A MODEL OF IMPLICIT ASSOCIATION LEARNING BASED ON
PLASTICITY IN THE PERIRHINAL CORTEX
Shigemitsu MOROKAMI∗ , Atsuo SUEMITSU∗∗ , Masahiko MORITA∗∗∗∗

Doctoral Program in System Information Engineering, University of Tsukuba∗∗
Doctoral Program in Engineering, University of Tsukuba∗∗∗
Institute of Engineering Mechanics and Systems, University of Tsukuba
1-1-1 Tennodai, Tsukuba, Ibaraki 305-8573, Japan
E-mail: morokami@bcl.esys.tsukuba.ac.jp

ABSTRACTIn the delayed match-to-sample task, responses of inferior temporal neurons to adjacent stimuli in the sequence are correlated to each other when the monkey
was trained repeatedly with the sequence of visual stimuli, although the monkey was not required to associate
the stimuli with each other. This correlation, however,is not observed for a monkey with lesions of the rhinal
cortex, which is not consistently explained by existingmodels of such correlated responses. In the present
study, we construct a model consisting of two networks
corresponding to area TE and the perirhinal cortex,
and show that perirhinal plasticity may underlie themechanism of implicit association learning.
1. INTRODUCTION
It is known that the inferior temporal cortex (IT) plays
a major role not only in visual recognition but also invisual short-term and long-term memories. Among themany examples of physiological evidences supporting
this, the data on memory-related neurons of the monkey reported by Miyashita[1] is one of the most important. This data reveals the phenomenon that after
a monkey was trained repeatedly on a delayed matchto-sample (DMS) task with a fixed sequence of visual
stimuli, the neuronal responses to adjacent stimuli inthe sequence are correlated to each other, although themonkey was not required to associate the stimuli explicitly. This is very interesting from the viewpoint of
the representation and structuralization of long-term
memory in the brain.
To explain this phenomenon, two theoretical models have been presented; however, they do not match
the physiological evidence in a critical point. In particular, they both have difficulty in explaining the resultof the lesion study on the rhinal cortex (perirhinal andentorhinal cortices) which plays an important role in
the formation process of visual memory.

In this paper, we present a model based on the findings on the perirhinal cortex, and show that the association between adjacent stimuli is quite possibly learned
by a mechanism different from those mentioned above.
2. BACKGROUND
2.1. IT neurons related to implicit association
The empirical study by Miyashita [1] is summarized asfollows.
First, he trained two monkeys repeatedly on a DMS
task for two weeks using 97 fractal pictures generated
by a computer. In this task, a picture is presented for ashort time as a sample stimulus, and the monkey mustjudge whether a picture (test stimulus) presented after
a delay of 16 seconds is the same as the sample or not.
The pictures were numbered from 1 to 97 and used asa sample always in that order.
Then he recorded the neuronal activities in IT (mainly
in area TE) while the trained monkeys were performing the task with the pictures used in training (learnedset) and with 97 novel pictures (unlearned set), andfound that many neurons exhibit strong activity during the delay period after the sample presentation ofsome specific learned pictures. The pictures eliciting astrong response to each of these neurons did not particularly have common features or similarity, but they
were often near one another in the picture sequencein training. That is, a neuron strongly responding to alearned picture tends to show a large response to neighboring pictures in the learned set.
This result is important in that the delay activities reflect the temporal relation between stimuli inthe learning process, which clearly differs from selective response to pictorial features as is usually seen in
IT. It should also be noted that the monkey has only
to remember the sample picture during a single trial
to perform the DMS task used in this experiment, andneed not associate pictures with each other. In thissense, such association between neighboring pictures
is formed implicitly, which we will refer to as implicit
association.
A similar phenomenon has been observed by Sakai
and Miyashita [2] in the case of a pair-association (PA)task, in which the monkey must associate a pair of visual stimuli explicitly. They reported that neurons responding to both of the paired pictures (‘pair-coding’neurons) were significantly more neurons than those responding to two unrelated pictures. Since in training,paired pictures were presented sequentially with a delay interval, we can regard such neuronal activity as
reflecting the temporal closeness of the visual stimuli.

