Modulating the granularity of category formation by global cortical states

The unsupervised categorization of sensory stimuli is typically attributed to feedforward processing in a hierarchy of cortical areas. This purely sensory-driven view of cortical processing, however, ignores any internal modulation, e.g., by top-down attentional signals or neuromodulator release. To isolate the role of internal signaling on category formation, we consider an unbroken continuum of stimuli without intrinsic category boundaries. We show that a competitive network, shaped by recurrent inhibition and endowed with Hebbian and homeostatic synaptic plasticity, can enforce stimulus categorization. The degree of competition is internally controlled by the neuronal gain and the strength of inhibition. Strong competition leads to the formation of many attracting network states, each being evoked by a distinct subset of stimuli and representing a category. Weak competition allows more neurons to be co-active, resulting in fewer but larger categories. We conclude that the granularity of cortical category formation, i.e., the number and size of emerging categories, is not simply determined by the richness of the stimulus environment, but rather by some global internal signal modulating the network dynamics. The model also explains the salient non-additivity of visual object representation observed in the monkey inferotemporal (IT) cortex. Furthermore, it offers an explanation of a previously observed, demand-dependent modulation of IT activity on a stimulus categorization task and of categorization-related cognitive deficits in schizophrenic patients.

Oncocytic ependymoma: a new morphological variant of high-grade ependymal neoplasm composed of mitochondrion-rich epithelioid cells

Oncocytomas are defined as tumors containing in excess of 50% large mitochondrion-rich cells, irrespective of histogenesis and dignity. Along the central neuraxis, oncocytomas are distinctly uncommon but relevant to the differential diagnosis of neoplasia marked by prominent cytoplasmic granularity. We describe an anaplastic ependymoma (WHO grade III) with a prevailing oncocytic component that was surgically resected from the right fronto-insular region of a 43-year-old female. Preoperative imaging showed a fairly circumscribed, partly cystic, contrast-enhancing mass of 2 cm × 2 cm × 1.7 cm. Histology revealed a biphasic neoplasm wherein conventional ependymal features coexisted with plump epithelioid cells replete with brightly eosinophilic granules. Whereas both components displayed an overtly ependymal immunophenotype, including positivity for S100 protein and GFAP, as well as "dot-like" staining for EMA, the oncocytic population also tended to intensely react with the antimitochondrial antibody 113-1. Conversely, failure to bind CD68 indicated absence of significant lysosomal storage. Negative reactions for both pan-cytokeratin (MNF 116) and low molecular weight cytokeratin (CAM 5.2), as well as synaptophysin and thyroglobulin, further assisted in ruling out metastatic carcinoma. In addition to confirming the presence of "zipper-like" intercellular junctions and microvillus-bearing cytoplasmic microlumina, electron microscopy allowed for the pervasive accumulation of mitochondria in tumor cells to be directly visualized. A previously not documented variant, oncocytic ependymoma, is felt to add a reasonably relevant novel item to the differential diagnosis of granule-bearing central nervous system neoplasia, in particular oncocytic meningioma, granular cell astrocytoma, as well as metastatic deposits by oncocytic malignancies from extracranial sites.

Model-Centric, Context-Aware Software Adaptation

Software must be constantly adapted to changing requirements. The time scale, abstraction level and granularity of adaptations may vary from short-term, fine-grained adaptation to long-term, coarse-grained evolution. Fine-grained, dynamic and context-dependent adaptations can be particularly difficult to realize in long-lived, large-scale software systems. We argue that, in order to effectively and efficiently deploy such changes, adaptive applications must be built on an infrastructure that is not just model-driven, but is both model-centric and context-aware. Specifically, this means that high-level, causally-connected models of the application and the software infrastructure itself should be available at run-time, and that changes may need to be scoped to the run-time execution context. We first review the dimensions of software adaptation and evolution, and then we show how model-centric design can address the adaptation needs of a variety of applications that span these dimensions. We demonstrate through concrete examples how model-centric and context-aware designs work at the level of application interface, programming language and runtime. We then propose a research agenda for a model-centric development environment that supports dynamic software adaptation and evolution.

The Web KnowARR Framework: Orchestrating Computational Intelligence with Graph Databases

This chapter presents fuzzy cognitive maps (FCM) as a vehicle for Web knowledge aggregation, representation, and reasoning. The corresponding Web KnowARR framework incorporates findings from fuzzy logic. To this end, a first emphasis is particularly on the Web KnowARR framework along with a stakeholder management use case to illustrate the framework’s usefulness as a second focal point. This management form is to help projects to acceptance and assertiveness where claims for company decisions are actively involved in the management process. Stakeholder maps visually (re-) present these claims. On one hand, they resort to non-public content and on the other they resort to content that is available to the public (mostly on the Web). The Semantic Web offers opportunities not only to present public content descriptively but also to show relationships. The proposed framework can serve as the basis for the public content of stakeholder maps.