The Evolution of Neuroplasticity and the Effect on Integrated Information
Information integration theory has been developed to quantify consciousness. Since conscious thought requires the integration of information, the degree of this integration can be used as a neural correlate (<inline-formula><math display="inline"><semantics><mi mathvar...
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doaj-4469de87fcf44df5a40dc88cd1dc7e832020-11-25T01:19:21ZengMDPI AGEntropy1099-43002019-05-0121552410.3390/e21050524e21050524The Evolution of Neuroplasticity and the Effect on Integrated InformationLeigh Sheneman0Jory Schossau1Arend Hintze2Department of Computer Science and Engineering, Michigan State University, East Lansing, MI 48823, USABEACON-Center for the Study of Evolution in Action, Michigan State University, East Lansing, MI 48823, USADepartment of Computer Science and Engineering, Michigan State University, East Lansing, MI 48823, USAInformation integration theory has been developed to quantify consciousness. Since conscious thought requires the integration of information, the degree of this integration can be used as a neural correlate (<inline-formula><math display="inline"><semantics><mi mathvariant="normal">Φ</mi></semantics></math></inline-formula>) with the intent to measure degree of consciousness. Previous research has shown that the ability to integrate information can be improved by Darwinian evolution. The value <inline-formula> <math display="inline"> <semantics> <mi mathvariant="normal">Φ</mi> </semantics> </math> </inline-formula> can change over many generations, and complex tasks require systems with at least a minimum <inline-formula> <math display="inline"> <semantics> <mi mathvariant="normal">Φ</mi> </semantics> </math> </inline-formula>. This work was done using simple animats that were able to remember previous sensory inputs, but were incapable of fundamental change during their lifetime: actions were predetermined or instinctual. Here, we are interested in changes to <inline-formula> <math display="inline"> <semantics> <mi mathvariant="normal">Φ</mi> </semantics> </math> </inline-formula> due to lifetime learning (also known as neuroplasticity). During lifetime learning, the system adapts to perform a task and necessitates a functional change, which in turn could change <inline-formula> <math display="inline"> <semantics> <mi mathvariant="normal">Φ</mi> </semantics> </math> </inline-formula>. One can find arguments to expect one of three possible outcomes: <inline-formula> <math display="inline"> <semantics> <mi mathvariant="normal">Φ</mi> </semantics> </math> </inline-formula> might remain constant, increase, or decrease due to learning. To resolve this, we need to observe systems that learn, but also improve their ability to learn over the many generations that Darwinian evolution requires. Quantifying <inline-formula> <math display="inline"> <semantics> <mi mathvariant="normal">Φ</mi> </semantics> </math> </inline-formula> over the course of evolution, and over the course of their lifetimes, allows us to investigate how the ability to integrate information changes. To measure <inline-formula> <math display="inline"> <semantics> <mi mathvariant="normal">Φ</mi> </semantics> </math> </inline-formula>, the internal states of the system must be experimentally observable. However, these states are notoriously difficult to observe in a natural system. Therefore, we use a computational model that not only evolves virtual agents (animats), but evolves animats to learn during their lifetime. We use this approach to show that a system that improves its performance due to feedback learning increases its ability to integrate information. In addition, we show that a system’s ability to increase <inline-formula> <math display="inline"> <semantics> <mi mathvariant="normal">Φ</mi> </semantics> </math> </inline-formula> correlates with its ability to increase in performance. This suggests that systems that are very plastic regarding <inline-formula> <math display="inline"> <semantics> <mi mathvariant="normal">Φ</mi> </semantics> </math> </inline-formula> learn better than those that are not.https://www.mdpi.com/1099-4300/21/5/524information integration theoryneuroevolutionautonomous learning |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Leigh Sheneman Jory Schossau Arend Hintze |
spellingShingle |
Leigh Sheneman Jory Schossau Arend Hintze The Evolution of Neuroplasticity and the Effect on Integrated Information Entropy information integration theory neuroevolution autonomous learning |
author_facet |
