Synchronized oscillation in a modular neural network composed of columns

The columnar organization is a ubiquitous feature in the cerebral cortex. In this study, a neural network model simulating the cortical columns has been constructed. When fed with random pulse input with constant rate, a column generates synchronized oscillations, with a frequency varying from 3 to 43 Hz depending on parameter values. The behavior of the model under periodic stimulation was studied and the input-output relationship was non-linear. When identical columns were sparsely interconnected, the column oscillator could be locked in synchrony. In a network composed of heterogeneous columns, the columns were organized by intrinsic properties and formed partially synchronized assemblies.     

Inhibition synchronizes sparsely connected cortical neurons within and between columns in realistic network models

Networks of compartmental model neurons were used to investigate the biophysical basis of the synchronization observed between sparsely-connected neurons in neocortex. A model of a single column in layer 5 consisted of 100 model neurons: 80 pyramidal and 20 inhibitory. The pyramidal cells had conductances that caused intrinsic repetitive bursting at different frequencies when driven with the same input. When connected randomly with a connection density of 10%, a single model column displayed synchronous oscillatory action potentials in response to stationary, uncorrelated Poisson spike-train inputs. Synchrony required a high ratio of inhibitory to excitatory synaptic strength; the optimal ratio was 41, within the range observed in cortex. The synchrony was insensitive to variation in amplitudes of postsynaptic potentials and synaptic delay times, even when the mean synaptic delay times were varied over the range 1 to 7 ms. Synchrony was found to be sensitive to the strength of reciprocal inhibition between the inhibitory neurons in one column: Too weak or too strong reciprocal inhibition degraded intra-columnar synchrony. The only parameter that affected the oscillation frequency of the network was the strength of the external driving input which could shift the frequency between 35 to 60 Hz. The same results were obtained using a model column of 1000 neurons with a connection density of 5%, except that the oscillation became more regular.Synchronization between cortical columns was studied in a model consisting of two columns with 100 model neurons each. When connections were made with a density of 3% between the pyramidal cells of each column there was no inter-columnar synchrony and in some cases the columns oscillated 180° out of phase with each other. Only when connections from the pyramidal cells in each column to the inhibitory cells in the other column were added was synchrony between the columns observed. This synchrony was established within one or two cycles of the oscillation and there was on average less than 1 ms phase difference between the two columns. Unlike the intra-columnar synchronization, the inter-columnar synchronization was found to be sensitive to the synaptic delay: A mean delay of greater than 5 ms virtually abolished synchronization between columns.     

An integrate-and-fire model for synchronized bursting in a network of cultured cortical neurons

It has been suggested that spontaneous synchronous neuronal activity is an essential step in the formation of functional networks in the central nervous system. The key features of this type of activity consist of bursts of action potentials with associated spikes of elevated cytoplasmic calcium. These features are also observed in networks of rat cortical neurons that have been formed in culture. Experimental studies of these cultured networks have led to several hypotheses for the mechanisms underlying the observed synchronized oscillations. In this paper, bursting integrate-and-fire type mathematical models for regular spiking (RS) and intrinsic bursting (IB) neurons are introduced and incorporated through a small-world connection scheme into a two-dimensional excitatory network similar to those in the cultured network. This computer model exhibits spontaneous synchronous activity through mechanisms similar to those hypothesized for the cultured experimental networks. Traces of the membrane potential and cytoplasmic calcium from the model closely match those obtained from experiments. We also consider the impact on network behavior of the IB neurons, the geometry and the small world connection scheme. Action Editor: David Golomb     

噪声诱导多细胞系统的同步切换

在以往的工作中讨论了对单细胞的基因开关系统噪声如何诱导连贯切换．在此,对多细胞的基因开关网络系统研究各种噪声（包括细胞内噪声和细胞环境噪声）对同步切换的影响．发现：细胞内基因调控过程中的合成率和降解率的随机涨落以及细胞内的附加噪声均能够诱导群体基因开关系统的同步切换,而且存在一个最优的噪声强度,它使得这种同步切换的效果最佳．另一方面,细胞环境的随机涨落所导致的噪声（即环境噪声）,不但能诱导上述同步切换,而且当细胞内噪声不足以诱导细胞群体的同步切换时,它通过压制内部噪声来达到增强群体系统的协作行为．最后,还分析了受噪声影响的信号分子的扩散率对细胞群体切换行为的影响．     

