Journal: Neural Computation
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Abbreviation
Neural Comput
Publisher
MIT Press
47 results
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Publications 1 - 10 of 47
- Mutual Inhibition with Few Inhibitory Cells via Nonlinear Inhibitory Synaptic InteractionItem type: Journal Article
Neural ComputationWeissenberger, Felix; Gauy, Marcelo Matheus; Zou, Xun; et al. (2019) - Learning the nonlinearity of neurons from natural visual stimuliItem type: Journal Article
Neural ComputationKayser, Christoph; Körding, Konrad P.; König, Peter (2003) - A Multimodal Fitting Approach to Construct Single-Neuron Models With Patch Clamp and High-Density Microelectrode ArraysItem type: Journal Article
Neural ComputationBuccino, Alessio Paolo; Damart, Tanguy; Bartram, Julian; et al. (2024)In computational neuroscience, multicompartment models are among the most biophysically realistic representations of single neurons. Constructing such models usually involves the use of the patch-clamp technique to record somatic voltage signals under different experimental conditions. The experimental data are then used to fit the many parameters of the model. While patching of the soma is currently the gold-standard approach to build multicompartment models, several studies have also evidenced a richness of dynamics in dendritic and axonal sections. Recording from the soma alone makes it hard to observe and correctly parameterize the activity of nonsomatic compartments. In order to provide a richer set of data as input to multicompartment models, we here investigate the combination of somatic patch-clamp recordings with recordings of high-density microelectrode arrays (HD-MEAs). HD-MEAs enable the observation of extracellular potentials and neural activity of neuronal compartments at subcellular resolution. In this work, we introduce a novel framework to combine patch-clamp and HD-MEA data to construct multicompartment models. We first validate our method on a ground-truth model with known parameters and show that the use of features extracted from extracellular signals, in addition to intracellular ones, yields models enabling better fits than using intracellular features alone. We also demonstrate our procedure using experimental data by constructing cell models from in vitro cell cultures. The proposed multimodal fitting procedure has the potential to augment the modeling efforts of the computational neuroscience community and provide the field with neuronal models that are more realistic and can be better validated. - Image segmentation by networks of spiking neuronsItem type: Journal Article
Neural ComputationBuhmann, Joachim M.; Lange, Tilman; Ramacher, Ulrich (2005) - Modeling short-term synaptic depression in siliconItem type: Journal Article
Neural ComputationBoegerhausen, Malte; Suter, Pascal; Liu, Shih-Chii (2003) - Learning Active Fusion of Multiple Experts’ Decisions: An Attention-Based ApproachItem type: Journal Article
Neural ComputationMirian, Maryam S.; Ahmadabadi, Majid N.; Araabi, Babak N.; et al. (2011) - Principles and Typical Computational Limitations of Sparse Speaker Separation Based on Deterministic Speech FeaturesItem type: Journal Article
Neural ComputationKern, Albert; Stoop, Ruedi (2011) - Hardware-Amenable Structural Learning for Spike-Based Pattern Classification Using a Simple Model of Active DendritesItem type: Journal Article
Neural ComputationHussain, S.; Liu, S.C.; Basu, A. (2015) - Computation with Spikes in a Winner-Take-All NetworkItem type: Journal Article
Neural ComputationOster, Matthias; Douglas, Rodney; Liu, Shih-Chii (2009) - Two-state membrane potential fluctuations driven by weak pairwise correlationsItem type: Journal Article
Neural ComputationBenucci, Andrea; Verschure, Paul F. M. J.; König, Peter (2004)
Publications 1 - 10 of 47