An algorithm to predict the connectome of neural microcircuits

Experimentally mapping synaptic connections, in terms of the numbers and locations of their synapses and estimating connection probabilities, is still not a tractable task, even for small volumes of tissue. In fact, the six layers of the neocortex contain thousands of unique types of synaptic connec...

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Main Authors:	Reimann, Michael W., King, James G., Muller, Eilif B., Ramaswamy, Srikanth, Markram, Henry
Format:	Online
Language:	English
Published:	Frontiers Media S.A. 2015
Online Access:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4597796/

id	pubmed-4597796
recordtype	oai_dc
spelling	pubmed-45977962015-10-23 An algorithm to predict the connectome of neural microcircuits Reimann, Michael W. King, James G. Muller, Eilif B. Ramaswamy, Srikanth Markram, Henry Neuroscience Experimentally mapping synaptic connections, in terms of the numbers and locations of their synapses and estimating connection probabilities, is still not a tractable task, even for small volumes of tissue. In fact, the six layers of the neocortex contain thousands of unique types of synaptic connections between the many different types of neurons, of which only a handful have been characterized experimentally. Here we present a theoretical framework and a data-driven algorithmic strategy to digitally reconstruct the complete synaptic connectivity between the different types of neurons in a small well-defined volume of tissue—the micro-scale connectome of a neural microcircuit. By enforcing a set of established principles of synaptic connectivity, and leveraging interdependencies between fundamental properties of neural microcircuits to constrain the reconstructed connectivity, the algorithm yields three parameters per connection type that predict the anatomy of all types of biologically viable synaptic connections. The predictions reproduce a spectrum of experimental data on synaptic connectivity not used by the algorithm. We conclude that an algorithmic approach to the connectome can serve as a tool to accelerate experimental mapping, indicating the minimal dataset required to make useful predictions, identifying the datasets required to improve their accuracy, testing the feasibility of experimental measurements, and making it possible to test hypotheses of synaptic connectivity. Frontiers Media S.A. 2015-10-08 /pmc/articles/PMC4597796/ /pubmed/26500529 http://dx.doi.org/10.3389/fncom.2015.00120 Text en Copyright © 2015 Reimann, King, Muller, Ramaswamy and Markram. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
repository_type	Open Access Journal
institution_category	Foreign Institution
institution	US National Center for Biotechnology Information
building	NCBI PubMed
collection	Online Access
language	English
format	Online
author	Reimann, Michael W. King, James G. Muller, Eilif B. Ramaswamy, Srikanth Markram, Henry
spellingShingle	Reimann, Michael W. King, James G. Muller, Eilif B. Ramaswamy, Srikanth Markram, Henry An algorithm to predict the connectome of neural microcircuits
author_facet	Reimann, Michael W. King, James G. Muller, Eilif B. Ramaswamy, Srikanth Markram, Henry
author_sort	Reimann, Michael W.
title	An algorithm to predict the connectome of neural microcircuits
title_short	An algorithm to predict the connectome of neural microcircuits
title_full	An algorithm to predict the connectome of neural microcircuits
title_fullStr	An algorithm to predict the connectome of neural microcircuits
title_full_unstemmed	An algorithm to predict the connectome of neural microcircuits
title_sort	algorithm to predict the connectome of neural microcircuits
description	Experimentally mapping synaptic connections, in terms of the numbers and locations of their synapses and estimating connection probabilities, is still not a tractable task, even for small volumes of tissue. In fact, the six layers of the neocortex contain thousands of unique types of synaptic connections between the many different types of neurons, of which only a handful have been characterized experimentally. Here we present a theoretical framework and a data-driven algorithmic strategy to digitally reconstruct the complete synaptic connectivity between the different types of neurons in a small well-defined volume of tissue—the micro-scale connectome of a neural microcircuit. By enforcing a set of established principles of synaptic connectivity, and leveraging interdependencies between fundamental properties of neural microcircuits to constrain the reconstructed connectivity, the algorithm yields three parameters per connection type that predict the anatomy of all types of biologically viable synaptic connections. The predictions reproduce a spectrum of experimental data on synaptic connectivity not used by the algorithm. We conclude that an algorithmic approach to the connectome can serve as a tool to accelerate experimental mapping, indicating the minimal dataset required to make useful predictions, identifying the datasets required to improve their accuracy, testing the feasibility of experimental measurements, and making it possible to test hypotheses of synaptic connectivity.
publisher	Frontiers Media S.A.
publishDate	2015
url	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4597796/
_version_	1613485151653724160

An algorithm to predict the connectome of neural microcircuits

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