Homeostatically regulated synchronized oscillations induced by short-term tetrodotoxin treatment in cultured neuronal network

Homeostatic plasticity plays a critical role in the stability of neuronal activities. Here, with high-density hippocampal networks cultured on multi-electrode arrays (MEAs), the transformation of spontaneous neuronal firing patterns induced by 1microM tetrodotoxin was clarified. Once tetrodotoxin was washed out after a 4-h treatment, spontaneous activities rose significantly with spike rate increasing approximately three times, and synchronized burst oscillations appeared throughout the network, with the cross-correlation coefficient between the active sites rising from 0.06+/-0.03 to 0.27+/-0.05. The long-term recording showed that the oscillations lasted for more than 4h before the network recovered. These results suggest that short-term treatment by tetrodotoxin may induce the homeostatically enhanced neuronal excitability, and that the spontaneous synchronized oscillations should be an indicator of homeostatic plasticity in cultured neuronal network. Furthermore, the non-invasive and long-term recording with MEAs as a novel sensing system is identified to be appropriate for pharmacological investigations of neuronal plasticity at the network level.     

Portrait of visual cortical circuits for generating neural oscillation dynamics

The mouse primary visual cortex (V1) has emerged as a classical system to study neural circuit mechanisms underlying visual function and plasticity. A variety of efferent-afferent neuronal connections exists within the V1 and between the V1 and higher visual cortical areas or thalamic nuclei, indicating that the V1 system is more than a mere receiver in information processing. Sensory representations in the V1 are dynamically correlated with neural activity oscillations that are distributed across different cortical layers in an input-dependent manner. Circuits consisting of excitatory pyramidal cells (PCs) and inhibitory interneurons (INs) are the basis for generating neural oscillations. In general, INs are clustered with their adjacent PCs to form specific microcircuits that gate or filter the neural information. The interaction between these two cell populations has to be coordinated within a local circuit in order to preserve neural coding schemes and maintain excitation–inhibition (E–I) balance. Phasic alternations of the E–I balance can dynamically regulate temporal rhythms of neural oscillation. Accumulating experimental evidence suggests that the two major sub-types of INs, parvalbumin-expressing (PV⁺) cells and somatostatin-expressing (SOM⁺) INs, are active in controlling slow and fast oscillations, respectively, in the mouse V1. The review summarizes recent experimental findings on elucidating cellular or circuitry mechanisms for the generation of neural oscillations with distinct rhythms in either developing or matured mouse V1, mainly focusing on visual relaying circuits and distinct local inhibitory circuits.     

Ring neural network qua a generator of rhythmic oscillation with period control mechanism

For the ring neural network to function as a generator of rhythmic oscillation, mechanisms are required by which rhythmic oscillation is generated and maintained and then its period controlled. This paper demonstrates by simulation that those mechanisms can be actualized by employing a synaptic modification algorithm and by applying inputs from the outside to excitatory and inhibitory cells. When the constants in the synaptic modification algorithm are fixed, it is possible to select two modes, that is, the modification mode and the non-modification mode, using the excitatory input level to excitatory cells alone. This property solves the problem of the re-modification caused by the dispersion of AIDs (average impulse densities) with the application of the excitatory synchronous input to inhibitory cells.     

