Kiyoshi Kotani

Motion Control of Self-Moving Trays for Human Supporting Production Cell “Attentive Workbench”

We propose “Attentive Workbench (AWB),” a new cell production system in which an intelligent system supports human workers. Using cameras, projectors, planar motor driven self-moving trays and other devices, the system supports workers from both physical and information aspects, recognizing worker’s condition and intention. This paper outlines AWB and deals with physical assembly support using self-moving parts trays. Two different schemes, centralized and decentralized, for controlling multiple parts trays are evaluated through experiments and simulations. The centralized control scheme is found to have the higher performance than the decentralized control scheme.

Network-wide integration of stem cell-derived neurons and mouse cortical neurons using microfabricated co-culture devices

Regeneration of damaged central nervous systems (CNS) is an important topic in neuroscience and neuroengineering. Grafting new neurons derived from pluripotent stem cells into damaged regions can be done to restore functions after injury. Little is known, however, about network-wide interactions between stem-cell-derived neurons and CNS neurons. In this study, we developed a co-culture method of stem cell-derived neuronal networks and CNS networks and observed spontaneous activity in the co-culture samples. By using a microfabricated poly(dimethylsiloxane) device having two culture compartments and 20 connecting microconduits, we are able to compartmentalize P19-derived neurons and mouse cortical neurons and connect them via the microconduits. Furthermore, we combined the co-culture device and a microelectrode array (MEA)-based recording system and recorded spontaneous activity in the co-cultured networks. We found that periodic synchronized bursting spreading over both neuronal networks occurred during the second week in vitro and that P19-derived neurons in the co-cultured networks had different developmental processes compared with those grown in monoculture. These findings suggest that functional interactions form between P19-dervived neurons and mouse cortical neurons and that the co-culture method is useful for exploring the network-wide integrations between stem cell-derived neurons and CNS neurons.

Postural-induced phase shift of respiratory sinus arrhythmia and blood pressure variations: insight from respiratory-phase domain analysis.

The purpose of this study is to evaluate the multiple effects of respiration on cardiovascular variability in different postures, by analyzing respiratory sinus arrhythmia (RSA) and respiratory-related blood pressure (BP) variations for systolic BP (SBP), diastolic BP (DBP), and pulse pressure (PP) in the respiratory-phase domain. The measurements were conducted for 420 s on healthy humans in the sitting and standing positions, while the subjects were continuously monitored for heart rate and BP variability and instantaneous lung volume. The waveforms of RSA and respiratory-related BP variations were extracted as a function of the respiratory phase. In the standing position, the waveforms of the BP variations for SBP, DBP, and PP show their maxima at around the end of expiration (pi rad) and the minima at around the end of inspiration (2 pi rad), while the waveform of RSA is delayed by approximately 0.35 pi rad compared with the BP waveforms. On the other hand, in the sitting position, the phase of the DBP waveform (1.69 pi rad) greatly and significantly (P < 0.01) differs from that in the standing position (1.20 pi rad). Also, the phase of PP is delayed and that of RSA is advanced in the sitting position (P < 0.01). In particular, the phase shift of the DBP waveform is sufficiently large to alter whole hemodynamic fluctuations, affecting the amplitudes of SBP and PP variations. We conclude that the postural change associated with an altered autonomic balance affects not only the amplitude of RSA, but also the phases of RSA and BP variations in a complicated manner, and the respiratory-phase domain analysis used in this study is useful for elucidating the dynamic mechanisms of RSA.

