ArticleFrontiers in computational neuroscience2019
Controlling Synchronization of Spiking Neuronal Networks by Harnessing Synaptic Plasticity.
Article in Frontiers in computational neuroscience, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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8 citing papers in PubMed.
- Proportional-Integral Controller-Based Deep Brain Stimulation Strategy for Controlling Excitatory-Inhibitory Network Synchronization.Proceedings of the ... American Control Conference. American Control Conference · 2025Article
- Time delays in computational models of neuronal and synaptic dynamics.Frontiers in computational neuroscience · 2025Article
- Coordinated reset stimulation of plastic neural networks with spatially dependent synaptic connections.Frontiers in network physiology · 2024Article
- Synchronization in STDP-driven memristive neural networks with time-varying topology.Journal of biological physics · 2023Article
- Engineering spatiotemporal patterns: information encoding, processing, and controllability in oscillator ensembles.Biomedical physics & engineering express · 2023Article
- Decoupling of interacting neuronal populations by time-shifted stimulation through spike-timing-dependent plasticity.PLoS computational biology · 2023Article
- Controlling neocortical epileptic seizures using forced temporal spike-time stimulation: anFrontiers in computational neuroscience · 2023Article
- Synaptic reshaping of plastic neuronal networks by periodic multichannel stimulation with single-pulse and burst stimuli.PLoS computational biology · 2022Article
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2 authors.
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Abstract
Disrupting the pathological synchronous firing patterns of neurons with high frequency stimulation is a common treatment for Parkinsonian symptoms and epileptic seizures when pharmaceutical drugs fail. In this paper, our goal is to design a desynchronization strategy for large networks of spiking neurons such that the neuronal activity of the network remains in the desynchronized regime for a long period of time after the removal of the stimulation. We develop a novel "Forced Temporal-Spike Time Stimulation (FTSTS)" strategy that harnesses the spike-timing dependent plasticity to control the synchronization of neural activity in the network by forcing the neurons in the network to artificially fire in a specific temporal pattern. Our strategy modulates the synaptic strengths of selective synapses to achieve a desired synchrony of neural activity in the network. Our simulation results show that the FTSTS strategy can effectively synchronize or desynchronize neural activity in large spiking neuron networks and keep them in the desired state for a long period of time after the removal of the external stimulation. Using simulations, we demonstrate the robustness of our strategy in desynchronizing neural activity of networks against uncertainties in the designed stimulation pulses and network parameters. Additionally, we show in simulation, how our strategy could be incorporated within the existing desynchronization strategies to improve their overall efficacy in desynchronizing large networks. Our proposed strategy provides complete control over the synchronization of neurons in large networks and can be used to either synchronize or desynchronize neural activity based on specific applications. Moreover, it can be incorporated within other desynchronization strategies to improve the efficacy of existing therapies for numerous neurological and psychiatric disorders associated with pathological synchronization.
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