Models of Cognition: Neurological possibility does not indicate neurological plausibility

Many activities in Cognitive Science involve complex computer models and simulations of both theoretical and real entities. Artificial Intelligence and the study of artificial neural nets in particular, are seen as major contributors in the quest for understanding the human mind. Computational models serve as objects of experimentation, and results from these virtual experiments are tacitly included in the framework of empirical science. Cognitive functions, like learning to speak, or discovering syntactical structures in language, have been modeled and these models are the basis for many claims about human cognitive capacities. Artificial neural nets (ANNs) have had some successes in the field of Artificial Intelligence, but the results from experiments with simple ANNs may have little value in explaining cognitive functions. The problem seems to be in relating cognitive concepts that belong in the `top-down' approach to models grounded in the `bottom-up' connectionist methodology. Merging the two fundamentally different paradigms within a single model can obfuscate what is really modeled. When the tools (simple artificial neural networks) to solve the problems (explaining aspects of higher cognitive functions) are mismatched, models with little value in terms of explaining functions of the human mind are produced. The ability to learn functions from data-points makes ANNs very attractive analytical tools. These tools can be developed into valuable models, if the data is adequate and a meaningful interpretation of the data is possible. The problem is, that with appropriate data and labels that fit the desired level of description, almost any function can be modeled. It is my argument that small networks offer a universal framework for modeling any conceivable cognitive theory, so that neurological possibility can be demonstrated easily with relatively simple models. However, a model demonstrating the possibility of implementation of a cognitive function using a distributed methodology, does not necessarily add support to any claims or assumptions that the cognitive function in question, is neurologically plausible

Field methods for rodent studies in Asia and the Indo-Pacific

Farm Management,

EFEMP1 binds the EGF receptor and activates MAPK and Akt pathways in pancreatic carcinoma cells

The EGF-related protein EFEMP1 (EGF-containing fibulin-like extracellular matrix protein 1) has been shown to promote tumor growth in human adenocarcinoma. To understand the mechanism of this action, the signal transduction activated upon treatment with this protein has been investigated. We show that EFEMP1 binds EGF receptor (EGFR) in a competitive manner relative to epidermal growth factor (EGF), implicating that EFEMP1 and EGF share the same or adjacent binding sites on the EGFR. Treatment of pancreatic carcinoma cells with purified EFEMP1 activates autophosphorylation of EGFR at the positions Tyr-992 and Tyr-1068, but not at the position Tyr-1048. This signal is further transduced to phosphorylation of Akt at position Thr-308 and p44/p42 MAPK (mitogen-activated protein kinase) at positions Thr-202 and Tyr-204. These downstream phosphorylation events can be inhibited by treatment with the EGFR kinase inhibitor PD 153035. The observed signal transduction upon treatment with EFEMP1 can contribute to the enhancement of tumor growth shown in pancreatic carcinoma cells overexpressing EFEMP1

On the Complexity of Bounded Context Switching

Bounded context switching (BCS) is an under-approximate method for finding violations to safety properties in shared-memory concurrent programs. Technically, BCS is a reachability problem that is known to be NP-complete. Our contribution is a parameterized analysis of BCS. The first result is an algorithm that solves BCS when parameterized by the number of context switches (cs) and the size of the memory (m) in O*(m^(cs)2^(cs)). This is achieved by creating instances of the easier problem Shuff which we solve via fast subset convolution. We also present a lower bound for BCS of the form m^o(cs / log(cs)), based on the exponential time hypothesis. Interestingly, the gap is closely related to a conjecture that has been open since FOCS\u2707. Further, we prove that BCS admits no polynomial kernel. Next, we introduce a measure, called scheduling dimension, that captures the complexity of schedules. We study BCS parameterized by the scheduling dimension (sdim) and show that it can be solved in O*((2m)^(4sdim)4^t), where t is the number of threads. We consider variants of the problem for which we obtain (matching) upper and lower bounds

Flood Management with SUDS: A Simulation-Optimization Framework

ABSTRACT: Urbanization and climate change are the main driving force in the development of sustainable strategies for managing water in cities, such as sustainable urban drainage systems (SUDS). Previous studies have identified the necessity to develop decision-making tools for SUDS in order to adequately implement these structures. This study proposes a simulation?optimization methodology that aims to ease the decision-making process when selecting and placing SUDS, with the specific goal of managing urban flooding. The methodology was applied to a real case study in Dresden, Germany. The most relevant variables when selecting SUDS were the spatial distribution of floods and the land uses in the catchment. Furthermore, the rainfall characteristics played an important role when selecting the different SUDS configurations. After the optimal SUDS configurations were determined, flood maps were developed, identifying the high potential that SUDS have for reducing flood volumes and depth, but showing them to be quite limited in reducing the flooded areas. The final section of the study proposes a combined frequency map of SUDS implementation, which is suggested for use as a final guide for the present study. The study successfully implemented a novel methodology that included land-use patterns and flood indicators to select SUDS in a real case study.This research was funded by ICETEX, grant number 5334506, granted to the first author. This research was funded by the German Federal Ministry of Education and Research, grant number 01LR2005A