Mumefural prevents insulin resistance and amyloid-beta accumulation in the brain by improving lowered interstitial fluid pH in type 2 diabetes mellitus.

The present study tried to clarify if mumefural would prevent hyperglycemia, one of the typical symptoms of type 2 diabetes mellitus (T2DM), since mumefural is an extract from Japanese apricots preventing hyperglycemia. To clarify if mumefural would prevent T2DM pathogenesis, we used Otsuka Long-Evans Tokushima fatty (OLETF) rats, T2DM model. Mumefural diminished hyperglycemia, HOMA-IR and plasma triglyceride concentration in OLETF rats under fasting conditions. In addition, mumefural elevated protein expression of sodium-coupled monocarboxylate transporter 1 (SMCT1) in the distal colon participating in absorption of weak organic acids, which behave as bases but not acids after absorption into the body. Mumefural also increased the interstitial fluid pH around the brain hippocampus lowered in OLETF rats compared with non-T2DM LETO rats used as control for OLETF rats. Amyloid-beta accumulation in the brain decreased in accordance with the pH elevation. On the one hand, mumefural didn't affect plasma concentrations of glucagon, GLP-1, GIP or PYY under fasting conditions. Taken together, these observations indicate that: 1) mumefural would be a useful functional food improving hyperglycemia, insulin resistance and the lowered interstitial fluid pH in T2DM; 2) the interstitial fluid pH would be one of key factors influencing the accumulation of amyloid-beta.

A neuronal prospect theory model in the brain reward circuitry.

Prospect theory, arguably the most prominent theory of choice, is an obvious candidate for neural valuation models. How the activity of individual neurons, a possible computational unit, obeys prospect theory remains unknown. Here, we show, with theoretical accuracy equivalent to that of human neuroimaging studies, that single-neuron activity in four core reward-related cortical and subcortical regions represents the subjective valuation of risky gambles in monkeys. The activity of individual neurons in monkeys passively viewing a lottery reflects the desirability of probabilistic rewards parameterized as a multiplicative combination of utility and probability weighting functions, as in the prospect theory framework. The diverse patterns of valuation signals were not localized but distributed throughout most parts of the reward circuitry. A network model aggregating these signals reconstructed the risk preferences and subjective probability weighting revealed by the animals' choices. Thus, distributed neural coding explains the computation of subjective valuations under risk.

Detection and categorization of severe cardiac disorders based solely on heart period measurements.

Cardiac disorders are common conditions associated with a high mortality rate. Due to their potential for causing serious symptoms, it is desirable to constantly monitor cardiac status using an accessible device such as a smartwatch. While electrocardiograms (ECGs) can make the detailed diagnosis of cardiac disorders, the examination is typically performed only once a year for each individual during health checkups, and it requires expert medical practitioners to make comprehensive judgments. Here we describe a newly developed automated system for alerting individuals about cardiac disorders solely by measuring a series of heart periods. For this purpose, we examined two metrics of heart rate variability (HRV) and analyzed 1-day ECG recordings of more than 1,000 subjects in total. We found that a metric of local variation was more efficient than conventional HRV metrics for alerting cardiac disorders, and furthermore, that a newly introduced metric of local-global variation resulted in superior capacity for discriminating between premature contraction and atrial fibrillation. Even with a 1-minute recording of heart periods, our new detection system had a diagnostic performance even better than that of the conventional analysis method applied to a 1-day recording.

Spatiotemporal dynamics of odor representations in the human brain revealed by EEG decoding.

