The evolution of neuroplasticity and the effect on integrated information
| Autor: | Jory Schossau, Leigh Sheneman, Arend Hintze |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
Computer science
media_common.quotation_subject neuroevolution General Physics and Astronomy lcsh:Astrophysics Bioinformatik och systembiologi Measure (mathematics) Article Task (project management) 03 medical and health sciences Animat 0302 clinical medicine lcsh:QB460-466 lcsh:Science autonomous learning 030304 developmental biology media_common 0303 health sciences Neuroevolution Bioinformatics and Systems Biology business.industry Integrated information theory Computer Sciences Autonomous learning lcsh:QC1-999 Information integration theory TheoryofComputation_MATHEMATICALLOGICANDFORMALLANGUAGES Datavetenskap (datalogi) lcsh:Q information integration theory Artificial intelligence Consciousness business Constant (mathematics) Value (mathematics) lcsh:Physics 030217 neurology & neurosurgery |
| Zdroj: | Entropy Volume 21 Issue 5 Entropy, Vol 21, Iss 5, p 524 (2019) |
| Popis: | 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 (&Phi ) 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 &Phi can change over many generations, and complex tasks require systems with at least a minimum &Phi 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 &Phi 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 &Phi One can find arguments to expect one of three possible outcomes: &Phi 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 &Phi 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 &Phi 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&rsquo s ability to increase &Phi correlates with its ability to increase in performance. This suggests that systems that are very plastic regarding &Phi learn better than those that are not. |
| Databáze: | OpenAIRE |
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