Social Brain: A Perspective of Swarm Intelligence in Humans

2018 ◽  
Author(s):  
Yong Tao
Author(s):  
A. Radhika ◽  
D. Haritha

Wireless Sensor Networks, have witnessed significant amount of improvement in research across various areas like Routing, Security, Localization, Deployment and above all Energy Efficiency. Congestion is a problem of  importance in resource constrained Wireless Sensor Networks, especially for large networks, where the traffic loads exceed the available capacity of the resources . Sensor nodes are prone to failure and the misbehaviour of these faulty nodes creates further congestion. The resulting effect is a degradation in network performance, additional computation and increased energy consumption, which in turn decreases network lifetime. Hence, the data packet routing algorithm should consider congestion as one of the parameters, in addition to the role of the faulty nodes and not merely energy efficient protocols .Nowadays, the main central point of attraction is the concept of Swarm Intelligence based techniques integration in WSN.  Swarm Intelligence based Computational Swarm Intelligence Techniques have improvised WSN in terms of efficiency, Performance, robustness and scalability. The main objective of this research paper is to propose congestion aware , energy efficient, routing approach that utilizes Ant Colony Optimization, in which faulty nodes are isolated by means of the concept of trust further we compare the performance of various existing routing protocols like AODV, DSDV and DSR routing protocols, ACO Based Routing Protocol  with Trust Based Congestion aware ACO Based Routing in terms of End to End Delay, Packet Delivery Rate, Routing Overhead, Throughput and Energy Efficiency. Simulation based results and data analysis shows that overall TBC-ACO is 150% more efficient in terms of overall performance as compared to other existing routing protocols for Wireless Sensor Networks.


2020 ◽  
Author(s):  
Davide Ghiglino ◽  
Agnieszka Wykowska

Mental activities are a fascinating mystery that humans have tried to unveil since the very beginning of philosophy. We all try to understand how other people “tick” and formulate hypotheses, predictions, expectations and, morebroadly, representations of the others’ goals, desires and intentions, and behaviors following from those. We “think” spontaneously about others’ and our own mental states. The advent of new technologies – seemingly smart artificialagents – is giving researchers new environments to test mindreading models, pushing the cognitive flexibility of the human social brain from the natural domain to towards the artificial.


2018 ◽  
Author(s):  
Mark Allen Thornton ◽  
Miriam E. Weaverdyck ◽  
Judith Mildner ◽  
Diana Tamir

One can never know the internal workings of another person – one can only infer others’ mental states based on external cues. In contrast, each person has direct access to the contents of their own mind. Here we test the hypothesis that this privileged access shapes the way people represent internal mental experiences, such that they represent their own mental states more distinctly than the states of others. Across four studies, participants considered their own and others’ mental states; analyses measured the distinctiveness of mental state representations. Two neuroimaging studies used representational similarity analyses to demonstrate that the social brain manifests more distinct activity patterns when thinking about one’s own states versus others’. Two behavioral studies support these findings. Further, they demonstrate that people differentiate between states less as social distance increases. Together these results suggest that we represent our own mind with greater granularity than the minds of others.


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