CONNECTIVITY AND CONDENSATION I N DREAhdING STANLEY R. P x o m o , M.D. Coiivergiiig developineiits i n the cognitive- and iieurosciences have broiiglit Freud’s hole of a bridge betrueeii psjchoaiialyis aiid psy choplysioloa iiearer to hand. This paper coiiceriis the relatioil betweeii dreaiii coirstrziclioii aiid iiieiiioiy in t e r m of these iiew developiiieiits. Tlie iietcral network arcliitectttre of iiieiiior).structures i n the braiii is described arid illustraled with simple exainples. JVe see horu a network is coiiiiected aiid how coiiiieclioii weights var). with exberielice. Tlie distributed represeiitatioii stored by the network alid its crucial properlies for iiieiital ficiictioiiiiig are clisctcssed. These concepts are rued to explaiti how particular iiieiiiories of past events are selected for iiicliuion in the dream. Tlie properties of the iieiiral iietwork suggest that images of clistiiict past events are coiiflated at tiiiies during the seleclion process. The appearmice of these coi$ated iiiiages inay complicate the riintchiiig of day residites ruitli representations of past events in the dreain itser/. Some like4 iiiijlicatioils f o r psjchoaiia~tictheory are ex/dored.

F

REUD’S HOPE FOR A CONVERGENCE between metapsychology and neuroscience has been coming closer to reality in recent years. An exciting neiv collaboration between the cognitive- and neurosciences has given us a fresh basis for making needed refinements in psychoanalytic theory. This paper will concentrate on the implications of this work for dream theory and more specifically for o u r understanding of the process of condensation in dreaming. T h e major focus of this neiv research is the development of the neztral 72etwork as a model architecture for memory and other major aspects of mental functioning. T h e neural network model suggests an answer to an intriguing question about the

Clinical Associate Professor o f Psychiatry, George IVashington University; Training and Supervising Analyst, Training Institute o f the New York Freudian Society.

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condensed imagery of dreams. This has to d o with the mechanism for selecting memories of past events to be matched with the day residues (Palombo, 1978, 1984~1,1988). T h e neural network model brings with it a new perspective on the nature of mental representation. This change in perspective leads in turn to an expanded and more detailed view of the condensed imagery of the dream. Freud often compared the mechanism of condensation in dreams with Galton’s.method of superimposing family photographs. In doing so he suggested, in effect, that condensation is an information processing procedure. I n “On Dreams,” Freud (1901) says:

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T h e dream-work then proceeds just as Francis Galton did in constructing his family photographs. It superimposes, as it were, the different components upon one another. T h e common element in them then stands out clearly in the composite picture, while contradictory details more o r less wipe one another out [p. 6491.

T h e dream imagery always contains representations of current events, which Freud called the day’s residues. Memories of past events, related to repressed impulses from the early years of life, are also present in every dream. It appeared that in dreaming an adaptive mechanism of some sort is purposefully seeking out a common element in these events (Palombo, 1976). Galton’s method dovetails neatly with the discovery that a critical step in the construction of an associative memory is the matching of new items with previously stored items (Newell, Shaiv and Simon, 1957). With the development of the neural network model, the issue of memory structure has received new attention. T h e question of superimposed representations has become central to the study of memory. In “A Distributed Model of Human Learning and Memory,” McClelland and Rumelhart (1986b) put forward their view of memory acquisition:

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We see the traces laid down by the processing of each input as contributing to the composite, superimposed memory representation. Each time a stimulus is processed, it gives rise to a slightly different memory trace-either because the item itself is different or because it occurs in a different context that conditions its representation. T h e logogen [meaningful event] is replaced by the set of specific traces, but the traces are not k e p separate. Each trace contributes to the composite, but the characteristics of particular experiences tend nevertheless to be preserved, at least until they are overridden by canceling characteristics of other traces. Also, the traces of one stimulus pattern can coexist with the traces of other stimuli, within the same composite memory trace [p. 1931. T h e similarity of terms in Freud’s description of Galton’s method and in this passage is striking. I shall attempt to make this correspondence (and its limitations) explicit.

