22,334 research outputs found
Redundant neural vision systems: competing for collision recognition roles
Ability to detect collisions is vital for future robots that interact with humans in complex visual environments. Lobula giant movement detectors (LGMD) and directional selective neurons (DSNs) are two types of identified neurons found in the visual pathways of insects such as locusts. Recent modelling studies showed that the LGMD or grouped DSNs could each be tuned for collision recognition. In both biological and artificial vision systems, however, which one should play the collision recognition role and the way the two types of specialized visual neurons could be functioning together are not clear. In this modeling study, we compared the competence of the LGMD and the DSNs, and also investigate the cooperation of the two neural vision systems for collision recognition via artificial evolution. We implemented three types of collision recognition neural subsystems – the LGMD, the DSNs and a hybrid system which combines the LGMD and the DSNs subsystems together, in each individual agent. A switch gene determines which of the three redundant neural subsystems plays the collision recognition role. We found that, in both robotics and driving environments, the LGMD was able to build up its ability for collision recognition quickly and robustly therefore reducing the chance of other types of neural networks to play the same role. The results suggest that the LGMD neural network could be the ideal model to be realized in hardware for collision recognition
Statistical Physics of the Spatial Prisoner's Dilemma with Memory-Aware Agents
We introduce an analytical model to study the evolution towards equilibrium
in spatial games, with `memory-aware' agents, i.e., agents that accumulate
their payoff over time. In particular, we focus our attention on the spatial
Prisoner's Dilemma, as it constitutes an emblematic example of a game whose
Nash equilibrium is defection. Previous investigations showed that, under
opportune conditions, it is possible to reach, in the evolutionary Prisoner's
Dilemma, an equilibrium of cooperation. Notably, it seems that mechanisms like
motion may lead a population to become cooperative. In the proposed model, we
map agents to particles of a gas so that, on varying the system temperature,
they randomly move. In doing so, we are able to identify a relation between the
temperature and the final equilibrium of the population, explaining how it is
possible to break the classical Nash equilibrium in the spatial Prisoner's
Dilemma when considering agents able to increase their payoff over time.
Moreover, we introduce a formalism to study order-disorder phase transitions in
these dynamics. As result, we highlight that the proposed model allows to
explain analytically how a population, whose interactions are based on the
Prisoner's Dilemma, can reach an equilibrium far from the expected one; opening
also the way to define a direct link between evolutionary game theory and
statistical physics.Comment: 7 pages, 5 figures. Accepted for publication in EPJ-
What traits are carried on mobile genetic elements, and why?
Although similar to any other organism, prokaryotes can transfer genes vertically from mother cell to daughter cell, they can also exchange certain genes horizontally. Genes can move within and between genomes at fast rates because of mobile genetic elements (MGEs). Although mobile elements are fundamentally self-interested entities, and thus replicate for their own gain, they frequently carry genes beneficial for their hosts and/or the neighbours of their hosts. Many genes that are carried by mobile elements code for traits that are expressed outside of the cell. Such traits are involved in bacterial sociality, such as the production of public goods, which benefit a cell's neighbours, or the production of bacteriocins, which harm a cell's neighbours. In this study we review the patterns that are emerging in the types of genes carried by mobile elements, and discuss the evolutionary and ecological conditions under which mobile elements evolve to carry their peculiar mix of parasitic, beneficial and cooperative genes
Opinion Dynamics in Social Networks with Hostile Camps: Consensus vs. Polarization
Most of the distributed protocols for multi-agent consensus assume that the
agents are mutually cooperative and "trustful," and so the couplings among the
agents bring the values of their states closer. Opinion dynamics in social
groups, however, require beyond these conventional models due to ubiquitous
competition and distrust between some pairs of agents, which are usually
characterized by repulsive couplings and may lead to clustering of the
opinions. A simple yet insightful model of opinion dynamics with both
attractive and repulsive couplings was proposed recently by C. Altafini, who
examined first-order consensus algorithms over static signed graphs. This
protocol establishes modulus consensus, where the opinions become the same in
modulus but may differ in signs. In this paper, we extend the modulus consensus
model to the case where the network topology is an arbitrary time-varying
signed graph and prove reaching modulus consensus under mild sufficient
conditions of uniform connectivity of the graph. For cut-balanced graphs, not
only sufficient, but also necessary conditions for modulus consensus are given.Comment: scheduled for publication in IEEE Transactions on Automatic Control,
2016, vol. 61, no. 7 (accepted in August 2015
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