Autism spectrum disorder (ASD) and schizophrenia are complex and heterogeneous mental disorders involving the dysfunction of multiple neural systems. The atypical and heterogenous temporal coordinations of neuronal activity, which are widely observed in these two disorders, are hypothesized to stem from an excitatory/inhibitory (E/I) imbalance in the brain. To investigate the association between the E/I imbalance and atypical neural activities, and to assess the influence of specific subtypes of inhibitory interneurons on network activity regulation, we developed a computational microcircuit model with biologically plausible layer 2/3 of visual cortex that combined excitatory pyramidal neurons with three subtypes of inhibitory interneurons (parvalbumin [PV], somatostatin [SOM], and vasoactive intestinal polypeptide [VIP]). We numerically explored the role of distinct types of E/I imbalance by changing the population size of different subtype neurons. We find that when the E/I balance is disrupted by decreasing the PV population size, activity of the PV population precedes that of the pyramidal population, which enhances beta and gamma oscillations. Conversely, pyramidal neuronal population activity was the precursor of PV interneuron activity when the E/I imbalance was induced by decreasing the SOM population size; this preferentially impaired gamma-frequency activity. The disruption of E/I balance altered the information flow between pyramidal and PV populations, modulating neuronal dynamics. Our results suggest that E/I imbalance due to different subtype interneurons would induce the distinct types of the atypical neural behaviors associated with neural system dysfunction.

Addressing driving fatigue is crucial among the various factors in traffic accidents. The origins of driver fatigue encompass prolonged driving, sleep deprivation, and the induction of stress inherent in driving. The causes of such driver fatigue were categorized into physical factors, including sleep deprivation and prolonged driving, and perceptional-behavioral factors such as perception of driving conditions. Numerous studies have explored technologies to detect driver fatigue in terms of relevance to sleep states, eye movements, electroencephalography (EEG) activation levels, and their functional connectivity. It was also found differences in the occurrence of driver fatigue and related theta and alpha band activity based on driving conditions. However, the factors contributing to these differences remained unexplored. This study seeks to investigate the causal relationship between driver fatigue induced in different driving environments and the corresponding brain activity. We focus on perceptional load, particularly the perception of traffic objects within the driving condition, and physical loads, such as steering, acceleration, and braking, associated with variations in driving environments. In the experiment, we evaluate the relationship between such differences in physical and mental load and the occurrence of fatigue dependent on the driving condition with considering EEG activation patterns. In the results, we found that the amount of load related to driver perception and operation inherent in driving environments plays a crucial role in driver fatigue.

Fluctuations in nonlinear systems can enhance the synchronization with weak input signals. Chaotic resonance (CR) is one of such phenomena, caused by a system-intrinsic chaotic fluctuation. CR is observed in systems with chaos-chaos intermittency (CCI), where a chaotic orbit appears between separate regions. Based on the characteristics of CR, we previously proposed a novel method for controlling the chaotic state to an appropriate CR state by adopting a feedback signal from the system itself. This method is called the reduced-region-of-orbit (RRO) feedback method. The RRO feedback method was applied to discrete and continuous time chaotic systems, and its versatility was confirmed. Moreover, we have applied this method to frontal cortex neural system model, and the effectiveness for controlling the system behavior by inducing CR was confirmed. In this study, we examined the responsiveness of CCI to a weak periodic signal by extending the model to the brain circuit level composed of frontal cortex, basal ganglia and thalamus. As a result, we confirmed the effectiveness of the RRO feedback method for stabilizing the neural activity observed in the brain circuit model of cerebral cortex-basal ganglia.

