title: Autaptic Connections Shift Network Excitability and Bursting.
abstract: We examine the role of structural autapses, when a neuron synapses onto itself, in driving network-wide bursting behavior. Using a simple spiking model of neuronal activity, we study how autaptic connections affect activity patterns, and evaluate if controllability significantly affects changes in bursting from autaptic connections. Adding more autaptic connections to excitatory neurons increased the number of spiking events and the number of network-wide bursts. We observed excitatory synapses contributed more to bursting behavior than inhibitory synapses. We evaluated if neurons with high average controllability, predicted to push the network into easily achievable states, affected bursting behavior differently than neurons with high modal controllability, thought to influence the network into difficult to reach states. Results show autaptic connections to excitatory neurons with high average controllability led to higher burst frequencies than adding the same number of self-looping connections to neurons with high modal controllability. The number of autapses required to induce bursting was lowered by adding autapses to high degree excitatory neurons. These results suggest a role of autaptic connections in controlling network-wide bursts in diverse cortical and subcortical regions of mammalian brain. Moreover, they open up new avenues for the study of dynamic neurophysiological correlates of structural controllability.
authors.affiliation: Department of Bioengineering, School of Engineering and Applied Sciences, University of Pennsylvania, Philadelphia, PA 19104, USA.
authors.forename: Laura
authors.initials: L
authors.lastname: Wiles
authors.affiliation: Department of Bioengineering, School of Engineering and Applied Sciences, University of Pennsylvania, Philadelphia, PA 19104, USA.|Applied Mathematics and Computational Science Graduate Program, School of Arts and Sciences, University of Pennsylvania, Philadelphia, PA 19104, USA.
authors.forename: Shi
authors.initials: S
authors.lastname: Gu
authors.affiliation: Department of Mechanical Engineering, University of California Riverside, CA, USA.
authors.forename: Fabio
authors.initials: F
authors.lastname: Pasqualetti
authors.affiliation: Department of Bioengineering, School of Engineering and Applied Sciences, University of Pennsylvania, Philadelphia, PA 19104, USA.
authors.forename: Brandon
authors.initials: B
authors.lastname: Parvesse
authors.affiliation: Department of Bioengineering, School of Engineering and Applied Sciences, University of Pennsylvania, Philadelphia, PA 19104, USA.
authors.forename: David
authors.initials: D
authors.lastname: Gabrieli
authors.affiliation: Department of Bioengineering, School of Engineering and Applied Sciences, University of Pennsylvania, Philadelphia, PA 19104, USA.|Department of Electrical and Systems Engineering, School of Engineering and Applied Sciences, University of Pennsylvania, Philadelphia, PA 19104, USA.
authors.forename: Danielle S
authors.initials: DS
authors.lastname: Bassett
authors.affiliation: Department of Bioengineering, School of Engineering and Applied Sciences, University of Pennsylvania, Philadelphia, PA 19104, USA.|Department of Neurosurgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA.
authors.forename: David F
authors.initials: DF
authors.lastname: Meaney
mesh_terms: D000200:Action Potentials; D003198:Computer Simulation; D008959:Models, Neurological; D009474:Neurons / Q000502:physiology*; D009435:Synaptic Transmission