A Neurodynamic Account of Spontaneous Behaviour

The current article suggests that deterministic chaos self-organized in cortical dynamics could be responsible for the generation of spontaneous action sequences. Recently, various psychological observations have suggested that humans and primates can learn to extract statistical structures hidden in perceptual sequences experienced during active environmental interactions. Although it has been suggested that such statistical structures involve chunking or compositional primitives, their neuronal implementations in brains have not yet been clarified. Therefore, to reconstruct the phenomena, synthetic neuro-robotics experiments were conducted by using a neural network model, which is characterized by a generative model with intentional states and its multiple timescales dynamics. The experimental results showed that the robot successfully learned to imitate tutored behavioral sequence patterns by extracting the underlying transition probability among primitive actions. An analysis revealed that a set of primitive action patterns was embedded in the fast dynamics part, and the chaotic dynamics of spontaneously sequencing these action primitive patterns was structured in the slow dynamics part, provided that the timescale was adequately set for each part. It was also shown that self-organization of this type of functional hierarchy ensured robust action generation by the robot in its interactions with a noisy environment. This article discusses the correspondence of the synthetic experiments with the known hierarchy of the prefrontal cortex, the supplementary motor area, and the primary motor cortex for action generation. We speculate that deterministic dynamical structures organized in the prefrontal cortex could be essential because they can account for the generation of both intentional behaviors of fixed action sequences and spontaneous behaviors of pseudo-stochastic action sequences by the same mechanism. Author Summary: Various psychological observations have suggested that the spontaneously generated behaviors of humans reflect statistical structures extracted via perceptual learning of everyday practices and experiences while interacting with the world. Although those studies have further suggested that such acquired statistical structures use chunking, which generates a variety of complex actions recognized in compositional manner, the underlying neural mechanism has not been clarified. The current neuro-robotics study presents a model prediction for the mechanism and an evaluation of the model through physically grounded experiments on action imitation learning. The model features learning of a mapping from intentional states to action sequences based on multiple timescales dynamics characteristics. The experimental results suggest that deterministic chaos self-organized in the slower timescale part of the network dynamics is responsible for generating spontaneous transitions among primitive actions by reflecting the extracted statistical structures. The robustness of action generation in a noisy physical environment is preserved. These results agree with other neuroscience evidence of the hierarchical organization in the cortex for voluntary actions. Finally, as presented in a discussion of the results, the deterministic cortical dynamics are presumed crucial in generating not only more intentional fixed action sequences but also less intentional spontaneously transitive action sequences.