fMRI Evidence for Modality-Specific Processing of Conceptual Knowledge on Six Modalities

Traditional theories assume that amodal representations, such as feature lists and semantic networks, represent conceptual knowledge about the world. According to this view, the sensory, motor, and introspective states that arise during perception and action are irrelevant to representing knowledge. Instead the conceptual system lies outside modality-specific systems and operates according to different principles. Increasingly, however, researchers report that modality-specific systems become active during purely conceptual tasks, suggesting that these systems play central roles in representing knowledge (for a review, see Martin, 2001, Handbook of Functional Neuroimaging of Cognition). In particular, researchers report that the visual system becomes active while processing visual properties, and that the motor system becomes active while processing action properties. The present study corroborates and extends these findings. During fMRI, subjects verified whether or not properties could potentially be true of concepts (e.g., BLENDER-loud). Subjects received only linguistic stimuli, and nothing was said about using imagery. Highly related false properties were used on false trials to block word association strategies (e.g., BUFFALOwinged). To assess the full extent of the modality-specific hypothesis, properties were verified on each of six modalities. Examples include GEMSTONE-glittering (vision), BLENDER-loud (audition), FAUCET-turned (motor), MARBLE-cool (touch), CUCUMBER-bland (taste), and SOAP-perfumed (smell). Neural activity during property verification was compared to a lexical decision baseline. For all six sets of the modalityspecific properties, significant activation was observed in the respective neural system. Finding modality-specific processing across six modalities contributes to the growing conclusion that knowledge is grounded in modality-specific systems of the brain

A contrasting look at self-organization in the Internet and next-generation communication networks

This article examines contrasting notions of self-organization in the Internet and next-generation communication networks, by reviewing in some detail recent evidence regarding several of the more popular attempts to explain prominent features of Internet structure and behavior as "emergent phenomena." In these examples, what might appear to the nonexpert as "emergent self-organization" in the Internet actually results from well conceived (albeit perhaps ad hoc) design, with explanations that are mathematically rigorous, in agreement with engineering reality, and fully consistent with network measurements. These examples serve as concrete starting points from which networking researchers can assess whether or not explanations involving self-organization are relevant or appropriate in the context of next-generation communication networks, while also highlighting the main differences between approaches to self-organization that are rooted in engineering design vs. those inspired by statistical physics

Empirical Analysis of the Necessary and Sufficient Conditions of the Echo State Property

The Echo State Network (ESN) is a specific recurrent network, which has gained popularity during the last years. The model has a recurrent network named reservoir, that is fixed during the learning process. The reservoir is used for transforming the input space in a larger space. A fundamental property that provokes an impact on the model accuracy is the Echo State Property (ESP). There are two main theoretical results related to the ESP. First, a sufficient condition for the ESP existence that involves the singular values of the reservoir matrix. Second, a necessary condition for the ESP. The ESP can be violated according to the spectral radius value of the reservoir matrix. There is a theoretical gap between these necessary and sufficient conditions. This article presents an empirical analysis of the accuracy and the projections of reservoirs that satisfy this theoretical gap. It gives some insights about the generation of the reservoir matrix. From previous works, it is already known that the optimal accuracy is obtained near to the border of stability control of the dynamics. Then, according to our empirical results, we can see that this border seems to be closer to the sufficient conditions than to the necessary conditions of the ESP.Comment: 23 pages, 14 figures, accepted paper for the IEEE IJCNN, 201

Integration of continuous-time dynamics in a spiking neural network simulator

Contemporary modeling approaches to the dynamics of neural networks consider two main classes of models: biologically grounded spiking neurons and functionally inspired rate-based units. The unified simulation framework presented here supports the combination of the two for multi-scale modeling approaches, the quantitative validation of mean-field approaches by spiking network simulations, and an increase in reliability by usage of the same simulation code and the same network model specifications for both model classes. While most efficient spiking simulations rely on the communication of discrete events, rate models require time-continuous interactions between neurons. Exploiting the conceptual similarity to the inclusion of gap junctions in spiking network simulations, we arrive at a reference implementation of instantaneous and delayed interactions between rate-based models in a spiking network simulator. The separation of rate dynamics from the general connection and communication infrastructure ensures flexibility of the framework. We further demonstrate the broad applicability of the framework by considering various examples from the literature ranging from random networks to neural field models. The study provides the prerequisite for interactions between rate-based and spiking models in a joint simulation

Evolutionary optimization within an intelligent hybrid system for design integration

An intelligent hybrid approach has been developed to integrate various stages in total design, including formulation of product design specifications, conceptual design, detail design, and manufacture. The integration is achieved by blending multiple artificial intelligence (AI) techniques and CAD/CAE/CAM into a single environment. It has been applied into power transmission system design. In addition to knowledge-based systems and artificial neural networks, another AI technique, genetic algorithms (GAs), are involved in the approach. The GA is used to conduct optimization tasks: (1) searching the best combination of design parameters to obtain optimum design of gears, and (2) optimization of the architecture of the artificial neural networks used in the hybrid system. In this paper, after a brief overview of the intelligent hybrid system, the GA applications are described in detail