# Likelihood Methods for Point Processes with Refractoriness

@article{Citi2014LikelihoodMF, title={Likelihood Methods for Point Processes with Refractoriness}, author={Luca Citi and Demba E. Ba and Emery N. Brown and Riccardo Barbieri}, journal={Neural Computation}, year={2014}, volume={26}, pages={237-263} }

Likelihood-based encoding models founded on point processes have received significant attention in the literature because of their ability to reveal the information encoded by spiking neural populations. We propose an approximation to the likelihood of a point-process model of neurons that holds under assumptions about the continuous time process that are physiologically reasonable for neural spike trains: the presence of a refractory period, the predictability of the conditional intensity…

## 26 Citations

On Quadrature Methods for Refractory Point Process Likelihoods

- Computer ScienceNeural Computation
- 2014

It is demonstrated that this method can be improved significantly by applying classical quadrature methods directly to the resulting continuous-time integral.

Algorithms for the analysis of ensemble neural spiking activity using simultaneous-event multivariate point-process models

- BiologyFront. Comput. Neurosci.
- 2014

It is shown how Solo's discrete-time formulation of the SEMPP model can be efficiently fit to ensemble neural spiking activity using a multinomial generalized linear model (mGLM) and the MPP time-rescaling theorem can be used to assess model goodness-of-fit.

Modeling neural activity with cumulative damage distributions

- Computer ScienceBiological Cybernetics
- 2015

The use of CD distributions are expanded to the modeling of neural spiking behavior, mainly by testing the suitability of the Birnbaum–Saunders distribution, which has not been studied in the setting of neural activity.

Sparse model-based estimation of functional dependence in high-dimensional field and spike multiscale networks.

- Computer ScienceJournal of neural engineering
- 2019

A sparse model-based estimation algorithm that can help study functional dependencies in high-dimensional spike-field networks and leads to more accurate multiscale encoding models.

Inferring synaptic inputs from spikes with a conductance-based neural encoding model

- BiologybioRxiv
- 2018

The model provides a novel interpretation of the popular Poisson generalized linear model (GLM) as a special kind of conductance-based model, where excitatory and inhibitory conductances are modulated in a “push-pull” manner so that total conductance remains constant.

Multiscale modeling and decoding algorithms for spike-field activity.

- Computer ScienceJournal of neural engineering
- 2019

A multiscale encoding model, adaptive learning algorithm, and decoder that explicitly incorporate the different statistical profiles and time-scales of spikes and fields is developed and validated within motor tasks.

Optimizing the learning rate for adaptive estimation of neural encoding models

- Computer SciencePLoS Comput. Biol.
- 2018

The calibration algorithm provides a novel analytical approach to predictably achieve a desired level of error and convergence time in adaptive learning, with application to closed-loop neurotechnologies and other signal processing domains.

Fast maximum likelihood estimation using continuous-time neural point process models

- Computer ScienceJournal of Computational Neuroscience
- 2015

A recent report estimates that the number of simultaneously recorded neurons is growing exponentially. A commonly employed statistical paradigm using discrete-time point process models of neural…

Inferring synaptic inputs from spikes with a conductance-based neural encoding model

- BiologyeLife
- 2019

It is shown that the conductance-based encoding model (CBEM) can be fit to extracellular spike train data and then used to predict excitatory and inhibitory synaptic currents and offers a novel quasi-biophysical interpretation of the Poisson generalized linear model (GLM) as a special case of the CBEM in which excitation and inhibition are perfectly balanced.

Reconstruction of recurrent synaptic connectivity of thousands of neurons from simulated spiking activity

- Computer Science, BiologyJournal of Computational Neuroscience
- 2015

The results demonstrate how synaptic connectivity could potentially be inferred from large-scale parallel spike train recordings and describe a minimal model that is optimized for large networks and an efficient scheme for its parallelized numerical optimization on generic computing clusters.

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