ArticlePLoS computational biology2017
Fundamental Activity Constraints Lead to Specific Interpretations of the Connectome.
Article in PLoS computational biology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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20 citing papers in PubMed.
- Habit learning is associated with efficiently controlled network dynamics in naive macaque monkeys.Npj complexity · 2026Article
- Building on models-a perspective for computational neuroscience.Cerebral cortex (New York, N.Y. : 1991) · 2025Article
- Reconciliation of weak pairwise spike-train correlations and highly coherent local field potentials across space.Cerebral cortex (New York, N.Y. : 1991) · 2024Article
- Multi-scale spiking network model of human cerebral cortex.Cerebral cortex (New York, N.Y. : 1991) · 2024Article
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- Cortical Representation of Touch in Silico.Neuroinformatics · 2022Article
- Fast Simulation of a Multi-Area Spiking Network Model of Macaque Cortex on an MPI-GPU Cluster.Frontiers in neuroinformatics · 2022Article
- Biological constraints on neural network models of cognitive function.Nature reviews. Neuroscience · 2021Review
- NetPyNE Implementation and Scaling of the Potjans-Diesmann Cortical Microcircuit Model.Neural computation · 2021Article
- DNN-assisted statistical analysis of a model of local cortical circuits.Scientific reports · 2020Article
- Optimal Interplay between Synaptic Strengths and Network Structure Enhances Activity Fluctuations and Information Propagation in Hierarchical Modular Networks.Brain sciences · 2020Article
- Stability of stochastic finite-size spiking-neuron networks: Comparing mean-field, 1-loop correction and quasi-renewal approximations.Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference · 2019Article
- A multi-scale layer-resolved spiking network model of resting-state dynamics in macaque visual cortical areas.PLoS computational biology · 2018Article
- Investigating the Correlation-Firing Rate Relationship in Heterogeneous Recurrent Networks.Journal of mathematical neuroscience · 2018Article
- Multi-scale account of the network structure of macaque visual cortex.Brain structure & function · 2018Article
- Toward Rigorous Parameterization of Underconstrained Neural Network Models Through Interactive Visualization and Steering of Connectivity Generation.Frontiers in neuroinformatics · 2018Article
- Towards a theory of cortical columns: From spiking neurons to interacting neural populations of finite size.PLoS computational biology · 2017Article
- Integration of Continuous-Time Dynamics in a Spiking Neural Network Simulator.Frontiers in neuroinformatics · 2017Article
- Integrating Visualizations into Modeling NEST Simulations.Frontiers in neuroinformatics · 2015Article
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5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The continuous integration of experimental data into coherent models of the brain is an increasing challenge of modern neuroscience. Such models provide a bridge between structure and activity, and identify the mechanisms giving rise to experimental observations. Nevertheless, structurally realistic network models of spiking neurons are necessarily underconstrained even if experimental data on brain connectivity are incorporated to the best of our knowledge. Guided by physiological observations, any model must therefore explore the parameter ranges within the uncertainty of the data. Based on simulation results alone, however, the mechanisms underlying stable and physiologically realistic activity often remain obscure. We here employ a mean-field reduction of the dynamics, which allows us to include activity constraints into the process of model construction. We shape the phase space of a multi-scale network model of the vision-related areas of macaque cortex by systematically refining its connectivity. Fundamental constraints on the activity, i.e., prohibiting quiescence and requiring global stability, prove sufficient to obtain realistic layer- and area-specific activity. Only small adaptations of the structure are required, showing that the network operates close to an instability. The procedure identifies components of the network critical to its collective dynamics and creates hypotheses for structural data and future experiments. The method can be applied to networks involving any neuron model with a known gain function.
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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.