Macroscopic Gamma Oscillation With Bursting Neuron Model Under Stochastic Fluctuation.

Gamma oscillations are thought to play a role in information processing in the brain. Bursting neurons, which exhibit periodic clusters of spiking activity, are a type of neuron that are thought to contribute largely to gamma oscillations. However, little is known about how the properties of bursting neurons affect the emergence of gamma oscillation, its waveforms, and its synchronized characteristics, especially when subjected to stochastic fluctuations. In this study, we proposed a bursting neuron model that can analyze the bursting ratio and the phase response function. Then we theoretically analyzed the neuronal population dynamics composed of bursting excitatory neurons, mixed with inhibitory neurons. The bifurcation analysis of the equivalent Fokker-Planck equation exhibits three types of gamma oscillations of unimodal firing, bimodal firing in the inhibitory population, and bimodal firing in the excitatory population under different interaction strengths. The analyses of the macroscopic phase response function by the adjoint method of the Fokker-Planck equation revealed that the inhibitory doublet facilitates synchronization of the high-frequency oscillations. When we keep the strength of interactions constant, decreasing the bursting ratio of the individual neurons increases the relative high-gamma component of the populational phase-coupling functions. This also improves the ability of the neuronal population model to synchronize with faster oscillatory input. The analytical frameworks in this study provide insight into nontrivial dynamics of the population of bursting neurons, which further suggest that bursting neurons have an important role in rhythmic activities.

Motion-capture technique-based interface screen displaying real-time probe position and angle in kidney ultrasonography.

Professional skill is required to reproduce ultrasound images of the kidney as an optimal cross-section is easily lost with slight deviation in scanning location or angle of the probe. We developed a motion-capture technique-based interface screen that displays the real-time probe position and angle to overlap those provided beforehand. When a professional operator captured the approximate kidney image, our system recorded the relative spatial relationship between the subject and the probe. Next, an amateur operator who had no experience of clinical practice manipulated the probe only with the aid of the interface until the probe position and angle coincided with the professional ones. Eventually, amateur operators could place the probe with a deviation of distance of (x = 2.7 ± 1.2 mm, y = 3.0 ± 1.7 mm, z = 6.6 ± 1.8 mm) and angle of (Rx = 1.5 ± 0.3 degrees, Ry = 2.6 ± 1.1 degrees, Rz = 1.1 ± 0.3 degrees) from the professional goal to produce very similar cross-sectional kidney images (N = 8). Also, motion-capture technique-based evaluation of relative locations of the probe and subject body revealed difficulty in reproducing those without the interface screen navigation. In summary, our motion-capture technique-based ultrasound guide system provides operators with the opportunity to handle the probe just as another operator would beforehand. This could help in medical procedures wherein the same cross-sectional image should be repeatedly obtained. Moreover, it requires no conventional probe training for beginners and could even shift the paradigm for ultrasound probe handling.

Initiation and termination of reentry-like activity in rat cardiomyocytes cultured in a microelectrode array.

Cardiac reentry is a lethal arrhythmia associated with cardiac diseases. Although arrhythmias are reported to be due to localized propagation abnormalities, little is known about the mechanisms underlying the initiation and termination of reentry. This is primarily because of a lack of an appropriate experimental system in which activity pattern switches between reentry and normal beating can be investigated. In this study, we aimed to develop a culture system for measuring the spatial dynamics of reentry-like activity during its onset and termination. Rat cardiomyocytes were seeded in microelectrode arrays and purified with a glucose-free culture medium to generate a culture with a heterogeneous cell density. Reentry-like activity was recorded in purified cardiomyocytes, but not in the controls. Reentry-like activity occurred by a unidirectional conduction block after shortening of the inter-beat interval. Furthermore, reentry-like activity was terminated after propagation with a conduction delay of less than 300 ms, irrespective of whether the propagation pattern changed or not. These results indicate that a simple purification process is sufficient to induce reentry-like activity. In the future, a more detailed evaluation of spatial dynamics will contribute to the development of effective treatment methods.

Synchronous firing patterns of induced pluripotent stem cell-derived cortical neurons depend on the network structure consisting of excitatory and inhibitory neurons.

