Charting epilepsy by searching for intelligence in network space with the help of evolving autonomous agents
| Autor: | Frank Y. Jin, Elan Liss Ohayon, Piotr Suffczynski, Donald S. Borrett, Paul W. Tsang, W. McIntyre Burnham, Hon C. Kwan, Stiliyan Kalitzin |
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| Rok vydání: | 2004 |
| Předmět: |
Computer science
Models Neurological Population Action Potentials Synaptic weight Neural ensemble Biological Clocks Physiology (medical) Animals Humans Computer Simulation education Motor Neurons Self-organization Brain Mapping education.field_of_study Epilepsy Neuronal Plasticity Artificial neural network business.industry General Neuroscience Robotics Complex dynamics Recurrent neural network Neural Networks Computer Artificial intelligence Nerve Net business Algorithms Mathematics Biological network |
| Zdroj: | Journal of Physiology-Paris. 98:507-529 |
| ISSN: | 0928-4257 |
| DOI: | 10.1016/j.jphysparis.2005.09.018 |
| Popis: | The problem of demarcating neural network space is formidable. A simple fully connected recurrent network of five units (binary activations, synaptic weight resolution of 10) has 3.2 ∗ 1026 possible initial states. The problem increases drastically with scaling. Here we consider three complementary approaches to help direct the exploration to distinguish epileptic from healthy networks. {1} First, we perform a gross mapping of the space of five-unit continuous recurrent networks using randomized weights and initial activations. The majority of weight patterns (>70%) were found to result in neural assemblies exhibiting periodic limit-cycle oscillatory behavior. {2} Next we examine the activation space of non-periodic networks demonstrating that the emergence of paroxysmal activity does not require changes in connectivity. {3} The next challenge is to focus the search of network space to identify networks with more complex dynamics. Here we rely on a major available indicator critical to clinical assessment but largely ignored by epilepsy modelers, namely: behavioral states. To this end, we connected the above network layout to an external robot in which interactive states were evolved. The first random generation showed a distribution in line with approach {1}. That is, the predominate phenotypes were fixed-point or oscillatory with seizure-like motor output. As evolution progressed the profile changed markedly. Within 20 generations the entire population was able to navigate a simple environment with all individuals exhibiting multiply-stable behaviors with no cases of default locked limit-cycle oscillatory motor behavior. The resultant population may thus afford us a view of the architectural principles demarcating healthy biological networks from the pathological. The approach has an advantage over other epilepsy modeling techniques in providing a way to clarify whether observed dynamics or suggested therapies are pointing to computational viability or dead space. |
| Databáze: | OpenAIRE |
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