How the human brain translates olfactory inputs into diverse perceptions, from pleasurable floral smells to sickening smells of decay, is one of the fundamental questions in olfaction. To examine how different aspects of olfactory perception emerge in space and time in the human brain, we performed time-resolved multivariate pattern analysis of scalp-recorded electroencephalogram responses to 10 perceptually diverse odors and associated the resulting decoding accuracies with perception and source activities. Mean decoding accuracies of odors exceeded the chance level 100 ms after odor onset and reached maxima at 350 ms. The result suggests that the neural representations of individual odors were maximally separated at 350 ms. Perceptual representations emerged following the decoding peak: unipolar unpleasantness (neutral to unpleasant) from 300 ms, and pleasantness (neutral to pleasant) and perceptual quality (applicability to verbal descriptors such as "fruity" or "flowery") from 500 ms after odor onset, with all these perceptual representations reaching their maxima after 600 ms. A source estimation showed that the areas representing the odor information, estimated based on the decoding accuracies, were localized in and around the primary and secondary olfactory areas at 100 to 350 ms after odor onset. Odor representations then expanded into larger areas associated with emotional, semantic, and memory processing, with the activities of these later areas being significantly associated with perception. These results suggest that initial odor information coded in the olfactory areas (<350 ms) evolves into their perceptual realizations (300 to >600 ms) through computations in widely distributed cortical regions, with different perceptual aspects having different spatiotemporal dynamics.

Different Activity Patterns in Retinal Ganglion Cells of TRPM1 and mGluR6 Knockout Mice.

TRPM1, the first member of the melanoma-related transient receptor potential (TRPM) subfamily, is the visual transduction channel downstream of metabotropic glutamate receptor 6 (mGluR6) on retinal ON bipolar cells (BCs). Human TRPM1 mutations are associated with congenital stationary night blindness (CSNB). In both TRPM1 and mGluR6 KO mouse retinas, OFF but not ON BCs respond to light stimulation. Here we report an unexpected difference between TRPM1 knockout (KO) and mGluR6 KO mouse retinas. We used a multielectrode array (MEA) to record spiking in retinal ganglion cells (RGCs). We found spontaneous oscillations in TRPM1 KO retinas, but not in mGluR6 KO retinas. We performed a structural analysis on the synaptic terminals of rod ON BCs. Intriguingly, rod ON BC terminals were significantly smaller in TRPM1 KO retinas than in mGluR6 KO retinas. These data suggest that a deficiency of TRPM1, but not of mGluR6, in rod ON bipolar cells may affect synaptic terminal maturation. We speculate that impaired signaling between rod BCs and AII amacrine cells (ACs) leads to spontaneous oscillations. TRPM1 and mGluR6 are both essential components in the signaling pathway from photoreceptors to ON BC dendrites, yet they differ in their effects on the BC terminal and postsynaptic circuitry.

[Electrophysiological Analysis of Retinal Oscillation in Normal and Degenerated Retina].

　The vertebrate retina is one of the most sophisticated parts of the nervous system. It comprises five classes of neurons and one glial type cell. During development, but prior to a vertebrate's eyes opening, retinal circuits are refined by endogenous neural activity. Characteristic patterns of activity, including oscillatory activity, occur in the normal retina, whereas distinctive alternative patterns occur in abnormal retinas. In this paper, we first describe the electrophysiological and spike sorting methods used to study retinal oscillations. Next, we describe the mechanisms and functions of oscillation in the normal retina. Finally, we characterize the distinctive oscillations and abnormal spontaneous activities in the degenerative retina.

Similarity in Neuronal Firing Regimes across Mammalian Species.

UNLABELLED: The architectonic subdivisions of the brain are believed to be functional modules, each processing parts of global functions. Previously, we showed that neurons in different regions operate in different firing regimes in monkeys. It is possible that firing regimes reflect differences in underlying information processing, and consequently the firing regimes in homologous regions across animal species might be similar. We analyzed neuronal spike trains recorded from behaving mice, rats, cats, and monkeys. The firing regularity differed systematically, with differences across regions in one species being greater than the differences in similar areas across species. Neuronal firing was consistently most regular in motor areas, nearly random in visual and prefrontal/medial prefrontal cortical areas, and bursting in the hippocampus in all animals examined. This suggests that firing regularity (or irregularity) plays a key role in neural computation in each functional subdivision, depending on the types of information being carried. SIGNIFICANCE STATEMENT: By analyzing neuronal spike trains recorded from mice, rats, cats, and monkeys, we found that different brain regions have intrinsically different firing regimes that are more similar in homologous areas across species than across areas in one species. Because different regions in the brain are specialized for different functions, the present finding suggests that the different activity regimes of neurons are important for supporting different functions, so that appropriate neuronal codes can be used for different modalities.

