@Article{Supelec807,
author = {Bassem Khouzam and Hervé Frezza-Buet},
title = {{Distributed Recurrent Self-Organization for Tracking the State of Non-Stationary Partially Observable Dynamical Systems}},
journal = {Biologically Inspired Cognitive Architectures},
year = {2013},
volume = {3},
pages = {87--104},
month = {january},
url = {http://dx.doi.org/10.1016/j.bica.2012.11.001},
doi = {10.1016/j.bica.2012.11.001},
abstract = {In this paper, a distributed recurrent self-organizing
architecture is presented. It can extract
the current state of a dynamical system from the sequence of the
recent observations provided by this
system, even if they are ambiguous. The recurrent network is an
adaptation of RecSOM to the context
of the simulation of large scale distributed neural
architectures, since it relies on a strictly local fine-
grained computation. The experiments show the ability of the
recurrent architecture to capture the
states, but also exhibit some unexpected dynamical effects, like
some instabilities of the learned
mappings. The presented architecture addresses the cognitive
ability to set up representations from
sequences at a mesoscopic level. At that intermediate level,
between cognition and neurons simulation,
some complex dynamics is unveiled. It needs to be identified and
understood in order to bridge the gap
between neuronal activities and high level cognition.
}
}