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A Compositionality Machine Realized by a Hierarchic Architecture of Synfire Chains
The composition of complex behavior is thought to rely on the concurrent and sequential activation of simpler action components, or primitives. Systems of synfire chains have previously been proposed to account for either the simultaneous or the sequential aspects of compositionality; however, the c...
Autores principales: | , , |
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Formato: | Texto |
Lenguaje: | English |
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Frontiers Research Foundation
2011
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3020397/ https://www.ncbi.nlm.nih.gov/pubmed/21258641 http://dx.doi.org/10.3389/fncom.2010.00154 |
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author | Schrader, Sven Diesmann, Markus Morrison, Abigail |
author_facet | Schrader, Sven Diesmann, Markus Morrison, Abigail |
author_sort | Schrader, Sven |
collection | PubMed |
description | The composition of complex behavior is thought to rely on the concurrent and sequential activation of simpler action components, or primitives. Systems of synfire chains have previously been proposed to account for either the simultaneous or the sequential aspects of compositionality; however, the compatibility of the two aspects has so far not been addressed. Moreover, the simultaneous activation of primitives has up until now only been investigated in the context of reactive computations, i.e., the perception of stimuli. In this study we demonstrate how a hierarchical organization of synfire chains is capable of generating both aspects of compositionality for proactive computations such as the generation of complex and ongoing action. To this end, we develop a network model consisting of two layers of synfire chains. Using simple drawing strokes as a visualization of abstract primitives, we map the feed-forward activity of the upper level synfire chains to motion in two-dimensional space. Our model is capable of producing drawing strokes that are combinations of primitive strokes by binding together the corresponding chains. Moreover, when the lower layer of the network is constructed in a closed-loop fashion, drawing strokes are generated sequentially. The generated pattern can be random or deterministic, depending on the connection pattern between the lower level chains. We propose quantitative measures for simultaneity and sequentiality, revealing a wide parameter range in which both aspects are fulfilled. Finally, we investigate the spiking activity of our model to propose candidate signatures of synfire chain computation in measurements of neural activity during action execution. |
format | Text |
id | pubmed-3020397 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2011 |
publisher | Frontiers Research Foundation |
record_format | MEDLINE/PubMed |
spelling | pubmed-30203972011-01-21 A Compositionality Machine Realized by a Hierarchic Architecture of Synfire Chains Schrader, Sven Diesmann, Markus Morrison, Abigail Front Comput Neurosci Neuroscience The composition of complex behavior is thought to rely on the concurrent and sequential activation of simpler action components, or primitives. Systems of synfire chains have previously been proposed to account for either the simultaneous or the sequential aspects of compositionality; however, the compatibility of the two aspects has so far not been addressed. Moreover, the simultaneous activation of primitives has up until now only been investigated in the context of reactive computations, i.e., the perception of stimuli. In this study we demonstrate how a hierarchical organization of synfire chains is capable of generating both aspects of compositionality for proactive computations such as the generation of complex and ongoing action. To this end, we develop a network model consisting of two layers of synfire chains. Using simple drawing strokes as a visualization of abstract primitives, we map the feed-forward activity of the upper level synfire chains to motion in two-dimensional space. Our model is capable of producing drawing strokes that are combinations of primitive strokes by binding together the corresponding chains. Moreover, when the lower layer of the network is constructed in a closed-loop fashion, drawing strokes are generated sequentially. The generated pattern can be random or deterministic, depending on the connection pattern between the lower level chains. We propose quantitative measures for simultaneity and sequentiality, revealing a wide parameter range in which both aspects are fulfilled. Finally, we investigate the spiking activity of our model to propose candidate signatures of synfire chain computation in measurements of neural activity during action execution. Frontiers Research Foundation 2011-01-05 /pmc/articles/PMC3020397/ /pubmed/21258641 http://dx.doi.org/10.3389/fncom.2010.00154 Text en Copyright © 2011 Schrader, Diesmann and Morrison. http://www.frontiersin.org/licenseagreement This is an open-access article subject to an exclusive license agreement between the authors and the Frontiers Research Foundation, which permits unrestricted use, distribution, and reproduction in any medium, provided the original authors and source are credited. |
spellingShingle | Neuroscience Schrader, Sven Diesmann, Markus Morrison, Abigail A Compositionality Machine Realized by a Hierarchic Architecture of Synfire Chains |
title | A Compositionality Machine Realized by a Hierarchic Architecture of Synfire Chains |
title_full | A Compositionality Machine Realized by a Hierarchic Architecture of Synfire Chains |
title_fullStr | A Compositionality Machine Realized by a Hierarchic Architecture of Synfire Chains |
title_full_unstemmed | A Compositionality Machine Realized by a Hierarchic Architecture of Synfire Chains |
title_short | A Compositionality Machine Realized by a Hierarchic Architecture of Synfire Chains |
title_sort | compositionality machine realized by a hierarchic architecture of synfire chains |
topic | Neuroscience |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3020397/ https://www.ncbi.nlm.nih.gov/pubmed/21258641 http://dx.doi.org/10.3389/fncom.2010.00154 |
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