Analyzing ensemble spiking activity using point process filters
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1
Boston University, United States
Brain areas are able to maintain dynamic representations of biological stimuli and behavioral variables through coordinated spiking activity of large neural ensembles. Technological advances in electrophysiology now allow us to record simultaneous activity from increasingly large populations of spiking neurons. Developing modeling methods to describe the neural representations present within these high dimensional signals presents an important statistical challenge for neural data analysis. The theory of point processes offers a unified, principled approach to modeling and estimating the firing properties of spiking neural systems, and assessing goodness-of-fit between a neural model and observed spike train data.
We develop a state space estimation framework to track the evolution of dynamic signals using spike train observations from large neural ensembles. This allows us to derive a toolbox of estimation algorithms and adaptive filters to address questions of static and dynamic encoding and decoding. In our analysis of these filtering algorithms, we draw analogies to well-studied linear estimation algorithms for continuous valued processes, such as the Kalman filter and its discrete and continuous time extensions.
These methods will be illustrated in the context of the analysis of place field activity in the rodent hippocampus. Place cells, which tend to fire preferentially when the animal is in specific locations, have been implicated in cognitive tasks such as navigation and decision making. Using simple point process models, we are able to accurately characterize the localized spiking activity of these neurons as a function of the animal's position in its environment, track plasticity in their firing properties, and reconstruct the animal's movements from the spiking of a hippocampal population.
Conference:
Neuroinformatics 2009, Pilsen, Czechia, 6 Sep - 8 Sep, 2009.
Presentation Type:
Oral Presentation
Topic:
Workshop 1- Advances in the automatic analysis of multi-dimensional data
Citation:
Eden
U
(2019). Analyzing ensemble spiking activity using point process filters.
Front. Neuroinform.
Conference Abstract:
Neuroinformatics 2009.
doi: 10.3389/conf.neuro.11.2009.08.128
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Received:
10 Jun 2009;
Published Online:
09 May 2019.
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Correspondence:
Uri Eden, Boston University, Boston, United States, tzvi@bu.edu