Another offshoot of this research that we would like to pursue further is developmen- tal perception. Building upon the categorical conceptual capacity research in section A.4 along with the neuromorphic principle of overlapping input fields, we have shown the development of perceptual primitives. Overlapping input fields within the same sensory modality is a principle that was partially refined out of the final embodied architecture, though overlapping input fields between distal and proximal modalities was preserved. This offshoot of research is satisfying because it allows the explo- ration of overlapping input field concepts further. An initial experiment is presented to show the promise of this avenue of future inquiry.
For this experiment, our architecture has overlapping input fields as in Figure 8.3. There are two sources of motivation for overlapping input fields, biological and categorical. We find biological motivation in the connective structure of simple cortical cells. As illustrated in Figure 8.2, simple cortical cells have rectangular receptive fields that respond to visual line segments in different orientations. Each simple cortical cell represents a different orientation. Multiple simple cortical cells,
Figure 8.1: Extended functional brain diagram from which embedded architecture is distilled.
representing different line orientations, receive stimulus from some of the same retinal ganglion cells[18]. Hence, the receptive fields of the simple cortical cells overlap. An additional function of separate neural functional subsections that take overlapping input is the implementation of parallel search for specific perceptual primitives (short line segments at specific orientations). The parallel speed-up is in comparison to one large classification system that attempts to match a set of line segments across the whole visual field at once. A general one-dimensional overlap is implemented so as not to bias the form of perceptual primitives that the synthetic organism will find. The form of overlap seen in the biological system is biased for oriented line segment identification by a simple neural perceptron. Reasonably, this form of architectural
Figure 8.2: Illustration of connection architecture between retinal ganglion cells and V1 simple cortical cells.
bias toward a certain perceptual primitive was probably developed through fitness and selection over evolutionary time.
For this investigation, we implement an architecture of multiple, relatively small, retinotopically mapped, neural modules with overlapping input fields. We use ART to classify the inputs of each module into an output vector. The experimental archi- tecture of small overlapping ART units has 32 ART units covering a 99 element array of visual sensors. The 99 visual sensor elements are uniformly distributed across the embodied synthetic organisms visual arc. Each ART unit inputs 6 visual sensor elements. Each visual sensor element has a red, green, and blue component. The inputs to each ART unit are complement coded. Therefore, each ART unit has an input vector of 36 real values. Each ART unit overlaps by half with its neighbors, with the end units only overlapping on one side. Figure 8.3 illustrates the architec- ture scheme, though not the specific number of ART units and inputs used for this experiment.
Figure 8.3: Simple one layer architecture, overlapping input fields.
world in the ART units, we non-parametrically cluster all formed templates. Nearest neighbor clustering is used on the vector of connection weights that encodes each template. Clusters represent perceptual concepts formed in the ART units. Analysis of which ART units have a member template in a given cluster provides information as to whether the cluster concept is a universal concept in the context of the simulated world, or if it is a somewhat unusual concept only seen in certain conditions and not likely to be useful as a perceptual primitive.
The more ART units in which a particular template is found, the more likely that template is to represent a perceptual primitive. For instance, if a line segment is a perceptual primitive for a particular system, then we would expect to see neural pat- terns that represent line segments occurring in locations throughout retinotopically mapped visual cortex. In biology, the simple cortical cell encodes line segments and is found encoding line segments is all locations throughout the visual field[18].
In section 8.7, the percentage of ART modules in which each template cluster is found is analyzed. In biological visual cortex, it would be expected to find the equiv- alent of clusters that represent oriented lines in 100% of visual field subsections. If our architecture displays common templates across its visual field, then a reasonable claim can be made to an analouge of developmental perception. The developmental claim is valid in part because the ART modules are self-organizing, they start with no designed in notion of a common perceptual primitive. Therefore, any common perceptual primitives which from across sections of the visual field must be a result of the simulated developmental process.
The developmental perception analysis looks for evidence of perceptual primitives formed in our artificial neural architecture. Each template, in an ART that encodes
a portion of the visual field, represents a certain pattern of inputs in that visual sub-field. Templates are encoded as weight vectors in the ART modules. As such, it is possible to take a measure of euclidean distance between two templates. With a distance measure, it is possible to cluster templates, and that is how we analyze the results.
Figure 8.4: Percent of templates represented per cluster, 30 degree visual arc.
Figure 8.5: Percent of templates represented per cluster, 1.25 degree visual arc.
Figures 8.4 and 8.5 show the percent of all ART units that have at least one member template in a given cluster for two different visual arc angles. We see a number of clusters with members in all ART units. These clusters give evidence that universal perceptual primitives are represented. With evidence that perceptual primitives can be represented at the lowest level of the architecture, more complex architectures can be considered.
As a corollary to the investigation on developmental perception, experiments are performed on a variation of ART that implements forgetting. An additional constraint is applied to the environment where ART is used in an embodied system. Our system is resource limited and therefor has a maximum number of F2 nodes with which to form abstractions. The main consequence of this constraint is realized when highly infrequent, and unusual, inputs are considered. Highly infrequent means that the inputs may be encountered only a few times in a simulated lifetime. Unusual
means that the inputs are not similar to any other inputs encountered. The ART orienting subsystem will make it likely that any unusual input gets a new abstraction. If that input, or anything like it, is never observed again, then the F2 node used to form it’s abstraction is wasted in the context of the rest of the system’s life and a fit system with constrained resources would forget.