Exploring Criticality as a Generic Adaptive Mechanism
The activity of many biological and cognitive systems is not poised deep within a specific regime of activity. Instead, they operate near points of critical behavior located at the boundary between different phases. Certain authors link some of the properties of criticality with the ability of livin...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2018-10-01
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| Series: | Frontiers in Neurorobotics |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/article/10.3389/fnbot.2018.00055/full |
| _version_ | 1818504472890966016 |
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| author | Miguel Aguilera Miguel Aguilera Manuel G. Bedia Manuel G. Bedia |
| author_facet | Miguel Aguilera Miguel Aguilera Manuel G. Bedia Manuel G. Bedia |
| author_sort | Miguel Aguilera |
| collection | DOAJ |
| description | The activity of many biological and cognitive systems is not poised deep within a specific regime of activity. Instead, they operate near points of critical behavior located at the boundary between different phases. Certain authors link some of the properties of criticality with the ability of living systems to generate autonomous or intrinsically generated behavior. However, these claims remain highly speculative. In this paper, we intend to explore the connection between criticality and autonomous behavior through conceptual models that show how embodied agents may adapt themselves toward critical points. We propose to exploit maximum entropy models and their formal descriptions of indicators of criticality to present a learning model that drives generic agents toward critical points. Specifically, we derive such a learning model in an embodied Boltzmann machine by implementing a gradient ascent rule that maximizes the heat capacity of the controller in order to make the network maximally sensitive to external perturbations. We test and corroborate the model by implementing an embodied agent in the Mountain Car benchmark test, which is controlled by a Boltzmann machine that adjusts its weights according to the model. We find that the neural controller reaches an apparent point of criticality, which coincides with a transition point of the behavior of the agent between two regimes of behavior, maximizing the synergistic information between its sensors and the combination of hidden and motor neurons. Finally, we discuss the potential of our learning model to answer questions about the connection between criticality and the capabilities of living systems to autonomously generate intrinsic constraints on their behavior. We suggest that these “critical agents” are able to acquire flexible behavioral patterns that are useful for the development of successful strategies in different contexts. |
| first_indexed | 2024-12-10T21:37:33Z |
| format | Article |
| id | doaj.art-051ab1fd69f946a883d539a99780e50c |
| institution | Directory Open Access Journal |
| issn | 1662-5218 |
| language | English |
| last_indexed | 2024-12-10T21:37:33Z |
| publishDate | 2018-10-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Neurorobotics |
| spelling | doaj.art-051ab1fd69f946a883d539a99780e50c2022-12-22T01:32:35ZengFrontiers Media S.A.Frontiers in Neurorobotics1662-52182018-10-011210.3389/fnbot.2018.00055349075Exploring Criticality as a Generic Adaptive MechanismMiguel Aguilera0Miguel Aguilera1Manuel G. Bedia2Manuel G. Bedia3Information and Autonomous Systems-Research Center for Life, Mind, and Society, University of the Basque Country, Donostia, SpainDepartment of Computer Science and Systems Engineering, University of Zaragoza, Zaragoza, SpainDepartment of Computer Science and Systems Engineering, University of Zaragoza, Zaragoza, SpainInteractive Systems, Adaptivity, Autonomy and Cognition Lab, Aragón Institute of Engineering Research, University of Zaragoza, Zaragoza, SpainThe activity of many biological and cognitive systems is not poised deep within a specific regime of activity. Instead, they operate near points of critical behavior located at the boundary between different phases. Certain authors link some of the properties of criticality with the ability of living systems to generate autonomous or intrinsically generated behavior. However, these claims remain highly speculative. In this paper, we intend to explore the connection between criticality and autonomous behavior through conceptual models that show how embodied agents may adapt themselves toward critical points. We propose to exploit maximum entropy models and their formal descriptions of indicators of criticality to present a learning model that drives generic agents toward critical points. Specifically, we derive such a learning model in an embodied Boltzmann machine by implementing a gradient ascent rule that maximizes the heat capacity of the controller in order to make the network maximally sensitive to external perturbations. We test and corroborate the model by implementing an embodied agent in the Mountain Car benchmark test, which is controlled by a Boltzmann machine that adjusts its weights according to the model. We find that the neural controller reaches an apparent point of criticality, which coincides with a transition point of the behavior of the agent between two regimes of behavior, maximizing the synergistic information between its sensors and the combination of hidden and motor neurons. Finally, we discuss the potential of our learning model to answer questions about the connection between criticality and the capabilities of living systems to autonomously generate intrinsic constraints on their behavior. We suggest that these “critical agents” are able to acquire flexible behavioral patterns that are useful for the development of successful strategies in different contexts.https://www.frontiersin.org/article/10.3389/fnbot.2018.00055/fullcriticalitylearningboltzmann machineIsing modelheat capacity |
| spellingShingle | Miguel Aguilera Miguel Aguilera Manuel G. Bedia Manuel G. Bedia Exploring Criticality as a Generic Adaptive Mechanism Frontiers in Neurorobotics criticality learning boltzmann machine Ising model heat capacity |
| title | Exploring Criticality as a Generic Adaptive Mechanism |
| title_full | Exploring Criticality as a Generic Adaptive Mechanism |
| title_fullStr | Exploring Criticality as a Generic Adaptive Mechanism |
| title_full_unstemmed | Exploring Criticality as a Generic Adaptive Mechanism |
| title_short | Exploring Criticality as a Generic Adaptive Mechanism |
| title_sort | exploring criticality as a generic adaptive mechanism |
| topic | criticality learning boltzmann machine Ising model heat capacity |
| url | https://www.frontiersin.org/article/10.3389/fnbot.2018.00055/full |
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