The Problem How are images of past events selected for superimposition on the day residues to form the composite condensed image experienced by the dreamer? These images represent significant events of childhood, events often associated with repressed wishes and impulses. T h e events of the dream day (or events within a few days of the dream day) have evoked or stirred them up. I n Freud’s word, these recent events are the “instigators” of the dream. My research has shown that the day residues appearing in the dream imagery are in fact associated with these instigating events (Palombo, 1978, 1988). T h e day residue is a displacement from or substitute for the significant but objectionable event that stirs the unconscious. T h e past events represented in the dream are related to the day residues by similarities of sensory image, narrative sequence, and affect. T h e formation of a condensed composite

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image in the dream would be impossible if this similarity did not preexist the matching. It would seem that a vast array of representations would have to be scanned so that only those past events that already had some degree of similarity to the day residue could be chosen for superimposition. Before the two representations could be matched in the sensory projection mechanisms, a minimal relation between them would have to be found. Whatever stirring may have taken place during the day, the activation of the repressed wishes to be represented in the dream cannot be coinpleted until the process of dream coiwtrzictioii has begun. A systematic search of the entire memory for past events related to the day residue would not be feasible. There are simply too many items to sort through, even using the heuristic search procedures that have become the hallmark of cognitive simulation in artificial intelligence. How is this preliminary selection accomplished? Without a plausible mechanism in place, the question has to be left unanswered.

Neural Networks and Distributed Re/wesentation.s A neural network is a set of neurons linked by connections that provide feedback when the cells are stimulated.' When Hebb (1949) proposed this model, scientists kneiv little about the actual connections in the brain. Computer simulation was the main research tool. Today's neurophysiological research techniques have confirmed that the neural network is the basic structure of brain organization (Nadel et al., 1989). Research on the neural network model now makes use of in viao experiments with mammal brains. I n computer simulations of neural networks the connections can be set u p in many different ways. Each set of connections will produce a distinct pattern of output when the network 'See Graubard (1988), Clark (1989), and Churchland (1990) for the philosophical background of neural network research. For concepts and tecliniques, the standard and highly recommended introduction is by hIcClelland and Rumelhart (1986a). hIcClelland and .Rumelhart (1'388) provide easy to use illustrative software. Caudhill and Butler (1990) cover the basic neural

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is activated. Every neuron in the network may be connected to every other neuron, but usually only a small fraction of the possible connections is needed to carry out a particular function. Most commonly the neurons are organized in layers, as in the brain, with the output of one layer forming the input to the next. Two factors determine the functional capability of a neural network. One is the pattern of connections between neurons just mentioned. This pattern remains fixed during the operation of the network. T h e second factor is the sensitivity of each neuron to feedback from each of the other neurons connected with it. These sensitivities are called the zoeiglits of the connections. They vary as the network is exposed to an external pattern of excitation. I n a simulated network, as in its biological counterpart, weights are adjusted at the point of contact where one neuron receives the signal coming to it from another. T h e weight at the synapse is the fraction of the incoming stirnulation the first neuron accepts from the second. Changes in the weights provide the means for the network to learn from experience. A key relation is that between the weights of the synapses of a neuron and the threshold of the neuron to external stimulation. As an illustration, we shall assume that the threshold to external stimulation of our sample neuron has a value of 1.0. One unit of external stimulation will cause the neuron to discharge. Any stimulation from neighboring neurons will lower the threshold to external stimulation to a value less than 1.0. If the neuron receives 0.2 units of excitation from its neighbors, the external threshold will decrease to 0.8. This means that the relation between the weights at the synapses and the threshold is inverse. T h e higher the weights, the greater the activation received from the neighboring neurons will be. As the charge from neighboring neurons rises, the threshold to an external stimulus will go down. network models. Nadel et al. (1989) discuss the interface betweeii computer and biological research.

