Estimating Parameters of Generalized Integrate-and-Fire Neurons from the Maximum Likelihood of Spike Trains
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Publication:2887011
DOI10.1162/NECO_a_00196zbMath1302.92022OpenAlexW2074678993WikidataQ41906572 ScholiaQ41906572MaRDI QIDQ2887011
Alexander Russell, Yi Dong, Ernst Niebur, Ştefan Mihalaş, Ralph Etienne-Cummings
Publication date: 15 May 2012
Published in: Neural Computation (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1162/neco_a_00196
Related Items (5)
Reconstruction of the input signal of the leaky integrate-and-fire neuronal model from its interspike intervals ⋮ The first passage time density of Ornstein-Uhlenbeck process with continuous and impulsive excitations ⋮ Responses of leaky integrate-and-fire neurons to a plurality of stimuli in their receptive fields ⋮ Fokker-Planck and Fortet equation-based parameter estimation for a leaky integrate-and-fire model with sinusoidal and stochastic forcing ⋮ Synchronization of Electrically Coupled Resonate-and-Fire Neurons
Cites Work
- A review of the integrate-and-fire neuron model: I. Homogeneous synaptic input
- A Novel Spike Distance
- Improved Integral Equation Solution for the First Passage Time of Leaky Integrate-and-Fire Neurons
- Exact Solutions for Rate and Synchrony in Recurrent Networks of Coincidence Detectors
- The Speed of Shor's R-algorithm
- A Generalized Linear Integrate-and-Fire Neural Model Produces Diverse Spiking Behaviors
- A new integral equation for the evaluation of first-passage-time probability densities
- Maximum Likelihood Estimation of a Stochastic Integrate-and-Fire Neural Encoding Model
- The Effects of Input Rate and Synchrony on a Coincidence Detector: Analytical Solution
- Event-Driven Simulations of Nonlinear Integrate-and-Fire Neurons
- A logical calculus of the ideas immanent in nervous activity
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