Leigh Sheneman Jory Schossau Arend Hintze |
author_sort |
Leigh Sheneman |
title |
The Evolution of Neuroplasticity and the Effect on Integrated Information |
title_short |
The Evolution of Neuroplasticity and the Effect on Integrated Information |
title_full |
The Evolution of Neuroplasticity and the Effect on Integrated Information |
title_fullStr |
The Evolution of Neuroplasticity and the Effect on Integrated Information |
title_full_unstemmed |
The Evolution of Neuroplasticity and the Effect on Integrated Information |
title_sort |
evolution of neuroplasticity and the effect on integrated information |
publisher |
MDPI AG |
series |
Entropy |
issn |
1099-4300 |
publishDate |
2019-05-01 |
description |
Information integration theory has been developed to quantify consciousness. Since conscious thought requires the integration of information, the degree of this integration can be used as a neural correlate (<inline-formula><math display="inline"><semantics><mi mathvariant="normal">Φ</mi></semantics></math></inline-formula>) with the intent to measure degree of consciousness. Previous research has shown that the ability to integrate information can be improved by Darwinian evolution. The value <inline-formula> <math display="inline"> <semantics> <mi mathvariant="normal">Φ</mi> </semantics> </math> </inline-formula> can change over many generations, and complex tasks require systems with at least a minimum <inline-formula> <math display="inline"> <semantics> <mi mathvariant="normal">Φ</mi> </semantics> </math> </inline-formula>. This work was done using simple animats that were able to remember previous sensory inputs, but were incapable of fundamental change during their lifetime: actions were predetermined or instinctual. Here, we are interested in changes to <inline-formula> <math display="inline"> <semantics> <mi mathvariant="normal">Φ</mi> </semantics> </math> </inline-formula> due to lifetime learning (also known as neuroplasticity). During lifetime learning, the system adapts to perform a task and necessitates a functional change, which in turn could change <inline-formula> <math display="inline"> <semantics> <mi mathvariant="normal">Φ</mi> </semantics> </math> </inline-formula>. One can find arguments to expect one of three possible outcomes: <inline-formula> <math display="inline"> <semantics> <mi mathvariant="normal">Φ</mi> </semantics> </math> </inline-formula> might remain constant, increase, or decrease due to learning. To resolve this, we need to observe systems that learn, but also improve their ability to learn over the many generations that Darwinian evolution requires. Quantifying <inline-formula> <math display="inline"> <semantics> <mi mathvariant="normal">Φ</mi> </semantics> </math> </inline-formula> over the course of evolution, and over the course of their lifetimes, allows us to investigate how the ability to integrate information changes. To measure <inline-formula> <math display="inline"> <semantics> <mi mathvariant="normal">Φ</mi> </semantics> </math> </inline-formula>, the internal states of the system must be experimentally observable. However, these states are notoriously difficult to observe in a natural system. Therefore, we use a computational model that not only evolves virtual agents (animats), but evolves animats to learn during their lifetime. We use this approach to show that a system that improves its performance due to feedback learning increases its ability to integrate information. In addition, we show that a system’s ability to increase <inline-formula> <math display="inline"> <semantics> <mi mathvariant="normal">Φ</mi> </semantics> </math> </inline-formula> correlates with its ability to increase in performance. This suggests that systems that are very plastic regarding <inline-formula> <math display="inline"> <semantics> <mi mathvariant="normal">Φ</mi> </semantics> </math> </inline-formula> learn better than those that are not. |
topic |
information integration theory neuroevolution autonomous learning |
url |
https://www.mdpi.com/1099-4300/21/5/524 |
work_keys_str_mv |
AT leighsheneman theevolutionofneuroplasticityandtheeffectonintegratedinformation AT joryschossau theevolutionofneuroplasticityandtheeffectonintegratedinformation AT arendhintze theevolutionofneuroplasticityandtheeffectonintegratedinformation AT leighsheneman evolutionofneuroplasticityandtheeffectonintegratedinformation AT joryschossau evolutionofneuroplasticityandtheeffectonintegratedinformation AT arendhintze evolutionofneuroplasticityandtheeffectonintegratedinformation |
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