# Dynamic Analysis of Neural Encoding by Point Process Adaptive Filtering

@article{Eden2004DynamicAO, title={Dynamic Analysis of Neural Encoding by Point Process Adaptive Filtering}, author={Uri T. Eden and Loren M. Frank and Riccardo Barbieri and Victor Solo and Emery N. Brown}, journal={Neural Computation}, year={2004}, volume={16}, pages={971-998} }

Neural receptive fields are dynamic in that with experience, neurons change their spiking responses to relevant stimuli. To understand how neural systems adapt the irrepresentations of biological information, analyses of receptive field plasticity from experimental measurements are crucial. Adaptive signal processing, the well-established engineering discipline for characterizing the temporal evolution of system parameters, suggests a framework for studying the plasticity of receptive fields…

## 355 Citations

### Point process adaptive filters for neural data analysis: Theory and applications

- Computer Science2007 46th IEEE Conference on Decision and Control
- 2007

A point process modeling framework for neural systems to perform inference, assess goodness-of-fit, and estimate a state variable from spiking observations is discussed, and a Bayesian approximate Gaussian filter is able to maintain accurate estimates of intended arm trajectories.

### Decoding Spike Trains from Neurons with Spatio-Temporal Receptive Fields

- Biology, Psychology2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
- 2018

A matched-filter point process filter (MF-PPF) that can decode behavioral states that are encoded transiently in neural activity when stimulus times are unknown is developed and used to decode visual saliency from simulated superior colliculus spiking activity.

### Optimal Decoding of Dynamic Stimuli by Heterogeneous Populations of Spiking Neurons: A Closed-Form Approximation

- Computer ScienceNeural Computation
- 2018

An analytically tractable Bayesian approximation to optimal filtering based on the observation of spiking activity is developed that greatly facilitates the analysis of optimal encoding in situations deviating from common assumptions of uniform coding.

### Stochastic Models for Multivariate Neural Point Processes: Collective Dynamics and Neural Decoding

- Computer Science
- 2010

This chapter reviews a stochastic point process framework for the modeling, analysis and decoding of neuronal ensembles and four related approaches for the statistical modeling of conditional intensity functions are presented: generalized linear models (GLM), penalized splines, hierarchical Bayesian P-splines, and nonparametric function approximation.

### A neural network implementing optimal state estimation based on dynamic spike train decoding

- Computer ScienceNIPS
- 2007

This work makes use of rigorous mathematical results from the theory of continuous time point process filtering, and shows how optimal real-time state estimation and prediction may be implemented in a general setting using linear neural networks.

### Particle filtering of point processes observation with application on the modeling of visual cortex neural spiking activity

- Biology2009 4th International IEEE/EMBS Conference on Neural Engineering
- 2009

The results of applying point process modeling on a real data from inferior temporal cortex of macaque monkey indicates that, based on the assessment of goodness-of-fit, the neural spiking activity and biophysical property of neuron could be captured more accurately in compare to conventional methods.

### Nonlinear point-process estimation of neural spiking activity based on variational Bayesian inference

- Computer ScienceJournal of neural engineering
- 2022

This work proposed a novel adaptive higher-order nonlinear point-process filter based on the variational Bayesian inference (VBI) framework, called the HON-VBI, which greatly reduces the decoding time of large-scale neural spike trains.

### Bayesian Filtering in Spiking Neural Networks: Noise, Adaptation, and Multisensory Integration

- Computer ScienceNeural Computation
- 2009

This work makes use of rigorous mathematical results from the theory of continuous time point process filtering and shows how optimal real-time state estimation and prediction may be implemented in a general setting using simple recurrent neural networks.

### CONTINUOUS-TIME FILTERS FOR STATE ESTIMATION FROM POINT PROCESS MODELS OF NEURAL DATA.

- Computer ScienceStatistica Sinica
- 2008

This work presents an accessible derivation of the well-known unnormalized conditional density equation for state evolution, construct a new continuous-time filter based on a Gaussian approximation, and proposes a method for assessing the validity of the approximation following an approach by Brockett and Clark.

### An analysis of hippocampal spatio-temporal representations using a Bayesian algorithm for neural spike train decoding

- Computer Science, BiologyIEEE Transactions on Neural Systems and Rehabilitation Engineering
- 2005

A Bayesian neural spike train decoding algorithm based on a point process model of individual neurons, a linear stochastic state-space model of the biological signal, and a temporal latency parameter is presented to study whether the representation of position by the ensemble spiking activity of pyramidal neurons in the CA1 region of the rat hippocampus is more consistent with prospective coding.

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