The neuron doctrine incorporates an atomistic assumption, that neurocomputation involves discrete signals communicated along fixed transmission lines between discrete computational elements. The western scientific tradition has a particular bias in favor of an atomistic view, not only of neurocomputation, but of the principles of computation in general. For the reductionist approach favored by western science involves breaking complex problems into simpler pieces which can then be analyzed individually. That is why the discovery by Cajal of the discrete cellular structure of the nervous system triggered an intensive program of investigation of the properties of the individual neuron, in the hope that this would shed light on the operation of the larger nervous system made up of those elementary units. There are certain classes of physical systems for which this analytical approach works well, specifically, for systems whose component elements operate relatively independently, i.e. when the processes and mechanisms active within the element are more closely coupled than those that operate between elements. However this atomistic view of neuroscience is nothing more than an optimistic assumption, because atomistic systems are incomparably easier to study and to characterize mathematically than are holistic or widely coupled and dynamic feedback systems. But although it would be very convenient for neuroscience if the brain worked on an atomistic principle, Gestalt theory has demonstrated with a great variety of different phenomena that this is in fact not the case, and that the brain operates on a holistic, emergent principle of computation.
Further evidence for holistic processes in the brain comes from electro-encephalogram (EEG) recordings which reveal global electrical oscillations that pervade the entire cortex. This global resonance is now beginning to manifest itself also in neurophysiological recordings in the form of synchronous oscillations. In fact the synchronous spiking of remote cortical neurons is difficult to account for in conventional neural terms, because the phase of the spiking signal should become scrambled as it propagates down the axon collaterals and dendrites of the pre- and post-synaptic cells respectively, due to the random thicknesses and irregular path lengths of those many parallel branches. The synchrony should be further disrupted by the spike train integration across the chemical synapses, each of which acts as a low-pass filter, blurring the sharp spike of the pre-synaptic action potential into a smooth rise and decay in the post-synaptic cell. The fact that a high resolution temporal synchrony is observed across remote cortical areas connected by countless parallel paths through countless synaptic junctures suggests that this synchrony is actually transmitted by some other means. In fact it has been shown (Pribram 1971, Bland et al. 1978) that the discrete spiking of the action potentials is superimposed on a more subtle graded potential oscillation, and Pribram (1971) showed that the graded potential oscillation persists even when the spiking discharge falls below threshold. This suggests that the spiking discharge is not the causal origin of the neural signal, but merely the overt manifestation of a more subtle underlying electrical oscillation, like the white caps on ocean waves, and that oscillation seems to pervade the neural tissue unrestricted by the boundaries of the cell. The whole concept of the neuron doctrine has blinded neurophysiologists to the possibility of significant signals that pervade the extracellular matrix, for it is assumed that signals which are neither channeled by the cell wall, nor gated by the chemical synapse, cannot possibly take part in meaningful computation. The Gestalt perspective on the other hand suggests that it is just that kind of holistic field-like process which must be sought out to account for the most significant and interesting aspects of neurocomputation.
The principal reason for the demise of the Gestalt movement was its failure to specify the vague holistic aspects of perception that it identified in more rigorous quantitative terms, in a manner that relates to known neurophysiology. In another paper (Lehar 2000) I have specified the elusive holistic Gestalt principles somewhat more precisely as the principles of emergence, reification, multistability, and invariance. In that paper I proposed a computational model of perception to demonstrate how those same Gestalt principles can serve a useful computational function in perception. However that model was expressed in terms that are independent of any neurophysiological assumptions. The objective of the present paper is to propose how those Gestalt aspects of perception can be related to our understanding of neurophysiology, in order to develop a neurophysiologically plausible Gestalt theory of neurocomputation.
The most significant general property of perception identified by Gestalt theory was the property of emergence, whereby a larger pattern or structure emerges under the simultaneous action of innumerable local forces. Koffka (1935) suggested a physical analogy of the soap bubble to demonstrate the operational principle behind emergence. The spherical shape of a soap bubble is not encoded in the form of a spherical template or abstract mathematical code, but rather that form emerges from the parallel action of innumerable local forces of surface tension acting in unison. The fine-grained and continuous character of emergence across both space and time is fundamentally at odds with the atomistic notion of neurocomputation embodied in the neuron doctrine.
