Multiple models to capture the variability in biological neurons and networks.
about
Wisdom of crowds for robust gene network inferenceThe BRAIN Initiative: developing technology to catalyse neuroscience discovery.Robust circuit rhythms in small circuits arise from variable circuit components and mechanismsExploiting mathematical models to illuminate electrophysiological variability between individualsComputational models in the age of large datasetsFluctuation-Driven Neural Dynamics Reproduce Drosophila Locomotor PatternsComputational modeling of seizure dynamics using coupled neuronal networks: factors shaping epileptiform activitySuccessful reconstruction of a physiological circuit with known connectivity from spiking activity aloneDistal spike initiation zone location estimation by morphological simulation of ionic current filtering demonstrated in a novel model of an identified Drosophila motoneuronPopulation of computational rabbit-specific ventricular action potential models for investigating sources of variability in cellular repolarisationAutomatic Construction of Predictive Neuron Models through Large Scale Assimilation of Electrophysiological DataThe theory of constructed emotion: an active inference account of interoception and categorization.HDAC2 regulates atypical antipsychotic responses through the modulation of mGlu2 promoter activity.The magnitudes of hyperpolarization-activated and low-voltage-activated potassium currents co-vary in neurons of the ventral cochlear nucleus.Computing with neural synchrony.Neural and genetic degeneracy underlies Caenorhabditis elegans feeding behavior.Homeostasis of functional maps in active dendrites emerges in the absence of individual channelostasis.The Electrogenic Na+/K+ Pump Is a Key Determinant of Repolarization Abnormality Susceptibility in Human Ventricular Cardiomyocytes: A Population-Based Simulation Study.On the nature of seizure dynamics.Patterns of presynaptic activity and synaptic strength interact to produce motor output.Adaptive transcriptional dynamics of A2 neurons and central cardiovascular control pathways.LEMS: a language for expressing complex biological models in concise and hierarchical form and its use in underpinning NeuroML 2.Using multi-compartment ensemble modeling as an investigative tool of spatially distributed biophysical balances: application to hippocampal oriens-lacunosum/moleculare (O-LM) cells.Identification of a functional connectome for long-term fear memory in miceThe effects of temperature on the stability of a neuronal oscillator.A calcium-dependent plasticity rule for HCN channels maintains activity homeostasis and stable synaptic learningConnecting a connectome to behavior: an ensemble of neuroanatomical models of C. elegans klinotaxisAutomatic parameter estimation of multicompartmental neuron models via minimization of trace error with control adjustment.Related neuropeptides use different balances of unitary mechanisms to modulate the cardiac neuromuscular system in the American lobster, Homarus americanus.A developmental approach to predicting neuronal connectivity from small biological datasets: a gradient-based neuron growth model.Inputs drive cell phenotype variability.Gene expression profile during functional maturation of a central mammalian synapse.Inter-subject variability in human atrial action potential in sinus rhythm versus chronic atrial fibrillation.Summary of the DREAM8 Parameter Estimation Challenge: Toward Parameter Identification for Whole-Cell Models.The influence of synaptic size on AMPA receptor activation: a Monte Carlo modelCompensatory plasticity restores locomotion after chronic removal of descending projections.Multifactoriality in Psychiatric Disorders: A Computational Study of SchizophreniaElectrosensory Midbrain Neurons Display Feature Invariant Responses to Natural Communication Stimuli.Network Plasticity as Bayesian InferenceAnimal-to-animal variability of connection strength in the leech heartbeat central pattern generator.
