ArticleeLife2022
Ultrafast simulation of large-scale neocortical microcircuitry with biophysically realistic neurons.
Article in eLife, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
10 citing papers in PubMed.
- Beyond-local neural information processing in neuronal networks.Computational and structural biotechnology journal · 2024Article
- Predicting the temporal-dynamic trajectories of cortical neuronal responses in non-human primates based on deep spiking neural network.Cognitive neurodynamics · 2024Article
- Multitask learning of a biophysically-detailed neuron model.PLoS computational biology · 2024Article
- Reproducibility of biophysical in silico neuron states and spikes from event-based partial histories.PLoS computational biology · 2023Article
- Simulations of cortical networks using spatially extended conductance-based neuronal models.The Journal of physiology · 2023Review
- Rapid estimation of cortical neuron activation thresholds by transcranial magnetic stimulation using convolutional neural networks.NeuroImage · 2023Article
- Quantitative models of auditory cortical processing.Hearing research · 2023Review
- A faster way to model neuronal circuitry.eLife · 2022Article
- Ultrafast simulation of large-scale neocortical microcircuitry with biophysically realistic neurons.eLife · 2022Article
- Bridging Hierarchies in Multi-Scale Models of Neural Systems: Look-Up Tables Enable Computationally Efficient Simulations of Non-linear Synaptic Dynamics.Frontiers in computational neuroscience · 2021Article
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3 authors.
Funding
Abstract
Understanding the activity of the mammalian brain requires an integrative knowledge of circuits at distinct scales, ranging from ion channel gating to circuit connectomics. Computational models are regularly employed to understand how multiple parameters contribute synergistically to circuit behavior. However, traditional models of anatomically and biophysically realistic neurons are computationally demanding, especially when scaled to model local circuits. To overcome this limitation, we trained several artificial neural network (ANN) architectures to model the activity of realistic multicompartmental cortical neurons. We identified an ANN architecture that accurately predicted subthreshold activity and action potential firing. The ANN could correctly generalize to previously unobserved synaptic input, including in models containing nonlinear dendritic properties. When scaled, processing times were orders of magnitude faster compared with traditional approaches, allowing for rapid parameter-space mapping in a circuit model of Rett syndrome. Thus, we present a novel ANN approach allowing for rapid, detailed network experiments using inexpensive and commonly available computational resources.
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Registered trials
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.