Neural Correlates of Familiar and Unfamiliar Action in Infancy

Behavioral evidence shows that experience with an action shapes action perception. Neural mirroring has been suggested as a mechanism underlying this behavioral phenomenon. Suppression of EEG power in the mu frequency band, an index of motor activation, typically reflects neural mirroring. However, contradictory findings exist regarding the association between mu suppression and motor familiarity in infant EEG studies. In this study, we investigated the neural underpinnings reflecting the role of familiarity on action perception. We measured neural processing of familiar (grasp) and novel (tool-use) actions in 9-and-12-month-old infants. Specifically, we measured infants’ distinct motor/visual activity and explored functional connectivity associated with these processes. Mu suppression was stronger for grasping than tool-use, while significant mu and occipital alpha (indexing visual activity) suppression were evident for both actions. Interestingly, selective visual-motor functional connectivity was found during observation of familiar action, a pattern not observed for novel action. Thus, the neural correlates of perception of familiar actions may be best understood in terms of a functional neural network, rather than isolated regional activity.Our findings provide novel insights on analytic approaches for identifying motor-specific neural activity while also considering neural networks involved in observing motorically familiar versus actions.

Functional neural network for decision processing, a racing network of programmable neurons with fuzzy logic where the target operating model relies on the network itself

In this paper, we are introducing a novel model of artificial intelligence, the functional neural network for modeling of human decision-making processes. This neural network is composed of multiple artificial neurons racing in the network. Each of these neurons has a similar structure programmed independently by the users and composed of an intention wheel, a motor core and a sensory core representing the user itself and racing at a specific velocity. The mathematics of the neuron’s formulation and the racing mechanism of multiple nodes in the network will be discussed, and the group decision process with fuzzy logic and the transformation of these conceptual methods into practical methods of simulation and in operations will be developed. Eventually, we will describe some possible future research directions in the fields of finance, education and medicine including the opportunity to design an intelligent learning agent with application in business operations supervision. We believe that this functional neural network has a promising potential to transform the way we can compute decision-making and lead to a new generation of neuromorphic chips for seamless human-machine interactions.

Neuroprotective Effect of Kinase Inhibition in Ischemic Factor Modeling In Vitro

The contribution of many neuronal kinases to the adaptation of nerve cells to ischemic damage and their effect on functional neural network activity has not yet been studied. The aim of this work is to study the role of the four kinases belonging to different metabolic cascades (SRC, Ikkb, eEF2K, and FLT4) in the adaptive potential of the neuron-glial network for modeling the key factors of ischemic damage. We carried out a comprehensive study on the effects of kinases blockade on the viability and network functional calcium activity of nerve cells under ischemic factor modeling in vitro. Ischemic factor modelling was performed on day 14 of culturing primary hippocampal cells obtained from mouse embryos (E18). The most significant neuroprotective effect was shown in the blockade of FLT4 kinase in the simulation of hypoxia. The studies performed revealed the role of FLT4 in the development of functional dysfunction in cerebrovascular accidents and created new opportunities for the study of this enzyme and its blockers in the formation of new therapeutic strategies.

Altered Resting State Brain Networks and Cognition in Familial Adenomatous Polyposis

Familial Adenomatous Polyposis (FAP) is an autosomal dominant disorder caused by mutation of the APC gene presenting with numerous colorectal adenomatous polyps and a near 100% risk of colon cancer. Preliminary research findings from our group indicate that FAP patients experience significant deficits across many cognitive domains. In the current study, fMRI brain metrics in a FAP population and matched controls were used to further the mechanistic understanding of reported cognitive deficits. This research identified and characterized any possible differences in resting brain networks and associations between neural network changes and cognition from 34 participants (18 FAP patients, 16 healthy controls). Functional connectivity analysis was performed using FSL with independent component analysis (ICA) to identify functional networks. Significant differences between cases and controls were observed in 8 well-established resting state networks. With the addition of an aggregate cognitive measure as a covariate, these differences were virtually non-existent, indicating a strong correlation between cognition and brain activity at the network level. The data indicate robust and pervasive effects on functional neural network activity among FAP patients and these effects are likely involved in cognitive deficits associated with this disease.

EXPLOITATION OF MCDA TO LEARN THE RADIAL BASE NEURAL NETWORK (RBFNN) AIM PHYSICAL AND SOCIAL VULNERABILITY ANALYSIS VERSUS THE EARTHQUAKE (CASE STUDY: SANANDAJ CITY, IRAN)

Despite years of research on natural hazards and efforts to reduce physical and psychological damage, earthquake as a natural disaster is catastrophic. Though, human is the main axis in dealing with crisis and vulnerability, and since the space of cities encompasses largest population spectrum, managing this space is considered as an essential issue. Accordingly, the vulnerability of the City of Sanandaj was defined by environmental, physical and social criteria. In this regard, with the aim of modeling, and assessing the risk and vulnerability, the MCDA-ANN hybrid model was introduced as a new method for teaching of learning models. In order to determine the final value of each of the criteria, AHP analysis was performed as one of the MCDA methods to solve complex and non-structural problems by creating a functional hierarchy, and after that, a training data base for learning ANN was created randomly based on the AHP classification map. Then, for modeling, the radial base functional neural network (RBFNN) was used as one of the techniques of artificial neural networks. After the modeling, 30% of the points were selected as validation data to determine the accuracy of the model. After the implementation of RBFNN model, the area of AUC curve resulted is 0.922, which indicates the high accuracy of the model in assessing the risk of an earthquake. The results show high vulnerability in urban areas1 and 2 and in downtown Sanandaj that in these zones the physical and social factors dramatically affect the vulnerability of these areas.