Neural dynamics and information representation in microcircuits of motor cortex.

The brain has to analyze and respond to external events that can change rapidly from time to time, suggesting that information processing by the brain may be essentially dynamic rather than static. The dynamical features of neural computation are of significant importance in motor cortex that governs the process of movement generation and learning. In this paper, we discuss these features based primarily on our recent findings on neural dynamics and information coding in the microcircuit of rat motor cortex. In fact, cortical neurons show a variety of dynamical behavior from rhythmic activity in various frequency bands to highly irregular spike firing. Of particular interest are the similarity and dissimilarity of the neuronal response properties in different layers of motor cortex. By conducting electrophysiological recordings in slice preparation, we report the phase response curves (PRCs) of neurons in different cortical layers to demonstrate their layer-dependent synchronization properties. We then study how motor cortex recruits task-related neurons in different layers for voluntary arm movements by simultaneous juxtacellular and multiunit recordings from behaving rats. The results suggest an interesting difference in the spectrum of functional activity between the superficial and deep layers. Furthermore, the task-related activities recorded from various layers exhibited power law distributions of inter-spike intervals (ISIs), in contrast to a general belief that ISIs obey Poisson or Gamma distributions in cortical neurons. We present a theoretical argument that this power law of in vivo neurons may represent the maximization of the entropy of firing rate with limited energy consumption of spike generation. Though further studies are required to fully clarify the functional implications of this coding principle, it may shed new light on information representations by neurons and circuits in motor cortex.

Optimal spike-based communication in excitable networks with strong-sparse and weak-dense links.

The connectivity of complex networks and functional implications has been attracting much interest in many physical, biological and social systems. However, the significance of the weight distributions of network links remains largely unknown except for uniformly- or Gaussian-weighted links. Here, we show analytically and numerically, that recurrent neural networks can robustly generate internal noise optimal for spike transmission between neurons with the help of a long-tailed distribution in the weights of recurrent connections. The structure of spontaneous activity in such networks involves weak-dense connections that redistribute excitatory activity over the network as noise sources to optimally enhance the responses of individual neurons to input at sparse-strong connections, thus opening multiple signal transmission pathways. Electrophysiological experiments confirm the importance of a highly broad connectivity spectrum supported by the model. Our results identify a simple network mechanism for internal noise generation by highly inhomogeneous connection strengths supporting both stability and optimal communication.

Power-law inter-spike interval distributions infer a conditional maximization of entropy in cortical neurons.

The brain is considered to use a relatively small amount of energy for its efficient information processing. Under a severe restriction on the energy consumption, the maximization of mutual information (MMI), which is adequate for designing artificial processing machines, may not suit for the brain. The MMI attempts to send information as accurate as possible and this usually requires a sufficient energy supply for establishing clearly discretized communication bands. Here, we derive an alternative hypothesis for neural code from the neuronal activities recorded juxtacellularly in the sensorimotor cortex of behaving rats. Our hypothesis states that in vivo cortical neurons maximize the entropy of neuronal firing under two constraints, one limiting the energy consumption (as assumed previously) and one restricting the uncertainty in output spike sequences at given firing rate. Thus, the conditional maximization of firing-rate entropy (CMFE) solves a tradeoff between the energy cost and noise in neuronal response. In short, the CMFE sends a rich variety of information through broader communication bands (i.e., widely distributed firing rates) at the cost of accuracy. We demonstrate that the CMFE is reflected in the long-tailed, typically power law, distributions of inter-spike intervals obtained for the majority of recorded neurons. In other words, the power-law tails are more consistent with the CMFE rather than the MMI. Thus, we propose the mathematical principle by which cortical neurons may represent information about synaptic input into their output spike trains.

Bayesian estimation of phase response curves.

