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tsuomela : neuralnetworks   7

Cooperation and the evolution of intelligence
"The high levels of intelligence seen in humans, other primates, certain cetaceans and birds remain a major puzzle for evolutionary biologists, anthropologists and psychologists. It has long been held that social interactions provide the selection pressures necessary for the evolution of advanced cognitive abilities (the ‘social intelligence hypothesis’), and in recent years decision-making in the context of cooperative social interactions has been conjectured to be of particular importance. Here we use an artificial neural network model to show that selection for efficient decision-making in cooperative dilemmas can give rise to selection pressures for greater cognitive abilities, and that intelligent strategies can themselves select for greater intelligence, leading to a Machiavellian arms race. Our results provide mechanistic support for the social intelligence hypothesis, highlight the potential importance of cooperative behaviour in the evolution of intelligence and may help us to explain the distribution of cooperation with intelligence across taxa."
intelligence  evolution  simulation  cooperation  neuralnetworks 
april 2012 by tsuomela
emergentTM (a major rewrite of PDP ) is a comprehensive simulation environment for creating complex, sophisticated models of the brain and cognitive processes using neural network models. These same networks can also be used for all kinds of other more pragmatic tasks, like predicting the stock market or analyzing data.
science  complexity  neuralnetworks  software  open-source  open-science  simulation 
july 2009 by tsuomela
[0901.2203] Neural Networks as dynamical systems
We consider neural networks from the point of view of dynamical systems theory. In this spirit we review recent results dealing with the following questions, adressed in the context of specific models. 1. Characterizing the collective dynamics
biology  neuralnetworks  neurology  model  dynamics  systems  complexity 
february 2009 by tsuomela
Self-organizing map - Wikipedia, the free encyclopedia
The self-organizing map (SOM) is a subtype of artificial neural networks. It is trained using unsupervised learning to produce low dimensional representation of the training samples while preserving the topological properties of the input space. This make
visualization  math  neuralnetworks  machine  learning 
may 2007 by tsuomela

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