2.2. Existing theories
At present, there exist two theoretical models explaining the mechanism of implicit association: the attractor
model by Griniasty et al. [3] and Brunel [4], and thelayer model by Wallis [5].
The former uses a recurrent neural network as amodel of the IT network, where the state encoding
a learned picture is an attractor of the network andthe activity pattern (‘code’) representing the sample ismaintained not only during the delay period but also
during the intertrial interval. That is, information on asample picture is preserved until sample presentation of
the next picture, which enables the model to correlate
the two picture codes.
On the other hand, the latter model uses a simple
layered network with the trace rule, which is an extended Hebb rule, where information on the previous
picture is preserved postsynaptically in the form of a
trace value that is elevated by excitation of the presynaptic neuron or the input signal and then decays gradually. Since the synapse is reinforced if the postsynaptic neuron is excited while some trace value remains,
the response pattern to a picture becomes similar tothe code of the previous picture.
However, neither mechanism of intertrial memory
retention in the two models has been physiologicallyverified or seems plausible. Moreover, these models do
not well explain some important findings concerning
the rhinal cortex.

2.3. Rhinal cortex
The rhinal cortex is an area composed of the perirhinal cortex (PRh), which is part of IT and adjacent toTE, and the entorhinal cortex which is in the medial

temporal lobe. This area, particularly PRh, has recently been drawing attention for its role in memory,and many physiological findings have been obtained;
we briefly describe some of them (see [6] for details).
First, lesions of PRh of the monkey moderately
impair the learning and performance of DMS tasks,
whereas simple visual recognition is not damaged. In
contrast, they cause critical impairments in association
learning; for example, a monkey with rhinal lesions iscompletely incapable of learning a new set of stimuli ina PA task.
Second, TE neurons of a monkey in the hemisphere,
from which the rhinal cortex had been removed before
training, show the same stimulus selectivity as thosein the intact hemisphere, but do not show the ‘paircoding effect’ [7], i.e., the high correlation between the
responses to paired pictures.
These findings indicate that the rhinal cortex is
critical for association learning of visual stimuli, which
does not accord well with either of the above models inwhich the rhinal cortex is not taken into considerationand information on the previous sample is retained at
the neurons exhibiting picture-selective delay activity;
according to those models, although they deal with the
implicit learning in DMS tasks, the pair-coding effectin TE will not disappear even after the removal of therhinal cortex because paired pictures in the PA task
are presented close together in time.
In connection with this, it is known that the rhinal
cortex, particularly PRh, has high plasticity in two respects. One is synaptic plasticity; for example, Tokuyama
et al. [8] demonstrated that the brain-derived neurotrophic factor was upregulated specifically in PRhduring PA learning, indicating that the synaptic plasticity in PRh is related to associating visual stimuli.
The other is the phenomenon that neurons exhibit
a reduced activity during the second or subsequent exposure to a stimulus relative to the first [9], which iscalled stimulus-specific adaptation (SSA) or the repetition inhibition effect. This adaptation effect lasts for along time (more than a few minutes), and is not greatly
affected by intervening stimuli presented between thefirst presentation and the second presentation of thestimulus.
Among the neurons exhibiting SSA, those in which
the response varies according to not the familiarity butby the recency of the stimulus are referred to as recency
neurons. Although recency neurons are also found in
TE and the entorhinal cortex, their ratio is particularly large in PRh, and the duration of adaptation ofrecency neurons is significantly longer in PRh than in
TE. These findings suggest that the information on asample picture is retained by the recency neurons in

x1

r N1

C +1

N

2

xn

C +i

C +n

1

x

s

C-

xi

r

1

x

C -i

C n-

rn

ri

Figure 2: Structure of the association network.

Figure 1: Block diagram of the model.
PRh to form implicit association.
3. THE MODEL OF IMPLICIT LEARNINGFORMATION
We previously constructed a model composed of twoneural networks corresponding to TE and PRh, andhave shown that it agrees well with the activities ofIT neurons during a PA task [10]. This model, however, is for explicit association and does not explain theimplicit association phenomenon.
Taking account of the above physiological findings,
we modify this model by introducing synaptic plasticity
and ‘recency cells’ showing the adaptation effect in the
network corresponding to PRh. The structure of themodel is shown in Fig.1, where association network N1
and trainer network N2 are interconnected.

of the inhibitory synapse from Ci+ to Ci− , and θ is a
threshold. The activation function f (u) of each cell isa monotonic sigmoid function increasing from 0 to 1
given byf (u) =

1.
1 + e−cu(4)

In parallel with this, using a learning signal generated by N2 , learning of the synaptic weights of N1 isperformed according to
+
dwij
dt
−
dw
ij
τ0
dt

τ0

+
= −wij+ α1 ri xj ,(5)−
= −wij− β1 ri xj + β2 xi xj + γ,

(6)where α1 , β1 and β2 are learning coefficients, γ is apositive constant representing lateral inhibition among
units, and τ 0 is a time constant of learning (τ 0  τ ).

