13,885 research outputs found
Quantifying the Evolutionary Self Structuring of Embodied Cognitive Networks
We outline a possible theoretical framework for the quantitative modeling of
networked embodied cognitive systems. We notice that: 1) information self
structuring through sensory-motor coordination does not deterministically occur
in Rn vector space, a generic multivariable space, but in SE(3), the group
structure of the possible motions of a body in space; 2) it happens in a
stochastic open ended environment. These observations may simplify, at the
price of a certain abstraction, the modeling and the design of self
organization processes based on the maximization of some informational
measures, such as mutual information. Furthermore, by providing closed form or
computationally lighter algorithms, it may significantly reduce the
computational burden of their implementation. We propose a modeling framework
which aims to give new tools for the design of networks of new artificial self
organizing, embodied and intelligent agents and the reverse engineering of
natural ones. At this point, it represents much a theoretical conjecture and it
has still to be experimentally verified whether this model will be useful in
practice.
Neuroevolution on the Edge of Chaos
Echo state networks represent a special type of recurrent neural networks.
Recent papers stated that the echo state networks maximize their computational
performance on the transition between order and chaos, the so-called edge of
chaos. This work confirms this statement in a comprehensive set of experiments.
Furthermore, the echo state networks are compared to networks evolved via
neuroevolution. The evolved networks outperform the echo state networks,
however, the evolution consumes significant computational resources. It is
demonstrated that echo state networks with local connections combine the best
of both worlds, the simplicity of random echo state networks and the
performance of evolved networks. Finally, it is shown that evolution tends to
stay close to the ordered side of the edge of chaos.Comment: To appear in Proceedings of the Genetic and Evolutionary Computation
Conference 2017 (GECCO '17
Evolution and Analysis of Embodied Spiking Neural Networks Reveals Task-Specific Clusters of Effective Networks
Elucidating principles that underlie computation in neural networks is
currently a major research topic of interest in neuroscience. Transfer Entropy
(TE) is increasingly used as a tool to bridge the gap between network
structure, function, and behavior in fMRI studies. Computational models allow
us to bridge the gap even further by directly associating individual neuron
activity with behavior. However, most computational models that have analyzed
embodied behaviors have employed non-spiking neurons. On the other hand,
computational models that employ spiking neural networks tend to be restricted
to disembodied tasks. We show for the first time the artificial evolution and
TE-analysis of embodied spiking neural networks to perform a
cognitively-interesting behavior. Specifically, we evolved an agent controlled
by an Izhikevich neural network to perform a visual categorization task. The
smallest networks capable of performing the task were found by repeating
evolutionary runs with different network sizes. Informational analysis of the
best solution revealed task-specific TE-network clusters, suggesting that
within-task homogeneity and across-task heterogeneity were key to behavioral
success. Moreover, analysis of the ensemble of solutions revealed that
task-specificity of TE-network clusters correlated with fitness. This provides
an empirically testable hypothesis that links network structure to behavior.Comment: Camera ready version of accepted for GECCO'1
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