Neural Computation

Journal: Neural Computation

Abbreviation

Neural Comput

Publisher

MIT Press

ISSN

0899-7667
1530-888X

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Publications 1 - 10 of 47

Mutual Inhibition with Few Inhibitory Cells via Nonlinear Inhibitory Synaptic Interaction
Weissenberger, Felix; Gauy, Marcelo Matheus; Zou, Xun; et al. (2019)
Neural Computation
Learning the nonlinearity of neurons from natural visual stimuli
Kayser, Christoph; Körding, Konrad P.; König, Peter (2003)
Neural Computation
A Multimodal Fitting Approach to Construct Single-Neuron Models With Patch Clamp and High-Density Microelectrode Arrays
Buccino, Alessio Paolo; Damart, Tanguy; Bartram, Julian; et al. (2024)
Neural Computation
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 neurons
Buhmann, Joachim M.; Lange, Tilman; Ramacher, Ulrich (2005)
Neural Computation
Modeling short-term synaptic depression in silicon
Boegerhausen, Malte; Suter, Pascal; Liu, Shih-Chii (2003)
Neural Computation
Learning Active Fusion of Multiple Experts’ Decisions: An Attention-Based Approach
Mirian, Maryam S.; Ahmadabadi, Majid N.; Araabi, Babak N.; et al. (2011)
Neural Computation
Principles and Typical Computational Limitations of Sparse Speaker Separation Based on Deterministic Speech Features
Kern, Albert; Stoop, Ruedi (2011)
Neural Computation
Hardware-Amenable Structural Learning for Spike-Based Pattern Classification Using a Simple Model of Active Dendrites
Hussain, S.; Liu, S.C.; Basu, A. (2015)
Neural Computation
Computation with Spikes in a Winner-Take-All Network
Oster, Matthias; Douglas, Rodney; Liu, Shih-Chii (2009)
Neural Computation
Two-state membrane potential fluctuations driven by weak pairwise correlations
Benucci, Andrea; Verschure, Paul F. M. J.; König, Peter (2004)
Neural Computation

Publications 1 - 10 of 47

Journal: Neural Computation

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