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T h e functional properties of the neural network are a consequence of this inverse relationship. If the weights oii the synapses of a particular netiron are high enotigli, the neuron zuill fire zohen only its neighbors are stimtlated, zuithoiit having to be s t i m l a t e d itseq. What makes the weights change? I n the absence of biological data, Hebb proposed a learning ride that would govern the shifts in the weights when the network is stimulated. T h e Hebb rule states that the weight of a syta/itic coniiectioii betzueeii two neiirons increases zuhenever the 1ieiiro)ts are activated at the saiiie time 6y a n external soiirce. Although the Hebb rule has been superseded in computer simulations by more efficient learning rules, it is still a useful first approximation for o u r purposes here. As an example of a very simple neural network, let us take a single layered 5 x 9 grid of light-sensitive cells, like those of the human retina. Think of the letter A projected onto this grid in the following pattern: A

5

[/I[

B

C

D

E

F

G

H

I

I[ I[ I[ I[ I [ .I[ I[\]

Neurons at E l , D2, F2, etc., are activated by matching elements of the stimulus pattern. When the activated neurons fire, the output of the network replicates the original stimulus. Without connections between the neurons, the grid would just be a grid. N o trace of the A would be left in it after it was discharged. T h e image formed on the cells of the grid without connections would be a local representation of the stimulus, a pictorial image of it. A local representation reproduces the shape and

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proportions of the stimulus in the geometry of the sensitive elements that record it. There is a one-to-one relation between the stimulus elements and the recording elements. T h e output I of a mental is an exact replica of the input. O u r L I S L I ~ idea representation is very much like a local o r pictorial representation. Now consider the same one-layer light-sensitive grid with every neuron connected to every other. This very simple configuration is called an nutonssociator. According to the Hebb rule, when the A is projected onto this grid each pair of neurons stimulated by the letter will also stimulate each other. T h e weights of their mutual synapses will go up, and the threshold of each of these neurons to further stimulation from the projected A will go down. In our network the excitation transmitted from one stimulated cell to another is (let us say) 0.15. T h e increase in weights with each application of the stimulus pattern is small, say 0.01. We begin with all the weights at 0.0. \Vhen the complete A is input, the complete A will be output as well. After 67 repetitions of the stimulus, the value of the weights at each synapse of the stimulated neurons will reach 0.67. T h e total feedback each stimulated cell receives from each of the others will be 0.67 x 0.15 = 0.1. T h e total received by each neuron will be 1.1, more than the 1.0 needed for discharge. If only 11 of the 12 neurons in the A are stimulated at this point, all 12 will still receive 0.1 X 10 and will fire. The network will output the whole A even if one of the 12 elements in the projected image is missing. After 100 o r more repetitions, the weights will reach the maximum value of 1.O. Each neuron will receive the full 1.O x 0.15 units from each of the other stimulated neurons. Now all twelve of the A neurons will fire when only seven of them have been externally stimulated (7 X 0.15 = 1.05 > 1.0). T h e whole A will be output when only a fragment of it, one leg and the crosspiece, for example, has been projected onto the grid. This frngiiient will act as n.probe. When iiiptit to the neliuork, it will I-elrieve the coiiiplete iiiiuge'of zuliich it is n part.

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T h e grid, now a neural network, remembers the A. It only has to be reminded of the image of the A by the partial stimulus of leg and crosspiece to retrieve all of it. T h e memory trace of the A, as distinct from the image itself, is not a local representation. T h e one-to-one relation between the elements of the stimulus and the elements of the grid that record them has been overridden. Individual neurons can fire without having been stimulated by a n external source. T h e representation is distributed over the whole network. This distribiited representation created by the neural network shares a number of critical properties with a human memory trace. Like a human memory trace, a distributed representation can be activated and retrieved by a partial stimulus. Proust’s story of his eating a pefite mzdeleine whose taste brings back an intense emotional experience of childhood is a good example of this phenomenon. T h e neutral madeleine acted as a probe for the recovery of the complete emotion-laden experience of many years before. One can address a distributed representation by its confent. It can be retrieved without knowledge of the actual physical location of the memory trace. A fragment of the content can be used as a probe to locate the complete representation, wherever it is physically located in memory. A distributed representation can reproduce the entire stimulus even when some of the neurons in the network have been disabled. We see this effect, called grucefil degradation, in the recovery of human patients with traumatic aphasia. Graceful degradation is not so evident in the case of the single layered autoassociator, where each neuron serves both as input and output device. I n the more usual memory network with two or three layers, each input neuron stimulates a number of output neurons. Each output neuron is stimulated in turn by a number of input neurons. T h e output is a complicated function of the input, not a physical replica of it. T h e loss of a single neuron in either layer will have relatively little effect on the calculation of the output function.

