Inhibitory dynamics in dual-route evidence accumulation account for response time distributions from conflict tasks.

Unlabelled: Laboratory data from conflict tasks, e.g. Simon and Eriksen tasks, reveal differences in response time distributions under different experimental conditions.

A memory access gate controlled by dynamic contexts.

Temporary difficulties in accessing the contents of memories are a common experience in everyday life, for example, when we try to recognize a known person in an unusual context. In addition, recent experiments seem to indicate that retrograde amnesia in the early stages of Alzheimer's disease is due to disorders in accessing memories that were installed normally. These facts suggest the existence of an intermediate step between the stimulus arrival and the associative recognition.

Load sharing between synergistic muscles characterized by a ligand-binding approach and elastography.

The skeletal muscle contraction is determined by cross-bridge formation between the myosin heads and the actin active sites. When the muscle contracts, it shortens, increasing its longitudinal shear elastic modulus ([Formula: see text]). Structurally, skeletal muscle can be considered analogous to the molecular receptors that form receptor-ligand complexes and exhibit specific ligand-binding dynamics.

Multiplicative processing in the modeling of cognitive activities in large neural networks.

Explaining the foundation of cognitive abilities in the processing of information by neural systems has been in the beginnings of biophysics since McCulloch and Pitts pioneered work within the biophysics school of Chicago in the 1940s and the interdisciplinary cybernetists meetings in the 1950s, inseparable from the birth of computing and artificial intelligence. Since then, neural network models have traveled a long path, both in the biophysical and the computational disciplines. The biological, neurocomputational aspect reached its representational maturity with the Distributed Associative Memory models developed in the early 70 s.

Stabilization through self-coupling in networks of small-world and scale-free topology.

Mechanisms that ensure the stability of dynamical systems are of vital importance, in particular in our globalized and increasingly interconnected world. The so-called connectivity-stability dilemma denotes the theoretical finding that increased connectivity between the components of a large dynamical system drastically reduces its stability. This result has promoted controversies within ecology and other fields of biology, especially, because organisms as well as ecosystems constitute systems that are both highly connected and stable.

A neurocomputational model for the processing of conflicting information in context-dependent decision tasks.

Context-dependent computation is a relevant characteristic of neural systems, endowing them with the capacity of adaptively modifying behavioral responses and flexibly discriminating between relevant and irrelevant information in a stimulus. This ability is particularly highlighted in solving conflicting tasks. A long-standing problem in computational neuroscience, flexible routing of information, is also closely linked with the ability to perform context-dependent associations.

Tensor Representation of Topographically Organized Semantic Spaces.

Human brains seem to represent categories of objects and actions as locations in a continuous semantic space across the cortical surface that reflects the similarity among categories. This vision of the semantic organization of information in the brain, suggested by recent experimental findings, is in harmony with the well-known topographically organized somatotopic, retinotopic, and tonotopic maps in the cerebral cortex. Here we show that these topographies can be operationally represented with context-dependent associative memories.

Exploring the sources and mechanisms of cognitive errors in medical diagnosis with associative memory models.

Background: One of the central challenges of third millennium medicine is the abatement of medical errors. Among the most frequent and hardiest causes of misdiagnosis are cognitive errors produced by faulty medical reasoning. These errors have been analyzed from the perspectives of cognitive psychology and empirical medical studies.

A Possible Neural Representation of Mathematical Group Structures.

Every cognitive activity has a neural representation in the brain. When humans deal with abstract mathematical structures, for instance finite groups, certain patterns of activity are occurring in the brain that constitute their neural representation. A formal neurocognitive theory must account for all the activities developed by our brain and provide a possible neural representation for them.

A modular approach to language production: models and facts.

Numerous cortical disorders affect language. We explore the connection between the observed language behavior and the underlying substrates by adopting a neurocomputational approach. To represent the observed trajectories of the discourse in patients with disorganized speech and in healthy participants, we design a graphical representation for the discourse as a trajectory that allows us to visualize and measure the degree of order in the discourse as a function of the disorder of the trajectories.

Dynamic searching in the brain.

Cognitive functions rely on the extensive use of information stored in the brain, and the searching for the relevant information for solving some problem is a very complex task. Human cognition largely uses biological search engines, and we assume that to study cognitive function we need to understand the way these brain search engines work. The approach we favor is to study multi-modular network models, able to solve particular problems that involve searching for information.

Looking for robust properties in the growth of an academic network: the case of the Uruguayan biological research community.

Graph-theoretical methods have recently been used to analyze certain properties of natural and social networks. In this work, we have investigated the early stages in the growth of a Uruguayan academic network, the Biology Area of the Programme for the Development of Basic Science (PEDECIBA). This transparent social network is a territory for the exploration of the reliability of clustering methods that can potentially be used when we are confronted with opaque natural systems that provide us with a limited spectrum of observables (happens in research on the relations between brain, thought and language).

The role of hippocampal atrophy in depression: a neurocomputational approach.

The role of hippocampal atrophy in the pathogenesis of major depression remains under investigation. Here, we show, within a neural network model, that the incorporation of atrophy reproduces the changes observed in cognitive impairment in depression and could also contribute to the maintenance of the depressed mood. Some other clinical observations, such as treatment resistance and frequent relapses of illness, could also be explained within the framework of the model.

Context-sensitive autoassociative memories as expert systems in medical diagnosis.

Background: The complexity of our contemporary medical practice has impelled the development of different decision-support aids based on artificial intelligence and neural networks. Distributed associative memories are neural network models that fit perfectly well to the vision of cognition emerging from current neurosciences.

Methods: We present the context-dependent autoassociative memory model.

Semantic graphs and associative memories.

Graphs have been increasingly utilized in the characterization of complex networks from diverse origins, including different kinds of semantic networks. Human memories are associative and are known to support complex semantic nets; these nets are represented by graphs. However, it is not known how the brain can sustain these semantic graphs.