Internal noise enhanced oscillation in a delayed circadian pacemaker

The effect of internal noise in a delayed circadian oscillator is studied by using both chemical Langevin equations and stochastic normal form theory. It is found that internal noise can induce circadian oscillation even if the delay time τ is below the deterministic Hopf bifurcation τ_h. We use signal-to-noise ratio (SNR) to quantitatively characterize the performance of such noise induced oscillations and a threshold value of SNR is introduced to define the so-called effective oscillation. Interestingly, the τ-range for effective stochastic oscillation, denoted as Δτ_EO, shows a bell-shaped dependence on the intensity of internal noise which is inversely proportional to the system size. We have also investigated how the rates of synthesis and degradation of the clock protein influence the SNR and thus Δτ_EO. The decay rate K_d could significantly affect Δτ_EO, while varying the gene expression rate K_e has no obvious effect if K_e is not too small. Stochastic normal form analysis and numerical simulations are in good consistency with each other. This work provides us comprehensive understandings of how internal noise and time delay work cooperatively to influence the dynamics of circadian oscillations.     

A synaptic modification algorithm in consideration of the generation of rhythmic oscillation in a ring neural network

In consideration of the generation of bursts of nerve impulses (that is, rhythmic oscillation in impulse density) in the ring neural network, a synaptic modification algorithm is newly proposed. Rhythmic oscillation generally occurs in the regular ring network with feedback inhibition and in fact such signals can be observed in the real nervous system. Since, however, various additional connections can cause a disturbance which easily extinguishes the rhythmic oscillation in the network, some function for maintaining the rhythmic oscillation is to be expected to exist in the synapses if such signals play an important part in the nervous system. Our preliminary investigation into the rhythmic oscillation in the regular ring network has led to the selection of the parameters, that is, the average membrane potential (AMP) and the average impulse density (AID) in the synaptic modification algorithm, where the decrease of synaptic strength is supposed to be essential. This synaptic modification algorithm using AMP and AID enables both the rhythmic oscillation and the non-oscillatory state to be dealt with in the algorithm without distinction. Simulation demonstrates cases in which the algorithm catches and holds the rhythmic oscillation in the disturbed ring network where the rhythmic oscillation was previously extinguished.     

Coupling between contraction rhythm and calcium oscillation in a cultured cardiac myocyte: Fluctuation behaviour and its modelling

Isolated and cultured neonatal cardiac myocytes show self-sustaining cyclic contraction, and have the properties of a nonlinear oscillator. We study the dynamics of mechanical contraction and cellular free Ca²⁺ in a single myocyte for the purposes of gaining an insight into the way in which excitation and contraction processes are inter-related. The concentration of intracellular Ca²⁺ in the myocyte is also found to vary periodically associated with its rhythmic contraction. The Ca²⁺ dynamics maintains its self-oscillatory nature when the spontaneous contraction is abolished by pharmacological treatment using 2,3-butanedione monoxime. However, fluctuation analysis of the Ca²⁺ oscillation intervals reveals that there occurs a characteristic change in the fluctuation behaviour due to the suppression of contraction; the mean value and fluctuation magnitude of the oscillation intervals and the persistency of the fluctuation correlations at short timescales all increase after pharmacological treatment. We develop a new nonlinear model based on Bonhoeffer - van der Pol oscillators to elucidate the mechanisms behind the observed effects of cardiac contraction on the Ca²⁺ oscillation. The model is composed of three coupled nonlinear differential equations that can describe the dynamics of both excitation (cellular free Ca²⁺) and contraction. Almost all the experimental findings are successfully reproduced by adjusting a parameter in the model responsible for excitation - contraction coupling.     

Multisensory integration in the superior colliculus: a neural network model

Neurons in the superior colliculus (SC) are known to integrate stimuli of different modalities (e.g., visual and auditory) following specific properties. In this work, we present a mathematical model of the integrative response of SC neurons, in order to suggest a possible physiological mechanism underlying multisensory integration in SC. The model includes three distinct neural areas: two unimodal areas (auditory and visual) are devoted to a topological representation of external stimuli, and communicate via synaptic connections with a third downstream area (in the SC) responsible for multisensory integration. The present simulations show that the model, with a single set of parameters, can mimic various responses to different combinations of external stimuli including the inverse effectiveness, both in terms of multisensory enhancement and contrast, the existence of within- and cross-modality suppression between spatially disparate stimuli, a reduction of network settling time in response to cross-modal stimuli compared with individual stimuli. The model suggests that non-linearities in neural responses and synaptic (excitatory and inhibitory) connections can explain several aspects of multisensory integration.     