3.1. The association network
The association network N1 consists of pairs of excitatory and inhibitory cells (Fig.2). The excitatory cellCi+ receives a signal ri from N2 with input intensityof λ and recurrent inputs from other units, and emits
output of the unit xi . The inhibitory cell Ci− sends astrong inhibitory signal to Ci+ . In mathematical terms,


n
X
−
yi = f 
wij
xj − θ ,
(1)
j=1

τ

dui
dt

=

xi

=

−ui +

n
X

+
wij
xj − w∗ yi + zi ,

(2)

j=1

f (ui ),

(3)+−
where wij
and wij
are the synaptic weights from the+jth unit to Ci and Ci− , w∗ represents the efficiency

3.2. The trainer network
The structure of the trainer network (N2 ) is shown in
Fig.3. This network consists of n pairs of output cell
Cio and recency cell Cir which inhibit Cio , corresponding
to the n units of N1 , and transforms the input patterns = (s1 , ..., sn ) into the learning signal r = (r1 , ..., rn )for the association network.
The ith cell Cio receives the input pattern s =(s1 , ..., sn ) and the feedback signal xj from N1 throughsynaptic weights pij and qij , respectively, and emits ri
to the ith unit of N1 . The output ri is also sent to
the recency cell Cir , which sends an inhibitory signal
zi back to Cio , and transiently increases the thresholdor the degree ei of fatigue depending on its value (wemodel SSA simply using the fatigue of cells, since themechanism of SSA is not clear and modeling it for itself is not our purpose). Accordingly if Cio is strongly

r x
1

r x
i

1

r x
n

i

X1

n

x2
X3
X4
X5
X6

r

r

r

Cn

Ci

C1

X7

C no

C oi

C1

Neuron No.

o

X8
X9
X10
X11
X12
X13
X14

s

s

1

s

i

X15

n

X16
X17

Figure 3: Structure of the trainer network.

X18
X19
X20

activated, the inhibitory signal from Cir decreases for awhile, when Cio is readily excited.
In mathematical terms,
τ

dvi
dt

=

−vi +

n
X
j=1

−ρ

X

pij sj +nX

qij xjj=1

rj + σri − ηri∗ ,

(7)

0

10

20

30

40

50

60

70

80

90

100

Pattern No.

Figure 4: Responses of the units to each input pattern.
cause of this effect, the degree of correlation between aand b gradually increases to a certain value by repeating the same learning process.

j6=i

ri
ri∗
dei
τ 00
dt

=
=

f (vi ),
(ζ − ei )ri ,

(8)
(9)

=

−ei + ιri ,(10)

where vi denotes the potential of Cio , ρ and σ represent
the efficiency of lateral inhibition and self-excitation,
respectively, η is the input weight from the recency
cell, and ζ and ι are positive constants.
This network generates the learning signal as follows. First, when N2 receives an external input pattern, say A, it emits a pattern a encoding A, and N1 istrained so that a state close (in the sense of the vector
direction) to the current learning signal r = a can bea stable attractor. Then if N2 receives a pattern B in
the next trial, it emits a different pattern b that was asmall correlation with a because of the adaptation effect of the recency cells. Since this effect decreases withtime and does not disappear immediately, the output
patterns c, d, and so forth of N2 corresponding to the
input patterns C, D, and so forth have some decreasing
degree of correlation with a.
At the same time, the synaptic weights pij are modified according toτ0dpij= −pij + α2 ri sj ,
dt

(11)

where α2 is a positive constant, which makes the input
signal to N2 similar to the current learning signal. Be-

4. COMPUTER SIMULATIONS
We carried out computer simulations on the model with
1000 units. First we prepared 100 patterns that were
1000-dimensional vectors with 10% of the elements being 1 and the rest 0, and trained the model by feeding
the patterns one by one for 4τ for each pattern. The
parameters wereτ 0 = 50000τ , τ 00 = 60τ , θ = 3.0,w ∗ = 10,
α1 = 50, α2 = 0.5, β1 = 25, β2 = 50,
γ = 0.05, ζ = 0.9, ι = 0.8, c = 10.
After training, we examined the response of theunits of N1 . Fig.4 displays the responses of some units
to the 100 learned patterns, where units with various
types of pattern selectivity can be seen; that is, someunits respond strongly to only a few patterns and moderately to their neighboring patterns, and others show
the same level of response to a long sequence of patterns. It should be noted that neurons of both types of
response are actually observed in monkey IT.
We analyzed this simulation data in the same way
as in Miyashita’s study [1]. The result is shown in
Fig.5, where correlation between the response to an input pattern and that to neighboring patterns is plottedby the solid line; the broken line represents the IT neurons (adapted from [1]). We see that the simulation
result agrees well with the empirical data.
Finally, to examine the learning process of implicit
association, we calculated the correlation between re-Correlation coefficient

