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Ensemble stacking mitigates biases in inference of synaptic connectivity
A promising alternative to directly measuring the anatomical connections in a neuronal population is inferring the connections from the activity. We employ simulated spiking neuronal networks to compare and contrast commonly used inference methods that identify likely excitatory synaptic connections...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MIT Press
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5989998/ https://www.ncbi.nlm.nih.gov/pubmed/29911678 http://dx.doi.org/10.1162/NETN_a_00032 |
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author | Chambers, Brendan Levy, Maayan Dechery, Joseph B. MacLean, Jason N. |
author_facet | Chambers, Brendan Levy, Maayan Dechery, Joseph B. MacLean, Jason N. |
author_sort | Chambers, Brendan |
collection | PubMed |
description | A promising alternative to directly measuring the anatomical connections in a neuronal population is inferring the connections from the activity. We employ simulated spiking neuronal networks to compare and contrast commonly used inference methods that identify likely excitatory synaptic connections using statistical regularities in spike timing. We find that simple adjustments to standard algorithms improve inference accuracy: A signing procedure improves the power of unsigned mutual-information-based approaches and a correction that accounts for differences in mean and variance of background timing relationships, such as those expected to be induced by heterogeneous firing rates, increases the sensitivity of frequency-based methods. We also find that different inference methods reveal distinct subsets of the synaptic network and each method exhibits different biases in the accurate detection of reciprocity and local clustering. To correct for errors and biases specific to single inference algorithms, we combine methods into an ensemble. Ensemble predictions, generated as a linear combination of multiple inference algorithms, are more sensitive than the best individual measures alone, and are more faithful to ground-truth statistics of connectivity, mitigating biases specific to single inference methods. These weightings generalize across simulated datasets, emphasizing the potential for the broad utility of ensemble-based approaches. |
format | Online Article Text |
id | pubmed-5989998 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | MIT Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-59899982018-06-15 Ensemble stacking mitigates biases in inference of synaptic connectivity Chambers, Brendan Levy, Maayan Dechery, Joseph B. MacLean, Jason N. Netw Neurosci Research A promising alternative to directly measuring the anatomical connections in a neuronal population is inferring the connections from the activity. We employ simulated spiking neuronal networks to compare and contrast commonly used inference methods that identify likely excitatory synaptic connections using statistical regularities in spike timing. We find that simple adjustments to standard algorithms improve inference accuracy: A signing procedure improves the power of unsigned mutual-information-based approaches and a correction that accounts for differences in mean and variance of background timing relationships, such as those expected to be induced by heterogeneous firing rates, increases the sensitivity of frequency-based methods. We also find that different inference methods reveal distinct subsets of the synaptic network and each method exhibits different biases in the accurate detection of reciprocity and local clustering. To correct for errors and biases specific to single inference algorithms, we combine methods into an ensemble. Ensemble predictions, generated as a linear combination of multiple inference algorithms, are more sensitive than the best individual measures alone, and are more faithful to ground-truth statistics of connectivity, mitigating biases specific to single inference methods. These weightings generalize across simulated datasets, emphasizing the potential for the broad utility of ensemble-based approaches. MIT Press 2018-03-01 /pmc/articles/PMC5989998/ /pubmed/29911678 http://dx.doi.org/10.1162/NETN_a_00032 Text en © 2017 Massachusetts Institute of Technology http://creativecommons.org/licenses/by/3.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Chambers, Brendan Levy, Maayan Dechery, Joseph B. MacLean, Jason N. Ensemble stacking mitigates biases in inference of synaptic connectivity |
title | Ensemble stacking mitigates biases in inference of synaptic connectivity |
title_full | Ensemble stacking mitigates biases in inference of synaptic connectivity |
title_fullStr | Ensemble stacking mitigates biases in inference of synaptic connectivity |
title_full_unstemmed | Ensemble stacking mitigates biases in inference of synaptic connectivity |
title_short | Ensemble stacking mitigates biases in inference of synaptic connectivity |
title_sort | ensemble stacking mitigates biases in inference of synaptic connectivity |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5989998/ https://www.ncbi.nlm.nih.gov/pubmed/29911678 http://dx.doi.org/10.1162/NETN_a_00032 |
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