Kuniyoshi Sakai, ¹ Yuji Naya, and Yasushi Miyashita Neuronal Tuning and Associative Mechanisms in Form Representation Department of Physiology School of Medicine University of Tokyo Bunkyo-ku, Tokyo 113, Japan
²Corresponding author. LEARNING & MEMORY 1:83-105 © 1994 by Cold Spring Harbor Laboratory Press ISSN1072-0502/94 $5.00 R E L A / N N G M o E M R Y occurs as overall identification or as a synthesis of structural components. Some theories of object recognition have proposed bottom-up determination of an object's components and subsequent matching of the arrangement of components with a memory representation (Marr 1982; Pentland 1986; Biederman 1987). The properties of face-selective cells found within the superior temporal sulcus (STS) may be in agreement with these theories (Perrett et al. 1987). However, there is psychological evidence that object features (not specifically related to parts) are matched directly with such overall features stored in long-term memory (Cave and Kosslyn 1993). Because this problem is inseparable from the nature of form representation itself, we will deepen this discussion in the section Is Form Representation 2D Viewer-centered or 3D Object-centered? (below) after presenting more relevant data. FORM SELECTIVITY AND ORIENTATION SELECTIVITY Whereas the orientation selectivity of V1 and V2 neurons has been well characterized (Hubel and Wiesel 1959; Bonhoeffer and Grinvald 1991; Blasdel 1992), the form selectivity of IT cells has not been thoroughly studied, owing in part to the complexity of form features (Schwartz et al. 1983; Desimone et al. 1984; Tanaka et al. 1991). When recording a V1 cell, its orientation preference and selectivity can be systematically determined. Orientation is of one-dimensional parameter ranging from 0° to 180°. In contrast, the range of form variety is almost infinite. Moreover, no critical parameters are known for the specification of general forms. If the form analysis in vision is hierarchical and bottom-up, then some aspects of the form selectivity of IT cells reflect the constraint in the L
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E L A R N   N G M E M o Y It should be noted that the PA task cannot be solved by employing short-term or working memory within a single trial. Instead, the PA task is essentially the memory recall task, which explicitly demands the memory retrieval and thus generation of images from the long-term memory. Therefore, memory components of the PA task present a sharp contrast to those of the delayed matching-to-sample (DMS) task. In the DMS task, the subject indicates whether a test stimulus matches a sample stimulus shown previously ( Gaffan 1985 ). When the stimulus set size is small and thus the same trial is repeated, working memory is mainly involved in the DMS task. When the stimulus set size is large and thus each trial is unique, recognition memory is also involved, because it is possible to indicate whether a test stimulus has appeared or not without employing working memory. We have to take these points into consideration in interpreting the results of behavioral and physiological studies. The Neuronal Tuning Mechanism in the AIT Cortex PARAMETRIC TRANSFORMATION AND GEOMETRIC TRANSFORMATION We have adopted computer-generated Fourier descriptors (FDs) as visual stimuli (Sakai and Miyashita 1991). FDs are appropriate in testing fine-form selectivity because they are specified by a set of parameters: harmonic amplitude (Ak) and phase angle (ak), where k = 1,2,. . ., is each term's ordinal number in a Fourier series (Zahn and Roskies 1972). The number of free parameters for each pattern shown in Figure 2 is only
G M E M o R Y one or three, which corresponds to one or two sets of [A k , a k ], respectively. (Note that one of the phase angles is not independent and affects only the overall rotation.) A slight alteration of one FD parameter from its original value produces a very similar form. This manipulation of an original pattern is called parametric transformation (Fig. 3A). The similarity of forms with slightly altered FD parameters is validated by the fact that the position of each point in the drawing plane is a continuous function of FD parameters (some examples are shown in Fig. 4, below). The parametric transformation should be regarded as a method for changing forms systematically in testing the fine-form selectivity. One previous study of IT cells reported that the neuronal responses to forms produced by combining two FD terms cannot be predicted from the responses to each component FD (Albright et al. 1985). However, this procedure leads to global alteration of forms, which is independent of the local parametric transformation. There is another type of manipulation, geometric transformation, in which an original pattern was rotated about its center with various rotation angles or was reflected as a mirror image (Fig. 3A). The geometric transformations show different views of the same form, whereas the parametric transformations show different forms with similar global features. It has been reported that IT cells and face-selective STS cells show invariant responses over changes in stimulus size, contrast, orientation, and retinal location, thereby activating a different set of V1 neurons (Schwartz et al. 1983; Rolls and Baylis 1986; Miyashita and Chang 1988). Furthermore, the form selectivity of IT cells did not vary
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E L A R N  N G M E M significant (P<0.01, n=54, Mann-Whitney U test) (Sakai and Miyashita 1994a). Because of the following reasons, we can exclude the possibility that the weaker response to unlearned transforms is attributable to purely sensory reasons. First, every tested transform was derived from one of the original patterns, which elicited the strongest or the second-strongest response. Therefore, these transforms are likely to produce equally strong responses as the original pattern, obviously stronger than other ineffective original patterns. Furthermore, it is impossible to predict beforehand the relative effectiveness between the original pattern and transforms that derived from it, because original patterns have no special form attributes and their FD parameters were selected randomly. Second, a cell's preference among the screening set should not be confused with fine-form selectivity among transforms, because the range of form variety in the screening set is incomparably wider than that in transforms. Therefore, the learning-dependent fine-form selectivity cannot be explained by the global form selectivity for the screening set. Furthermore, the weaker neuronal response caused by the parametric transformation is not the result of extraretinal factors such as attention. The geometric transformation did not produce a comparable reduction in neuronal responses even with the use of the same procedure to impose a new association with a transformed pattern (Sakai and Miyashita 1994a). This significant difference should be ascribed to the parametric manipulation of forms. These results suggest that AIT neurons are subject to tuning mechanisms for particular forms in a learning process.
