Modeling Rate-Modulated Selfexciting Point Processes.

Abstract

This paper addresses several issues arising in the modeling of discrete event processes for which the sample-path evolution depends on the past trajectory and is also controlled by an independent modulating process. While information on local, sample-path evolution is sometimes readily obtainable or measurable, in many applications it is more important to predict ensemble averaged responses to variations in the modulation process. The authors discuss this problem in the framework of a general model for rate-modulated selfexciting processes and, under certain assumptions, derive a nonlinear ordinary differential equation for approximately predicting ensemble behavior from known sample-path evolution laws. A successful application of this method to a neural encoding process has already been made.

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Document Details

Document Type
Technical Report
Publication Date
Dec 01, 1983
Accession Number
ADA142745

Entities

People

  • A. M. Bruckstein
  • Thomas Kailath

Organizations

  • Stanford University

Tags

DTIC Thesaurus Topics

  • Availability
  • Classification
  • Coding
  • Computer Programming
  • Differential Equations
  • Electrical Engineering
  • Engineering
  • Equations
  • Frequency Modulation
  • Information Systems
  • Modulation
  • Nonlinear Differential Equations
  • Notation
  • Personal Information Managers
  • Pulse Frequency Modulation
  • Security
  • Stochastic Processes

Readers

  • Control Systems Engineering.
  • Neural Network Machine Learning.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - Neural Networks