The balance between glutamate-mediated excitation and GABA-mediated inhibition is critical to cortical functioning. However, the contribution of network structure consisting of the both neurons to cortical functioning has not been elucidated. We aimed to evaluate the relationship between the network structure and functional activity patterns in vitro. We used mouse induced pluripotent stem cells (iPSCs) to construct three types of neuronal populations; excitatory-rich (Exc), inhibitory-rich (Inh), and control (Cont). Then, we analyzed the activity patterns of these neuronal populations using microelectrode arrays (MEAs). Inhibitory synaptic densities differed between the three types of iPSC-derived neuronal populations, and the neurons showed spontaneously synchronized bursting activity with functional maturation for one month. Moreover, different firing patterns were observed between the three populations; Exc demonstrated the highest firing rates, including frequent, long, and dominant bursts. In contrast, Inh demonstrated the lowest firing rates and the least dominant bursts. Synchronized bursts were enhanced by disinhibition via GABAA receptor blockade. The present study, using iPSC-derived neurons and MEAs, for the first time show that synchronized bursting of cortical networks in vitro depends on the network structure consisting of excitatory and inhibitory neurons.

Functional innervation of human induced pluripotent stem cell-derived cardiomyocytes by co-culture with sympathetic neurons developed using a microtunnel technique.

Microelectrode array (MEA) based-drug screening with human induced pluripotent stem cell-derived cardiomyocytes (hiPSCM) is a potent pre-clinical assay for efficiently assessing proarrhythmic risks in new candidates. Furthermore, predicting sympathetic modulation of the proarrhythmic side-effects is an important issue. Although we have previously developed an MEA-based co-culture system of rat primary cardiomyocyte and sympathetic neurons (rSNs), it is unclear if this co-culture approach is applicable to develop and investigate sympathetic innervation of hiPSCMs. In this study, we developed a co-culture of rSNs and hiPSCMs on MEA substrate, and assessed functional connections. The inter-beat interval of hiPSCM was significantly shortened by stimulation in SNs depending on frequency and pulse number, indicating functional connections between rSNs and hiPSCM and the dependency of chronotropic effects on rSN activity pattern. These results suggest that our co-culture approach can evaluate sympathetic effects on hiPSCMs and would be a useful tool for assessing sympathetic modulated-cardiotoxicity in human cardiac tissue.

Deriving theoretical phase locking values of a coupled cortico-thalamic neural mass model using center manifold reduction.

Cognitive functions such as sensory processing and memory processes lead to phase synchronization in the electroencephalogram or local field potential between different brain regions. There are a lot of computational researches deriving phase locking values (PLVs), which are an index of phase synchronization intensity, from neural models. However, these researches derive PLVs numerically. To the best of our knowledge, there have been no reports on the derivation of a theoretical PLV. In this study, we propose an analytical method for deriving theoretical PLVs from a cortico-thalamic neural mass model described by a delay differential equation. First, the model for generating neural signals is transformed into a normal form of the Hopf bifurcation using center manifold reduction. Second, the normal form is transformed into a phase model that is suitable for analyzing synchronization phenomena. Third, the Fokker-Planck equation of the phase model is derived and the phase difference distribution is obtained. Finally, the PLVs are calculated from the stationary distribution of the phase difference. The validity of the proposed method is confirmed via numerical simulations. Furthermore, we apply the proposed method to a working memory process, and discuss the neurophysiological basis behind the phase synchronization phenomenon. The results demonstrate the importance of decreasing the intensity of independent noise during the working memory process. The proposed method will be of great use in various experimental studies and simulations relevant to phase synchronization, because it enables the effect of neurophysiological changes on PLVs to be analyzed from a mathematical perspective.

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).

Linking Neuromodulated Spike-Timing Dependent Plasticity with the Free-Energy Principle.

The free-energy principle is a candidate unified theory for learning and memory in the brain that predicts that neurons, synapses, and neuromodulators work in a manner that minimizes free energy. However, electrophysiological data elucidating the neural and synaptic bases for this theory are lacking. Here, we propose a novel theory bridging the information-theoretical principle with the biological phenomenon of spike-timing dependent plasticity (STDP) regulated by neuromodulators, which we term mSTDP. We propose that by integrating an mSTDP equation, we can obtain a form of Friston's free energy (an information-theoretical function). Then we analytically and numerically show that dopamine (DA) and noradrenaline (NA) influence the accuracy of a principal component analysis (PCA) performed using the mSTDP algorithm. From the perspective of free-energy minimization, these neuromodulatory changes alter the relative weighting or precision of accuracy and prior terms, which induces a switch from pattern completion to separation. These results are consistent with electrophysiological findings and validate the free-energy principle and mSTDP. Moreover, our scheme can potentially be applied in computational psychiatry to build models of the faulty neural networks that underlie the positive symptoms of schizophrenia, which involve abnormal DA levels, as well as models of the NA contribution to memory triage and posttraumatic stress disorder.