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A neural network can be taught a number of similar but not identical patterns, all variations on a single prototype. It will then recognize the unlearned prototype more accurately than any of the learned variations. Taught to recognize the representations of several breeds of dog, it will create a distributed representation of a generic dog. This property, known as generalization, is familiar in human memory. Distributed representations are not confined to a single region of contiguous neurons, but can be spread out over a large and discontinuous domain. IVhiIe the input layer may have to mimic the geometry of the input stimulus pattern, as in the case of our autoassociator, other layers are not restricted in the same way. They may be distributed over large regions of the brain. What matters is the topology of the connections, not the geometry of cell location. Perhaps the most remarkable property, from the point of view of the psychoanalyst, is the ability of a neural network to store a number of distributed representations simultaneously. T h e number that can be stored efficiently in a given network depends on two factors. One is the number of neurons in the network. T h e other is the degree of differentiation among the patterns being stored. Besides the A stored in our autoassociator, we can store other letters of the alphabet as well. A is actually an ideal case, since no other letter overlaps it in more than two or three isolated points. A probe made u p of the leg and crosspiece of the A will retrieve only the A from a network containing the entire alphabet. For similar letters like 0 and D, it is not quite so simple. If the probe consists of a region unique to one of the two letters, that letter will be retrieved no matter how closely it resembles any other stored in the network. However, if the probe consists of a region common to both letters, the output of the network may be one, both, or an unpredictable conflation of the two. T h e more elements there are in the network, the more elements there will be that are distinct for each of the two representations. T h e more distinct elements there are, the easier it will be for the network to distinguish between the two patterns.

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How Far Can the Neural Nelzuorli Model Take Us? Is it possible that the entire long-term memory can be organized as a single neural network structure? T h e answer to this is no. \Vith an upper limit of about 10,000 synapses for each neuron in the brain, a fully connected network could have only that number of neurons. Perhaps a more sparsely connected multilayered network could have as many as lo5. Actual numbers in the mammalian brain are in this range. T h e hippocampal region of the rat brain is a set of interacting neural network groups. Estimates based on the functional anatomy of the hippocampus indicate a value in the range of lo5 to lo6 neurons per group (O'Keefe, 1989). With 10'" or 10" neurons in the human brain, hundreds of thousands of neuron groups this size would be available for memory. These groups would be organized as a hierarchical system, with higher-level neural network groups taking the output of lower-level groups as their input (Edelman, 1989; Finkel el al., 1989). Higher-level outputs would link the elementary units of image and affect into larger and more meaningful groupings. T h e juxtaposition of images in dreams and free associations suggests a hierarchical associative memory with a basic structure like a tree (Palombo, 1973, 1976). T h e main branches of the tree seem to connect familiar experiences that resemble one another in form o r feeling. They are usually linked by theme rather than by chronology, although later events on each branch are always further from the root of the tree than earlier ones. T h e more central elements of experience can be accessed by moving along these main branches. Many other experiences are stored in the more distal branches of the memory tree. They appear to be accessible only through association with other experiences that lie closer to the main branches. Since its direct connections with the main branches are blocked, a repressed memory acts as if it has been relegated to the outlying branches of the tree, where access is difficult and unpredictable.