0.4

to other theoretical models in that it matches physiological evidence more closely, particularly the fact thatthe implicit association phenomenon in TE disappears
when the rhinal cortex is removed. Another advantage
is that this model can learn and perform a PA task
and thus gives a unified theory of implicit and explicit
association learning processes.
Empirical verification of the model remains for future study as well as the development of a model forrecency neurons based on a more plausible mechanism.

Simulation result
Empirical data

0.3

0.2

0.1

0.0

-0.1
0

1

2

3

4

5

6

7

8

9

10

Proximity of patterns along sequence

Figure 5: Correlation coefficient between responses toa pair of learned patterns versus their distance in thepattern sequence.

Correlation coefficient

0.35[1] Y. Miyashita, “Neural correlate of visual associative
long-term memory in the primate temporal cortex,”Nature, vol.335, pp.817–820, Oct. 1988.
[2] K. Sakai and Y. Miyashita, “Neural organization
for the long-term memory or paired association,” Nature, vol.354, pp.152–155, Nov. 1991.

Normal condition
Without recency effectWithout plasticity

0.30

6. REFERENCES[3] M. Griniasty, M.V. Tsodyks and D.J. Amit, “Conversion of the temporal correlations between stimuli
to spatial correlations between attractors,” NeuralComputation, vol.5, pp.1–17, 1993.

0.25
0.20
0.15

[4] N. Brunel, “Hebbian learning of context in recurrent neural networks,” Neural Computation, vol.8,
pp.1677–1710, 1996.

0.10
0.05
0.00
0

1

2

3

4

5

6

7

8

9 10 11 12 13 14 15

Learning times
Figure 6: Learning process of implicit association.

sponses to adjacent patterns for every cycle of training.
For comparison, we repeated simulations in the casesthat ei ≡ 0, or the recency effect was removed, andin the case that N2 has no synaptic plasticity. Fig.6
shows the result, where the solid line indicates the intact model, and the broken and dotted lines indicate
the cases without the recency effect and without synaptic learning of N2 , respectively. As is evident fromthis figure, no implicit association phenomenon was observed without the recency effect cells; even with therecency effect, if N2 does not have synaptic plasticity, the degree of correlation does not increase much.
This suggests that both the recency effect and synaptic
learning in PRh are important for implicit association
learning in TE.

[5] G. Wallis, “Spatio-temporal influences at the neurallevel of object recognition,” Network: Computationin Neural Systems, vol.9, pp.265–278, 1998.
[6] E.A. Murray and T.J. Bussey, “Perceptualmnemonic functions of the perirhinal cortex,” Trends
in Cognitive Sciences, Vol.3, pp.142–151, April.
1999.
[7] S. Higuchi and Y. Miyashita, “Formation of
mnemonic neural response to visual paired associates
in inferotemporal cortex is impaired by perirhinaland entorhinal lesions,” Proc. Natl. Acad. Sci. USA,vol.93, pp.739–743, Jan. 1996.
[8] W. Tokuyama, H. Okuno, T. Hashimoto, Y.X.
Li and Y. Miyashita, “BDNF upregulation during
declarative memory formation in monkey inferior
temporal cortex,” Nature Neuroscience, vol.3, no.11,
pp.1134–1142, Nov. 2000.

5. CONCLUDING REMARKS[9] J.L. Ringo, “Stimulus specific adaptation in inferior
temporal and medial temporal cortex of the monkey,” Behavioural Brain Research, vol.76, pp.191–
197, 1996.

Based on the findings on the rhinal cortex, we haveconstructed a neural network model that forms implicit
association, and have shown that it agrees well with theempirical data on IT neurons. This model is superior[10] M. Morita and A. Suemitsu, “Computationalmodeling of pair-association memory in inferior temporal cortex,” Cognitive Brain Research, vol.13,
pp.169–178, 2002.