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.

Accurate connection strength estimation based on variational bayes for detecting synaptic plasticity.

Connection strength estimation is widely used in detecting the topology of neuronal networks and assessing their synaptic plasticity. A recently proposed model-based method using the leaky integrate-and-fire model neuron estimates membrane potential from spike trains by calculating the maximum a posteriori (MAP) path. We further enhance the MAP path method using variational Bayes and dynamic causal modeling. Several simulations demonstrate that the proposed method can accurately estimate connection strengths with an error ratio of less than 20%. The results suggest that the proposed method can be an effective tool for detecting network structure and synaptic plasticity.

Recording axonal conduction to evaluate the integration of pluripotent cell-derived neurons into a neuronal network.

Stem cell transplantation is a promising therapy to treat neurodegenerative disorders, and a number of in vitro models have been developed for studying interactions between grafted neurons and the host neuronal network to promote drug discovery. However, methods capable of evaluating the process by which stem cells integrate into the host neuronal network are lacking. In this study, we applied an axonal conduction-based analysis to a co-culture study of primary and differentiated neurons. Mouse cortical neurons and neuronal cells differentiated from P19 embryonal carcinoma cells, a model for early neural differentiation of pluripotent stem cells, were co-cultured in a microfabricated device. The somata of these cells were separated by the co-culture device, but their axons were able to elongate through microtunnels and then form synaptic contacts. Propagating action potentials were recorded from these axons by microelectrodes embedded at the bottom of the microtunnels and sorted into clusters representing individual axons. While the number of axons of cortical neurons increased until 14 days in vitro and then decreased, those of P19 neurons increased throughout the culture period. Network burst analysis showed that P19 neurons participated in approximately 80% of the bursting activity after 14 days in vitro. Interestingly, the axonal conduction delay of P19 neurons was significantly greater than that of cortical neurons, suggesting that there are some physiological differences in their axons. These results suggest that our method is feasible to evaluate the process by which stem cell-derived neurons integrate into a host neuronal network.

Limiting parameter range for cortical-spherical mapping improves activated domain estimation for attention modulated auditory response.

BACKGROUND: Attention is one of the factors involved in selecting input information for the brain. We applied a method for estimating domains with clear boundaries using magnetoencephalography (the domain estimation method) for auditory-evoked responses (N100m) to evaluate the effects of attention in milliseconds. However, because the surface around the auditory cortex is folded in a complicated manner, it is unknown whether the activity in the auditory cortex can be estimated. NEW METHOD: The parameter range to express current sources was set to include the auditory cortex. Their search region was expressed as a direct product of the parameter ranges used in the adaptive diagonal curves. RESULTS: Without a limitation of the range, activity was estimated in regions other than the auditory cortex in all cases. However, with the limitation of the range, the activity was estimated in the primary or higher auditory cortex. Further analysis of the limitation of the range showed that the domains activated during attention included the regions activated during no attention for the participants whose amplitudes of N100m were higher during attention. COMPARISON WITH EXISTING METHOD: We proposed a method for effectively limiting the search region to evaluate the extent of the activated domain in regions with complex folded structures. CONCLUSION: To evaluate the extent of activated domains in regions with complex folded structures, it is necessary to limit the parameter search range. The area of the activated domains in the auditory cortex may increase by attention on the millisecond timescale.

Experimental validation of the free-energy principle with in vitro neural networks.

Empirical applications of the free-energy principle are not straightforward because they entail a commitment to a particular process theory, especially at the cellular and synaptic levels. Using a recently established reverse engineering technique, we confirm the quantitative predictions of the free-energy principle using in vitro networks of rat cortical neurons that perform causal inference. Upon receiving electrical stimuli-generated by mixing two hidden sources-neurons self-organised to selectively encode the two sources. Pharmacological up- and downregulation of network excitability disrupted the ensuing inference, consistent with changes in prior beliefs about hidden sources. As predicted, changes in effective synaptic connectivity reduced variational free energy, where the connection strengths encoded parameters of the generative model. In short, we show that variational free energy minimisation can quantitatively predict the self-organisation of neuronal networks, in terms of their responses and plasticity. These results demonstrate the applicability of the free-energy principle to in vitro neural networks and establish its predictive validity in this setting.

