The NEURON simulation environment
Neural Computation
Spikes: exploring the neural code
Spikes: exploring the neural code
Spiking Neuron Models: An Introduction
Spiking Neuron Models: An Introduction
Dynamics of the firing probability of noisy integrate-and-fire neurons
Neural Computation
A universal model for spike-frequency adaptation
Neural Computation
Characterization of subthreshold voltage fluctuations in neuronal membranes
Neural Computation
Minimal Models of Adapted Neuronal Response to In Vivo–lLike Input Currents
Neural Computation
Patterns of Synchrony in Neural Networks with Spike Adaptation
Neural Computation
Biophysics of Computation: Information Processing in Single Neurons (Computational Neuroscience Series)
Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems
Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems
2008 Special Issue: The state of MIIND
Neural Networks
A spiking neural network model of an actor-critic learning agent
Neural Computation
Applying the multivariate time-rescaling theorem to neural population models
Neural Computation
Statistical properties of superimposed stationary spike trains
Journal of Computational Neuroscience
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We propose a Markov process model for spike-frequency adapting neural ensembles that synthesizes existing mean-adaptation approaches, population density methods, and inhomogeneous renewal theory, resulting in a unified and tractable framework that goes beyond renewal and mean-adaptation theories by accounting for correlations between subsequent interspike intervals. A method for efficiently generating inhomogeneous realizations of the proposed Markov process is given, numerical methods for solving the population equation are presented, and an expression for the first-order interspike interval correlation is derived. Further, we show that the full five-dimensional master equation for a conductance-based integrate-and-fire neuron with spike-frequency adaptation and a relative refractory mechanism driven by Poisson spike trains can be reduced to a two-dimensional generalization of the proposed Markov process by an adiabatic elimination of fast variables. For static and dynamic stimulation, negative serial interspike interval correlations and transient population responses, respectively, of Monte Carlo simulations of the full five-dimensional system can be accurately described by the proposed two-dimensional Markov process.