Function Identification in Neuron Populations via Information Bottleneck
It is plausible to hypothesize that the spiking responses of certain neurons represent functions of the spiking signals of other neurons. A natural ensuing question concerns how to use experimental data to infer what kind of a function is being computed. Model-based approaches typically require assu...
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doaj-cd2515741e654195bff4df2859431b982020-11-24T23:48:50ZengMDPI AGEntropy1099-43002013-05-011551587160810.3390/e15051587Function Identification in Neuron Populations via Information BottleneckS. Kartik BuddhaKelvin SoJose M. CarmenaMichael C. GastparIt is plausible to hypothesize that the spiking responses of certain neurons represent functions of the spiking signals of other neurons. A natural ensuing question concerns how to use experimental data to infer what kind of a function is being computed. Model-based approaches typically require assumptions on how information is represented. By contrast, information measures are sensitive only to relative behavior: information is unchanged by applying arbitrary invertible transformations to the involved random variables. This paper develops an approach based on the information bottleneck method that attempts to find such functional relationships in a neuron population. Specifically, the information bottleneck method is used to provide appropriate compact representations which can then be parsed to infer functional relationships. In the present paper, the parsing step is specialized to the case of remapped-linear functions. The approach is validated on artificial data and then applied to recordings from the motor cortex of a macaque monkey performing an arm-reaching task. Functional relationships are identified and shown to exhibit some degree of persistence across multiple trials of the same experiment.http://www.mdpi.com/1099-4300/15/5/1587information theoryinformation bottleneck methodneuroscience |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
S. Kartik Buddha Kelvin So Jose M. Carmena Michael C. Gastpar |
spellingShingle |
S. Kartik Buddha Kelvin So Jose M. Carmena Michael C. Gastpar Function Identification in Neuron Populations via Information Bottleneck Entropy information theory information bottleneck method neuroscience |
author_facet |
S. Kartik Buddha Kelvin So Jose M. Carmena Michael C. Gastpar |
author_sort |
S. Kartik Buddha |
title |
Function Identification in Neuron Populations via Information Bottleneck |
title_short |
Function Identification in Neuron Populations via Information Bottleneck |
title_full |
Function Identification in Neuron Populations via Information Bottleneck |
title_fullStr |
Function Identification in Neuron Populations via Information Bottleneck |
title_full_unstemmed |
Function Identification in Neuron Populations via Information Bottleneck |
title_sort |
function identification in neuron populations via information bottleneck |
publisher |
MDPI AG |
series |
Entropy |
issn |
1099-4300 |
publishDate |
2013-05-01 |
description |
It is plausible to hypothesize that the spiking responses of certain neurons represent functions of the spiking signals of other neurons. A natural ensuing question concerns how to use experimental data to infer what kind of a function is being computed. Model-based approaches typically require assumptions on how information is represented. By contrast, information measures are sensitive only to relative behavior: information is unchanged by applying arbitrary invertible transformations to the involved random variables. This paper develops an approach based on the information bottleneck method that attempts to find such functional relationships in a neuron population. Specifically, the information bottleneck method is used to provide appropriate compact representations which can then be parsed to infer functional relationships. In the present paper, the parsing step is specialized to the case of remapped-linear functions. The approach is validated on artificial data and then applied to recordings from the motor cortex of a macaque monkey performing an arm-reaching task. Functional relationships are identified and shown to exhibit some degree of persistence across multiple trials of the same experiment. |
topic |
information theory information bottleneck method neuroscience |
url |
http://www.mdpi.com/1099-4300/15/5/1587 |
work_keys_str_mv |
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