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Probabilistic inference in discrete spaces can be implemented into networks of LIF neurons
The means by which cortical neural networks are able to efficiently solve inference problems remains an open question in computational neuroscience. Recently, abstract models of Bayesian computation in neural circuits have been proposed, but they lack a mechanistic interpretation at the single-cell...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2015
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4325917/ https://www.ncbi.nlm.nih.gov/pubmed/25729361 http://dx.doi.org/10.3389/fncom.2015.00013 |
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author | Probst, Dimitri Petrovici, Mihai A. Bytschok, Ilja Bill, Johannes Pecevski, Dejan Schemmel, Johannes Meier, Karlheinz |
author_facet | Probst, Dimitri Petrovici, Mihai A. Bytschok, Ilja Bill, Johannes Pecevski, Dejan Schemmel, Johannes Meier, Karlheinz |
author_sort | Probst, Dimitri |
collection | PubMed |
description | The means by which cortical neural networks are able to efficiently solve inference problems remains an open question in computational neuroscience. Recently, abstract models of Bayesian computation in neural circuits have been proposed, but they lack a mechanistic interpretation at the single-cell level. In this article, we describe a complete theoretical framework for building networks of leaky integrate-and-fire neurons that can sample from arbitrary probability distributions over binary random variables. We test our framework for a model inference task based on a psychophysical phenomenon (the Knill-Kersten optical illusion) and further assess its performance when applied to randomly generated distributions. As the local computations performed by the network strongly depend on the interaction between neurons, we compare several types of couplings mediated by either single synapses or interneuron chains. Due to its robustness to substrate imperfections such as parameter noise and background noise correlations, our model is particularly interesting for implementation on novel, neuro-inspired computing architectures, which can thereby serve as a fast, low-power substrate for solving real-world inference problems. |
format | Online Article Text |
id | pubmed-4325917 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2015 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-43259172015-02-27 Probabilistic inference in discrete spaces can be implemented into networks of LIF neurons Probst, Dimitri Petrovici, Mihai A. Bytschok, Ilja Bill, Johannes Pecevski, Dejan Schemmel, Johannes Meier, Karlheinz Front Comput Neurosci Neuroscience The means by which cortical neural networks are able to efficiently solve inference problems remains an open question in computational neuroscience. Recently, abstract models of Bayesian computation in neural circuits have been proposed, but they lack a mechanistic interpretation at the single-cell level. In this article, we describe a complete theoretical framework for building networks of leaky integrate-and-fire neurons that can sample from arbitrary probability distributions over binary random variables. We test our framework for a model inference task based on a psychophysical phenomenon (the Knill-Kersten optical illusion) and further assess its performance when applied to randomly generated distributions. As the local computations performed by the network strongly depend on the interaction between neurons, we compare several types of couplings mediated by either single synapses or interneuron chains. Due to its robustness to substrate imperfections such as parameter noise and background noise correlations, our model is particularly interesting for implementation on novel, neuro-inspired computing architectures, which can thereby serve as a fast, low-power substrate for solving real-world inference problems. Frontiers Media S.A. 2015-02-12 /pmc/articles/PMC4325917/ /pubmed/25729361 http://dx.doi.org/10.3389/fncom.2015.00013 Text en Copyright © 2015 Probst, Petrovici, Bytschok, Bill, Pecevski, Schemmel and Meier. 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. |
spellingShingle | Neuroscience Probst, Dimitri Petrovici, Mihai A. Bytschok, Ilja Bill, Johannes Pecevski, Dejan Schemmel, Johannes Meier, Karlheinz Probabilistic inference in discrete spaces can be implemented into networks of LIF neurons |
title | Probabilistic inference in discrete spaces can be implemented into networks of LIF neurons |
title_full | Probabilistic inference in discrete spaces can be implemented into networks of LIF neurons |
title_fullStr | Probabilistic inference in discrete spaces can be implemented into networks of LIF neurons |
title_full_unstemmed | Probabilistic inference in discrete spaces can be implemented into networks of LIF neurons |
title_short | Probabilistic inference in discrete spaces can be implemented into networks of LIF neurons |
title_sort | probabilistic inference in discrete spaces can be implemented into networks of lif neurons |
topic | Neuroscience |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4325917/ https://www.ncbi.nlm.nih.gov/pubmed/25729361 http://dx.doi.org/10.3389/fncom.2015.00013 |
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