Phase response curve (PRC) of an oscillatory neuron describes the response of the neuron to external perturbation. The PRC is useful to predict synchronized dynamics of neurons; hence, its measurement from experimental data attracts increasing interest in neural science. This paper introduces a Bayesian method for estimating PRCs from data, which allows for the correlation of errors in explanatory and response variables of the PRC. The method is implemented with a replica exchange Monte Carlo technique; this avoids local minima and enables efficient calculation of posterior averages. A test with artificial data generated by the noisy Morris-Lecar equation shows that the proposed method outperforms conventional regression that ignores errors in the explanatory variable. Experimental data from the pyramidal cells in the rat motor cortex is also analyzed with the method; a case is found where the result with the proposed method is considerably different from that obtained by conventional regression.

Made-to-order spiking neuron model equipped with a multi-timescale adaptive threshold.

Information is transmitted in the brain through various kinds of neurons that respond differently to the same signal. Full characteristics including cognitive functions of the brain should ultimately be comprehended by building simulators capable of precisely mirroring spike responses of a variety of neurons. Neuronal modeling that had remained on a qualitative level has recently advanced to a quantitative level, but is still incapable of accurately predicting biological data and requires high computational cost. In this study, we devised a simple, fast computational model that can be tailored to any cortical neuron not only for reproducing but also for predicting a variety of spike responses to greatly fluctuating currents. The key features of this model are a multi-timescale adaptive threshold predictor and a nonresetting leaky integrator. This model is capable of reproducing a rich variety of neuronal spike responses, including regular spiking, intrinsic bursting, fast spiking, and chattering, by adjusting only three adaptive threshold parameters. This model can express a continuous variety of the firing characteristics in a three-dimensional parameter space rather than just those identified in the conventional discrete categorization. Both high flexibility and low computational cost would help to model the real brain function faithfully and examine how network properties may be influenced by the distributed characteristics of component neurons.

Balanced excitatory and inhibitory inputs to cortical neurons decouple firing irregularity from rate modulations.

In vivo cortical neurons are known to exhibit highly irregular spike patterns. Because the intervals between successive spikes fluctuate greatly, irregular neuronal firing makes it difficult to estimate instantaneous firing rates accurately. If, however, the irregularity of spike timing is decoupled from rate modulations, the estimate of firing rate can be improved. Here, we introduce a novel coding scheme to make the firing irregularity orthogonal to the firing rate in information representation. The scheme is valid if an interspike interval distribution can be well fitted by the gamma distribution and the firing irregularity is constant over time. We investigated in a computational model whether fluctuating external inputs may generate gamma process-like spike outputs, and whether the two quantities are actually decoupled. Whole-cell patch-clamp recordings of cortical neurons were performed to confirm the predictions of the model. The output spikes were well fitted by the gamma distribution. The firing irregularity remained approximately constant regardless of the firing rate when we injected a balanced input, in which excitatory and inhibitory synapses are activated concurrently while keeping their conductance ratio fixed. The degree of irregular firing depended on the effective reversal potential set by the balance between excitation and inhibition. In contrast, when we modulated conductances out of balance, the irregularity varied with the firing rate. These results indicate that the balanced input may improve the efficiency of neural coding by clamping the firing irregularity of cortical neurons. We demonstrate how this novel coding scheme facilitates stimulus decoding.

Layer and frequency dependencies of phase response properties of pyramidal neurons in rat motor cortex.