Spontaneous Calcium Transients in Cultured Cortical Networks During Development

Spontaneous neuronal activity plays an important role in the development of the brain. Developmental changes in the spontaneous activity pattern of neuronal networks in vitro have been extensively studied by using the microelectrode array (MEA) recording system. However, little is known about the transition of spontaneous intracellular calcium dynamics, and the relationship between calcium transients and electrical activity during development. In the present paper, we carry out simultaneous recording of spontaneous electrical activity and intracellular calcium transients of rat cortical networks cultured on MEA. In one-week cultures, periodic synchronized bursts are observed and are followed by synchronized calcium transients. In three-week cultures, synchronized calcium transients are rarely observed despite the presence of highly complicated synchronized activity. Between these two states, in two-week cultures, slow, radial propagation of calcium waves independent of electrical activity is observed. Pharmacological treatments with the purinergic receptor antagonist suramin and gap junction blocker 18-beta glycyrrhetinic acid reveal that the spontaneous radial calcium waves are mediated by the astrocytic network, and suggest that the astrocytic calcium waves can influence the electrical firing patterns of networks by locally affecting neuronal signaling. These results indicate that the various dynamics of intracellular calcium transients regulate the network maturation processes.

Relationship between working memory performance and neural activation measured using near-infrared spectroscopy.

Working memory (WM) is a key function for various cognitive processes. Near‐infrared spectroscopy (NIRS) is a powerful technique for noninvasive functional imaging. However, a study has yet to be published on the application of NIRS for evaluating WM performance. The objective was to evaluate NIRS for measuring WM performance.

Reduction theories elucidate the origins of complex biological rhythms generated by interacting delay-induced oscillations.

Time delay is known to induce sustained oscillations in many biological systems such as electroencephalogram (EEG) activities and gene regulations. Furthermore, interactions among delay-induced oscillations can generate complex collective rhythms, which play important functional roles. However, due to their intrinsic infinite dimensionality, theoretical analysis of interacting delay-induced oscillations has been limited. Here, we show that the two primary methods for finite-dimensional limit cycles, namely, the center manifold reduction in the vicinity of the Hopf bifurcation and the phase reduction for weak interactions, can successfully be applied to interacting infinite-dimensional delay-induced oscillations. We systematically derive the complex Ginzburg-Landau equation and the phase equation without delay for general interaction networks. Based on the reduced low-dimensional equations, we demonstrate that diffusive (linearly attractive) coupling between a pair of delay-induced oscillations can exhibit nontrivial amplitude death and multimodal phase locking. Our analysis provides unique insights into experimentally observed EEG activities such as sudden transitions among different phase-locked states and occurrence of epileptic seizures.

Respiratory-phase domain analysis of heart rate variability can accurately estimate cardiac vagal activity during a mental arithmetic task.

OBJECTIVES The objectives of this paper were to present a method to extract the amplitude of RSA in the respiratory-phase domain, to compare that with subjective or objective indices of the MWL (mental workload), and to compare that with a conventional frequency analysis in terms of its accuracy during a mental arithmetic task. METHODS HRV (heart rate variability), ILV (instantaneous lung volume), and motion of the throat were measured under a mental arithmetic experiment and subjective and objective indices were also obtained. The amplitude of RSA was extracted in the respiratory-phase domain, and its correlation with the load level was compared with the results of the frequency domain analysis, which is the standard analysis of the HRV. RESULTS The subjective and objective indices decreased as the load level increased, showing that the experimental protocol was appropriate. Then, the amplitude of RSA in the respiratory-phase domain also decreased with the increase in the load level. The results of the correlation analysis showed that the respiratory-phase domain analysis has higher negative correlations, -0.84 and -0.82, with the load level as determined by simple correlation and rank correlation, respectively, than does frequency analysis, for which the correlations were found to be -0.54 and -0.63, respectively. In addition, it was demonstrated that the proposed method could be applied to the short-term extraction of RSA amplitude. CONCLUSIONS We proposed a simple and effective method to extract the amplitude of the respiratory sinus arrhythmia (RSA) in the respiratory-phase domain and the results show that this method can estimate cardiac vagal activity more accurately than frequency analysis.

Cultured Cortical Neurons Can Perform Blind Source Separation According to the Free-Energy Principle.

