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Designing spontaneous behavioral switching via chaotic itinerancy.

ABSTRACT: Chaotic itinerancy is a frequently observed phenomenon in high-dimensional nonlinear dynamical systems and is characterized by itinerant transitions among multiple quasi-attractors. Several studies have pointed out that high-dimensional activity in animal brains can be observed to exhibit chaotic itinerancy, which is considered to play a critical role in the spontaneous behavior generation of animals. Thus, how to design desired chaotic itinerancy is a topic of great interest, particularly for neurorobotics researchers who wish to understand and implement autonomous behavioral controls. However, it is generally difficult to gain control over high-dimensional nonlinear dynamical systems. In this study, we propose a method for implementing chaotic itinerancy reproducibly in a high-dimensional chaotic neural network. We demonstrate that our method enables us to easily design both the trajectories of quasi-attractors and the transition rules among them simply by adjusting the limited number of system parameters and by using the intrinsic high-dimensional chaos.

SUBMITTER: Inoue K

PROVIDER: S-EPMC7673744 | biostudies-literature | 2020 Nov

REPOSITORIES: biostudies-literature

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Designing spontaneous behavioral switching via chaotic itinerancy.

Inoue Katsuma K Nakajima Kohei K Kuniyoshi Yasuo Y

Science advances 20201111 46

Chaotic itinerancy is a frequently observed phenomenon in high-dimensional nonlinear dynamical systems and is characterized by itinerant transitions among multiple quasi-attractors. Several studies have pointed out that high-dimensional activity in animal brains can be observed to exhibit chaotic itinerancy, which is considered to play a critical role in the spontaneous behavior generation of animals. Thus, how to design desired chaotic itinerancy is a topic of great interest, particularly for n ...[more]

PMID: 33177080

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Project description:Goal-directed decision making often requires evaluating the outcomes of our decisions, assessing any gains or losses, learning from performance-related feedback, and deciding whether to alter our future decisions. However, it is unclear how these processes can be influenced by the saliency of an outcome (e.g., when one aspect of the outcome is accentuated more than another). Here we investigated whether decision strategies changed when certain aspects of the task outcome (win/loss or correct/incorrect) became more salient and how our brain encoded such saliency signals. We employed a simple two-alternative forced choice gambling task and quantified the frequency at which participants switched decisions to an alternative option in the subsequent trial after receiving feedback on their current selection. We conducted three experiments. In Experiment 1, we established the baseline decision switching behavior: participants switched more frequently following incorrect trials than correct trials, but there was no significant difference between win and loss trials. In Experiment 2, we highlighted the utility (win or loss) or performance (correct or incorrect) dimension of the chosen outcome and we found that the difference in switching frequency was enlarged along the highlighted dimension. However, Experiment 3 showed that when using non-specific saliency emphasis of the outcome, the saliency effect was abolished. We further conducted simultaneous EEG recordings using specific saliency emphasis and found that the feedback-related negativity, P300, and late positive potential could collectively encode saliency modulation of behavioral switching. Lastly, both the frontal and parietal theta-band power encoded the outcome when it was made more salient. Together, our findings suggest that specific outcome saliency can modulate behavioral decision switching between choices and our results have further revealed the neural signatures underlying such saliency modulation. Altering the saliency of an outcome may change how information is weighed during outcome evaluation and thus influence future decisions.

Dataset Information

Designing spontaneous behavioral switching via chaotic itinerancy.

Publications

Designing spontaneous behavioral switching via chaotic itinerancy.

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