It is postulated that synchronous firing of cortical neurons plays an active role in cognitive functions of the brain. An important issue is whether pyramidal neurons in different cortical layers exhibit similar tendencies to synchronise. To address this issue, we performed intracellular and whole-cell recordings of regular-spiking pyramidal neurons in slice preparations of the rat motor cortex (18-45 days old) and analysed the phase response curves of these pyramidal neurons in layers 2/3 and 5. The phase response curve represents how an external stimulus affects the timing of spikes immediately after the stimulus in repetitively firing neurons. The phase response curve can be classified into two categories, type 1 (the spike is always advanced) and type 2 (the spike is advanced or delayed depending on the stimulus phase), and are important determinants of whether or not rhythmic synchronization of neuron pairs occurs. We found that pyramidal neurons in layer 2/3 tend to display type-2 phase response curves whereas those in layer 5 tend to exhibit type-1 phase response curves. The differences were prominent particularly in the gamma-frequency range (20-45 Hz). Our results imply that the layer-2/3 pyramidal neurons, when coupled mutually through fast excitatory synapses, may exhibit a much stronger tendency for rhythmic synchronization than layer-5 neurons in the gamma-frequency range.

Synchronization of excitatory neurons with strongly heterogeneous phase responses.

In many real-world oscillator systems, the phase response curves are highly heterogeneous. However, the dynamics of heterogeneous oscillator networks has not been seriously addressed. We propose a theoretical framework to analyze such a system by dealing explicitly with the heterogeneous phase response curves. We develop a method to solve the self-consistent equations for order parameters by using formal complex-valued phase variables, and apply our theory to networks of in vitro cortical neurons. We find a novel state transition that is not observed in previous oscillator network models.

Synchrony of limit-cycle oscillators induced by random external impulses.

The mechanism of phase synchronization between uncoupled limit-cycle oscillators induced by common random impulsive forcing is analyzed. By reducing the dynamics of the oscillator to a random phase map, it is shown that phase synchronization generally occurs when the oscillator is driven by weak random impulsive forcing in the limit of large interimpulse intervals. The case where the interimpulse intervals are finite is also analyzed perturbatively for small impulse intensity. For weak Poisson impulses, it is shown that the phase synchronization persists up to the first order approximation.

Synchrony of neural oscillators induced by random telegraphic currents.

When a neuron receives a randomly fluctuating input current, its reliability of spike generation improves compared with the case of a constant input current [Mainen and Sejnowski, Science 268, 1503 (1995)]. This phenomenon can be interpreted as phase synchronization between uncoupled nonlinear oscillators subject to a common external input. We analyze this phenomenon using dynamical models of neurons, assuming the input current to be a simple random telegraphic signal that jumps between two values, and the neuron to be always purely self-oscillatory. The internal state of the neuron randomly jumps between two limit cycles corresponding to the input values, which can be described by random phase maps when the switching time of the input current is sufficiently long. Using such a random map description, we discuss the synchrony of neural oscillators subject to fluctuating inputs. Especially when the phase maps are monotonic, we can generally show that the Lyapunov exponent is negative, namely, phase synchronization is stable and reproducibility of spike timing improves.

Predicting spike timings of current-injected neurons.

Typical neurospiking models were examined on the ability to reproduce and predict the spike sequences of a biological neuron for a variety of fluctuating currents, using a fixed set of parameter values. The predicting accuracy was found to be particularly good for the Hodgkin-Huxley models augmented with the Ca(2+)-dependent potassium current generating slow afterhyper-polarization and/or the muscarine-sensitive potassium current. In the successful parameter determination method, the effective membrane time constant is estimated very short, typically about 5 ms. The biological neurons we examined were distinctly classified into two types according to the estimated percent contents of those ionic channels.

Universality of chaotic rare fluctuations in a locally coupled phase map model.

Chaotic fluctuations of the order parameter in a coupled two-dimensional phase map model are numerically investigated. We discuss the system-size N dependence of the statistical properties of rare fluctuations observed in the transition range between the quasiordered chaotic state and the fully developed one. It is found that the normalized probability distribution function (PDF) has a unique functional form irrespective of N. The asymptotic form of the PDF is discussed in connection with the universal distribution for correlated systems proposed by Bramwell et al. [Nature (London) 396, 552 (1998)]. Moreover, it is observed that the power spectrum P(N)(omega) of rare fluctuations asymptotically takes the power-law form P(N)(omega) equivalent to omega(-(1+alpha)) (alpha=0.6 equivalent to 0.7) irrespective of N. This result suggests that the temporal correlation decays as a stretched exponential.