ObjectiveThe functional strategic mechanisms in the brain during performing visuospatial working memory tasks, especially tasks with heavy load, are controversial. We conducted the functional magnetic resonance imaging (fMRI) while sixteen subjects were performing face- and location-matching n-back tasks to examine causal relations within the frontoparietal networks.MethodsWe applied a sophisticated method, the structural equation modeling (SEM), to the fMRI data. The imaging data were analyzed by extracting the task-related eigenseries using the principal component analysis (PCA) and then by applying a form of data-driven model called the automated search method.ResultsThe SEM analyses revealed a functional shift of network connectivity from the right to the left hemisphere with increasing load in the face-matching n-back tasks while the location-matching tasks required bilateral activation. In the locating matching n-back tasks, a pattern of parallel processing was observed in the left phonological loop and the right inferior parietal regions. Furthermore, object working memory-related activities in the left hemisphere reliably contributed to performance of both the face- and location-matching 2-back tasks.ConclusionOur results are consistent with previous reports in terms of demonstrating parallel and distributed information processing during performing working memory tasks with heavy loads. Our results additionally suggest a dynamic shift between the fast imagery circuit (right hemisphere) and the stable verbal circuit (left hemisphere), depending on task load.
Abstract-We describe a "molecular" evolutionary algorithm that can be implemented in DNA computing in vitro to learn the recently-proposed hypernetwork model of cognitive memory. The molecular learning process is designed to make it possible to perform wet-lab experiments using DNA molecules and bio-lab tools. We present the bio-experimental protocols for selection, amplification and mutation operators for evolving hypernetworks. We analyze the convergence properties of the molecular evolutionary algorithms on simulated DNA computers. The performance of the algorithms is demonstrated on the task of simulating the cognitive process of learning a language model from a drama corpus to identify the style of an unknown drama. We also discuss other applications of the molecular evolutionary algorithms. In addition to their feasibility in DNA computing, which opens a new horizon of in vitro evolutionary computing, the molecular evolutionary algorithm provides unique properties that are distinguished from conventional evolutionary algorithms and makes a new addition to the arsenal of tools in evolutionary computation.
Many DNA-based technologies, such as DNA computing, DNA nanoassembly and DNA biochips, rely on DNA hybridization reactions. Previous hybridization models have focused on macroscopic reactions between two DNA strands at the sequence level. Here, we propose a novel population-based Monte Carlo algorithm that simulates a microscopic model of reacting DNA molecules. The algorithm uses two essential thermodynamic quantities of DNA molecules: the binding energy of bound DNA strands and the entropy of unbound strands. Using this evolutionary Monte Carlo method, we obtain a minimum free energy configuration in the equilibrium state. We applied this method to a logical reasoning problem and compared the simulation results with the experimental results of the wet-lab DNA experiments performed subsequently. Our simulation predicted the experimental results quantitatively.
The global minimum search problem is important in neural networks because the error cost involved is formed as multiminima potential in weight parametric space. Therefore, parameters that produce a global minimum in a cost function are the best values for enhancing the performance of neural networks. Previously, a global minimum search based on a damped oscillator equation known as the heavy ball with friction (HBF) was studied. The kinetic energy overcomes a local minimum if the kinetic energy is sufficiently large or else the heavy ball will converge into a local minimum due to the action of friction. However, an appropriate damping coefficient has not been found in the HBF; therefore, the ball has to be shot again after it arrives at each local minimum until it finds a global minimum. In order to solve this problem, we determined an adaptive damping coefficient using the geodesic of Newtonian dynamics Lagrangian. This geometric method produces a second-order adaptively damped oscillator equation, the damping coefficient of which is the negative time derivative of the logarithmic function of the cost potential. Furthermore, we obtained a novel adaptive steepest descent by discretizing this second-order equation. To investigate the performance of this novel steepest descent, we applied our first-order update rule to the Rosenbrock-and Griewank-type potentials. The results show that our method determined the global minimum in most cases from various initial points. Our adaptive steepest descent may be applied in
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