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R G M E M o R Y TUNING AS LOCAL MAXIMA Suppose the parameter space of FDs, in which each axis corresponds to one FD parameter (x,) (e.g., amplitude A, in Fig. 4). Because every FD pattern is specified by a set of parameters (x 1 , x 2 , . . , xn ), each pattern corresponds to a single point in this parameter space. Note that the 24 points of original patterns in the screening set are chosen randomly and sparsely. The parametric transformation corresponds to a local perturbation (x 1 °, . ., x k - 1 °, x k ° + Ax k , x k + 1 °, . ., x n °) around the point of an original pattern (x, °, x2°, . . , xn°), where | Ax, | is less than half the minimum separation of the points of original patterns (Fig. 5). We assume a continuous mapping from each point in the parameter space to a single-cell discharge rate f(x,, x2,. . . , xn), that is, there are no break points in the response curve. As emphasized in the section Fine-form Selectivity (above), there is no guarantee that the point of an original pattern (x 1 °, x 2 °, . . , x n °), which elicited the strongest response among the screening set, gives a local maximum of f(x 1 °,.., Xk- 1 °, Xk, Xk+1 °,..., xn°) (Fig. 5). Nevertheless, the parametric transformation always caused a that the parametric transformation causes a decrease in neuronal responses for any tested points, as shown by the thick curved line where x,° gives a local maximum. Accordingly, neuronal responses should be optimally tuned to the original patterns.
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L E A R N l N G M E M o R Y The Associative Mechanism in the AIT Cortex PAIR-CODING NEURON across-category deficit is the similarity of their forms among exemplars. We propose that the neuronal tuning mechanism in the temporal association area subserves category-related recognition that requires fine discrimination among similar items in each category. It has been reported that monkeys with IT lesions did not show categorical perception of visual stimuli (Iversen and Weiskrantz 1967; Dean 1976; Wilson and DeBauche 1981). Hence, AIT neurons would be critical for categorization of various forms, thereby organizing the stored information by the neuronal tuning mechanism.
