ArticleFrontiers in computational neuroscience2015
An algorithm to predict the connectome of neural microcircuits.
Article in Frontiers in computational neuroscience, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 61 papers.
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61 citing papers in PubMed.
- Structural characteristics of local cortical networks wired by distance dependent connectivity rules.BMC neuroscience · 2026Article
- Cortical PV and VIP interneurons similarly influence SST neuron output despite distinct unitary properties.Communications biology · 2026Article
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- Intensity-dependent tACS entrainment effects in a cortical microcircuit: a computational study.Scientific reports · 2026Article
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- Reconstruction of a connectome of single neurons in mouse brains by cross-validating multi-scale multi-modality data.Nature methods · 2025Article
- Combined topological and spatial constraints are required to capture the structure of neural connectomes.Network neuroscience (Cambridge, Mass.) · 2025Article
- A connectome manipulation framework for the systematic and reproducible study of structure-function relationships through simulations.Network neuroscience (Cambridge, Mass.) · 2025Article
- Specific inhibition and disinhibition in the higher-order structure of a cortical connectome.Cerebral cortex (New York, N.Y. : 1991) · 2024Article
- Community-based reconstruction and simulation of a full-scale model of the rat hippocampus CA1 region.PLoS biology · 2024Article
- Reconciliation of weak pairwise spike-train correlations and highly coherent local field potentials across space.Cerebral cortex (New York, N.Y. : 1991) · 2024Article
- Large-Scale Mechanistic Models of Brain Circuits with Biophysically and Morphologically Detailed Neurons.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2024Review
- Modeling the impact of neuromorphological alterations in Down syndrome on fast neural oscillations.PLoS computational biology · 2024Article
- Cortical cell assemblies and their underlying connectivity: An in silico study.PLoS computational biology · 2024Article
- The impact of Parkinson's disease on striatal network connectivity and corticostriatal drive: An in silico study.Network neuroscience (Cambridge, Mass.) · 2024Article
- Simulation-based inference for efficient identification of generative models in computational connectomics.PLoS computational biology · 2023Article
- Simulations of cortical networks using spatially extended conductance-based neuronal models.The Journal of physiology · 2023Review
- Relative contributions of different neural sources to the EEG.NeuroImage · 2023Article
- Thalamic control of sensory processing and spindles in a biophysical somatosensory thalamoreticular circuit model of wakefulness and sleep.Cell reports · 2023Article
- Model simulations unveil the structure-function-dynamics relationship of the cerebellar cortical microcircuit.Communications biology · 2022Article
1 more citing papers are in PubMed but not listed here.
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5 authors.
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Abstract
Experimentally mapping synaptic connections, in terms of the numbers and locations of their synapses and estimating connection probabilities, is still not a tractable task, even for small volumes of tissue. In fact, the six layers of the neocortex contain thousands of unique types of synaptic connections between the many different types of neurons, of which only a handful have been characterized experimentally. Here we present a theoretical framework and a data-driven algorithmic strategy to digitally reconstruct the complete synaptic connectivity between the different types of neurons in a small well-defined volume of tissue-the micro-scale connectome of a neural microcircuit. By enforcing a set of established principles of synaptic connectivity, and leveraging interdependencies between fundamental properties of neural microcircuits to constrain the reconstructed connectivity, the algorithm yields three parameters per connection type that predict the anatomy of all types of biologically viable synaptic connections. The predictions reproduce a spectrum of experimental data on synaptic connectivity not used by the algorithm. We conclude that an algorithmic approach to the connectome can serve as a tool to accelerate experimental mapping, indicating the minimal dataset required to make useful predictions, identifying the datasets required to improve their accuracy, testing the feasibility of experimental measurements, and making it possible to test hypotheses of synaptic connectivity.
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