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T h e initial registration of a new event in the memory tree most likely involves a number of neural network units. There is probably a separate unit (at least) for each variety of sensory experience and affective toning. I n isolation, these component neural network registers would not be able to store complete and fully meaningful psychological events. To achieve that, integration of the outputs of the component networks would be necessary. T h e difference between the contents of the basic neural network units and the fully integrated representation of the event is like the distinction between a sense datum and a percejtion. T h e perception I am speaking of here is a very complicated form, including narrative elements and affect as well as imagery. First-order neural networks would be responsible for the registration of sense data, but the integration of sense data into perceptions would take place at a higher level. (See Edelman [1989] for a theory of higher-level integration using a sophisticated neural network model of the brain.) Let me review the problem in dream theory we began with. T h e nzatching of representations of present and past events in the dream is a cognitive operation at a high level. T h e matching process would require fully integrated representations, projected as images, to be effective at this level. These arc more like perceptions, although they need not be true, if perceptions can ever be said to be true. For the seleclion of past representations to be matched with the day residues a much less discriminating procedure would be needed. T h e probing of the component neural networks by the partial stimulus of the day residue would work at the level of stored sense data. A relatively simple criterion should suffice for selecting the past representations for matching from the diverse output to the probe. Perhaps a count of component representations retrieved by the day residue would be enough. More likely, the responses from the component networks would be tallied nonlinearly through a higher-level network. Several representations of past

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events could be selected in this way for each day residue probing the system of component networks. We often see multiple past events represented in individual dreams and even more often in sequences of dreams. Freud (1900) seems to have anticipated this solution to the selection problem in The Interp-etation of Dream. In this rather uncanny passage he casts his vote for a higher-level network rather than a simple count:

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Thus a dream is not constructed by each individual dreamthought, or group of dream-thoughts, finding (in abbreviated form) separate representation in the content of the dream-in the kind of way in which a n electorate chooses parliamentary representatives; a dream is constructed, rather, by the whole mass of dream-thoughts being submitted to a sort of manipulative process in which those elements which have the most numerous and strongest supports acquire the right of entry into the dream-concent-in a manner analogous to election by scriitin cfe liste [p. 2841. Scriihi de liste, or proportional representation, is an apt

metaphor for the connectivity of the neural network, though not an exact analogy to it (Forrest, 1991). The important point is that any such mechanical method of selection would be quite primitive by information-processing standards. It takes into account only that similarities have been recognized in the probing of the component networks. It does not actually compare the contents in detail, as the dream itself will do.

The Dream Image The composite dream image would then be built up in three stages. In the first stage, as outlined above, representations of past events would be selected when they are retrieved from

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the component neural network structures by the probing day residue. We shall look at Stage I in some detail. In Stage 11, the representations selected in Stage I are completed by the activation of any remaining components that have not already been stimulated by the probe. T h e completed representation becomes a unit in Stage 11. I n the third stage, the completed representations are reconstructed as irnages in the sensory projection mechanisms. How this is done is not well understood. That it is done is clear, however. Freud discovered that every condensed image contains a reference both to the present (the day residue) and to the past (the repressed childhood wish). T h e original iinages superimposed to form the final composite can be recovered from memory in the majority of cases (Palombo, 1984b). I n Stage 111 the completed representations of past events are superimposed on and matched with the day residues. This final step takes place in the sensory projection mechanisms of the brain, where events can be portrayed in a realistic way and affects can be fully activated. This is the stage in which the actual comparison between past and present is made, the stage that corresponds most directly with Galton’s method of matching photographs. For the purpose of matching by superimposition, the distributed representations that have been so useful up to this point a r e no longer useful. Local representations, pictorial images in the case of visual data, are reconstructed to provide an integration of data that is more like actual experience. It is this step that makes dreaming unique among mental operations taking place out of conscious awareness. Nevertheless, some representations of past events that emerge from the neural networks in Stage I may already appear to be the products of superimposition at the time they become images in Stage 111. These are the conflated representations remarked on earlier. An ambiguous day residue in Stage I will cause the retrieval of a representation conflatecl from two or more distinct past events. This will lead to a situation in

