Simple cyclic movements as a distinct autism feature - computational approach

Diversity of symptoms in autism dictates a broad definition of Autism Spectrum of Disorders(ASD). Each year percentage of children diagnosed with ASD is growing. One common diag-nostic feature in individuals with ASD is the tendency to atypical simple cyclic movements.The motor brain activity seems to generate periodic attractor state that is hard to escape.Despite numerous studies scientists and clinicians do not know exactly if ASD is a result ofa simple but general mechanism, or a complex set of mechanisms, both on neural, molecularand system levels. Simulations using biologically relevant neural network model presentedhere may help to reveal simplest mechanisms that may be responsible for specific behavior.Abnormal neural fatigue mechanisms may be responsible for motor as well as many if notall other symptoms observed in ASD

Error-preceding brain activity reflects (mal-)adaptive adjustments of cognitive control: a modeling study

Errors in choice tasks are preceded by gradual changes in brain activity presumably related to fluctuations in cognitive control that promote the occurrence of errors. In the present paper, we use connectionist modeling to explore the hypothesis that these fluctuations reflect (mal-)adaptive adjustments of cognitive control. We considered ERP data from a study in which the probability of conflict in an Eriksen-flanker task was manipulated in sub-blocks of trials. Errors in these data were preceded by a gradual decline of N2 amplitude. After fitting a connectionist model of conflict adaptation to the data, we analyzed simulated N2 amplitude, simulated response times (RTs), and stimulus history preceding errors in the model, and found that the model produced the same pattern as obtained in the empirical data. Moreover, this pattern is not found in alternative models in which cognitive control varies randomly or in an oscillating manner. Our simulations suggest that the decline of N2 amplitude preceding errors reflects an increasing adaptation of cognitive control to specific task demands, which leads to an error when these task demands change. Taken together, these results provide evidence that error-preceding brain activity can reflect adaptive adjustments rather than unsystematic fluctuations of cognitive control, and therefore, that these errors are actually a consequence of the adaptiveness of human cognition

Cognitive Control Reflects Context Monitoring, Not Motoric Stopping, in Response Inhibition

The inhibition of unwanted behaviors is considered an effortful and controlled ability. However, inhibition also requires the detection of contexts indicating that old behaviors may be inappropriate – in other words, inhibition requires the ability to monitor context in the service of goals, which we refer to as context-monitoring. Using behavioral, neuroimaging, electrophysiological and computational approaches, we tested whether motoric stopping per se is the cognitively-controlled process supporting response inhibition, or whether context-monitoring may fill this role. Our results demonstrate that inhibition does not require control mechanisms beyond those involved in context-monitoring, and that such control mechanisms are the same regardless of stopping demands. These results challenge dominant accounts of inhibitory control, which posit that motoric stopping is the cognitively-controlled process of response inhibition, and clarify emerging debates on the frontal substrates of response inhibition by replacing the centrality of controlled mechanisms for motoric stopping with context-monitoring

A model of proactive and reactive cognitive control with anterior cingulate cortex and the neuromodulatory system

Abstract Proactive and reactive cognitive control are often associated with anterior cingulate cortex (ACC). How ACC affects processing in other brain areas, however, is often not explicitly delineated. In this work, we describe a model of how ACC computes measures of conflict and surprise that are in turn relayed to the basal forebrain (BF) and locus coeruleus (LC) in that order. BF and LC signals then respectively sharpen posterior cortical processing and trigger the reframing of prefrontal cortical decision-making frames. We implemented this theory in a large-scale neurocognitive model that performs simulated geospatial intelligence tasks. Experiments demonstrate improved performance while minimizing additional processing. Alternate interpretations of neuromodulatory signals are also discussed.

DANA: Distributed (asynchronous) Numerical and Adaptive modelling framework

International audienceDANA is a python framework (http://dana.loria.fr) whose computational paradigm is grounded on the notion of a unit that is essentially a set of time dependent values varying under the influence of other units via adaptive weighted connections. The evolution of a unit's value are defined by a set of differential equations expressed in standard mathematical notation which greatly ease their definition. The units are organized into groups that form a model. Each unit can be connected to any other unit (including itself) using a weighted connection. The DANA framework offers a set of core objects needed to design and run such models. The modeler only has to define the equations of a unit as well as the equations governing the training of the connections. The simulation is completely transparent to the modeler and is handled by DANA. This allows DANA to be used for a wide range of numerical and distributed models as long as they fit the proposed framework (e.g. cellular automata, reaction-diffusion system, decentralized neural networks, recurrent neural networks, kernel-based image processing, etc.)