海马记忆功能的神经网络模型

综合神经心理学,神经生理学、解剖学与神经网络研究的成果,提出一个海马记忆功能的神经网络模型。模型由三个神经网络所组成;海马的ＣＡ１和ＣＡ３网络和大脑皮层联合区,ＣＡ３的功能是将不同感觉输入联合起来,而ＣＡ１的作用是将它们结成一个单一的记忆。而大脑皮层则是长期记忆的部位。在ＶＡＸ１１／７５０上进行计算机仿真,仿真证明模型有近期及长期记忆功能,破坏模拟海马的部分,模型显示出与顺行性遗忘症相似的特性。在     

The role of phase synchronization in memory processes

In recent years, studies ranging from single-unit recordings in animals to electroencephalography and magnetoencephalography studies in humans have demonstrated the pivotal role of phase synchronization in memory processes. Phase synchronization - here referring to the synchronization of oscillatory phases between different brain regions - supports both working memory and long-term memory and acts by facilitating neural communication and by promoting neural plasticity. There is evidence that processes underlying working and long-term memory might interact in the medial temporal lobe. We propose that this is accomplished by neural operations involving phase-phase and phase-amplitude synchronization. A deeper understanding of how phase synchronization supports the flexibility of and interaction between memory systems may yield new insights into the functions of phase synchronization in general.     

The Spatiotemporal Dynamics of Phase Synchronization during Epileptogenesis in Amygdala-Kindling Mice

The synchronization among the activities of neural populations in functional regions is one of the most important electrophysiological phenomena in epileptic brains. The spatiotemporal dynamics of phase synchronization was investigated to reveal the reciprocal interaction between different functional regions during epileptogenesis. Local field potentials (LFPs) were recorded simultaneously from the basolateral amygdala (BLA), the cornu ammonis 1 of hippocampus (CA1) and the mediodorsal nucleus of thalamus (MDT) in the mouse amygdala-kindling models during the development of epileptic seizures. The synchronization of LFPs was quantified between BLA, CA1 and MDT using phase-locking value (PLV). During amygdala kindling, behavioral changes (from stage 0 to stage 5) of mice were accompanied by after-discharges (ADs) of similar waveforms appearing almost simultaneously in CA1, MDT, as well as BLA. AD durations were positively related to the intensity of seizures. During seizures at stages 1~2, PLVs remained relatively low and increased dramatically shortly after the termination of the seizures; by contrast, for stages 3~5, PLVs remained a relatively low level during the initial period but increased dramatically before the seizure termination. And in the theta band, the degree of PLV enhancement was positively associated with seizure intensity. The results suggested that during epileptogenesis, the functional regions were kept desynchronized rather than hyper-synchronized during either the initial or the entire period of the seizures; so different dynamic patterns of phase synchronization may be involved in different periods of the epileptogenesis, and this might also reflect that during seizures at different stages, the mechanisms underlying the dynamics of phase synchronization were different.     

Influence of connection type on phase synchrony: analysis of a neural mass model

Empirical studies have demonstrated synchronized frontal and parietal electrophysiological signals at 22-34?Hz during a conjunctive visual search task and at 36-56?Hz during a pop-out visual search task. Bidirectional (conjunctive) versus unidirectional (pop-out) information transfer between neuronal populations is hypothesized to underly this difference in synchronization frequency. This study modeled the influence of connection type (i.e., unidirectional vs. bidirectional) on phase synchrony between two neural populations using a neural mass model. Phase-locking values (PLVs) were used as the measure of synchrony between populations. Consistent with the connectivity hypothesis, the model revealed greater PLVs at 22-34?Hz when the two populations were connected bidirectionally than unidirectionally, but greater PLVs at 34-52?Hz when connected unidirectionally than bidirectionally. The model suggests that inter-population connectivity also changes with bottom-up versus top-down control of attention.     

