Cortical Integration: Possible Solutions to the Binding and Linking Problems in Perception, Reasoning and Long Term Memory
Nick Bostrom
Page 3
Source: http://www.nickbostrom.com/old/cortical.html
4. Integration through synchronization
4.1 The idea
The idea that binding might be achieved in the brain by means of temporal co-ordination of spiking activity was first formulated, not very explicitly, by Milner, and later by Von der Malsburg (1981). It is only in later years, however, that it has been subjected to serious empirical investigations and simulation studies. The reason for this delay is that informative multi-electrode recordings have been technically difficult to perform and evaluate. Another factor for the delay is probably that the original idea was not sufficiently elaborated, so that it was not clear exactly what was supposed to be bound to what, or which areas in the brain that were involved. There is still much to be worked out on this theoretical level before we can say that we have even the outlines of a theory about how the linking and binding problems could be solved by synchronization. We shall return to this issue in section 6. For present purposes, it may suffice to think of the synchronization proposal as the hypothesis that some complex representations consist of distinct groups of activated nerve cells that are firing in synchronization with each other but not with other active cells in their neighborhood.
4.2 The advantages of synchronization
Apart from being a way of dealing with the problems of linking and binding, synchronization would bestow several other advantages onto the nervous system. Let us just list them here; we shall later see how they relate to the evaluation of the evidence for the synchronization hypotheses.
First: if what makes a subset of neurons in a population an active representation is the synchronization of their activity, rather than their level of activation (i.e. firing rate), then the level of activation can be invested with representative significance. For example, in sensory processing, the firing rate could conveniently be used to represent the intensity of the stimulus, or the degree of fit between stimulus features and perceptual categories.
Another advantage is that the synchronization mechanism might increase the processing capacity of cortex through enabling multiple representations to be simultaneously active in the same territory without fusing. On a coarse time scale (>40ms) they are overlapping, but on a finer scale they are alternating. Not only will this provide for more active patterns at any given time, but it will accommodate several patterns at the same location, which might be important for some types of operations and comparisons.
Moreover, co-ordinating spiking can serve to increase the impact of the regimented group of neurons. This might be necessary to achieve an immediate and certain effect in such a noisy environment as the central nervous system, where the contributions of individual neurons or even of groups of neurons are easily lost, unless they all arrive at their target cells within a narrowly defined time interval.
A fourth advantage has to do with quickness of operation. Since an excited neuron typically continues to respond for several 100 ms, an unsynchronized network could have difficulty going through more than five or six effective updating cycles per second. With synchrony, however, a neuron could be virtually switched off in a few milliseconds by having its firing schedule displaced with a small phase term; the neuron could continue to fire, but it would no longer exert a significant influence as it would be out of phase with the other neurons.
This brings with it yet another bonus. Since the total incoming activation to a cell at any given moment is very much greater if the inputs are synchronised and get there at exactly that moment, rather than unsynchronized and arriving at all times, the network can use a double threshold function to make fine temporal distinctions in its synaptic modifications. If the activity is lower than the lower threshold, no changes are made. If it is higher than the lower threshold, but lower than the upper threshold, then the synapses undergo long-term depression (LTD). If the activity exceeds the upper threshold, the synapses get potentiated (LTP). This means that in-phase inputs will be enhanced; out-of-phase inputs depressed; and unsynchronized input channels will remain unmodified. This arrangement reduces the effects of irrelevant noise on learning and makes modifications especially tailored to the essential factors that influenced the neuron at the time of the probing. Neuronal learning in accordance with these principles has been observed; for a review see Singer(1990).