Reification is the constructive, or generative aspect of perception identified by Gestalt theory. Reification is seen in visual illusions like the Kanizsa figure, where the subjective experience of the illusion encodes more explicit spatial information than the stimulus on which it is based. Specifically, illusory edges are seen in places where there are no edges in the stimulus, and those edges bound a continuous surface percept whose illusory brightness pervades the entire illusory surface as a spatial continuum. Reification in perception indicates that perception is not merely a passive process of recognition of features in the visual input, as suggested in the neuron doctrine, but that perception creates the perceived world as a constructive or generative process.
Multistability is seen in a variety of visual illusions, including the Necker cube, and Rubin's figure / vase illusion. The significance for theories of perception is that it reveals perception as a dynamic system whose stable states represent the final percept. Multistability and reification work hand-in-hand, because each perceptual state is reified as a full surface or volume percept in each of its alternate states, i.e. the subjective reversal of a figure like the Necker cube is not experienced as a change in a cognitive interpretation, or the flipping of a single cognitive variable, but is vividly experienced as an inversion of a perceptual data structure, changing the perceived depth of every point in the perceived structure.
A central focus of Gestalt theory was the issue of invariance, i.e. how an object, like a square or a triangle, can be recognized regardless of its rotation, translation, or scale, or whatever its contrast polarity against the background, or whether it is depicted solid or in outline form, or whether it is defined in terms of texture, motion, or binocular disparity. Invariance is also seen in the perception of color and brightness, where the color of an object is generally judged independent of the color of the light falling on it. Recognition is also invariant to elastic deformation of non-rigid objects, for example animal bodies are recognized independent of their postural configuration, and faces are recognized despite distortions imposed by facial expressions, or even more extreme distortions often observed in caricatures. Even normally rigid objects like houses or cars are recognized in deformed form, as when seen through distorting mirrors or lenses, or as often depicted in cartoon renditions. Although isolated counter-examples exist, for example the recognition of complex figures and of faces is not completely rotation invariant, the fact that invariance is observed through so many stimulus variations and across such a wide variety of perceptual modalities suggests that invariance is fundamental to perception, and therefore reflects a fundamental characteristic of the mechanism of biological computation.
One of the most disturbing properties of the phenomenal world for models of the perceptual mechanism involves the subjective impression that the phenomenal world rotates relative to our perceived head as our head rotates relative to the world, and that objects in perception are observed to translate and rotate while maintaining their perceived structural integrity and recognized identity in their motions through the perceived world. If we assume that the structural percept of the world is represented by a spatial pattern of activation of some sort in the tissue of the brain, this suggests that the internal representation of external objects and surfaces is not anchored to the tissue of the brain, as suggested by current concepts of neural representation, but is free to rotate and translate coherently relative to the neural substrate, as suggested in Köhler's field theory (Köhler & Held 1947). In other words the perceptual picture of the world can move relative to the representational substrate, and discrete patterns of perceptual structure can move relative to that background while maintaining their perceptual integrity and recognized identity.
It is small wonder that in the face of this formidable array of most enigmatic properties, theories of vision have generally been restricted to simplistic models of isolated aspects of the problem in a piecemeal manner. This does not however in any way justify the fact that the Gestalt properties of perception, discovered and identified almost a century ago, are so under-represented in contemporary theories of neurocomputation. Our failure to find a neurophysiological explanation for Gestalt phenomena does not suggest that no such explanation exists, only that we must be looking for it in the wrong places. The enigmatic nature of Gestalt phenomena only highlights the importance of the search for a computational mechanism that exhibits these same properties. In fact, any model that fails to address the Gestalt phenomena of perception is worse than no model at all, for it is a diversion from the real issues of perception.