Excitatory/inhibitory (E/I) balance is significantly associated with cognitive function. Its imbalance impairs cognitive function, particularly in patients with psychiatric disorders. Recent physiological and modeling findings show that excitatory postsynaptic potentials (EPSPs) have a long-tailed distribution and contribute to the generation of spontaneous activity. Moreover, this spontaneous activity and its response to the external stimulus significantly alternate under the different E/I balance. However, the effects of the E/I balance under long-tailed EPSPs at the functional level remain unknown. Hence, to elucidate this relationship, we constructed a reservoir computing (RC) model generating the long-tailed distribution of EPSPs and investigated the effect of the E/I balance on the learning performance of RC in the memory capacity (MC) task, which measures how correctly delayed input signals can be reproduced. The results revealed that an appropriate E/I balance maximized the MC. This high MC was realized by recurrent spike propagation under long-tailed EPSPs. These findings contribute to the understanding of the effect of the E/I balance in physiologically relevant neural networks.

Chaotic resonance, resembling stochastic resonance, is induced by internal fluctuations (i.e., chaos). This phenomenon has been observed in numerous systems. The most representative form of chaotic resonance is the synchronization with respect to weak applied signals under chaos-chaos intermittency, where a chaotic orbit moves among multiple attractors. Chaotic resonance exhibits higher sensitivity than stochastic resonance, but its engineering applications are limited by concerns such as the requirement to adjust the state of chaos by internal system parameters to induce chaotic resonance rather than the external noise strength. However, achieving this adjustment is challenging, especially in biological systems. Therefore, to handle this limitation, we proposed a novel double-Gaussian-filtered reduced region of orbit (RRO) method (called the DG-RRO method) for obtaining perturbed feedback signals lower than the conventional RRO method. This DG-RRO feedback signal is determined by the inverse sign of the map function and double-Gaussian filters around the local maximum/minimum values of the map. Because of its fine local specification, the DG-RRO feedback signal induces a chaotic resonance by one-third the feedback strength of the conventional signal. This method may pave the way for using chaotic resonance in engineering applications.

An echo state network (ESN), consisting of an input layer, reservoir, and output layer, provides a higher learning-efficient approach than other recurrent neural networks (RNNs). In the design of ESNs, a sufficiently large number of reservoir neurons is required compared to the dimension of the input signal. Thus, the number of neurons must be increased for high-dimensional input to achieve good performance. However, an increase in the number of neurons increases the computational load. To solve this problem, we propose an assembly ESN (AESN) architecture comprising a feature extraction part that uses multiple sub-ESNs with segregated components of high-dimensional input and a feature integration part. To validate the effectiveness of the proposed AESN, we investigated and compared the conventional ESN with the AESN under high-dimensional input. The results show that the AESN is possibly superior to the conventional ESN in accuracy, memory performance, and computational load. We believe that the AESN also has a correct integration function. Therefore, the proposed method is expected to solve high-dimensional problems with improved accuracy.

Locus coeruleus (LC) overactivity, especially in the right hemisphere, is a recognized pathophysiology of attention-deficit/hyperactivity disorder (ADHD) and may be related to inattention. LC activity synchronizes with the kinetics of the pupil diameter and reflects neural activity related to cognitive functions such as attention and arousal. Recent studies highlight the importance of the complexity of the temporal patterns of pupil diameter. Moreover, asymmetrical pupil diameter, which correlates with the severity of inattention, impulsivity, and hyperactivity in ADHD, might be attributed to a left-right imbalance in LC activity. We recently constructed a computational model of pupil diameter based on the newly discovered contralateral projection from the LC to the Edinger-Westphal nucleus (EWN), which demonstrated mechanisms for the complex temporal patterns of pupil kinetics; however, it remains unclear how LC overactivity and its asymmetry affect pupil diameter. We hypothesized that a neural model of pupil diameter control featuring left-right differences in LC activity and projections onto two opponent sides may clarify the role of pupil behavior in ADHD studies. Therefore, we developed a pupil diameter control model reflecting LC overactivity in the right hemisphere by incorporating a contralateral projection from the LC to EWN and evaluated the complexity of the temporal patterns of pupil diameter generated by the model. Upon comparisons with experimentally measured pupil diameters in adult patients with ADHD, the parameter region of interest of the neural model was estimated, which was a region in the two-dimensional plot of complexity versus left-side LC baseline activity and that of the right. A region resulting in relatively high right-side complexity, which corresponded to the pathophysiological indexes, was identified. We anticipate that the discovery of lateralization of complexity in pupil diameter fluctuations will facilitate the development of biomarkers for accurate diagnosis of ADHD.

