12.2 Contour Extraction and Grouping

actions like ‘grab with fingers’,‘move to mouth’,‘bite’. Neither the chair, nor the apple has to be structurally verified during visual recognition. In short, perceptual category representations would directly trigger motor frames. A structural verification would only seldomly occur because there may not be any particular need for it.

12.2Contour Extraction and Grouping

To extract contours we have used a single neuronal layer with local connections and adjustable spiking thresholds or spike latencies. The resulting contour image looks like a contour image obtained with a computer vision algorithm. The contour image is sufficient for encoding the regions which are necessary to perform a perceptual categorization. One may seek to improve the contour image by applying perceptual grouping processes that are either purely computationally motivated, as Marr did (Marr, 1982), or that are motivated by psychophysical studies on contour grouping. There is a good amount of psychophysical work that may be easily applicable for that purpose (e.g. (Hess and Field, 1999; Bruce et al., 2003)). Such aspirations should not however result in the pursuit of an immaculate contour image. As we have already pointed out, there is enough region information in the contour image as it is obtained with our retina circuits. The long-term effort should be rather spent in finding efficient region and category representations.

12.3Neuroscientific Inspiration

Much of current computational neuroscience has focused on unraveling the spike code that the brain maybe using (Dayan and Abbott, 2001; Gerstner and Kistler, 2002; Koch, 1999; Trappenberg, 2002). Given the blazing speed with which the brain operates and given that neurons fire at a low frequency (sections 3.2-3.4), it may well be that the brain does not use a spike code at all. Because the computations in this book are done with waves, the viewpoint that one therefore may adopt is that spikes appear as part of a wave: a wave propagating through the neuron, or a wave triggered by the neuron. With waves we have solved tasks like contour detection, contour binding and speed estimation, whereby the specifics of the implemented waves are variable: To signal contours, the wave is a charge-propagation mechanism; to encode space - or to bind contours -, the wave is an actively propagating wave; to estimate speed, the wave is inert and responds to preferred speeds only. For contour detection and speed estimation, the wave does not have to be particularly fast, because both tasks could possibly be performed within milliseconds. In contrast, for region encoding of the SAT a wave needs to propagate rapidly. We argued that fast waves may run through cortical areas (section 7.5).

On the other hand, if the visual system used something like contourpropagation fields, then a wave would not have to be as fast due to the wide-spread representation of the shape. The idea that the brain operates according to some broadcast receiver principle, is certainly unusual too for contemporary computational neuroscience; but given that we have solved some tasks in a relatively simple way with waves, we can only encourage other neuroscientists to consider this viewpoint as well.

The neuronal models we have used in our neuromorphic simulations are merely integrate-and-fire neurons, which are embedded into excitable maps (propagation maps). Their parameter values were sometimes tuned to make the model operate as coincidence detectors that sense when two waves collide (chapter 7). Again, this is not to say that the neuron’s function can be reduced to this simple model. The neuron’s anatomical and physiological diversity may well be the cause for a variety of distinct wave propagation characteristics.

12.4Neuromorphic Implementation

We have not presented any analog hardware implementation of our envisioned wave-propagation mechanisms. But they are conceptually simple enough to be implementable with the existing ‘silicon ingredients’, as for example the circuits presented in chapter 4, which mimic synaptic responses, dendritic propagation and somatic spiking. The wiring substrate, that would enable communication between maps, also already exists: a multichip architecture - the silicon cortex - provides the ‘fluent’ communication between analog chips (section 4.5). A first step towards a neural hardware realization of our networks would be to build a wave-propagating map as presented in section 6.3, from which one would derive the various variants. It is this analog hardware that allows for a timeand energy-efficient emulation of our wave-propagating maps. The computation of such wave maps in digital computers is and will always be too slow or too energy-consuming. It is therefore the neuromorphic hardware approach that offers the best solution to our envisioned wave networks.

12.5Future Approach

Short term One short-term goal is to refine some of the map simulations presented in this essay and I am currently in the process of doing so. A next step would be to implement those maps. But even if some of those proposed maps are implemented, it likely requires another round of simulations to ensure that they properly operate in the silicon cortex. This may not be only a minor technical issue, but may also require extensive adjustment and tuning of the dynamics of the respective maps. For example, the SAT architecture (figure 36) or

the CPFM system (figure 51), seem straightforward at first, but may bear some intricacies regarding the matching of the map dynamics. Another short-term goal should therefore be the establishment of a soft-ware simulation methodology that guarantees that the envisioned architecture is also transferable into a silicon cortex system.

Long term The challenge of finding the loose basic-level category representations is too vast, that one can give a detailed, meticulous plan on how to proceed. But we have given two broad approaches which may benefit from each other, or may be even converge. One approach was envisioned as the hybrid categorization system in which a neuromorphic front-end extracts contours and encodes regions by the SAT; a computer vision back-end would associate the generated symaxes (chapter 7). This system can be particularly useful for exploring certain ‘high-level’ aspects; like the degree of looseness necessary for representing the part-alignment variability and part redundancy. After this looseness has been further characterized, the appropriate networks can be designed. The existent scene recognition approaches (section 2.3.4) may thereby be helpful in determining this looseness - even though they do not exploit the idea of encoding space as we have pursued it here. Thus, we regard the combined employment of computer vision methods and neuromorphic methods as a possibly fruitful approach to explore and develop the future architecture necessary for recognition.

The second approach is the pursuit of a pure neuromorphic system using the CPFM system as a basis (chapters 9 and 10). This approach encodes the region completely as opposed to the SAT. The system learns simple shapes in a single pass and recognizes them even under noisy circumstances. If this network can be extended to basic-level objects, may be with insight from the hybrid approach, then that would be a formidable starting point for the construction of a self-organizing neuromorphic visual system. The most reasonable route would be to extend this system and to make it succeed on linedrawing objects similar to the ones used in chapter 5; then one would further refine the system and make it functioning with gray-scale image input.

Thus, regarding the search for basic-level category representations, the Odysee continues in some sense. But a significant part of it has been completed by constructing networks that encode space. The remainder of the Odysee is now rather a directed one, an exploration towards associating regions somehow.

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