Stochastic computations in cortical microcircuit models.
Experimental data from neuroscience suggest that a substantial amount of knowledge is stored in the brain in the form of probability distributions over network states and trajectories of network states. We provide a theoretical foundation for this hypothesis by showing that even very detailed models...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2013-01-01
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| Series: | PLoS Computational Biology |
| Online Access: | http://europepmc.org/articles/PMC3828141?pdf=render |
| _version_ | 1819047012616634368 |
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| author | Stefan Habenschuss Zeno Jonke Wolfgang Maass |
| author_facet | Stefan Habenschuss Zeno Jonke Wolfgang Maass |
| author_sort | Stefan Habenschuss |
| collection | DOAJ |
| description | Experimental data from neuroscience suggest that a substantial amount of knowledge is stored in the brain in the form of probability distributions over network states and trajectories of network states. We provide a theoretical foundation for this hypothesis by showing that even very detailed models for cortical microcircuits, with data-based diverse nonlinear neurons and synapses, have a stationary distribution of network states and trajectories of network states to which they converge exponentially fast from any initial state. We demonstrate that this convergence holds in spite of the non-reversibility of the stochastic dynamics of cortical microcircuits. We further show that, in the presence of background network oscillations, separate stationary distributions emerge for different phases of the oscillation, in accordance with experimentally reported phase-specific codes. We complement these theoretical results by computer simulations that investigate resulting computation times for typical probabilistic inference tasks on these internally stored distributions, such as marginalization or marginal maximum-a-posteriori estimation. Furthermore, we show that the inherent stochastic dynamics of generic cortical microcircuits enables them to quickly generate approximate solutions to difficult constraint satisfaction problems, where stored knowledge and current inputs jointly constrain possible solutions. This provides a powerful new computing paradigm for networks of spiking neurons, that also throws new light on how networks of neurons in the brain could carry out complex computational tasks such as prediction, imagination, memory recall and problem solving. |
| first_indexed | 2024-12-21T10:53:36Z |
| format | Article |
| id | doaj.art-6d23a9627aa54feeb1dca31bc840212f |
| institution | Directory Open Access Journal |
| issn | 1553-734X 1553-7358 |
| language | English |
| last_indexed | 2024-12-21T10:53:36Z |
| publishDate | 2013-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS Computational Biology |
| spelling | doaj.art-6d23a9627aa54feeb1dca31bc840212f2022-12-21T19:06:35ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582013-01-01911e100331110.1371/journal.pcbi.1003311Stochastic computations in cortical microcircuit models.Stefan HabenschussZeno JonkeWolfgang MaassExperimental data from neuroscience suggest that a substantial amount of knowledge is stored in the brain in the form of probability distributions over network states and trajectories of network states. We provide a theoretical foundation for this hypothesis by showing that even very detailed models for cortical microcircuits, with data-based diverse nonlinear neurons and synapses, have a stationary distribution of network states and trajectories of network states to which they converge exponentially fast from any initial state. We demonstrate that this convergence holds in spite of the non-reversibility of the stochastic dynamics of cortical microcircuits. We further show that, in the presence of background network oscillations, separate stationary distributions emerge for different phases of the oscillation, in accordance with experimentally reported phase-specific codes. We complement these theoretical results by computer simulations that investigate resulting computation times for typical probabilistic inference tasks on these internally stored distributions, such as marginalization or marginal maximum-a-posteriori estimation. Furthermore, we show that the inherent stochastic dynamics of generic cortical microcircuits enables them to quickly generate approximate solutions to difficult constraint satisfaction problems, where stored knowledge and current inputs jointly constrain possible solutions. This provides a powerful new computing paradigm for networks of spiking neurons, that also throws new light on how networks of neurons in the brain could carry out complex computational tasks such as prediction, imagination, memory recall and problem solving.http://europepmc.org/articles/PMC3828141?pdf=render |
| spellingShingle | Stefan Habenschuss Zeno Jonke Wolfgang Maass Stochastic computations in cortical microcircuit models. PLoS Computational Biology |
| title | Stochastic computations in cortical microcircuit models. |
| title_full | Stochastic computations in cortical microcircuit models. |
| title_fullStr | Stochastic computations in cortical microcircuit models. |
| title_full_unstemmed | Stochastic computations in cortical microcircuit models. |
| title_short | Stochastic computations in cortical microcircuit models. |
| title_sort | stochastic computations in cortical microcircuit models |
| url | http://europepmc.org/articles/PMC3828141?pdf=render |
| work_keys_str_mv | AT stefanhabenschuss stochasticcomputationsincorticalmicrocircuitmodels AT zenojonke stochasticcomputationsincorticalmicrocircuitmodels AT wolfgangmaass stochasticcomputationsincorticalmicrocircuitmodels |