Spontaneous evolution of neural cells was recorded around 4-34 days in vitro (DIV) with high-density CMOS microelectrode array, which enables detailed study of the spatiotemporal activity of cultured neurons. We used the CMOS array to characterize 1) the evolution of activation patterns of each putative neurons, 2) the developmental change in cell-cell interactions, and finally, 3) emergence of multiple timescales for neurons to exchange information with each other. The results revealed not only the topology of the physical connectivity of the neurons but also the functional connectivity of the neurons within different time scales. We finally argued the relationship of the results with "functional networks", which interact with each other to support multiple cognitive functions in the mature human brain.

This study investigated the effects of stressful life events (SLE) on the quality of sleep among university students. The subjects were 410 Chinese undergraduate students (CU), 201 Japanese undergraduate students (JU), and 111 Chinese international students living in Japan (CI). Four questionnaires were administered: (a) the Pittsburgh Sleep Quality Index; (b) the Depression Anxiety Stress Scale-21; (c) the Negative Life Events checklist; and (d) the Adolescent Self-Rating Life Events Checklist. Structural Equation Modeling (SEM) indicated that the experience of SLE directly affects and decreases the quality of sleep, and also increases negative emotions thereby indirectly influencing sleep quality. The multiple-group SEM suggested that negative emotions elicited from SLE, influencing the quality of sleep, were higher for CU and CI than for JU. Thus, it could be suggested that measures to improve the quality of sleep need to be managed differently for Japanese and Chinese students.

Vicarious trial-and-error (VTE) is a behavior observed in rat experiments that seems to suggest self-conflict. This behavior is seen mainly when the rats are uncertain about making a decision. The presence of VTE is regarded as an indicator of a deliberative decision-making process, that is, searching, predicting, and evaluating outcomes. This process is slower than automated decision-making processes, such as reflex or habituation, but it allows for flexible and ongoing control of behavior. In this study, we propose for the first time a robotic model of VTE to see if VTE can emerge just from a body-environment interaction and to show the underlying mechanism responsible for the observation of VTE and the advantages provided by it. We tried several robots with different parameters, and we have found that they showed three different types of VTE: high numbers of VTE at the beginning of learning, decreasing numbers afterward (similar VTE pattern to experiments with rats), low during the whole learning period, and high numbers all the time. Therefore, we were able to reproduce the phenomenon of VTE in a model robot using only a simple dynamical neural network with Hebbian learning, which suggests that VTE is an emergent property of a plastic and embodied neural network. From a comparison of the three types of VTE, we demonstrated that 1) VTE is associated with chaotic activity of neurons in our model and 2) VTE-showing robots were robust to environmental perturbations. We suggest that the instability of neuronal activity found in VTE allows ongoing learning to rebuild its strategy continuously, which creates robust behavior. Based on these results, we suggest that VTE is caused by a similar mechanism in biology and leads to robust decision making in an analogous way.

Spontaneous development of neuronal cells was recorded around 4-34 days in vitro (DIV) with high-density CMOS array, which enables detailed study of the spatio-temporal activity of neuronal culture. We used the CMOS array to characterize the evolution of the inter-spike interval (ISI) distribution from putative single neurons, and estimate the network structure based on transfer entropy analysis, where each node corresponds to a single neuron. We observed that the ISI distributions gradually obeyed the power law with maturation of the network. The amount of information transferred between neurons increased at the early stage of development, but decreased as the network matured. These results suggest that both ISI and transfer entropy were very useful for characterizing the dynamic development of cultured neural cells over a few weeks. I IntroductionThere are many techniques for training artificial neural networks to optimize an explicit objective function, e.g., back propagation, genetic algorithm, and Q-learning [3], [5], [10]. But with biological systems in general, there are no explicit functions that need to be optimized. Indeed, a characteristic trait of a biological system is its autonomy, which is found in the spontaneous primitive movements of a bacterium, and even in the sophisticated learning skills and action selection of human beings. Our main interest is the study of developmental processes and of the emergent learning capabilities expressed by a biological neural system. As a first step, we focused on the development of cultured neural cells.Biological neural cells cultured in vitro show development, aging, and spontaneous activities, which are rarely studied in computational neural cells. These features of biological cells are the focus of the present research. More specifically, we aim to reveal the underlying dynamics of the developmental processes.By recording the development of cultured biological neural cells on a CMOS (complementary meta-oxide-semiconductor) array glass plate, we monitored the temporal evolution of electrical signals over a few weeks. From direct observations, we noticed that Alternatively, optical imaging can be used to study the Ca ++ dynamics of any neuron of interest; however, the temporal resolution is not high enough to characterize the action potentials of each neuron. Europe PMC Funders GroupHigh-density CMOS array is an emerging instrument for investigation of the spatio-temporal activity of neuronal culture cells in detail. The array has 11,000 recording sites with an interelectrode distance of 17 µm, i.e., in the order of cell body size, and a sampling rate of over 20 kHz. This high spatio-temporal resolution allows precise recording of action potentials from the identified cell bodies of neurons. In the present work, we use the CMOS array to characterize inter-spike interval (ISI) distributions from putative single neurons, and estimate network structure based on transfer entropy, where each node corresponds to a single neuron.The paper is organized as fo...