Chaotic Neurodynamics for Autonomous Agents

D. Harter, R. Kozma
<span title="">2005</span> <i title="Institute of Electrical and Electronics Engineers (IEEE)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/22mhkeaq5zdqlmtti5oidj26fi" style="color: black;">IEEE Transactions on Neural Networks</a> </i> &nbsp;
Mesoscopic level neurodynamics study the collective dynamical behavior of neural populations. Such models are becoming increasingly important in understanding large-scale brain processes. Brains exhibit aperiodic oscillations with a much more rich dynamical behavior than fixed-point and limitcycle approximation allow. Here we present a discretized model inspired by Freeman's K-set mesoscopic level population model. We show that this version is capable of replicating the important principles of
more &raquo; ... periodic/chaotic neurodynamics while being fast enough for use in real-time autonomous agent applications. This simplification of the K model provides many advantages not only in terms of efficiency but in simplicity and its ability to be analyzed in terms of its dynamical properties. We study the discrete version using a multi-layer, highly recurrent model of the neural architecture of perceptual brain areas. We use this architecture to develop example action selection mechanisms in an autonomous agent.
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/tnn.2005.845086">doi:10.1109/tnn.2005.845086</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/15940987">pmid:15940987</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/pdpqczhsovgx7ch2hwxanrstaq">fatcat:pdpqczhsovgx7ch2hwxanrstaq</a> </span>
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