Classification of ECG arrhythmia by a modular neural network based on Mixture of Experts and Negatively Correlated Learning

In this paper, we propose a novel ECG arrhythmia classification method using the complementary features of Mixture of Experts (ME) and Negatively Correlated Learning (NCL). Negative Correlation Learning and Mixture of Experts methods utilize different error functions for simultaneous training of negatively correlated Neural Networks. The capability of a control parameter for NCL is incorporated in the error function of ME, which enables the training algorithm of ME to establish a balance in bias-variance-covariance trade-offs. ECG records from the MIT-BIH arrhythmia database are selected as test data. It is observed that the proposed classification method not only preserves the advantages and alleviates the disadvantages of its basis approaches, but also offering significantly improved performance over the original methods.     

Caffeine-induced modulation of network oscillation in a molluscan olfactory center

Calcium release from intracellular stores has various actions in neurons, but its effects on network oscillation have not been well understood. The olfactory center (procerebrum, PC) of the terrestrial slug Limax valentianus shows a regular oscillation in the local field potential (LFP). Here we report that caffeine, which is an agonist for ryanodine receptors and triggers calcium release from intra-cellular stores, has strong modulatory effects on the PC. In isolated PC neurons, caffeine enhanced the cytoplasmic calcium concentration, and this was blocked by ryanodine. Caffeine elevated the frequency and amplitude of the LFP oscillation, which was also blocked by ryanodine. The time lag between the frequency and amplitude effects suggests distinct mechanisms for the modulation of these two parameters. These results suggest that calcium release from intracellular stores through ryanodine receptors activates network activity in the PC.     

Synchronized dynamics and non-equilibrium steady states in a stochastic yeast cell-cycle network

Applying the mathematical circulation theory of Markov chains, we investigate the synchronized stochastic dynamics of a discrete network model of yeast cell-cycle regulation where stochasticity has been kept rather than being averaged out. By comparing the network dynamics of the stochastic model with its corresponding deterministic network counterpart, we show that the synchronized dynamics can be soundly characterized by a dominant circulation in the stochastic model, which is the natural generalization of the deterministic limit cycle in the deterministic system. Moreover, the period of the main peak in the power spectrum, which is in common use to characterize the synchronized dynamics, perfectly corresponds to the number of states in the main cycle with dominant circulation. Such a large separation in the magnitude of the circulations, between a dominant, main cycle and the rest, gives rise to the stochastic synchronization phenomenon.     

Analysis of reflex modulation with a biologically realistic neural network

In this study, a neuromusculoskeletal model was built to give insight into the mechanisms behind the modulation of reflexive feedback strength as experimentally identified in the human shoulder joint. The model is an integration of a biologically realistic neural network consisting of motoneurons and interneurons, modeling 12 populations of spinal neurons, and a one degree-of-freedom musculoskeletal model, including proprioceptors. The model could mimic the findings of human postural experiments, using presynaptic inhibition of the Ia afferents to modulate the feedback gains. In a pathological case, disabling one specific neural connection between the inhibitory interneurons and the motoneurons could mimic the experimental findings in complex regional pain syndrome patients. It is concluded that the model is a valuable tool to gain insight into the spinal contributions to human motor control. Applications lay in the fields of human motor control and neurological disorders, where hypotheses on motor dysfunction can be tested, like spasticity, clonus, and tremor. Action Editor: Karen Sigvardt     

Artificial neural network method for predicting HIV protease cleavage sites in protein

Knowledge of the polyprotein cleavage sites by HIV protease will refine our understanding of its specificity, and the information thus acquired will be useful for designing specific and efficient HIV protease inhibitors. The search for inhibitors of HIV protease will be greatly expedited if one can find and accurate, robust, and rapid method for predicting the cleavage sites in proteins by HIV protease. In this paper, Kohonen’s self-organization model, which uses typical artificial neural networks, is applied to predict the cleavability of oligopeptides by proteases with multiple and extended specificity subsites. We selected HIV-1 protease as the subject of study. We chose 299 oligopeptides for the training set, and another 63 oligopeptides for the test set. Because of its high rate of correct prediction (58/63=92.06%) and stronger fault-tolerant ability, the neural network method should be a useful technique for finding effective inhibitors of HIV protease, which is one of the targets in designing potential drugs against AIDS. The principle of the artificial neural network method can also be applied to analyzing the specificity of any multisubsite enzyme.     