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Stage I11 in which the day residue will be matched with and superimposed on conflated images of past events. A conflated representation may o r may not bc a coherent compromise between the images from which it is created. A well-known simulation by Rumelhart et al. (1986) shows the subtle effects that can occur in this situation. I n the simulation, a multilayered network is trained to identify five rooms of a typical house (an office, a living room, a bedroom, a kitchen, and a bathroom). Each room type is represented by a pattern of features, which include size, from very large to very small, structures like doors and windows, and fixtures like sinks and toilets. Furniture (beds, sofas), appliances (toasters, computers), and furnishings (drapes, pictures) are also o n the list. There are forty features in all. When the network is probed by a feature unique to one room (e.g., a bathtub), its output is a feature list typical of that room (very small, sink, toilet, etc.). T h e network includes inhibitory connections, so that a probe consisting of a feature common to more than one room will most often retrieve only one of the possible choices. For example, a single feature common to both living room and bedroom (a television) will retrieve only the feature list for the living room. This is because the activation level of the living room will be increased by connections between the television and other features of the living room. A probe consisting of two features present in the same room will retrieve the feature list of that room. A probe made u p of two features, each of which is unique to a different room, will produce a conflation. Sofa and bathtub will retrieve the feature list typical of a living room, with a bathtub anomalously and bizarrely included. However, sofa and bed will retrieve the feature list of a typical bedroom, but larger than usual, with an added grouping including sofa, easy chair, and floor lamp. In this case the network has created a new room type not preiriously learned, a bed-sitting room.

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The difference between the sofa/bathtub and the sofdbed probes is that while the sofa and bed are unique to different rooms, they each have connections with other features that are common to them both (bookshelf, desk, television). These common features serve as a bridge for the mutual activation of bedroom and living room features in the combined output. Compared with the autoassociator, this network has an active and creative aspect much more like human memory. T h e conflated representations in a dream image can be either coherent or incoherent. Coherent conflations may be the basis for the feeling of smooth narrative movement we often experience when the 'dream creates new events out of old. \\'hen conflations are not coherent, dreams may appear to be bizarre and discontinuous. i V e may now ask about the fate of conflated imagery in the matching process itself. There may be cases in which a conflatcd image is less likely to be matched with a particular day residue. T h e features similar to those of the day residue may be diluted or obscured by the conflation. However, one can also imagine that at times these features are reinforced by similarities between the conflated components, as in the case of the bedsitting room, making the match more likely to take place. Freud suggested that repressed impulses may pool their quanta of psychic energy to reach a combined level high enough to break through the repression barrier of the unconscious. Perhaps the idea that psychic energy is pooled in the id is equivalent to what we see here as the pooling of weighted connections in the neural network structures of memory.

Wlierc Docs the Cemor Intemeiie? How can we relate the information-processing aspects of dreaming and memory to the motivational concepts of psychoanalysis? This is, of course, a large question. I shall only begin to touch on it here. The action of the defenses is an example of motivated activity of particular interest to the study of dreams.

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T h e motivation of defensive activity is to reduce tlie acting out of impulses considered to be dangerous or objectionable in other ways. T h e basic mechanism common to the defenses is displacement. It achieves its purpose by substituting elements that d o not arouse anxiety for the objectionable items that raise an alarm. Although the aim is to reduce the content of relevant information carried by the objectionable item, the choice of substitutes is not completely open. Some associative continuity with the item substituted for must be maintained. My idea here is that the defenses, and the dream censor in particular, take advantage of the natural capacity of the neural network to retrieve items related to the objectionable ones but less likely to cause alarm. (For the mechanism of the dream censor, see Palombo, 1978 and 1988.) This would be a natural and economical use of background brain functioning. It would eliminate the effort that would be required to select each substitute item on a basis other than relatedness and low anxiety potential. One might go further to ask whether the neural network model suggests a n opportunity for the dream censor to intervene in the process of selection during Stage I. This would actually be a very uneconomical use of the neural network architecture. Although we might imagine that the weights of the connections in the network could be manipulated to serve the goal of repression, this is not really a practical mechanism. We might expect that the defenses modifying tlie retrieval mechanism in Stage I could react in a different way to day residues that appear to be dangerous. Perhaps the connection weights might be reduced when the potential of the stimulus to arouse anxiety is high. We might think that the learning rule could be designed to reduce the influence on the output of an objectionable input. A defensive response to the content of the day residue would require a very much more complex set of rules to modify the weights, however. One would have to imagine a single,