P2860
Q24604615-48DF26F4-92FA-4310-83D6-DEF24804B72BQ26849487-B5AE01C5-24D5-48FC-A766-7FC63BF65D46Q26991423-1E2D4C2C-C2D7-4FD5-8DFE-BC615D26F75AQ27011715-E7F746B0-3A68-4187-9914-8C76DE407619Q27027352-D0FA582A-0EA1-48E4-ACB6-44213BFBF7ECQ27317256-1B542BC1-AD5D-4411-830C-D3E080E029F1Q27319493-67F195B2-612E-474F-BBF5-F64991AA85C2Q28534619-34E200C3-7069-49EE-BC4E-B4797459B2D8Q28547254-C7130B45-FB63-4892-96E2-88983AF46DE1Q28658394-B7D32E1C-DB40-480E-89D0-C76FB1DC9BB8Q30275980-E61B9B40-1371-4D89-8D15-97719D22FD97Q30394702-ABDBA8F8-1A32-4E70-98B9-FD604B5CFF49Q30457800-E9FA76D2-5C94-4BAE-AE6A-70FD730C9BABQ30465266-61379B16-CE45-4E17-90AB-BAF1A8DE1447Q30466487-BF1019A8-1D6C-4478-8FB3-134428A7B35EQ30585942-2C8D547B-C6E8-4B7A-968C-BB5E5D4E162FQ33358196-B37CA3F8-761E-4626-9271-E06D2D7C1A81Q33639071-EAB14BF8-7CEA-4B65-9666-0C9E09055C58Q33937473-EDDFF0CB-4AB1-4518-A564-509892FE8753Q34021765-2A2F1B4B-41BD-4376-8B5B-7F709241B021Q34049074-FF3D844F-B4B6-47F8-8F15-E86823C3DB40Q34242670-2371D98A-DEF8-49CF-95A0-E363515D5B24Q34434045-FF0E0B85-4766-4CCF-85C7-A9F4A94CDEDBQ34539738-F114D722-14C6-4B0B-9AE9-6657E5F8D485Q34550712-054468C0-F585-4E0B-84FE-3A33F6E39014Q34580112-298CA981-DB0B-4EFC-8F17-B0FA234AF438Q34585640-48D55348-BA38-4FD9-B215-7F465609F8F4Q34763030-8E4C3EE0-CE4A-4252-AC8F-386A44C123DCQ35034969-79B0EA8A-2F3F-48A4-B39E-461AD9BC16FDQ35107373-92B03894-511A-4D4B-8577-97A005E0A3ADQ35132534-ABA1D70F-8FEF-4363-8D28-9CB4AE0B12CBQ35201269-0D972A08-329A-46B5-8934-7509E7D827A8Q35231876-0790A0F3-2C86-4B76-9EB6-98054BE8AE9CQ35644952-8FF36CAD-E76D-4A16-985A-C39B5C8D38DAQ35673230-E68D19E1-E0B7-48DE-AA6F-09E00CAF48F3Q35709305-53389CE0-9D48-4D21-A9BA-2D89E72094B0Q35738982-91E4DA9C-BCDF-40AA-8255-B63F00D3EFDFQ35810665-CE95E3A1-F825-45A0-A463-D94E64FCE64AQ35834437-306A9AA2-1E19-4BD1-A241-BEF0D4BDD94EQ35850664-FF8130A0-AB8D-4720-8819-F33E41746197
P2860
Multiple models to capture the variability in biological neurons and networks.
description
2011 nî lūn-bûn
@nan
2011年の論文
@ja
2011年論文
@yue
2011年論文
@zh-hant
2011年論文
@zh-hk
2011年論文
@zh-mo
2011年論文
@zh-tw
2011年论文
@wuu
2011年论文
@zh
2011年论文
@zh-cn
name
Multiple models to capture the variability in biological neurons and networks.
@en
type
label
Multiple models to capture the variability in biological neurons and networks.
@en
prefLabel
Multiple models to capture the variability in biological neurons and networks.
@en
P2860
P356
P1433
P1476
Multiple models to capture the variability in biological neurons and networks.
@en
P2093
Adam L Taylor
P2860
P2888
P304
P356
10.1038/NN.2735
P407
P50
P577
2011-02-01T00:00:00Z