Biophysical mechanism of the interaction between default mode network and working memory network

Default mode network (DMN) is a functional brain network with a unique neural activity pattern that shows high activity in resting states but low activity in task states. This unique pattern has been proved to relate with higher cognitions such as learning, memory and decision-making. But neural mechanisms of interactions between the default network and the task-related network are still poorly understood. In this paper, a theoretical model of coupling the DMN and working memory network (WMN) is proposed. The WMN and DMN both consist of excitatory and inhibitory neurons connected by AMPA, NMDA, GABA synapses, and are coupled with each other only by excitatory synapses. This model is implemented to demonstrate dynamical processes in a working memory task containing encoding, maintenance and retrieval phases. Simulated results have shown that: (1) AMPA channels could produce significant synchronous oscillations in population neurons, which is beneficial to change oscillation patterns in the WMN and DMN. (2) Different NMDA conductance between the networks could generate multiple neural activity modes in the whole network, which may be an important mechanism to switch states of the networks between three different phases of working memory. (3) The number of sequentially memorized stimuli was related to the energy consumption determined by the network''s internal parameters, and the DMN contributed to a more stable working memory process. (4) Finally, this model demonstrated that, in three phases of working memory, different memory phases corresponded to different functional connections between the DMN and WMN. Coupling strengths that measured these functional connections differed in terms of phase synchronization. Phase synchronization characteristics of the contained energy were consistent with the observations of negative and positive correlations between the WMN and DMN reported in referenced fMRI experiments. The results suggested that the coupled interaction between the WMN and DMN played important roles in working memory.Supplementary InformationThe online version contains supplementary material available at 10.1007/s11571-021-09674-1.     

A linear model for characterization of synchronization frequencies of neural networks

The synchronization frequency of neural networks and its dynamics have important roles in deciphering the working mechanisms of the brain. It has been widely recognized that the properties of functional network synchronization and its dynamics are jointly determined by network topology, network connection strength, i.e., the connection strength of different edges in the network, and external input signals, among other factors. However, mathematical and computational characterization of the relationships between network synchronization frequency and these three important factors are still lacking. This paper presents a novel computational simulation framework to quantitatively characterize the relationships between neural network synchronization frequency and network attributes and input signals. Specifically, we constructed a series of neural networks including simulated small-world networks, real functional working memory network derived from functional magnetic resonance imaging, and real large-scale structural brain networks derived from diffusion tensor imaging, and performed synchronization simulations on these networks via the Izhikevich neuron spiking model. Our experiments demonstrate that both of the network synchronization strength and synchronization frequency change according to the combination of input signal frequency and network self-synchronization frequency. In particular, our extensive experiments show that the network synchronization frequency can be represented via a linear combination of the network self-synchronization frequency and the input signal frequency. This finding could be attributed to an intrinsically-preserved principle in different types of neural systems, offering novel insights into the working mechanism of neural systems.     

Synchronization of Two Homodromy Rotors Installed on a Double Vibro-Body in a Coupling Vibration System

A new mechanism is proposed to implement synchronization of the two unbalanced rotors in a vibration system, which consists of a double vibro-body, two induction motors and spring foundations. The coupling relationship between the vibro-bodies is ascertained with the Laplace transformation method for the dynamics equation of the system obtained with the Lagrange’s equation. An analytical approach, the average method of modified small parameters, is employed to study the synchronization characteristics between the two unbalanced rotors, which is converted into that of existence and the stability of zero solutions for the non-dimensional differential equations of the angular velocity disturbance parameters. By assuming the disturbance parameters that infinitely approach to zero, the synchronization condition for the two rotors is obtained. It indicated that the absolute value of the residual torque between the two motors should be equal to or less than the maximum of their coupling torques. Meanwhile, the stability criterion of synchronization is derived with the Routh-Hurwitz method, and the region of the stable phase difference is confirmed. At last, computer simulations are preformed to verify the correctness of the approximate solution of the theoretical computation for the stable phase difference between the two unbalanced rotors, and the results of theoretical computation is in accordance with that of computer simulations. To sum up, only the parameters of the vibration system satisfy the synchronization condition and the stability criterion of the synchronization, the two unbalanced rotors can implement the synchronization operation.     

