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Browsing by Author "Baker, Brittany"

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    Effects of Macrostructure on Synchrony in SONET Model Neuron Networks
    (2017-04) Kirkeide, Marina; Nykamp, Duane; Baker, Brittany
    The SONET model randomly generates neural networks with microstructure using four second order statistics. In order to make the SONET model more realistic, another parameter was added to the model that would control the macrostructure. The new parameter affects the probability that two neurons are connected based on the distance between the neurons; the closer two neurons are to each other, the more likely they are to be connected. To test the new parameter, L, against the existing parameters, alpha-chain, alpha-converge, alpha-diverge, and alpha-reciprocal, 400 neuron networks were generated with random values of each variable, and a 2000 millisecond simulation was run on each network using the Brian2 neuron simulating software. The synchrony of each network was then measured monotonically. Before the new parameter was added, it was known that the rate of the chain motif, alpha-chain, had the greatest effect on the synchrony of a network. The testing with macrostructure showed alpha-chain was still the most important factor for predicting synchrony, though L, the intensity of the macrostructure, did somewhat affect the synchrony. When the macrostructure of a network was more prominent, had a smaller L value, the network tended to be more synchronous.
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    How dynamical regime and neuronal network structure influence synchronous events
    (2019-06) Baker, Brittany
    Synchronization of spiking neuronal activity plays a role in many important processes in the human body. In 2011, Zhao, Beverlin, Netoff, and Nykamp explored the relationship between synchrony and network structure by developing the SONET model where one can modulate the microstructure of the network by adjusting frequencies of pairs of directed connections between nodes, which correspond to the second order statistics of the network. We extended the SONET framework to allow for the prescription of probabilities of neuronal connections based on location to modulate spatial macrostructure. We used this spatial SONET model to explore how both network microstructure (SONET motif frequencies) and macrostructure influence the emergence of synchrony. To enable a consistent analysis of synchrony across a wide range of networks, we developed a novel measure of synchrony based on the rate of synchronous events. We discovered that the microstructure played the dominant role in shaping synchrony. Moreover, we found that the influence of the microstructure can depend on the dynamics of the inputs to the network.