ArticlePLoS computational biology2016
Plasticity-Driven Self-Organization under Topological Constraints Accounts for Non-random Features of Cortical Synaptic Wiring.
Article in PLoS computational biology, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 29 papers.
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29 citing papers in PubMed, 63 citations in OpenAlex.
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- The impact of neuron morphology on cortical network architecture.Cell reports · 2022Article
- Microcircuits for spatial coding in the medial entorhinal cortex.Physiological reviews · 2022Review
- Dynamics and Information Import in Recurrent Neural Networks.Frontiers in computational neuroscience · 2022Article
- Self-Organized Structuring of Recurrent Neuronal Networks for Reliable Information Transmission.Biology · 2021Article
- Local Homeostatic Regulation of the Spectral Radius of Echo-State Networks.Frontiers in computational neuroscience · 2021Article
- Autonomous emergence of connectivity assemblies via spike triplet interactions.PLoS computational biology · 2020Article
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- The self-organized learning of noisy environmental stimuli requires distinct phases of plasticity.Network neuroscience (Cambridge, Mass.) · 2020Article
- Emergence of Stable Synaptic Clusters on Dendrites Through Synaptic Rewiring.Frontiers in computational neuroscience · 2020Article
- The Interplay of Synaptic Plasticity and Scaling Enables Self-Organized Formation and Allocation of Multiple Memory Representations.Frontiers in neural circuits · 2020Article
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- Emergence of spontaneous assembly activity in developing neural networks without afferent input.PLoS computational biology · 2018Article
- Bridging structure and function: A model of sequence learning and prediction in primary visual cortex.PLoS computational biology · 2018Article
- 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
- Ensemble stacking mitigates biases in inference of synaptic connectivity.Network neuroscience (Cambridge, Mass.) · 2018Article
- A Knowledge-Based Arrangement of Prototypical Neural Representation Prior to Experience Contributes to Selectivity in Upcoming Knowledge Acquisition.Frontiers in human neuroscience · 2018Article
- Challenges in Reproducibility, Replicability, and Comparability of Computational Models and Tools for Neuronal and Glial Networks, Cells, and Subcellular Structures.Frontiers in neuroinformatics · 2018Article
Corrections and comments
- Erratum issued
Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Understanding the structure and dynamics of cortical connectivity is vital to understanding cortical function. Experimental data strongly suggest that local recurrent connectivity in the cortex is significantly non-random, exhibiting, for example, above-chance bidirectionality and an overrepresentation of certain triangular motifs. Additional evidence suggests a significant distance dependency to connectivity over a local scale of a few hundred microns, and particular patterns of synaptic turnover dynamics, including a heavy-tailed distribution of synaptic efficacies, a power law distribution of synaptic lifetimes, and a tendency for stronger synapses to be more stable over time. Understanding how many of these non-random features simultaneously arise would provide valuable insights into the development and function of the cortex. While previous work has modeled some of the individual features of local cortical wiring, there is no model that begins to comprehensively account for all of them. We present a spiking network model of a rodent Layer 5 cortical slice which, via the interactions of a few simple biologically motivated intrinsic, synaptic, and structural plasticity mechanisms, qualitatively reproduces these non-random effects when combined with simple topological constraints. Our model suggests that mechanisms of self-organization arising from a small number of plasticity rules provide a parsimonious explanation for numerous experimentally observed non-random features of recurrent cortical wiring. Interestingly, similar mechanisms have been shown to endow recurrent networks with powerful learning abilities, suggesting that these mechanism are central to understanding both structure and function of cortical synaptic wiring.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.