Synchronization also suggest itself as a way of organizing attention. It is not yet known whether the thalamic nuclei, or the basal ganglia, play a role in achieving synchronization in cortico-cortical activity, but if it turns out that it does, then this would be an obvious candidate to serve as an attention mechanism. With the synchronization being driven both from the local area in cortex and from deeper regions of the brain, we would have a physical correlate to the psychological endowment that allows attention to be shifted either by will or by the intrinsic salience of the stimulus. For example, a stimulus could attract our attention by being so intense that its representation in the early processing is active enough to win the competition for impact on a higher level of processing. Or the stimulus could include an organized movement, for instance, that would tend to synchronize the cells in visual cortex and thereby increase the likelihood that their influence become predominant for the continued abstract processing. An finally, there is also the possibility that input from the thalamic nuclei or the basal ganglia prejudices the probability that synchronization should occur among certain groups of cortical cells, which would in effect mean that the system were actively searching for particular patterns or concentrating on some aspects of its perceptual field.
4.3 Review of some simulation studies of synchronization as a binding mechanism
Computer simulations aimed at cast light on the feasibility of achieving linking and binding by means of synchronization range form highly idealized models to ones that pay close attention to the detailed organization of the central nervous system. The highly idealized models of brain function can sometimes acquire relevance for neuroscience through discovering or illustrating computational principles which may also be employed by the brain, though presumably the implementation is rather different from how it is done in the simulation model. More realistic models can test whether a certain algorithm is efficient for dealing with the sort of tasks the nervous system is good at solving; if so, then this suggests that we look further into the matter to see whether Evolution has struck upon the same solution. Models that are still more realistic can be used to determine constants which can be directly compared to empirical data.
A model by Mani et. al (1993) belongs to the first category, the one with the high degree of idealization. It is a hybrid system for knowledge representation and abstract reasoning based on a type hierarchy and a rule-based reasoner. It involves synchrony as a means of binding variables. The system makes swift deductions and the time it takes to answer questions is independent of the size of the knowledge base. There are, however, limitations as to the sort of sentences it can handle, and it is not clear what biological entities in the brain are supposed to correlate to the various elements of the model. The issue of learning is not addressed.
Nenov & Dyer (1993, I & II) have developed a system they call DETE, which exhibits many features which are biologically realistic compared to Mani's et. al system. DETE is also a neural/procedural hybrid. In its structure and performance, however, it is more realistic than Mani's et al. model. DETE is set a twofold task: to verbally describe elements of its two-dimensional blob world, and to ostend objects in the blob world in response to verbal input. Its problem situation is similar to that of on infant learning to speak. Corresponding to objects like "Mama", "ball", "milk", in the infants environment, DETE's blob world contains simple geometrical shapes of varying size, colour and motion. These distinct types of features are projected onto several feature planes by a procedural mechanism. Thus there is one feature plane where the shapes of the objects are represented as distinct spots of activity, another feature plane that encodes information about their direction of motion, and so forth. The binding problem arises here as the necessity of somehow keeping track of which activation spots in one feature plan are caused by the same object as which patterns on another feature plane. DETE handles this binding problem by means of having the ontologically related representations arranged to fire in synchrony. This is done by a procedural mechanism. The resulting performance is quite impressive: DETE uses a moveable token in the blob world, representing a hand, to single out or manipulate objects in response to sentences like "Push left the red square.". It also has circle in the blob world which represents its focus of attention, and the position and size of which can be determined by the experimenter. For example, by having the attention window set successively to different parts of the blob world, DETE generated the sentence "Two objects. Medium circle in the center. Small blue square up right." More challenging sentences involving bouncing objects were also successfully processed.
While DETE is a quite powerful and flexible, its efficiency is to a considerable extent due to clever hard-wiring. The procedurally managed distribution of feature representations to the proper feature planes unloads the neural system of part of the learning burden. It is indicated, however, that these procedural mechanisms could be replaced by parallelized processing in forthcoming developments. Additional biological realism could also be added by having the verbal input presented in a rawer form, so that neural modules had to pre-process it for phoneme extraction, word recognition etc. This seems relatively straightforward in principle. A potentially more problematic feature is the employment of procedural mechanisms to determine the phase of the activation patterns on the various feature planes. This solution to some extent begs the question, if what we wanted to find out was whether synchronization would be an efficient and biologically realistic way of modelling the brain's way of integrating neural representation.
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