Even without external random input, cortical networks in vivo sustain asynchronous irregular firing with low firing rate. In addition to detailed balance between excitatory and inhibitory activities, recent theoretical studies have revealed that another feature commonly observed in cortical networks, i.e., long-tailed distribution of excitatory synapses implying coexistence of many weak and a few extremely strong excitatory synapses, plays an essential role in realizing the self-sustained activity in recurrent networks of biologically plausible spiking neurons. The previous studies, however, have not considered highly non-random features of the synaptic connectivity, namely, bidirectional connections between cortical neurons are more common than expected by chance and strengths of synapses are positively correlated between pre- and postsynaptic neurons. The positive correlation of synaptic connections may destabilize asynchronous activity of networks with the long-tailed synaptic distribution and induce pathological synchronized firing among neurons. It remains unclear how the cortical network avoids such pathological synchronization. Here, we demonstrate that introduction of the correlated connections indeed gives rise to synchronized firings in a cortical network model with the long-tailed distribution. By using a simplified feed-forward network model of spiking neurons, we clarify the underlying mechanism of the synchronization. We then show that the synchronization can be efficiently suppressed by highly heterogeneous distribution, typically a lognormal distribution, of inhibitory-to-excitatory connection strengths in a recurrent network model of cortical neurons.

Abstract:Because of an increasing demand on electric power and limited resources for conventional fuels, highly efficient engine that is capable of converting energy resource to electric power with a small loss is awaited. In this respect, Stirling engine provides a strong potential because of its efficient thermodynamic cycle, which is ideally close to the theoretical limit of the Carnot cycle. Practical use of the Stirling engine, however, has been limited because of its low output power. Towards its wider applicability, simultaneous operation of many individual Stirling engines is indispensable to increase the output power. This paper presents an experimental study of synchronized dynamics of two coupled Stirling engines. It is shown that the synchronized operation of the population of engines provides a key technology to extend the system size so as to produce a large-scale electric energy.

Cortical networks both in vivo and in vitro sustain asynchronous irregular firings with extremely low frequency. To realize such self-sustained activity in neural network models, balance between excitatory and inhibitory activities is known to be one of the keys. In addition, recent theoretical studies have revealed that another feature commonly observed in cortical networks, i.e., sparse but strong connections and dense weak connections, plays an essential role. The previous studies, however, have not thoroughly considered the cooperative dynamics between a network of such heterogeneous synaptic connections and intrinsic noise. The noise stimuli, representing inherent nature of the neuronal activities, e.g., variability of presynaptic discharges, should be also of significant importance for sustaining the irregular firings in cortical networks. Here, we numerically demonstrate that highly heterogeneous distribution, typically a lognormal type, of excitatory-to-excitatory connections, reduces the amount of noise required to sustain the network firing activities. In the sense that noise consumes an energy resource, the heterogeneous network receiving less amount of noise stimuli is considered to realize an efficient dynamics in cortex. A noise-driven network of bi-modally distributed synapses further shows that many weak and a few very strong synapses are the key feature of the synaptic heterogeneity, supporting the network firing activity.

Abstract:As an experimental analogy of synchronized hands clapping , sound-coupled electronic metronomes are introduced. Clicking sounds generated from the driving metronome serve as the sounds of hands clapping, whereas a microphone attached to the driven electronic metronome detects the clicking sounds. In contrast to popular experimental systems of synchronization, e.g., mechanical metronomes put on a same beam, which directly connects oscillators through a material medium, the present system utilizes sound as an indirect coupling medium. In cases of both unidirectional and bidirectional couplings, our experiments showed that the two electronic metronomes are synchronized in such a way that a slow metronome catches up with a fast one. Phase slips, which give rise to intermittent switching between synchronized and desynchronized rhythms, were also observed, resembling the hands clapping in a concert hall. Our mathematical model based on phase oscillators with positive interaction function elucidates the observed results very well. Our system may provide a basic experimental framework for studying synchronization in sound-coupled oscillators including the rhythmic applause.

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In the absence of sensory stimuli, continuous neuronal firings are observed in cortical networks. Such self-sustained ongoing activity is referred to as "spontaneous activity," the dynamics of which is characterized by (1) low firing frequency, (2) irregularity, and (3) asynchronous firings among neurons. Despite numerous theoretical attempts, the mechanism that underlies the spontaneous firing activity has remained unclear. Recently, Teramae et al. proposed a neuronal network model with excitatory postsynaptic potentials (EPSPs) obeying a lognormal distribution, as observed in physiological experiments. The model successfully reproduced the key features of the spontaneous activity. Their model, however, focused mainly on the lognormal distribution of the network connectivity, where the correlation of EPSPs observed between bidirectionally coupled neurons was disregarded. The present paper introduces the correlated EPSPs to the lognormal network model and shows that a physiologically plausible level of such correlation causes (i) synchronous firings among neurons, (ii) extremely high firing frequencies observed in a group of neurons, and (iii) intermittent switching between asynchronous and synchronous firing states.

Abstract:The Stirling engine, originally development in 1816 by R. Stirling, has been considered as one of the most efficient systems that convert energy resource to electric power using a thermodynamic cycle ideally close to the Carnot cycle. Use of the Stirling engine for realworld problems, however, has been limited because of its relatively low output power. Towards its more practical applicability, simultaneous operation of many individual Stirling engines is indispensable to increase the output power. This paper presents an experimental study of entraining two Stirling engines to an external pacemaker. Our aim is to achieve synchronized oscillations of the Stirling engines without lowering their oscillation frequencies, because both synchrony and high frequencies are important factors to enhance the total output power. Compared to our previous study of directly coupled Stirling engines, it is shown that the output power is significantly improved in the present framework.

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