ArticleNature communications2020
An efficient analytical reduction of detailed nonlinear neuron models.
Article in Nature communications, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed.
- The Neural Analysis Toolkit Unifies Semi-Analytical Techniques to Simplify, Understand, and Simulate Dendrites.Neuroinformatics · 2026Article
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- Predicting neural responses to intra- and extra-cranial electric brain stimulation by means of the reciprocity theorem.PLoS computational biology · 2025Article
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- Neuromorphic algorithms for brain implants: a review.Frontiers in neuroscience · 2025Review
- Dendritic synaptome of calcium-binding protein containing GABAergic interneurons in the mouse primary visual cortex.Frontiers in neural circuits · 2025Article
- Regulation of XOR function of reduced human L2/3 pyramidal neurons.Cognitive neurodynamics · 2024Article
- Sub-threshold neuronal activity and the dynamical regime of cerebral cortex.Nature communications · 2024Article
- Network-neuron interactions underlying sensory responses of layer 5 pyramidal tract neurons in barrel cortex.PLoS computational biology · 2024Article
- How neuronal morphology impacts the synchronisation state of neuronal networks.PLoS computational biology · 2024Article
- Introducing the Dendrify framework for incorporating dendrites to spiking neural networks.Nature communications · 2023Article
- Virtual Intelligence: A Systematic Review of the Development of Neural Networks in Brain Simulation Units.Brain sciences · 2022Review
- Ultrafast simulation of large-scale neocortical microcircuitry with biophysically realistic neurons.eLife · 2022Article
- Brain signal predictions from multi-scale networks using a linearized framework.PLoS computational biology · 2022Article
- Analog transmission of action potential fine structure in spiral ganglion axons.Journal of neurophysiology · 2021Article
- A convolutional neural-network framework for modelling auditory sensory cells and synapses.Communications biology · 2021Article
- A convolutional neural-network model of human cochlear mechanics and filter tuning for real-time applications.Nature machine intelligence · 2021Article
- Data-driven reduction of dendritic morphologies with preserved dendro-somatic responses.eLife · 2021Article
- Coding Capacity of Purkinje Cells With Different Schemes of Morphological Reduction.Frontiers in computational neuroscience · 2019Article
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7 authors.
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Abstract
Detailed conductance-based nonlinear neuron models consisting of thousands of synapses are key for understanding of the computational properties of single neurons and large neuronal networks, and for interpreting experimental results. Simulations of these models are computationally expensive, considerably curtailing their utility. Neuron_Reduce is a new analytical approach to reduce the morphological complexity and computational time of nonlinear neuron models. Synapses and active membrane channels are mapped to the reduced model preserving their transfer impedance to the soma; synapses with identical transfer impedance are merged into one NEURON process still retaining their individual activation times. Neuron_Reduce accelerates the simulations by 40-250 folds for a variety of cell types and realistic number (10,000-100,000) of synapses while closely replicating voltage dynamics and specific dendritic computations. The reduced neuron-models will enable realistic simulations of neural networks at unprecedented scale, including networks emerging from micro-connectomics efforts and biologically-inspired "deep networks". Neuron_Reduce is publicly available and is straightforward to implement.
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