A population density approach that facilitates large-scale modeling of neural networks: analysis and an application to orientation tuning.
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The influence of synaptic weight distribution on neuronal population dynamicsA dynamic causal model study of neuronal population dynamicsfMRI models of dendritic and astrocytic networks.Divisive gain modulation with dynamic stimuli in integrate-and-fire neuronsSystematic fluctuation expansion for neural network activity equations.Mechanisms explaining transitions between tonic and phasic firing in neuronal populations as predicted by a low dimensional firing rate modelAccurate and fast simulation of channel noise in conductance-based model neurons by diffusion approximationModels of cardiac excitation-contraction coupling in ventricular myocytesBeyond mean field theory: statistical field theory for neural networks.An effective kinetic representation of fluctuation-driven neuronal networks with application to simple and complex cells in visual cortex.Modeling neuronal assemblies: theory and implementation.Stochastic models of neuronal dynamicsDynamic finite size effects in spiking neural networks.A complex-valued firing-rate model that approximates the dynamics of spiking networksMacroscopic equations governing noisy spiking neuronal populations with linear synapsesA neuronal network model of macaque primary visual cortex (V1): orientation selectivity and dynamics in the input layer 4Calpha.A probability density approach to modeling local control of calcium-induced calcium release in cardiac myocytes.Cross validation for selection of cortical interaction models from scalp EEG or MEG.Will big data yield new mathematics? An evolving synergy with neuroscience.Towards a theory of cortical columns: From spiking neurons to interacting neural populations of finite size.Dynamics of the subthalamo-pallidal complex in Parkinson's disease during deep brain stimulationAn embedded network approach for scale-up of fluctuation-driven systems with preservation of spike informationConductance-based refractory density approach: comparison with experimental data and generalization to lognormal distribution of input current.Inferring network dynamics and neuron properties from population recordings.Critical analysis of dimension reduction by a moment closure method in a population density approach to neural network modeling.Population density methods for stochastic neurons with realistic synaptic kinetics: firing rate dynamics and fast computational methods.How adaptation shapes spike rate oscillations in recurrent neuronal networks.Mean-field models for heterogeneous networks of two-dimensional integrate and fire neurons.On conductance-based neural field models.Integrate-and-fire neurons driven by correlated stochastic input.The faithful copy neuron.Bifurcations of large networks of two-dimensional integrate and fire neurons.On the phase reduction and response dynamics of neural oscillator populations.A principled dimension-reduction method for the population density approach to modeling networks of neurons with synaptic dynamics.Minimal models of adapted neuronal response to in vivo-like input currents.Variable synaptic strengths controls the firing rate distribution in feedforward neural networks.Mean-field approximations for coupled populations of generalized linear model spiking neurons with Markov refractoriness.Synchronization of an excitatory integrate-and-fire neural network.Populations of tightly coupled neurons: the RGC/LGN system.Stochastic dynamics of a finite-size spiking neural network.
P2860
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P2860
A population density approach that facilitates large-scale modeling of neural networks: analysis and an application to orientation tuning.
description
2000 nî lūn-bûn
@nan
2000年の論文
@ja
2000年学术文章
@wuu
2000年学术文章
@zh
2000年学术文章
@zh-cn
2000年学术文章
@zh-hans
2000年学术文章
@zh-my
2000年学术文章
@zh-sg
2000年學術文章
@yue
2000年學術文章
@zh-hant
name
A population density approach ...... ication to orientation tuning.
@en
A population density approach ...... ication to orientation tuning.
@nl
type
label
A population density approach ...... ication to orientation tuning.
@en
A population density approach ...... ication to orientation tuning.
@nl
prefLabel
A population density approach ...... ication to orientation tuning.
@en
A population density approach ...... ication to orientation tuning.
@nl
P356
P1476
A population density approach ...... ication to orientation tuning.
@en
P2093
D Q Nykamp
D Tranchina
P2888
P356
10.1023/A:1008912914816
P577
2000-01-01T00:00:00Z
P6179
1049294956