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simple defensive learning rule for each neural network component of memory. This would require that a multitude of individual learning rules be coordinated from moment to moment. Each learning rule would have to be independently modifiable by experience. T h e neiv learning rule would have to be able to compare the input with a large number of unacceptable examples. There is no reasonable way of setting a limit to this number. Even the moderately complicated learning rules of computer simulations seem less than likely to play a role in the brain. T h e most plausible analog in the brain for the learning rules of simulated networks is a process called long-tel-inpoteiztiation (Lynch et al., 1989). Long-term potentiation works at the level‘of cell membrane ionization. It is not a mechanism likely to respond to a perceived psychological threat. Even so, there is some reason to view the differences in the weights as a quantitative factor similar conceptually to Freud’s notion of psychic energy. The weighting of the connections in a neural network is very close to Freud’s concept of cathexis. T h e German term, Besifzting, has connotations similar to the idea of weight. Perhaps it would make sense to think of psychic energy not as an undifferentiated sort of fluid, but as a complex summation of hierarchically ordered synaptic weights. While defensive activity at Stage I is unlikely, the intervention of the defenses at Stage I1 in the formation of the dream image fits o u r usual psychoanalytic observations. T h e decision to keep objectionable ideas out of consciousness is unconscious. We learn of the decision when the unconscious material displaced from a symptom or parapraxis comes to light in free association. T h e Stage I selection mechanism is too simple to participate at this level of complexity.

Disczcssion Freud used two vivid images to illustrate his views on the storage of neiv experience. “The mystic writing pad” represents

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tlie normal situation for memory. “The cauldron of seething excitations” is more like the case where repression is dominant. Both images are suggestive of the neural network model. In the case of the mystic writing pad, Freud describes a representation of current experience sinking into a long-term storage device in the nature of a palimpsest (Freud, 1925; Hawkins, 1966; Greenberg and Leiderman, 1966; Palombo, 1985a). T h e representation loses its own identity as it is superimposed on a composite image formed by earlier superimpositions. This composite image is similar to the photographic image created by Galton’s method. T h e mystic writing pad helps us visualize the sinking of the current representation into the composite long-term structure, as does the neural network model. Unlike the neural network model, however, the mystic writing pad does not allow us to visualize the intact retrieval of the current representation at a much later date. T h e current representation can only contribute to an overall tendency of tlie system, at the cost of its individual features. T h e cailldron of seething excitations (Freud, 1923) is a more subtle case. Although “impressions” sink into it, they are not absorbed into a composite image. They remain separate and intact throughout, even though the seething cauldron image suggests a very intense kind of mixing and blending. Freud was trying to understand the emergence of repressed impulses from the id. He had several ideas about how this might happen, each of which attributed a different degree of autonomy to the impulses. T h e seething cauldron image suggests that the impulses are truly autonomous. They contain such a charge of restless and unbound energy that they simply overwhelm the repression barrier that holds them in. Freud’s idea that condensation is a method for building u p a sufficient charge of psychic energy to penetrate the repression barrier is a variant on this theme. In this version, however, the individual features of the condensed impulses would be lost as the composite was formed.

