Dynamic models of neural spiking activity

Gabriela Czanner, Anna A. Dreyer, Uri T. Eden, Sylvia Wirth, Hubert H. Lim, Wendy A. Suzuki, Emery N. Brown

Research output: Chapter in Book/Report/Conference proceedingConference contribution


We present a state-space generalized linear model (SS-GLM) for characterizing neural spiking activity in multiple trials. We estimate the model parameters by maximum likelihood using an approximate Expectation-Maximization (EM) algorithm which employs a recursive point process filter, fixed-interval smoothing and state-space covariance algorithms. We assess model goodness-of-fit using the time-rescaling theorem and guide the choice of model order with Akaike's information criterion. We illustrate our approach in two applications. In the analysis of hippocampal neural activity recorded from a monkey performing a location-scene association task, we use the model to quantify the neural changes related to learning. In the analysis of primary auditory cortex responses to different levels of electrical stimulation in the rat midbrain, we use the method to analyze auditory threshold detection. Our findings have important implications for developing theoretically-sound and practical tools to characterize the dynamics of spiking activity.

Original languageEnglish (US)
Title of host publicationProceedings of the 46th IEEE Conference on Decision and Control 2007, CDC
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages6
ISBN (Print)1424414989, 9781424414987
StatePublished - 2007
Event46th IEEE Conference on Decision and Control 2007, CDC - New Orleans, LA, United States
Duration: Dec 12 2007Dec 14 2007

Publication series

NameProceedings of the IEEE Conference on Decision and Control
ISSN (Print)0743-1546
ISSN (Electronic)2576-2370


Other46th IEEE Conference on Decision and Control 2007, CDC
Country/TerritoryUnited States
CityNew Orleans, LA

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Modeling and Simulation
  • Control and Optimization


Dive into the research topics of 'Dynamic models of neural spiking activity'. Together they form a unique fingerprint.

Cite this