ArticlePLoS computational biology2015
Automated High-Throughput Characterization of Single Neurons by Means of Simplified Spiking Models.
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 39 papers.
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Who cites it
39 citing papers in PubMed.
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- Nonlinear recurrent inhibition through facilitating serotonin release in the raphe.Nature neuroscience · 2025Article
- Biologically inspired heterogeneous learning for accurate, efficient and low-latency neural network.National science review · 2025Article
- Simplified two-compartment neuron with calcium dynamics capturing brain-state specific apical-amplification, -isolation and -drive.Frontiers in computational neuroscience · 2025Article
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- Neural networks with optimized single-neuron adaptation uncover biologically plausible regularization.PLoS computational biology · 2024Article
- Short-term neuronal and synaptic plasticity act in synergy for deviance detection in spiking networks.PLoS computational biology · 2023Article
- Adult-born granule cells improve stimulus encoding and discrimination in the dentate gyrus.eLife · 2023Article
- Low-dimensional models of single neurons: a review.Biological cybernetics · 2023Review
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- Mapping input noise to escape noise in integrate-and-fire neurons: a level-crossing approach.Biological cybernetics · 2021Article
- Spike frequency adaptation supports network computations on temporally dispersed information.eLife · 2021Article
- An adaptive threshold neuron for recurrent spiking neural networks with nanodevice hardware implementation.Nature communications · 2021Article
- A convolutional neural-network framework for modelling auditory sensory cells and synapses.Communications biology · 2021Article
- Linear-nonlinear cascades capture synaptic dynamics.PLoS computational biology · 2021Article
- Nonlinear effects of intrinsic dynamics on temporal encoding in a model of avian auditory cortex.PLoS computational biology · 2021Article
- Data-driven reduction of dendritic morphologies with preserved dendro-somatic responses.eLife · 2021Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Single-neuron models are useful not only for studying the emergent properties of neural circuits in large-scale simulations, but also for extracting and summarizing in a principled way the information contained in electrophysiological recordings. Here we demonstrate that, using a convex optimization procedure we previously introduced, a Generalized Integrate-and-Fire model can be accurately fitted with a limited amount of data. The model is capable of predicting both the spiking activity and the subthreshold dynamics of different cell types, and can be used for online characterization of neuronal properties. A protocol is proposed that, combined with emergent technologies for automatic patch-clamp recordings, permits automated, in vitro high-throughput characterization of single neurons.
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