Blind source separation is the computation underlying the cocktail party effect––a partygoer can distinguish a particular talker’s voice from the ambient noise. Early studies indicated that the brain might use blind source separation as a signal processing strategy for sensory perception and numerous mathematical models have been proposed; however, it remains unclear how the neural networks extract particular sources from a complex mixture of inputs. We discovered that neurons in cultures of dissociated rat cortical cells could learn to represent particular sources while filtering out other signals. Specifically, the distinct classes of neurons in the culture learned to respond to the distinct sources after repeating training stimulation. Moreover, the neural network structures changed to reduce free energy, as predicted by the free-energy principle, a candidate unified theory of learning and memory, and by Jaynes’ principle of maximum entropy. This implicit learning can only be explained by some form of Hebbian plasticity. These results are the first in vitro (as opposed to in silico) demonstration of neural networks performing blind source separation, and the first formal demonstration of neuronal self-organization under the free energy principle.

Population dynamics of the modified theta model: macroscopic phase reduction and bifurcation analysis link microscopic neuronal interactions to macroscopic gamma oscillation.

Gamma oscillations of the local field potential are organized by collective dynamics of numerous neurons and have many functional roles in cognition and/or attention. To mathematically and physiologically analyse relationships between individual inhibitory neurons and macroscopic oscillations, we derive a modification of the theta model, which possesses voltage-dependent dynamics with appropriate synaptic interactions. Bifurcation analysis of the corresponding Fokker–Planck equation (FPE) enables us to consider how synaptic interactions organize collective oscillations. We also develop the adjoint method (infinitesimal phase resetting curve) for simultaneous equations consisting of ordinary differential equations representing synaptic dynamics and a partial differential equation for determining the probability distribution of the membrane potential. This method provides a macroscopic phase response function (PRF), which gives insights into how it is modulated by external perturbation or internal changes of parameters. We investigate the effects of synaptic time constants and shunting inhibition on these gamma oscillations. The sensitivity of rising and decaying time constants is analysed in the oscillatory parameter regions; we find that these sensitivities are not largely dependent on rate of synaptic coupling but, rather, on current and noise intensity. Analyses of shunting inhibition reveal that it can affect both promotion and elimination of gamma oscillations. When the macroscopic oscillation is far from the bifurcation, shunting promotes the gamma oscillations and the PRF becomes flatter as the reversal potential of the synapse increases, indicating the insensitivity of gamma oscillations to perturbations. By contrast, when the macroscopic oscillation is near the bifurcation, shunting eliminates gamma oscillations and a stable firing state appears. More interestingly, under appropriate balance of parameters, two branches of bifurcation are found in our analysis of the FPE. In this case, shunting inhibition can effect both promotion and elimination of the gamma oscillation depending only on the reversal potential.

A Novel Unsupervised Adaptive Learning Method for Long-Term Electromyography (EMG) Pattern Recognition

Performance degradation will be caused by a variety of interfering factors for pattern recognition-based myoelectric control methods in the long term. This paper proposes an adaptive learning method with low computational cost to mitigate the effect in unsupervised adaptive learning scenarios. We presents a particle adaptive classifier (PAC), by constructing a particle adaptive learning strategy and universal incremental least square support vector classifier (LS-SVC). We compared PAC performance with incremental support vector classifier (ISVC) and non-adapting SVC (NSVC) in a long-term pattern recognition task in both unsupervised and supervised adaptive learning scenarios. Retraining time cost and recognition accuracy were compared by validating the classification performance on both simulated and realistic long-term EMG data. The classification results of realistic long-term EMG data showed that the PAC significantly decreased the performance degradation in unsupervised adaptive learning scenarios compared with NSVC (9.03% ± 2.23%, p < 0.05) and ISVC (13.38% ± 2.62%, p = 0.001), and reduced the retraining time cost compared with ISVC (2 ms per updating cycle vs. 50 ms per updating cycle).