Blind intensity estimation from shot-noise data

Sequeira, R.E.; Gubner, John A.

doi:10.1109/78.554306

Cited by 9 publications

(1 citation statement)

References 28 publications

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“…For the case when both the intensity function and the system impulse response are unknown (blind estimation), the paper [9] (which was revised during 95-96) first estimates a histogram of the underlying point process and then constructs a kernel estimate of the intensity. The estimate of the unknown impulse response is constructed via a regularized back-substitution of a discrete-time convolution with the estimated histogram.…”

Section: Accomplishments/new Findingsmentioning

confidence: 99%

Photon-Limited Image Detection Using Shot-Noise Models.

Gubner¹

1996

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Section: Accomplishments/new Findingsmentioning

confidence: 99%

Photon-Limited Image Detection Using Shot-Noise Models.

Gubner¹

1996

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Nonstationary filtered shot-noise processes and applications to neuronal membranes

Brigham

Destexhe

2015

Phys. Rev. E

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Filtered shot noise processes have proven to be very effective in modeling the evolution of systems exposed to shot noise sources and have been applied to a wide variety of fields ranging from electronics through biology. In particular, they can model the membrane potential Vm of neurons driven by stochastic input, where these filtered processes are able to capture the nonstationary characteristics of Vm fluctuations in response to presynaptic input with variable rate. In this paper we apply the general framework of Poisson point processes transformations to analyze these systems in the general case of nonstationary input rates. We obtain exact analytic expressions, as well as different approximations, for the joint cumulants of filtered shot noise processes with multiplicative noise. These general results are then applied to a model of neuronal membranes subject to conductance shot noise with a continuously variable rate of presynaptic spikes. We propose very effective approximations for the time evolution of the Vm distribution and a simple method to estimate the presynaptic rate from a small number of Vm traces. This work opens the perspective of obtaining analytic access to important statistical properties of conductance-based neuronal models such as the first passage time.

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Bayesian estimation of filtered point processes using Markov chain Monte Carlo methods

Andrieu¹,

Doucet²,

Duvaut³

Conference Record of the Thirty-First Asilomar Conference on Signals, Systems and Computers (Cat. No.97CB36136)

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Blind intensity estimation from shot-noise data

Cited by 9 publications

References 28 publications

Photon-Limited Image Detection Using Shot-Noise Models.

Photon-Limited Image Detection Using Shot-Noise Models.

Nonstationary filtered shot-noise processes and applications to neuronal membranes

Bayesian estimation of filtered point processes using Markov chain Monte Carlo methods

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