A. Lazar, and Y. Slutskiy. Advances in Neural Information Processing Systems 23, page 1261-1269. J. Lafferty and C. K. I. Williams and J. Shawe-Taylor and R.S. Zemel and A. Culotta, (2010)
Abstract
In system identification both the input and the output of a
system are available to an observer and an algorithm is sought
to identify parameters of a hypothesized model of that
system. Here we present a novel formal methodology for
identifying dendritic processing in a neural circuit
consisting of a linear dendritic processing filter in cascade
with a spiking neuron model. The input to the circuit is an
analog signal that belongs to the space of bandlimited
functions. The output is a time sequence associated with the
spike train. We derive an algorithm for identification of the
dendritic processing filter and reconstruct its kernel with
arbitrary precision.
%0 Conference Paper
%1 lazar_identifying_2010
%A Lazar, Aurel A.
%A Slutskiy, Yevgeniy B.
%B Advances in Neural Information Processing Systems 23
%D 2010
%I J. Lafferty and C. K. I. Williams and J. Shawe-Taylor and R.S. Zemel and A. Culotta
%K identification system
%P 1261-1269
%T Identifying Dendritic Processing
%U /brokenurl#books.nips.cc/papers/files/nips23/NIPS2010_0495.pdf
%X In system identification both the input and the output of a
system are available to an observer and an algorithm is sought
to identify parameters of a hypothesized model of that
system. Here we present a novel formal methodology for
identifying dendritic processing in a neural circuit
consisting of a linear dendritic processing filter in cascade
with a spiking neuron model. The input to the circuit is an
analog signal that belongs to the space of bandlimited
functions. The output is a time sequence associated with the
spike train. We derive an algorithm for identification of the
dendritic processing filter and reconstruct its kernel with
arbitrary precision.
@inproceedings{lazar_identifying_2010,
abstract = {In system identification both the input and the output of a
system are available to an observer and an algorithm is sought
to identify parameters of a hypothesized model of that
system. Here we present a novel formal methodology for
identifying dendritic processing in a neural circuit
consisting of a linear dendritic processing filter in cascade
with a spiking neuron model. The input to the circuit is an
analog signal that belongs to the space of bandlimited
functions. The output is a time sequence associated with the
spike train. We derive an algorithm for identification of the
dendritic processing filter and reconstruct its kernel with
arbitrary precision.},
added-at = {2014-01-19T15:18:48.000+0100},
author = {Lazar, Aurel A. and Slutskiy, Yevgeniy B.},
bdsk-url-1 = {books.nips.cc/papers/files/nips23/NIPS2010_0495.pdf},
biburl = {https://www.bibsonomy.org/bibtex/2339281ff27dc7c381e4ff23c97bf56b4/neurokernel},
booktitle = {Advances in Neural Information Processing Systems 23},
date-modified = {2013-10-30 03:47:34 +0000},
interhash = {113c515928a6d2a1c17fcac9f925aefe},
intrahash = {339281ff27dc7c381e4ff23c97bf56b4},
keywords = {identification system},
pages = {1261-1269},
publisher = {J. Lafferty and C. K. I. Williams and J. Shawe-Taylor and R.S. Zemel and A. Culotta},
timestamp = {2014-01-19T15:18:48.000+0100},
title = {Identifying Dendritic Processing},
url = {/brokenurl#books.nips.cc/papers/files/nips23/NIPS2010_0495.pdf},
year = 2010
}