Ever since Ramon y Cajal demonstrated that the nervous system is composed of discrete cells rather than a continuous network, the neuron doctrine (Barlow 1972, 1995, Shepard 1991) has emerged as the dominant paradigm of neurocomputation. The rapid propagation of ions through the intracellular fluid, in combination with the relatively slow transmission across the chemical synapse suggest that the neuron behaves as a quasi-independent processor that responds to the input signals received through its dendrites to produce an output signal through the axon and its collaterals. Hodgkin & Huxley (1939) demonstrated that the frequency of action potentials correlates with the input applied to the cell presynaptically. This in turn suggests a rate code, in which the significant signal is not carried by the individual action potentials, as had been previously assumed, but by the frequency of their occurrence.
Neuroscience experienced something of a revolution with the discovery by Hubel and Wiesel of cells in the visual cortex that respond to particular features presented at specific locations in the visual field. Some cells were found to respond to simple features such as a local edge of a particular location and orientation, while other cells had more complex response functions, as if in response to spatial combinations of simple cell responses. Eventually whole hierarchies of cells were identified in various regions of the visual cortex, with cells in the higher cortical areas responding to ever more complex combinations of lower level primitives. This data suggests that the cortex has a hierarchical organization that encodes the presence of particular patterns in the visual field by the activation of lower and higher order cells tuned specifically to those patterns.
The computational principle behind these cortical feature detector cells has also been proposed. Hubel (1988) suggests that lower level feature detectors are triggered by visual edges by the same essential principle as that used in edge detectors employed in computer image processing, i.e. each cell is equipped with a receptive field whose spatial pattern of excitatory and inhibitory synapses match the spatial feature that the cell is tuned to detect. In essence these lower level feature detectors perform a local template match to the pattern of activation detected in their input field, with the pattern of synapses in the dendritic field acting as the spatial template. Higher order cells respond to more complex combinations of lower level features by being connected to the corresponding lower order feature cells by way of the appropriate pattern of excitatory and inhibitory connections.
At the highest levels of the cortex information is presumed to be encoded by a massively interconnected network of cells, each cell representing a complex combination of lower level featural primitives (Barlow 1972). The higher cortical neurons each represent some aspect of the complex perceptual and cognitive experience, and conversely, any particular experience is represented by a characteristic constellation of innumerable activations of higher cortical neurons.
One essential aspect of this paradigm of representation is that extended elements of perceptual experience, such as the perception of whole objects in a scene, are encoded in a compressed manner in higher cortical centers by the activation of a single cell or small set of cells dedicated individually or collectively to the representation of those objects. The number of cells required to encode that object at the higher level therefore is much smaller than the number of lower level cells that encode the object's component features. In other words information is progressively reduced or abstracted as it progresses up through the cortical hierarchy, from the more explicit primary areas to the more abstract association areas. There is a sequential progression implied in this paradigm of representation, with information flowing bottom-up from primary to higher areas, although reciprocal feedback pathways implicate some kind of top-down process, presumably for the purposes of cognitive expectation and perceptual completion.
Another essential aspect of the neuron doctrine is its fragmentary or distributed nature. For although the primary cortical areas reveal topographical maps of the sensory world, as in the primary visual and somatosensory cortices, higher cortical areas are fragmented into multiple cortical maps of those same sensory areas, as in the secondary visual and somatosensory areas. Each of these multiple copies of the same sensory field appears to be specialized for the representation of particular aspects or modalities of that sensory experience, such as color, shape, motion, and binocular disparity. Even within each of these maps, the features are represented in fragmentary form, with separate cells dedicated to encoding features of different orientations, shape, binocular disparities, color, directions of motion, etc. at every location within that visual map. This is suggestive of an analytical representational strategy in which the sensory world is broken down into its component features, each of which is represented by distinct cortical mechanisms tuned to detect those features.
The neuron doctrine is by no means universally accepted in neuroscience. Alternative paradigms have been proposed, such as Köhler & Held's electric field theory (Köhler & Held 1947), Pribram's holographic theory (Barrett 1969, Pribram et al. 1974, Pribram 1999), De Valois & De Valois' theory of Fourier coding (De Valois & De Valois 1979, 1988), von der Malsburg's temporal correlation hypothesis (von der Malsburg & Schneider 1986, von der Malsburg 1987), Penrose's theory of quantum consciousness (Penrose 1989, 1994), Harrison's and Smythies' theory of consciousness in hidden dimensions (Harrison 1989, Smythies & Beloff 1989, Smythies 1994), to name a few. However none of these paradigms has ever been worked out in enough detail to specify exactly how perceptual information is encoded or processed in the brain. By contrast, the neuron doctrine has the distinct merit of being clear and explicitly defined, and therefore amenable to quantitative computer simulations. Therefore in the absence of a more viable alternative, the neuron doctrine remains to this day the dominant paradigm of neurocomputation, and much of the contemporary research, and discussion of the results of that research, is based implicitly or explicitly on the assumptions of this paradigm. The neuron doctrine is also fairly consistent with contemporary understanding of the neuron at the cellular level. However this concept of neurocomputation has some serious shortcomings that come to light when considering a larger systems level of analysis of brain function.<
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