Variability and control of activity in small neural networks: Effects of neuron feedback dynamics

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Hooper, Ryan Michael
Prinz, Astrid A.
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Rhythmic neural networks are dynamic systems that reliably generate stereotyped activity that drives numerous biological processes essential to life, including motor pattern generation. Due to these networks’ reliable pattern generation, as well as the broad wealth of insights into fundamental questions in neuroscience that have been gained in their study without considering their fundamentally stochastic nature, the variability in their pattern generation is often overlooked. But such rhythmic networks are typically composed of a richly diverse ensemble of neurons, synapses, and their underlying properties and kinetics, each of which possesses individual dynamics that interact to contribute to the collective network dynamics that determine not just steady-state neural network activity, but also the presence or absence of network reliability and stability in the face of perturbations and stochastic processes. Because the crustacean stomatogastric network is a well studied and understood network, is experimentally amenable, and has been modeled extensively, it serves as a good system for investigating the role specific features of network composition play in determining network activity variability. Advances here may readily be adapted to inform models that are currently the focus of intense study aimed at gaining an understanding of the connection between underlying molecular and genetic cell properties and rhythmic neural network activity. The primary focus of this research is to explore the impacts of one such feature of network composition that is involved in stochastic network activity—the dynamics of synaptic feedback—and in turn determining its impact on variability of the pacemaker network. We have discovered that synaptic feedback dynamics in the crustacean stomatogastric pattern generating network tend to be ordered in multiple senses that optimally minimize rhythmic variability: in terms of both feedback neuron phase response properties, and cycle-by-cycle phase maintenance of synaptic feedback burst width. Our findings have implications for neural network design and optimization as well as neural network model and database construction.
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