Journal
MATHEMATICAL BIOSCIENCES
Volume 196, Issue 1, Pages 1-13Publisher
ELSEVIER SCIENCE INC
DOI: 10.1016/j.mbs.2005.04.002
Keywords
Volterra kernels; Wiener kernels; nonlinear modeling; Poisson inputs; point-process inputs; neural systems; neuronal modeling
Categories
Funding
- PHS HHS [01861] Funding Source: Medline
Ask authors/readers for more resources
This paper presents a general methodological framework for the practical modeling of neural systems with point-process inputs (sequences of action potentials or, more broadly, identical events) based on the Volterra and Wiener theories of functional expansions and system identification. The paper clarifies the distinctions between Volterra and Wiener kernels obtained from Poisson point-process inputs. It shows that only the Wiener kernels can be estimated via cross-correlation, but must be defined as zero along the diagonals. The Volterra kernels can be estimated far more accurately (and from shorter data-records) by use of the Laguerre expansion technique adapted to point-process inputs, and they are independent of the mean rate of stimulation (unlike their P-W counterparts that depend on it). The Volterra kernels can also be estimated for broadband point-process inputs that are not Poisson. Useful applications of this modeling approach include cases where we seek to determine (model) the transfer characteristics between one neuronal axon (a point-process 'input') and another axon (a point-process 'output') or some other measure of neuronal activity (a continuous 'output', such as population activity) with which a causal link exists. (c) 2005 Elsevier Inc. All rights reserved.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available