title: Charting epilepsy by searching for intelligence in network space with the help of evolving autonomous agents.
abstract: 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 *10(26) 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.
authors.affiliation: University of Toronto Epilepsy Research Program, Institute of Medical Science, Medical Sciences Building, University of Toronto, Ont., Canada. ohayon@chass.utoronto.ca
authors.forename: Elan L
authors.initials: EL
authors.lastname: Ohayon
authors.forename: Stiliyan
authors.initials: S
authors.lastname: Kalitzin
authors.forename: Piotr
authors.initials: P
authors.lastname: Suffczynski
authors.forename: Frank Y
authors.initials: FY
authors.lastname: Jin
authors.forename: Paul W
authors.initials: PW
authors.lastname: Tsang
authors.forename: Donald S
authors.initials: DS
authors.lastname: Borrett
authors.forename: W McIntyre
authors.initials: WM
authors.lastname: Burnham
authors.forename: Hon C
authors.initials: HC
authors.lastname: Kwan
mesh_terms: D000200:Action Potentials / Q000502:physiology; D000465:Algorithms; D000818:Animals; D001683:Biological Clocks / Q000502:physiology; D001931:Brain Mapping / Q000379:methods*; D003198:Computer Simulation; D004827:Epilepsy / Q000503:physiopathology* / Q000628:therapy; D006801:Humans; D008433:Mathematics; D008959:Models, Neurological; D009046:Motor Neurons / Q000502:physiology; D009415:Nerve Net / Q000503:physiopathology*; D016571:Neural Networks, Computer; D009473:Neuronal Plasticity / Q000502:physiology; D012371:Robotics