Improving the accuracy of decoding monkey brain-machine interface data by estimating the state of unobserved cell assemblies.

BACKGROUND: The brain-machine interface is a technology that has been used for improving the quality of life of individuals with physical disabilities and also healthy individuals. It is important to improve the methods used for decoding the brain-machine interface data as the accuracy and speed of movements achieved using the existing technology are not comparable to the normal body. COMPARISON WITH THE EXISTING METHOD: Decoding of brain-machine interface data using the proposed method resulted in improved decoding accuracy compared to the existing method. CONCLUSIONS: The results demonstrated the usefulness of cell assembly state estimation method for decoding the brain-machine interface data. NEW METHOD: We incorporated a novel method of estimating cell assembly states using spike trains with the existing decoding method that used only firing rate data. Synaptic connectivity pattern was used as feature values in addition to firing rate. Publicly available monkey brain-machine interface datasets were used in the study. RESULTS: As long as the decoding was successful, the root mean square error of the proposed method was significantly smaller than the existing method. Artificial neural netowork-based decoding method resulted in more stable decoding, and also improved the decoding accuracy due to incorporation of synaptic connectivity pattern.

Controlling fluidic oscillator flow dynamics by elastic structure vibration.

In this study, we introduce a design of a feedback-type fluidic oscillator with elastic structures surrounding its feedback channel. By employing phase reduction theory, we extract the phase sensitivity function of the complex fluid-structure coupled system, which represents the system's oscillatory characteristics. We show that the frequency of the oscillating flow inside the fluidic oscillator can be modulated by inducing synchronization with the weak periodic forcing from the elastic structure vibration. This design approach adds controllability to the fluidic oscillator, where conventionally, the intrinsic oscillatory characteristics of such device were highly determined by its geometry. The synchronization-induced control also changes the physical characteristics of the oscillatory fluid flow, which can be beneficial for practical applications, such as promoting better fluid mixing without changing the overall geometry of the device. Furthermore, by analyzing the phase sensitivity function, we demonstrate how the use of phase reduction theory gives good estimation of the synchronization condition with minimal number of experiments, allowing for a more efficient control design process. Finally, we show how an optimal control signal can be designed to reach the fastest time to synchronization.

Development of a Hypersensitivity Evaluation Method for Cultured Sensory Neurons Using Electrical Activity Recording.

Investigation of hypersensitivity caused by peripheral sensitization progression is important for developing novel pain treatments. Existing methods cannot record plastic changes in neuronal activity because they occur over a few days. We aimed to establish an efficient method to evaluate neuronal activity alterations caused by peripheral sensitization on high-density microelectrode arrays (HD-MEAs) which can record neuronal activity for a long time. Rat dorsal root ganglion (DRG) neurons were dissected from rat embryos and cultured on HD-MEAs. DRG neurons were labeled with NeuO, live staining dye. Neurons were detected with the fluorescence signal and electrodes were selected with the fluorescence images. The number of DRG neurons, whose activity were recorded, detected based on fluorescence observation was five times greater than that based on neuronal activity. Analysis of changes in neuronal activity observed in pharmacological stimulation experiments suggested that substance P induced peripheral sensitization and enhanced capsaicin sensitivity. In addition, results of immunofluorescence staining suggested that peripheral sensitization occurred mostly in neurons that co-expressed transient receptor potential vanilloid 1 (TRPV1) and neurokinin 1 receptor (NK1R). In conclusion, we established an efficient method for assessing the effects of peripheral sensitization on DRG neurons cultured on HD-MEAs.

Adjoint method provides phase response functions for delay-induced oscillations.

Limit-cycle oscillations induced by time delay are widely observed in various systems, but a systematic phase-reduction theory for them has yet to be developed. Here we present a practical theoretical framework to calculate the phase response function Z(θ), a fundamental quantity for the theory, of delay-induced limit cycles with infinite-dimensional phase space. We show that Z(θ) can be obtained as a zero eigenfunction of the adjoint equation associated with an appropriate bilinear form for the delay differential equations. We confirm the validity of the proposed framework for two biological oscillators and demonstrate that the derived phase equation predicts intriguing multimodal locking behavior.

Observing Cell Assemblies From Spike Train Recordings Based on the Biological Basis of Synaptic Connectivity.