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At the intermediate level of autonomy is Freud’s idea that repressed impulses emerge from the unconscious o r the id by attaching themselves to the day residues (Freud, 1900, p. 563). Freud tried to minimize the dependence of the impulses on the day residues by declaring them “trivial” and “clear of associations.” Their function would only be to provide a disguise from the censor. But the day residues are not, in fact, clear of associations. Finally, Freud conceived of the repressed impulses as being stirred u p by specific events of the dream day. This is the sense in which the day residues act as “instigators” for the formation of the dream (Freud, 1900, p. 561). T h e recording of events of the present in which fresh impulses are acted out induces the retrieval of specific events of the past associated with similar impulses. T h e neural network model offers a biologically plausible mechanism for the last of these three hypotheses of Freud’s. T h e central issue is whether impulses can float freely in the seething cauldron, ready at any moment to begin their attack on the repression barrier. Evidence from both the clinic and the laboratory suggests that impulses are stored as action components of memories. These memories are found in associative structures that must be entered and searched before the action component of a particular memory and can be activated (Palombo, 1978, 1985b). T h e involvement of neural network mechanisms in this process would be helpful in a number of ways. T h e probing of a neural network is a much more immediate and spontaneous process than a step-by-step heuristic search of an associative network would be. T h e mobility and substitutability of impulses seen so often in clinical work may be d u e to the fact that multiple outputs can result when a network of many components is probed. T h e phenomenon of multiple outputs suggests a major role for the defenses in making the choice of outputs for further activation.

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T h e concepts of the neural network and the distributed representation have other implications for the solution of theoretical problems that go well beyond the scope of this paper. In the case of dreaming, neiiral networks help to explain the intermediate stages leading to tlie final act of superimposition that forms the dream image. However, local representations make u p the final dream product. They are drawn out of the deeper regions of unconscious mental life, so that Galton’s method may be applied in many dimensions at once, with the vividness and complexity of actual experience. REFERENCES hl. 8; BUTLER, C. (1990). A’aturalb Intelfigeiit Sjsfem. Cambridge, CAUDHILL, hlass.: hi. I. T. Press. P. hl. (1990). A h’euroconrpirtatioiinl Perspective. Cambridge, CHURCHLAND, hiass.: hl. I. T. Press. CLARK, A. (1989). Microcognition. Cambritlge, hlass.: hl. I. T. Press. EDELmW, G. (1989). The Remembered Present. New York: Basic Books. FIXKEI.,L. H. et al. (1989). A population approach to the neural basis of perceptual categorization. In Nadel et al. (1989), pp. 146-179. FORREST, D. V. (1991). Mental, neuropsychic, and brain patterns of defense. J . Amer. Acad. Psychoaiial., 19:100-124. FREUD, S. (1900). T h e interpretation of dreams. S. E., 4 & 5. (1901). On dreams. S. E., 5. (1923). T h e ego and tlie id. S. E., 19. (1925). A note upon the mystic writing pad. S. E., 19. GRAUBARD, S. R., Ed. (1988). Artificial Intelligence. Daedalrcs, 117: 1-3 12. R. 8: LEIDERXIAN, P. H. (1966). Perceptions, the dream process GREENBERG, and memory: an up-to-date version of ‘‘a note upon the ‘mystic writing pad.’ ” Comnpreheii. Psycfiiat., 7:5 17-523. HAWKINS, D. (1966). A review of psychoanalytic dream theory in the light of recent psycho-physiological studies of sleep and dreaming. Brit. J . hied. PS~CliOl.,39: 85- 104. HEBB,D. 0.(1949). The Organizatioa ofUehauior. New York: IViley. LYNCH,G. et al. (1989). Cortical encoding of memory: hypotheses derived from analysis and sitnulation of physiological learning rules in anatomical structures. In L. Nadel et al. (1989), pp. 180-224. hkCLELLAND, J. L. 8; R U ~ ~ E L H A RD.T ,E., Eds. (1986a). Parallel Disfributed Processing, Vol. 2 . Cambridge, hlass.: hl. 1. T. Press. --(1986b). A distributed model of human learning and memory. In hlcClelland 8: Rumelhart (1986a), pp. 170-215. --(1988). Explorations in Para!lel Distributed Processing. Cambridge, hlass.: hi. I. T. Press.

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