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.
Traumatic brain injury (TBI) can lead to neurodegeneration in the injured circuitry, either through primary structural damage to the neuron or secondary effects that disrupt key cellular processes. Moreover, traumatic injuries can preferentially impact subpopulations of neurons, but the functional network effects of these targeted degeneration profiles remain unclear. Although isolating the consequences of complex injury dynamics and long-term recovery of the circuit can be difficult to control experimentally, computational networks can be a powerful tool to analyze the consequences of injury. Here, we use the Izhikevich spiking neuron model to create networks representative of cortical tissue. After an initial settling period with spike-timing-dependent plasticity (STDP), networks developed rhythmic oscillations similar to those seen in vivo. As neurons were sequentially removed from the network, population activity rate and oscillation dynamics were significantly reduced. In a successive period of network restructuring with STDP, network activity levels returned to baseline for some injury levels and oscillation dynamics significantly improved. We next explored the role that specific neurons have in the creation and termination of oscillation dynamics. We determined that oscillations initiate from activation of low firing rate neurons with limited structural inputs. To terminate oscillations, high activity excitatory neurons with strong input connectivity activate downstream inhibitory circuitry. Finally, we confirm the excitatory neuron population role through targeted neurodegeneration. These results suggest targeted neurodegeneration can play a key role in the oscillation dynamics after injury.
18Traumatic brain injury (TBI) can lead to neurodegeneration in the injured circuitry, either through primary 19 structural damage to the neuron or secondary effects that disrupt key cellular processes. Moreover, 20 traumatic injuries can preferentially impact subpopulations of neurons, but the functional network effects 21 of these targeted degeneration profiles remain unclear. Although isolating the consequences of complex 22 Author Summary 37In this study, we study the impact of neuronal degeneration -a process that commonly occurs after 38 traumatic injury and neurodegenerative disease -on the neuronal dynamics in a cortical network. We 39 create computational models of neural networks and include spike timing plasticity to alter the synaptic 40 strength among connections as networks remodel after simulated injury. We find that spike-timing 41 dependent plasticity helps recover the neural dynamics of an injured microcircuit, but it frequently 42 cannot recover the original oscillation dynamics in an uninjured network. In addition, we find that 43 selectively injuring excitatory neurons with the highest firing rate reduced the neuronal oscillations in a 44 circuit much more than either random deletion or the removing neurons with the lowest firing rate. In 45 all, these data suggest (a) plasticity reduces the consequences of neurodegeneration and (b) losing the 46 most active neurons in the network has the most adverse effect on neural oscillations. 47
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