ArticlePLoS computational biology2015
Scalability of Asynchronous Networks Is Limited by One-to-One Mapping between Effective Connectivity and Correlations.
Article in PLoS computational biology, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 41 papers.
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Who cites it
41 citing papers in PubMed.
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- Visualization and simulation of full-scale point-neuron circuits via the Neural Circuit Visualizer web platform.Scientific reports · 2026Article
- Iso-orientation bias of layer 2/3 connections unifies spontaneous, visually and optogenetically driven V1 dynamics.Nature communications · 2026Article
- Building on models-a perspective for computational neuroscience.Cerebral cortex (New York, N.Y. : 1991) · 2025Article
- Simultaneously determining regional heterogeneity and connection directionality from neural activity and symmetric connection.PLoS computational biology · 2025Article
- Modeling neuron-astrocyte interactions in neural networks using distributed simulation.PLoS computational biology · 2025Article
- Competition and cooperation of assembly sequences in recurrent neural networks.PLoS computational biology · 2025Article
- Cell-type-specific firing patterns in a V1 cortical column model depend on feedforward and feedback-driven states.PLoS computational biology · 2025Article
- Functional connectivity drifts during sleep as a marker of fluctuations in the level of consciousness.Neuroscience of consciousness · 2025Article
- Synaptic plasticity facilitates oscillations in a V1 cortical column model with multiple interneuron types.Frontiers in computational neuroscience · 2025Article
- Multi-scale spiking network model of human cerebral cortex.Cerebral cortex (New York, N.Y. : 1991) · 2024Article
- Reconciliation of weak pairwise spike-train correlations and highly coherent local field potentials across space.Cerebral cortex (New York, N.Y. : 1991) · 2024Article
- A comprehensive data-driven model of cat primary visual cortex.PLoS computational biology · 2024Article
- Efficient parameter calibration and real-time simulation of large-scale spiking neural networks with GeNN and NEST.Frontiers in neuroinformatics · 2023Article
- Editorial: Neuroscience, computing, performance, and benchmarks: Why it matters to neuroscience how fast we can compute.Frontiers in neuroinformatics · 2023Article
- Toward reproducible models of sequence learning: replication and analysis of a modular spiking network with reward-based learning.Frontiers in integrative neuroscience · 2023Article
- Connectivity concepts in neuronal network modeling.PLoS computational biology · 2022Review
- Bringing Anatomical Information into Neuronal Network Models.Advances in experimental medicine and biology · 2022Article
- A Spiking Neural Network Builder for Systematic Data-to-Model Workflow.Frontiers in neuroinformatics · 2022Article
- A Modular Workflow for Performance Benchmarking of Neuronal Network Simulations.Frontiers in neuroinformatics · 2022Article
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3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Network models are routinely downscaled compared to nature in terms of numbers of nodes or edges because of a lack of computational resources, often without explicit mention of the limitations this entails. While reliable methods have long existed to adjust parameters such that the first-order statistics of network dynamics are conserved, here we show that limitations already arise if also second-order statistics are to be maintained. The temporal structure of pairwise averaged correlations in the activity of recurrent networks is determined by the effective population-level connectivity. We first show that in general the converse is also true and explicitly mention degenerate cases when this one-to-one relationship does not hold. The one-to-one correspondence between effective connectivity and the temporal structure of pairwise averaged correlations implies that network scalings should preserve the effective connectivity if pairwise averaged correlations are to be held constant. Changes in effective connectivity can even push a network from a linearly stable to an unstable, oscillatory regime and vice versa. On this basis, we derive conditions for the preservation of both mean population-averaged activities and pairwise averaged correlations under a change in numbers of neurons or synapses in the asynchronous regime typical of cortical networks. We find that mean activities and correlation structure can be maintained by an appropriate scaling of the synaptic weights, but only over a range of numbers of synapses that is limited by the variance of external inputs to the network. Our results therefore show that the reducibility of asynchronous networks is fundamentally limited.
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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.