exposure to 
the compound AB (Lubow. Ritkin & Alek. 1976). The theory attributes this effect to 
redundancy compression in the pre-exposure phase. and therefore again predicts that the 
effect should disappear in the hippocampal-lesioned animal. 
5 CONCLUSIONS 
There are many hippocampal-dependent phenomena which the model. in its present form. 
does not address. For example. the model does not consider real-time temporal effects. or 
operant choice behavior. Because it is a trial-level model. it does not address the issue of 
a consolidation period durirg which memories gradually become independent of the 
hippocampus. We have also not considered here the physiological mechanisms or 
structures within the hippocampal region which might implement the proposed 
hippocampal function. Finally. the model would require extensions before it could apply 
to such high-level behaviors as spatial navigation. human declarative memory. and 
working memory -- all of which are known to be disrupted by hippocmnpal lesions. 
Despite the theory's restricted scope. it provides a simple and unified account of a wide 
range of trial-level conditioning data. It also makes several novel predictions which 
remain to be investigated in lesioned animals. The theory suggests that the effects of 
hippocampal damage may be especially informative in studies of two-phase transfer 
tasks. In these paradigms. both intacl and hippocampal-lesioned animals are expected to 
behave similarly on a simple initial learning task. but exhibit different behaviors on a 
subsequent transfer or generalization task. 
Adaptive Stimulus Representations: Computational Theory of Hippocampal-Region Function 943 
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