Solving Constraint-Satisfaction Problems with Distributed Neocortical-Like Neuronal Networks

How does the "default mode" network contribute to semantic cognition?

This review examines whether and how the "default mode" network (DMN) contributes to semantic processing. We review evidence implicating the DMN in the processing of individual word meanings and in sentence- and discourse-level semantics. Next, we argue that the areas comprising the DMN contribute to semantic processing by coordinating and integrating the simultaneous activity of local neuronal ensembles across multiple unimodal and multimodal cortical regions, creating a transient, global neuronal ensemble. The resulting ensemble implements an integrated simulation of phenomenological experience - that is, an embodied situation model - constructed from various modalities of experiential memory traces. These situation models, we argue, are necessary not only for semantic processing but also for aspects of cognition that are not traditionally considered semantic. Although many aspects of this proposal remain provisional, we believe it provides new insights into the relationships between semantic and non-semantic cognition and into the functions of the DMN.

Two kinds of memory signals in neurons of the human hippocampus

SignificanceEpisodic memories represent the "what," "when," and "where" of specific episodes. In epilepsy patients, we detected single-unit activity reflecting episodic memory only in the hippocampus. This neural signal is sparsely coded and pattern-separated, consistent with predictions from neurocomputational models. We also detected single-unit activity reflecting a generic memory signal, coding whether an item is old or new without item-specific episodic information. Similar to concept cells, this generic repetition/novelty neural signal was found in multiple brain regions, including the hippocampus. In contrast, the item-specific signal was found only in the hippocampus. Our results indicate the coexistence of two memory signals in the human brain and suggest that the sparsely coded, hippocampus-specific signal is fundamental, whereas the often-studied generic signal is derivative.

A Place for Viruses on the Tree of Life

Viruses are ubiquitous. They infect almost every species and are probably the most abundant biological entities on the planet, yet they are excluded from the Tree of Life (ToL). However, there can be no doubt that viruses play a significant role in evolution, the force that facilitates all life on Earth. Conceptually, viruses are regarded by many as non-living entities that hijack living cells in order to propagate. A strict separation between living and non-living entities places viruses far from the ToL, but this may be theoretically unsound. Advances in sequencing technology and comparative genomics have expanded our understanding of the evolutionary relationships between viruses and cellular organisms. Genomic and metagenomic data have revealed that co-evolution between viral and cellular genomes involves frequent horizontal gene transfer and the occasional co-option of novel functions over evolutionary time. From the giant, ameba-infecting marine viruses to the tiny Porcine circovirus harboring only two genes, viruses and their cellular hosts are ecologically and evolutionarily intertwined. When deciding how, if, and where viruses should be placed on the ToL, we should remember that the Tree functions best as a model of biological evolution on Earth, and it is important that models themselves evolve with our increasing understanding of biological systems.

A Deterministic Annealing Neural Network Algorithm for the Minimum Concave Cost Transportation Problem

In this article, a deterministic annealing neural network algorithm is proposed to solve the minimum concave cost transportation problem. Specifically, the algorithm is derived from two neural network models and Lagrange-barrier functions. The Lagrange function is used to handle linear equality constraints, and the barrier function is used to force the solution to move to the global or near-global optimal solution. In both neural network models, two descent directions are constructed, and an iterative procedure for the optimization of the neural network is proposed. As a result, two corresponding Lyapunov functions are naturally obtained from these two descent directions. Furthermore, the proposed neural network models are proved to be completely stable and converge to the stable equilibrium state, therefore, the proposed algorithm converges. At last, the computer simulations on several test problems are made, and the results indicate that the proposed algorithm always generates global or near-global optimal solutions.

The Low-Dimensional Neural Architecture of Cognitive Complexity Is Related to Activity in Medial Thalamic Nuclei

Cognitive activity emerges from large-scale neuronal dynamics that are constrained to a low-dimensional manifold. How this low-dimensional manifold scales with cognitive complexity, and which brain regions regulate this process, are not well understood. We addressed this issue by analyzing sub-second high-field fMRI data acquired during performance of a task that systematically varied the complexity of cognitive reasoning. We show that task performance reconfigures the low-dimensional manifold and that deviations from these patterns relate to performance errors. We further demonstrate that individual differences in thalamic activity relate to reconfigurations of the low-dimensional architecture during task engagement.

An Ant Colony Optimization Based on Information Entropy for Constraint Satisfaction Problems

Solving the constraint satisfaction problem (CSP) is to find an assignment of values to variables that satisfies a set of constraints. Ant colony optimization (ACO) is an efficient algorithm for solving CSPs. However, the existing ACO-based algorithms suffer from the constructed assignment with high cost. To improve the solution quality of ACO for solving CSPs, an ant colony optimization based on information entropy (ACOE) is proposed in this paper. The proposed algorithm can automatically call a crossover-based local search according to real-time information entropy. We first describe ACOE for solving CSPs and show how it constructs assignments. Then, we use a ranking-based strategy to update the pheromone, which weights the pheromone according to the rank of these ants. Furthermore, we introduce the crossover-based local search that uses a crossover operation to optimize the current best assignment. Finally, we compare ACOE with seven algorithms on binary CSPs. The experimental results revealed that our method outperformed the other compared algorithms in terms of the cost comparison, data distribution, convergence performance, and hypothesis test.