3.8 Recapitulation and Conclusion

old. When synaptic input reaches the (spiking) threshold, the spikegenerating unit will trigger a spike and reset the activity level back to 0 (ground). If desired, a refractory period can be simulated by clamping the activity level to 0 for a short while. This model just suffices to simulate simple synaptic integration. A more elaborate variant is the leaky I&F neuron (figure 20b), which consists of the perfect I&F circuit plus a membrane resistor. Or put with above terminology, it is the RC circuit plus a spiking unit. The membrane resistance has the effect that any input is only transient, which renders this model much more realistic than the perfect I&F model. This model is for example suitable for detecting coincidences amongst synaptic input.

3.8 Recapitulation and Conclusion

The experimental results in visual systems neuroscience are fascinating but remain puzzling as a whole. The innumerous recordings and measurements have been interpreted in many different ways and have not led to a unifying picture of the recognition process but rather to a continuing diversification in theories and approaches. The prevailing idea of feature integrating does not directly help us out in specifying the loose category representations we look for and that is primarily for two reasons:

1) If the features found in the visual field are integrated, then a single category instance has been described, an object of the identity level in some sense (figure 3). What would have to follow that description, is a continued evolvement leading to the abstract category representation, which is able to deal with the structural variability.

2) Most of the feature or object detectors found in higher areas do not exactly represent structure seen in the real world. The exception are a few neurons firing for real-world objects, yet they hardly represent abstract representations but rather objects of the identity level (figure 3). The feature integration concept would be much more compelling if there were neurons that fired for different instances of the same category, for example a cell responding to structurally different trees, fruits or flowers.

The neural networks performing this feature integration do not make an explicit distinction between representation and evolvement - as opposed to the computer vision systems reviewed in the previous chapter. Evolvement is representation and vice versa. The strength of many of these neural network architectures is that they learn automatically after some initial help. Their downside is that their neuronal units or connections share too many object representations and this shared substrate leads to a considerable overlap. Thus, they are structurally not specific enough to represent different categories. In order to differentiate between structurally more diverse objects, a network has to evolve structurally more distinct features and contain

distinct representations. The cases of visual agnosia suggest that the visual system may indeed have mechanisms that perceive specific structures and that object representations maybe spatially very distinct.

The low spiking frequency found with complex stimuli presentations stands in apparent contrast with the lightning processing speed and casts serious doubts on the prevailing rate-code concept. Several researchers have therefore looked for alternative codes, ranging from wave propagation, over to timing codes, to cortical potential distributions. As we have already pointed out in chapter 1, we also consider speed a pivotal characteristic that needs to be explained when one attempts to unravel the evolvement and representation of the visual recognition process - and likely any computation in the nervous system.

If one adopts the idea that waves perform computation - as we will do later - then one may regard a spike merely as part of a wave running across the neuron. In order to account for the processing speed, such a wave would need to be fast. We argue in our later chapters - in particular chapter 7 - that fast waves may exist (section 7.5). Taking the ‘wave view’, the occasionally observed high firing frequencies (for simple stimuli) may then be interpreted as the continuous accumulation (or collision) of waves, because the visual system is idling due to the lack of meaningful input.

The detailed function of the neuron remains also an enigma, despite quite a number of experiments and model simulations: Is it a simple integrator? A sophisticated calculator? A coincidence detector? Is its dendrite a fine-tuned ‘spatio-temporal’ filter? In the remaining chapters we will only make use of the ‘crude’ I&F neuron type, not because we think that is what the neuron can be reduced to, but because it suffices for the wave-propagation processes we will describe in later chapters.