Uni-cellular integration of complex spatial information in slime moulds and ciliates

Representing Stimulus Information in an Energy Metabolism Pathway

We explored a computational model of astrocytic energy metabolism and demonstrated the theoretical plausibility that this type of pathway might be capable of coding information about stimuli in addition to its known functions in cellular energy and carbon budgets. Simulation results indicate that glycogenolytic glycolysis triggered by activation of adrenergic receptors can capture the intensity and duration features of a neuromodulator waveform and can respond in a dose-dependent manner, including non-linear state changes that are analogous to action potentials. We show how this metabolic pathway can translate information about external stimuli to production profiles of energy-carrying molecules such as lactate with a precision beyond simple signal transduction or non-linear amplification. The results suggest the operation of a metabolic state-machine from the spatially discontiguous yet interdependent metabolite elements. Such metabolic pathways might be well-positioned to code an additional level of salient information about a cell's environmental demands to impact its function. Our hypothesis has implications for the computational power and energy efficiency of the brain.

Accumulation of Tetrahymena pyriformis on Interfaces

The behavior of ciliates has been studied for many years through environmental biology and the ethology of microorganisms, and recent hydrodynamic studies of microswimmers have greatly advanced our understanding of the behavioral dynamics at the single-cell level. However, the association between single-cell dynamics captured by microscopic observation and pattern dynamics obtained by macroscopic observation is not always obvious. Hence, to bridge the gap between the two, there is a need for experimental results on swarming dynamics at the mesoscopic scale. In this study, we investigated the spatial population dynamics of the ciliate, Tetrahymena pyriformis, based on quantitative data analysis. We combined the image processing of 3D micrographs and machine learning to obtain the positional data of individual cells of T. pyriformis and examined their statistical properties based on spatio-temporal data. According to the 3D spatial distribution of cells and their temporal evolution, cells accumulated both on the solid wall at the bottom surface and underneath the air–liquid interface at the top. Furthermore, we quantitatively clarified the difference in accumulation levels between the bulk and the interface by creating a simple behavioral model that incorporated quantitative accumulation coefficients in its solution. The accumulation coefficients can be compared under different conditions and between different species.

Design and Implementation of Human-Computer Interface for Participatory Art Video Development Platform Based on Interactive Non-linear Algorithm

Artificial intelligence (AI) technology is innovatively combined with participatory video for artistic creation and communication to improve the enthusiasm of art lovers for artistic creation and communication and expand the application range of AI technology. First, the interactive framework of interactive participation video is proposed based on the analysis of the related literature of interactive non-linear video. Then, a questionnaire is designed accordingly to analyze the social needs of people on art social platforms. According to the survey results, the participatory art video online communication platform is designed and preliminarily realized. Finally, a participant video eye movement control experiment is conducted to test the performance of the participatory art video development platform. Meanwhile, the platform is evaluated through field research from two aspects of test efficiency and user experience. The results show that the operation time of the participatory art video development platform is much shorter than that of the control group. It takes only approximately 15 s to complete the annotation operation with low SD, indicating that the system performance is stable. The accuracy of the platform also reaches 100%.

Adaptive behaviour and learning in slime moulds: the role of oscillations

The slime mould Physarum polycephalum, an aneural organism, uses information from previous experiences to adjust its behaviour, but the mechanisms by which this is accomplished remain unknown. This article examines the possible role of oscillations in learning and memory in slime moulds. Slime moulds share surprising similarities with the network of synaptic connections in animal brains. First, their topology derives from a network of interconnected, vein-like tubes in which signalling molecules are transported. Second, network motility, which generates slime mould behaviour, is driven by distinct oscillations that organize into spatio-temporal wave patterns. Likewise, neural activity in the brain is organized in a variety of oscillations characterized by different frequencies. Interestingly, the oscillating networks of slime moulds are not precursors of nervous systems but, rather, an alternative architecture. Here, we argue that comparable information-processing operations can be realized on different architectures sharing similar oscillatory properties. After describing learning abilities and oscillatory activities of P. polycephalum, we explore the relation between network oscillations and learning, and evaluate the organism's global architecture with respect to information-processing potential. We hypothesize that, as in the brain, modulation of spontaneous oscillations may sustain learning in slime mould. This article is part of the theme issue 'Basal cognition: conceptual tools and the view from the single cell'.