Artificial neural network method for predicting HIV protease cleavage sites in protein

Knowledge of the polyprotein cleavage sites by HIV protease will refine our understanding of its specificity, and the information thus acquired will be useful for designing specific and efficient HIV protease inhibitors. The search for inhibitors of HIV protease will be greatly expedited if one can find and accurate, robust, and rapid method for predicting the cleavage sites in proteins by HIV protease. In this paper, Kohonen’s self-organization model, which uses typical artificial neural networks, is applied to predict the cleavability of oligopeptides by proteases with multiple and extended specificity subsites. We selected HIV-1 protease as the subject of study. We chose 299 oligopeptides for the training set, and another 63 oligopeptides for the test set. Because of its high rate of correct prediction (58/63=92.06%) and stronger fault-tolerant ability, the neural network method should be a useful technique for finding effective inhibitors of HIV protease, which is one of the targets in designing potential drugs against AIDS. The principle of the artificial neural network method can also be applied to analyzing the specificity of any multisubsite enzyme.     

Validity of a novel method to measure vertical oscillation during running using a depth camera

Recent advancements in low-cost depth cameras may provide a clinically accessible alternative to conventional three-dimensional (3D) multi-camera motion capture systems for gait analysis. However, there remains a lack of information on the validity of clinically relevant running gait parameters such as vertical oscillation (VO). The purpose of this study was to assess the validity of measures of VO during running gait using raw depth data, in comparison to a 3D multi-camera motion capture system. Sixteen healthy adults ran on a treadmill at a standard speed of 2.7 m/s. The VO of their running gait was simultaneously collected from raw depth data (Microsoft Kinect v2) and 3D marker data (Vicon multi-camera motion capture system). The agreement between the VO measures obtained from the two systems was assessed using a Bland-Altman plot with 95% limits of agreement (LOA), a Pearson’s correlation coefficient (r), and a Lin’s concordance correlation coefficient (r_c). The depth data from the Kinect v2 demonstrated excellent results across all measures of validity (r = 0.97; r_c = 0.97; 95% LOA = −8.0 mm – 8.7 mm), with an average absolute error and percent error of 3.7 (2.1) mm and 4.0 (2.0)%, respectively. The findings of this study have demonstrated the ability of a low cost depth camera and a novel tracking method to accurately measure VO in running gait.     

Gravitational force modulates muscle activity during mechanical oscillation of the tibia in humans

Mechanical oscillation (vibration) is an osteogenic stimulus for bone in animal models and may hold promise as an anti-osteoporosis measure in humans with spinal cord injury (SCI). However, the level of reflex induced muscle contractions associated with various loads (g force) during limb segment oscillation is uncertain. The purpose of this study was to determine whether certain gravitational loads (g forces) at a fixed oscillation frequency (30 Hz) increases muscle reflex activity in individuals with and without SCI. Nine healthy subjects and two individuals with SCI sat with their hip and knee joints at 90° and the foot secured on an oscillation platform. Vertical mechanical oscillations were introduced at 0.3, 0.6, 1.2, 3 and 5g force for 20 s at 30 Hz. Non-SCI subjects received the oscillation with and without a 5% MVC background contraction. Peak soleus and tibialis anterior (TA) EMG were normalized to M-max. Soleus and TA EMG were <2.5% of M-max in both SCI and non-SCI subjects. The greatest EMG occurred at the highest acceleration (5g). Low magnitude mechanical oscillation, shown to enhance bone anabolism in animal models, did not elicit high levels of reflex muscle activity in individuals with and without SCI. These findings support the g force modulated background muscle activity during fixed frequency vibration. The magnitude of muscle activity was low and likely does not influence the load during fixed frequency oscillation of the tibia.