Synaptic reorganization of synchronized neuronal networks with synaptic weight and structural plasticity DOI Creative Commons
Kanishk Chauhan, Alexander Neiman, Peter A. Tass

et al.

PLoS Computational Biology, Journal Year: 2024, Volume and Issue: 20(7), P. e1012261 - e1012261

Published: July 9, 2024

Abnormally strong neural synchronization may impair brain function, as observed in several disorders. We computationally study how neuronal dynamics, synaptic weights, and network structure co-emerge, particular, during (de)synchronization processes they are affected by external perturbation. To investigate the impact of different types plasticity mechanisms, we combine a excitatory integrate-and-fire neurons with weight and/or structural mechanisms: (i) only spike-timing-dependent (STDP), (ii) homeostatic (hSP), i.e., without weight-dependent pruning STDP, (iii) combination STDP hSP, pruning, (iv) (SP) that includes hSP pruning. accommodate diverse time scales firing, SP, introduce simple stochastic SP model, enabling detailed numerical analyses. With tools from theory, reveal reorganization remarkably enhance network’s level synchrony. When weaker contacts preferentially eliminated synchrony is achieved significantly sparser connections than randomly structured networks STDP-only model. In strengthening higher natural firing rates to those lower weakening opposite direction, followed selective removal weak contacts, allows for fewer connections. This activity-led results emergence degree-frequency, degree-degree correlations, mixture degree assortativity. compare stimulation-induced desynchronization synchronized states model models (iv). The latter require stimuli intensity achieve long-term desynchronization. These findings inform future pre-clinical clinical studies invasive or non-invasive stimulus modalities aiming at inducing long-lasting relief symptoms, e.g., Parkinson’s disease.

Language: Английский

Synaptic reorganization of synchronized neuronal networks with synaptic weight and structural plasticity DOI Creative Commons
Kanishk Chauhan, Alexander Neiman, Peter A. Tass

et al.

PLoS Computational Biology, Journal Year: 2024, Volume and Issue: 20(7), P. e1012261 - e1012261

Published: July 9, 2024

Abnormally strong neural synchronization may impair brain function, as observed in several disorders. We computationally study how neuronal dynamics, synaptic weights, and network structure co-emerge, particular, during (de)synchronization processes they are affected by external perturbation. To investigate the impact of different types plasticity mechanisms, we combine a excitatory integrate-and-fire neurons with weight and/or structural mechanisms: (i) only spike-timing-dependent (STDP), (ii) homeostatic (hSP), i.e., without weight-dependent pruning STDP, (iii) combination STDP hSP, pruning, (iv) (SP) that includes hSP pruning. accommodate diverse time scales firing, SP, introduce simple stochastic SP model, enabling detailed numerical analyses. With tools from theory, reveal reorganization remarkably enhance network’s level synchrony. When weaker contacts preferentially eliminated synchrony is achieved significantly sparser connections than randomly structured networks STDP-only model. In strengthening higher natural firing rates to those lower weakening opposite direction, followed selective removal weak contacts, allows for fewer connections. This activity-led results emergence degree-frequency, degree-degree correlations, mixture degree assortativity. compare stimulation-induced desynchronization synchronized states model models (iv). The latter require stimuli intensity achieve long-term desynchronization. These findings inform future pre-clinical clinical studies invasive or non-invasive stimulus modalities aiming at inducing long-lasting relief symptoms, e.g., Parkinson’s disease.

Language: Английский

Citations

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