Estimating a state-space model from point process observations
Neural Computation
Characterization of subthreshold voltage fluctuations in neuronal membranes
Neural Computation
Dynamic analysis of neural encoding by point process adaptive filtering
Neural Computation
A Review of the Integrate-and-fire Neuron Model: I. Homogeneous Synaptic Input
Biological Cybernetics
Biological Cybernetics - Special Issue: Quantitative Neuron Modeling
Estimating instantaneous irregularity of neuronal firing
Neural Computation
A new look at state-space models for neural data
Journal of Computational Neuroscience
Comparison of brain---computer interface decoding algorithms in open-loop and closed-loop control
Journal of Computational Neuroscience
Distinguishing the causes of firing with the membrane potential slope
Neural Computation
| Hi-index | 0.00 |
The set of firing rates of the presynaptic excitatory and inhibitory neurons constitutes the input signal to the postsynaptic neuron. Estimation of the time-varying input rates from intracellularly recorded membrane potential is investigated here. For that purpose, the membrane potential dynamics must be specified. We consider the Ornstein-Uhlenbeck stochastic process, one of the most common single-neuron models, with time-dependent mean and variance. Assuming the slow variation of these two moments, it is possible to formulate the estimation problem by using a state-space model. We develop an algorithm that estimates the paths of the mean and variance of the input current by using the empirical Bayes approach. Then the input firing rates are directly available from the moments. The proposed method is applied to three simulated data examples: constant signal, sinusoidally modulated signal, and constant signal with a jump. For the constant signal, the estimation performance of the method is comparable to that of the traditionally applied maximum likelihood method. Further, the proposed method accurately estimates both continuous and discontinuous time-variable signals. In the case of the signal with a jump, which does not satisfy the assumption of slow variability, the robustness of the method is verified. It can be concluded that the method provides reliable estimates of the total input firing rates, which are not experimentally measurable.