learning. Here, this type of coding is termed associative coding, in which the involvement of associative learning is essential for memory storage. The associative coding proposed here provides one organizing principle by which the special selectivity of neuronal responses is produced. The spatiotemporal patterns of neuronal discharges selective to object forms thus constitute the basis of ensemble coding. A possible molecular mechanism of the associative coding lies in the change of L E A R N
G M E M o R Y synaptic connections through repetitive learning (Frégnac et al. 1988; Artola and Singer 1990), whereby two inputs are always paired with each other. This mechanism is supported further by a recent model of the neuronal network that learns invariance to shift in retinal position through exposure to temporal sequences of patterns undergoing the transformation (Földiák 1991). The associative mechanism based on temporal contiguity will be discussed further in the section The Implicit Association Process and Object Recognition (below). PAIR—RECALL. NEURON We found another type of neuron (pair-recall neuron) that is presumably involved in the process of memory retrieval (Sakai and Miyashita 1991). Recall that in our experiments the monkey is required to recognize the paired associate of a cue stimulus after a delay period. Pair-recall neurons exhibited form-selective delay activity (Fig. 7). This response is closely coupled with the paired associate that is not actually seen but retrieved by the cue stimulus. There are two possibilities for the critical process during the delay period. One is to hold a retrospective code, which is a cuc stimulus, in working memory. The other is to generate a prospective code by converting a cue into its paired associate. The increasing delay activity of the pair-recall neurons is consistent with the claim that subjects can employ a prospective code. On the grounds that the AIT cortex serves as the memory storehouse (see A Model of the Cognitive Memory System, below), these neurons could serve as memory storage elements, also activated in the retrieval process. The finding of pair-recall neurons is the first neurophysiological demonstration that visual imagery is also implemented by the same neuronal mechanism that subserves memory retrieval. Pair-coding neurons and pair-recall neurons can participate, respectively, in the coding and recall processes of visual long-term memory. The pair-recall neuron represents a typical example of interaction between sustained activation and association among the neuronal mechanisms that subserve the formation or expression of memory traces (Desimone 1992). Evidence for form-selective or color-selective sustained activation has been documented in the IT cortex (Fuster and Jervey 1982; Miyashita and Chang 1988; Fuster 1990; Chelazzi et al. 1993) and the L
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E L A R N l N G M E M o Y several views encoded by pair-coding neurons, the independence of each view is still preserved by AIT neurons that respond selectively to each view, as we have already found neurons of both types. Using this scheme, one standard view can be naturally associated with any observed views of the same object, thus enabling correct recognition. The face-selective cells that are responsive to multiple views of the head can also be explained within this scheme, such that the invariance over changes of vantage points was actually acquired through associative learning. As noted by Perrett et al. (1987), "for the majority of objects only learning can specify that the back and front views of an object belong to the same object." In our visual world, multiple views of an object are nearly always presented in succession, resulting from relative movement between the observer's eyes and the object. This situation is an example of the implicit associative process in that two different views of an object are associated and memorized by AIT neurons (Sakai and Miyashita 1991). As Stryker (1991) pointed out, "if the temporal cortex contains a general mechanism for the association of successively presented stimuli, then these views would automatically become associated, obviating the need for special mechanisms for doing geometrical transformations or for hierarchically associating conjunctions of trigger features." Such an associative mechanism based on temporal contiguity agrees well with the principle of generic image of sampling, proposed by Nakayama and Shimojo (1992). This is a theoretical framework based on the idea of a mobile observer sampling images from random vantage points in space. For example, generic or usual images of a cube have three faces; accidental or unusual images of a cube have two or only one face. This principle indicates that, "when faced with more than one surface interpretation of an image, the visual system assumes it is viewing the scene from a generic, not an accidental, vantage point" (Nakayama and Shimojo 1992). They argued further that the visual system learns associative connections between images and surfaces through the observer's experience of sampling. Incorporating this significant conceptual framework, the implicit associative process can be elucidated further: The relationship of generic 2D views is automatically acquired by the neuronal mechanism of association. A model of the cognitive memory system that we have proposed (Sakai and Miyashita 1993a, 1994b) is shown in Figure 8A. This scheme is based on structures and functions of the visual memory system for object recognition (Fig. 8B), and it contains some ideas that have been put forward by several researchers (Gross 1973; Mishkin 1982; Squire and Zola-Morgan 1991; Van Essen et al. 1992; Ungerleider and Haxby 1994). In the memory acquisition process, sensory information is transformed into a memory code of neuronal responses (encoding) with the bottom-up information flow from feature analyzers to a memory storehouse. The primary visual area and the prestriate area serve as feature analyzers in vision (Van Essen et al. 1992; Ungerleider and Haxby 1994). A possible candidate for the memory storehouse is the temporal association area (Gross 1973; Mishkin 1982; Sakai and Miyashita 1993a). In visual perception, prominent features in a visual field are selected and located by a focal attention controller. As noted above, one candidate for the focal attention controller is the pulvinar nucleus. We hypothesize
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N / G M E M A Model of the Cognitive Memory System Memory acquisition and retrieval
Memory consolidation
B Memory consolidation that perception is implemented by the interaction between memory acquisition (encoding) and focal attention mechanisms (Fig. 8A). To establish a long-lasting representation in the memory storehouse, memory controllers are responsible for consolidation. During this memory consolidation process, the memory code is reorganized dynamically (recoding) by the interaction between the memory storehouse and memory controllers. Medial temporal structures, including the hippocampus, are regarded as memory controllers because of their plasticity (e.g., long-term potentiation and depression) (Bliss and Collingridge 1993; Malenka 1993), neural networks (e.g., autoassociation) (McNaughton and Morris 1987; Rolls 1989), clinical observations (e.g., medial temporal lobe amnesia) (Milner 1972; Squire
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