Cell assemblies are difficult to observe because they consist of many neurons. We aimed to observe cell assemblies based on biological statistics, such as synaptic connectivity. We developed an estimation method to estimate the activity and synaptic connectivity of cell assemblies from spike trains using mathematical models of individual neurons and cell assemblies. Synaptic transmissions were averaged to generate postsynaptic currents with the same timing and waveform but different amplitudes, as the number of presynaptic neurons was large. We estimated the average synaptic transmission and synaptic connectivity from active cell assemblies based on the stochastic prediction of membrane potentials and verified the estimation ability of the average synaptic transmission and synaptic connectivity using the proposed method on simulated neural activity. Different cell assembly activities evoked by electrical stimuli were correctly sorted into various clusters in experiments using rat cortical neurons cultured on microelectrode arrays. We observed multiple cell assemblies from the spontaneous activity of rat cortical networks on microelectrode arrays, based on the synaptic connectivity patterns estimated by the proposed method. The proposed method was superior to the conventional method for detecting the activity of multiple cell assemblies. Using the proposed method, it is possible to observe multiple cell assemblies based on the biological basis of synaptic connectivity. In summary, we report a novel method to observe cell assemblies from spike train recordings based on the biological basis of synaptic connectivity, rather than merely relying on a statistical method.

Recording Saltatory Conduction Along Sensory Axons Using a High-Density Microelectrode Array.

Myelinated fibers are specialized neurological structures used for conducting action potentials quickly and reliably, thus assisting neural functions. Although demyelination leads to serious functional impairments, little is known the relationship between myelin structural change and increase in conduction velocity during myelination and demyelination processes. There are no appropriate methods for the long-term evaluation of spatial characteristics of saltatory conduction along myelinated axons. Herein, we aimed to detect saltatory conduction from the peripheral nervous system neurons using a high-density microelectrode array. Rat sensory neurons and intrinsic Schwann cells were cultured. Immunofluorescence and ultrastructure examination showed that the myelinating Schwann cells appeared at 1 month, and compact myelin was formed by 10 weeks in vitro. Activity of rat sensory neurons was evoked with optogenetic stimulation, and axon conduction was detected with high-density microelectrode arrays. Some conductions included high-speed segments with low signal amplitude. The same segment could be detected with electrical recording and immunofluorescent imaging for a myelin-related protein. The spatiotemporal analysis showed that some segments show a velocity of more than 2 m/s and that ends of the segments show a higher electrical sink, suggesting that saltatory conduction occurred in myelinated axons. Moreover, mathematical modeling supported that the recorded signal was in the appropriate range for axon and electrode sizes. Overall, our method could be a feasible tool for evaluating spatial characteristics of axon conduction including saltatory conduction, which is applicable for studying demyelination and remyelination.

Coupling of in vitro Neocortical-Hippocampal Coculture Bursts Induces Different Spike Rhythms in Individual Networks.

Brain-state alternation is important for long-term memory formation. Each brain state can be identified with a specific process in memory formation, e.g., encoding during wakefulness or consolidation during sleeping. The hippocampal-neocortical dialogue was proposed as a hypothetical framework for systems consolidation, which features different cross-frequency couplings between the hippocampus and distributed neocortical regions in different brain states. Despite evidence supporting this hypothesis, little has been reported about how information is processed with shifts in brain states. To address this gap, we developed an in vitro neocortical-hippocampal coculture model to study how activity coupling can affect connections between coupled networks. Neocortical and hippocampal neurons were cultured in two different compartments connected by a micro-tunnel structure. The network activity of the coculture model was recorded by microelectrode arrays underlying the substrate. Rhythmic bursting was observed in the spontaneous activity and electrical evoked responses. Rhythmic bursting activity in one compartment could couple to that in the other via axons passing through the micro-tunnels. Two types of coupling patterns were observed: slow-burst coupling (neocortex at 0.1-0.5 Hz and hippocampus at 1 Hz) and fast burst coupling (neocortex at 20-40 Hz and hippocampus at 4-10 Hz). The network activity showed greater synchronicity in the slow-burst coupling, as indicated by changes in the burstiness index. Network synchronicity analysis suggests the presence of different information processing states under different burst activity coupling patterns. Our results suggest that the hippocampal-neocortical coculture model possesses multiple modes of burst activity coupling between the cortical and hippocampal parts. With the addition of external stimulation, the neocortical-hippocampal network model we developed can elucidate the influence of state shifts on information processing.