Dynamic Analysis of Neural Encoding by Point Process Adaptive Filtering

  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},
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… 

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

  • U. Eden
  • Computer Science
    2007 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

  • Nitin SadrasM. Shanechi
  • Biology, Psychology
    2018 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

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

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

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

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

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

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.


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

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.



An analysis of neural receptive field plasticity by point process adaptive filtering

An adaptive filter algorithm for tracking neural receptive field plasticity based on point process models of spike train activity is presented and an instantaneous steepest descent algorithm is derived by using as the criterion function the instantaneous log likelihood of a point process spike train model.

Contrasting Patterns of Receptive Field Plasticity in the Hippocampus and the Entorhinal Cortex: An Adaptive Filtering Approach

An adaptive point process filtering algorithm was developed that allowed us to estimate the dynamics of both the spatial receptive field and the interspike interval structure of neural spike trains on a millisecond time scale without binning over time or space.

The Time-Rescaling Theorem and Its Application to Neural Spike Train Data Analysis

The time-rescaling theorem may be used to develop goodness-of-fit tests for both parametric and histogram-based point process models of neural spike trains, and a proof using only elementary probability theory arguments is presented.

A Statistical Paradigm for Neural Spike Train Decoding Applied to Position Prediction from Ensemble Firing Patterns of Rat Hippocampal Place Cells

The statistical paradigm provides a reliable approach for quantifying the spatial information in the ensemble place cell firing patterns and defines a generally applicable framework for studying information encoding in neural systems.

Estimating a State-Space Model from Point Process Observations

Inspired by neurophysiology experiments in which neural spiking activity is induced by an implicit (latent) stimulus, an algorithm to estimate a state-space model observed through point process measurements is developed.

Neural Decoding of Cursor Motion Using a Kalman Filter

A control-theoretic approach that explicitly models the motion of the hand and the probabilistic relationship between this motion and the mean firing rates of the cells in 70ms bins is developed and provides insights into the nature of the neural coding of movement.

Experience-Dependent Asymmetric Shape of Hippocampal Receptive Fields

Dynamic changes in receptive-field size in cat primary visual cortex.

  • M. PettetC. Gilbert
  • Biology
    Proceedings of the National Academy of Sciences of the United States of America
  • 1992
The findings support the idea that even in adult animals RFs are dynamic, capable of being altered by the sensory context, and propose that the expansion may account for visual illusions, such as perceptual fill-in of stabilized images and illusory contours.

Experience-dependent, asymmetric expansion of hippocampal place fields.

Indirect evidence for Hebbian synaptic plasticity and a functional explanation for why place cells become directionally selective during route following are provided, namely, to preserve the synaptic asymmetry necessary to encode the sequence direction.