首页|Swiss Federal Institute of Technology Zurich (ETH) Reports Findings in Neural Co mputation (A Multimodal Fitting Approach to Construct Single-Neuron Models with Patch Clamp and High- Density Microelectrode Arrays)
Swiss Federal Institute of Technology Zurich (ETH) Reports Findings in Neural Co mputation (A Multimodal Fitting Approach to Construct Single-Neuron Models with Patch Clamp and High- Density Microelectrode Arrays)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Computation - Neural C omputation is the subject of a report. According to news reporting originating f rom Basel, Switzerland, by NewsRx correspondents, research stated, “In computati onal neuroscience, multicompartment models are among the most biophysically real istic representations of single neurons. Constructing such models usually involv es the use of the patch-clamp technique to record somatic voltage signals under different experimental conditions.”Our news editors obtained a quote from the research from the Swiss Federal Insti tute of Technology Zurich (ETH), “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 ev idenced 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-c lamp 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 patchclamp and HD-MEA data to construct multicompar tment models. We first validate our method on a ground-truth model with known pa rameters and show that the use of features extracted from extracellular signals, in addition to intracellular ones, yields models enabling better fits than usin g intracellular features alone. We also demonstrate our procedure using experime ntal data by constructing cell models from in vitro cell cultures.”
BaselSwitzerlandEuropeComputationa l NeuroscienceHealth and MedicineNeural Computation
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