Stress signalling in acellular slime moulds and its detection by conspecifics

Unicellular organisms live in unpredictable environments. Therefore, they need to continuously assess environmental conditions and respond appropriately to survive and thrive. When subjected to rapid changes in their environment or to cellular damages, unicellular organisms such as bacteria exhibit strong physiological reactions called stress responses that can be sensed by conspecifics. The ability to detect and use stress-related cues released by conspecifics to acquire information about the environment constitutes an adaptive survival response by prompting the organism to avoid potential dangers. Here, we investigate stress signalling and its detection by conspecifics in a unicellular organism, Physarum polycephalum. Slime moulds were subjected to either biotic (i.e. nutritional) or abiotic (i.e. chemical and light) stressors or left undisturbed while they were exploring a homogeneous environment. Then, we observed the responses of slime moulds facing a choice between cues released by stressed clone mates and cues released by undisturbed ones. We found that slime moulds actively avoided environments previously explored by stressed clone mates. These results suggest that slime moulds, like bacteria or social amoeba, exhibit physiological responses to biotic and abiotic stresses that can be sensed by conspecifics. Our results establish slime moulds as a promising new model to investigate the use of social information in unicellular organisms. This article is part of the theme issue 'Signal detection theory in recognition systems: from evolving models to experimental tests'.

A Complex Hierarchy of Avoidance Behaviors in a Single-Cell Eukaryote

Complex behavior is associated with animals with nervous systems, but decision-making and learning also occur in non-neural organisms [1], including singly nucleated cells [2-5] and multi-nucleate synctia [6-8]. Ciliates are single-cell eukaryotes, widely dispersed in aquatic habitats [9], with an extensive behavioral repertoire [10-13]. In 1906, Herbert Spencer Jennings [14, 15] described in the sessile ciliate Stentor roeseli a hierarchy of responses to repeated stimulation, which are among the most complex behaviors reported for a singly nucleated cell [16, 17]. These results attracted widespread interest [18, 19] and exert continuing fascination [7, 20-22] but were discredited during the behaviorist orthodoxy by claims of non-reproducibility [23]. These claims were based on experiments with the motile ciliate Stentor coeruleus. We acquired and maintained the correct organism in laboratory culture and used micromanipulation and video microscopy to confirm Jennings' observations. Despite significant individual variation, not addressed by Jennings, S. roeseli exhibits avoidance behaviors in a characteristic hierarchy of bending, ciliary alteration, contractions, and detachment, which is distinct from habituation or conditioning. Remarkably, the choice of contraction versus detachment is consistent with a fair coin toss. Such behavioral complexity may have had an evolutionary advantage in protist ecosystems, and the ciliate cortex may have provided mechanisms for implementing such behavior prior to the emergence of multicellularity. Our work resurrects Jennings' pioneering insights and adds to the list of exceptional features, including regeneration [24], genome rearrangement [25], codon reassignment [26], and cortical inheritance [27], for which the ciliate clade is renowned.

Behavioural analysis of single-cell aneural ciliate, Stentor roeseli, using machine learning approaches

There is still a significant gap between our understanding of neural circuits and the behaviours they compute-i.e. the computations performed by these neural networks (Carandini 2012 Nat. Neurosci.15, 507-509. (doi:10.1038/nn.3043)). Cellular decision-making processes, learning, behaviour and memory formation-all that have been only associated with animals with neural systems-have also been observed in many unicellular aneural organisms, namely Physarum, Paramecium and Stentor (Tang & Marshall2018 Curr. Biol.28, R1180-R1184. (doi:10.1016/j.cub.2018.09.015)). As these are fully functioning organisms, yet being unicellular, there is a much better chance to elucidate the detailed mechanisms underlying these learning processes in these organisms without the complications of highly interconnected neural circuits. An intriguing learning behaviour observed in Stentor roeseli (Jennings 1902 Am. J. Physiol. Legacy Content8, 23-60. (doi:10.1152/ajplegacy.1902.8.1.23)) when stimulated with carmine has left scientists puzzled for more than a century. So far, none of the existing learning paradigm can fully encapsulate this particular series of five characteristic avoidance reactions. Although we were able to observe all responses described in the literature and in a previous study (Dexter et al. 2019), they do not conform to any particular learning model. We then investigated whether models inferred from machine learning approaches, including decision tree, random forest and feed-forward artificial neural networks could infer and predict the behaviour of S. roeseli. Our results showed that an artificial neural network with multiple 'computational' neurons is inefficient at modelling the single-celled ciliate's avoidance reactions. This has highlighted the complexity of behaviours in aneural organisms. Additionally, this report will also discuss the significance of elucidating molecular details underlying learning and decision-making processes in these unicellular organisms, which could offer valuable insights that are applicable to higher animals.