An echo state network (ESN) is a reservoir computing framework consisting of an input layer, a reservoir, and a readout layer. A reservoir is a recursive network comprising neuron models. In the reservoir, various models use analog neurons with a logistic output function. Time-series learning for ESNs requires a high memory capacity for storing the historical information of past inputs. However, analog neurons are incapable of storing time history by themselves. Therefore, historical information gained by introducing internal neural dynamics can enhance the memory capacity of ESNs. In this context, we hypothesized that the evaluation of the functions of internal-neural decay factors and optimal balances between the decay factors of chaotic neurons can provide useful results for improving the performance of ESNs comprising chaotic neural networks. Therefore, to validate this hypothesis, we investigated the performance of an ESN using a reservoir comprising a chaotic neural network (ChESN). The ChESN significantly outperformed a conventional ESN owing to its high memory capacity at a large temporal scale, even when the spectral radius of the reservoir synaptic weight was small. The proposed approach is expected to have wide applications in reservoir computing.

Recent studies on stochastic resonance have been considered in various fields for engineering applications. Chaotic dynamics derive a phenomenon called chaotic resonance, which is similar to stochastic resonance. The engineering applications of chaotic resonance are limited due to its controlling difficulty, although it exhibits a high sensitivity to signal responses. To address this limitation, we previously proposed a "reduced region of orbit" (RRO) feedback method which induces chaotic resonance using external feedback signals. This method was evaluated under noise-free conditions. However, in practical scenarios, background noise and measurement error are observed when estimating the RRO feedback strength. The influence of these factors on chaotic resonance must be evaluated for preliminary practical application of chaotic resonance. Therefore, in this study, we evaluated chaotic resonance induced by the RRO feedback method in chaotic neural systems under stochastic noise. We focus on chaotic resonance induced by RRO feedback signals in a discrete neural system comprising excitatory and inhibitory neurons, which are typical neural systems that entail chaotic resonance under additive noise and feedback signals, including measurement errors (called contaminant noise). Although both types of noise commonly degrade the degree of synchronization of chaotic resonance induced by the RRO feedback strength, their characteristics are considerably different. In actual neural systems, the influence of noise is inevitable; therefore, this study highlighted the importance of noise countermeasures during the application of chaotic resonance.

Studies of structural connectivity at the synaptic level show that in synaptic connections of the cerebral cortex, the excitatory postsynaptic potential (EPSP) in most synapses exhibits sub-mV values, while a small number of synapses exhibit large EPSPs ( >~1.0 [mV]). This means that the distribution of EPSP fits a log-normal distribution. While not restricting structural connectivity, skewed and long-tailed distributions have been widely observed in neural activities, such as the occurrences of spiking rates and the size of a synchronously spiking population. Many studies have been modeled this long-tailed EPSP neural activity distribution; however, its causal factors remain controversial. This study focused on the long-tailed EPSP distributions and interlateral synaptic connections primarily observed in the cortical network structures, thereby having constructed a spiking neural network consistent with these features. Especially, we constructed two coupled modules of spiking neural networks with excitatory and inhibitory neural populations with a log-normal EPSP distribution. We evaluated the spiking activities for different input frequencies and with/without strong synaptic connections. These coupled modules exhibited intermittent intermodule-alternative behavior, given moderate input frequency and the existence of strong synaptic and intermodule connections. Moreover, the power analysis, multiscale entropy analysis, and surrogate data analysis revealed that the long-tailed EPSP distribution and intermodule connections enhanced the complexity of spiking activity at large temporal scales and induced nonlinear dynamics and neural activity that followed the long-tailed distribution.

