ArticleFrontiers in neuroanatomy2016
Automatic Generation of Connectivity for Large-Scale Neuronal Network Models through Structural Plasticity.
Article in Frontiers in neuroanatomy, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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24 citing papers in PubMed.
- Calcium-based synaptic and structural plasticity link pathological activity to synaptic reorganization in Parkinson's disease.Science advances · 2025Article
- Article
- Resolving inconsistent effects of tDCS on learning using a homeostatic structural plasticity model.Frontiers in network physiology · 2025Article
- Arbor-TVB: a novel multi-scale co-simulation framework with a case study on neural-level seizure generation and whole-brain propagation.Frontiers in computational neuroscience · 2025Article
- Synaptic reorganization of synchronized neuronal networks with synaptic weight and structural plasticity.PLoS computational biology · 2024Article
- Repetitive transcranial magnetic stimulation (rTMS) triggers dose-dependent homeostatic rewiring in recurrent neuronal networks.PLoS computational biology · 2023Article
- STDP-Driven Rewiring in Spiking Neural Networks under Stimulus-Induced and Spontaneous Activity.Biomimetics (Basel, Switzerland) · 2023Article
- Dynamics of phase oscillator networks with synaptic weight and structural plasticity.Scientific reports · 2022Article
- Connectivity concepts in neuronal network modeling.PLoS computational biology · 2022Review
- Homeostatic control of synaptic rewiring in recurrent networks induces the formation of stable memory engrams.PLoS computational biology · 2022Article
- Bringing Anatomical Information into Neuronal Network Models.Advances in experimental medicine and biology · 2022Article
- On the Role of the Excitation/Inhibition Balance of Homeostatic Artificial Neural Networks.Entropy (Basel, Switzerland) · 2021Article
- Growth rules for the repair of Asynchronous Irregular neuronal networks after peripheral lesions.PLoS computational biology · 2021Article
- Long-Term Desynchronization by Coordinated Reset Stimulation in a Neural Network Model With Synaptic and Structural Plasticity.Frontiers in physiology · 2021Article
- Routing Brain Traffic Through the Von Neumann Bottleneck: Parallel Sorting and Refactoring.Frontiers in neuroinformatics · 2021Article
- Firing rate homeostasis counteracts changes in stability of recurrent neural networks caused by synapse loss in Alzheimer's disease.PLoS computational biology · 2020Article
- Efficient Communication in Distributed Simulations of Spiking Neuronal Networks With Gap Junctions.Frontiers in neuroinformatics · 2020Article
- Network remodeling induced by transcranial brain stimulation: A computational model of tDCS-triggered cell assembly formation.Network neuroscience (Cambridge, Mass.) · 2019Article
- Multicontact Co-operativity in Spike-Timing-Dependent Structural Plasticity Stabilizes Networks.Cerebral cortex (New York, N.Y. : 1991) · 2018Article
- Associative properties of structural plasticity based on firing rate homeostasis in recurrent neuronal networks.Scientific reports · 2018Article
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4 authors.
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No grant is acknowledged in the PubMed record.
Abstract
With the emergence of new high performance computation technology in the last decade, the simulation of large scale neural networks which are able to reproduce the behavior and structure of the brain has finally become an achievable target of neuroscience. Due to the number of synaptic connections between neurons and the complexity of biological networks, most contemporary models have manually defined or static connectivity. However, it is expected that modeling the dynamic generation and deletion of the links among neurons, locally and between different regions of the brain, is crucial to unravel important mechanisms associated with learning, memory and healing. Moreover, for many neural circuits that could potentially be modeled, activity data is more readily and reliably available than connectivity data. Thus, a framework that enables networks to wire themselves on the basis of specified activity targets can be of great value in specifying network models where connectivity data is incomplete or has large error margins. To address these issues, in the present work we present an implementation of a model of structural plasticity in the neural network simulator NEST. In this model, synapses consist of two parts, a pre- and a post-synaptic element. Synapses are created and deleted during the execution of the simulation following local homeostatic rules until a mean level of electrical activity is reached in the network. We assess the scalability of the implementation in order to evaluate its potential usage in the self generation of connectivity of large scale networks. We show and discuss the results of simulations on simple two population networks and more complex models of the cortical microcircuit involving 8 populations and 4 layers using the new framework.
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