In coupled chaotic systems, chaos synchronization is widely observed under the condition with specific coupled forms, such as complete, phase, and generalized synchronizations. Recently, several methods for controlling this chaos synchronization by a nonlinear feedback controller have been proposed. In this study, by focusing on coupled cubic maps, we developed a new method to control synchronization of chaos-chaos intermittency by a nonlinear feedback controller to adjust the range of existence of chaotic orbits. Through the evaluation of the dependence of system behaviors on the feedback strength and the coupled strength, we confirmed that the synchronization of chaos-chaos intermittency could be induced by this nonlinear feedback controller. Especially, the degree of synchronization becomes high at the edge between the parameter region of the feedback strength of chaos-chaos intermittency and the region of nonchaos-chaos intermittency.

A growing number of nonlinear EEG studies have elucidated the reduced fractality in Alzheimer's disease (AD). However, despite the importance of studying EEG dynamics within physiologically relevant frequency ranges, far fewer studies have explored temporal-dependent fractal properties in AD. This study was aimed at assessing the temporal-scale specific fractal properties in AD using Higuchi's fractal algorithm. As a result, we found both enhanced and reduced fractality in a temporal-scale relevant manner. Our findings suggest that investigating temporal specific fractal properties in EEG might serve as a useful approach for characterizing neural basis of AD.

Spiking neuron models, which can realize diverse kinds of neural coding by describing spiking activity of membrane potential, have been widely utilized. Among these models, several hybrid spiking neuron models, which combine continuous spike-generation mechanisms and discontinuous resetting process after spiking, have been proposed as a simple transition scheme for membrane potential between spike and hyperpolarization. Izhikevich neuron model as this kind of model can reproduce many spiking patterns. It has also become clear that this model has various kinds of bifurcation and routes to chaos under the effect of the state dependent jump in the resetting process. In response to this situation, we have further gotten interested in the relation between chaotic behaviors and the state dependent jump. In this paper, we approach the subject from the comparison of spiking neuron models without the resetting process and with it. We first adopt a continuous two-dimensional spiking neuron model where the orbit at spiking state does not exhibit the divergent behavior and next insert the resetting process to it.

Synaptic plasticity is widely recognized to support adaptable information processing in the brain. Spike-timing-dependent plasticity, one subtype of plasticity, can lead to synchronous spike propagation with temporal spiking coding information. Recently, it was reported that in a noisy environment, like the actual brain, the spike-timing-dependent plasticity may be made efficient by the effect of stochastic resonance. In the stochastic resonance, the presence of noise helps a nonlinear system in amplifying a weak (under barrier) signal. However, previous studies have ignored the full variety of spiking patterns and many relevant factors in neural dynamics. Thus, in order to prove the physiological possibility for the enhancement of spike-timing-dependent plasticity by stochastic resonance, it is necessary to demonstrate that this stochastic resonance arises in realistic cortical neural systems. In this study, we evaluate this stochastic resonance phenomenon in the realistic cortical neural system described by the Izhikevich neuron model and compare the characteristics of typical spiking patterns of regular spiking, intrinsically bursting and chattering experimentally observed in the cortex.

It is well known that cerebellar motor control is fine-tuned by the learning process adjusted according to rich error signals from inferior olive (IO) neurons. Schweighofer and colleagues proposed that these signals can be produced by chaotic irregular firing in the IO neuron assembly; such chaotic resonance (CR) was replicated in their computer demonstration of a Hodgkin-Huxley (HH)-type compartment model. In this study, we examined the response of CR to a periodic signal in the IO neuron assembly comprising the Llinás approach IO neuron model. This system involves empirically observed dynamics of the IO membrane potential and is simpler than the HH-type compartment model. We then clarified its dependence on electrical coupling strength, input signal strength, and frequency. Furthermore, we compared the physiological validity for IO neurons such as low firing rate and sustaining subthreshold oscillation between CR and conventional stochastic resonance (SR) and examined the consistency with asynchronous firings indicated by the previous model-based studies in the cerebellar learning process. In addition, the signal response of CR and SR was investigated in a large neuron assembly. As the result, we confirmed that CR was consistent with the above IO neuron's characteristics, but it was not as easy for SR.