Stability and synchronization analysis of inertial memristive neural networks with time delays

This paper is concerned with the problem of stability and pinning synchronization of a class of inertial memristive neural networks with time delay. In contrast to general inertial neural networks, inertial memristive neural networks is applied to exhibit the synchronization and stability behaviors due to the physical properties of memristors and the differential inclusion theory. By choosing an appropriate variable transmission, the original system can be transformed into first order differential equations. Then, several sufficient conditions for the stability of inertial memristive neural networks by using matrix measure and Halanay inequality are derived. These obtained criteria are capable of reducing computational burden in the theoretical part. In addition, the evaluation is done on pinning synchronization for an array of linearly coupled inertial memristive neural networks, to derive the condition using matrix measure strategy. Finally, the two numerical simulations are presented to show the effectiveness of acquired theoretical results.     

Visuomotor synchronization: Analysis of the initiation and stable synchronization phases

The initiation phase of visuomotor synchronization and the phase of stable synchronization were studied in an experiment where eight adult subjects synchronized their motor responses with an isochronous sequence of visual stimuli. The period of the sequence varied in a wide range (from 500 to 2200 ms). Analysis of the statistical characteristics of synchronization errors (asynchronies) showed that the phase of stable visuomotor synchronization fit the model of current phase correction of a central timer; as in the case of audiomotor synchronization, the variability of the intervals of the central timer and the phase correction coefficient increased with increasing period of the stimulus sequence. The initiation phase of visuomotor synchronization was characterized by a considerable inter-and intraindividual variability of the form (exponential, linear, or step) and duration (from one to ten responses) of the transition from reacting to a sensory signal to synchronization. The shape and duration of the transitional region depend on the phase correction coefficient and the possibility of using an estimate of the sequence period stored in memory. The obtained data indicate that the initiation stage is not automatic throughout the studied range of periods of the visual stimulus sequence; in particular, working memory plays a substantial role in its organization.     

选择方向强化学习的神经网络模型

提出了神经网络模型的一种选择方向强化学习规则,定义并导出了新模型与Hopfield模型两种不同的筛选曲线,由此表明新模型对相关图样的分辨力优于Hopfield模型。在微机上模拟了由100个神经元构成的网络,结果显示新模型具有重复记忆这一神经生理学特点。定义并分行了记忆强度因子,模拟结果表明记忆强度因子愈大的记忆态,联想性能愈好,学习周期愈短。     

A model of stimulus-specific neural assemblies in the insect antennal lobe

It has been proposed that synchronized neural assemblies in the antennal lobe of insects encode the identity of olfactory stimuli. In response to an odor, some projection neurons exhibit synchronous firing, phase-locked to the oscillations of the field potential, whereas others do not. Experimental data indicate that neural synchronization and field oscillations are induced by fast GABA(A)-type inhibition, but it remains unclear how desynchronization occurs. We hypothesize that slow inhibition plays a key role in desynchronizing projection neurons. Because synaptic noise is believed to be the dominant factor that limits neuronal reliability, we consider a computational model of the antennal lobe in which a population of oscillatory neurons interact through unreliable GABA(A) and GABA(B) inhibitory synapses. From theoretical analysis and extensive computer simulations, we show that transmission failures at slow GABA(B) synapses make the neural response unpredictable. Depending on the balance between GABA(A) and GABA(B) inputs, particular neurons may either synchronize or desynchronize. These findings suggest a wiring scheme that triggers stimulus-specific synchronized assemblies. Inhibitory connections are set by Hebbian learning and selectively activated by stimulus patterns to form a spiking associative memory whose storage capacity is comparable to that of classical binary-coded models. We conclude that fast inhibition acts in concert with slow inhibition to reformat the glomerular input into odor-specific synchronized neural assemblies.     

Synchronization of neuronal assemblies in reciprocally connected cortical areas

Summary To investigate scene segmentation in the visual system we present a model of two reciprocally connected visual areas comprising spiking neurons. The peripheral area P is modeled similar to the primary visual cortex, while the central area C is modeled as an associative memory representing stimulus objects according to Hebbian learning. Without feedback from area C, spikes corresponding to stimulus representations in P are synchronized only locally (slow state). Feedback from C can induce fast oscillations and an increase of synchronization ranges (fast state). Presenting a superposition of several stimulus objects, scene segmentation happens on a time scale of hundreds of milliseconds by alternating epochs of the slow and fast state, where neurons representing the same object are simultaneously in the fast state. We relate our simulation results to various phenomena observed in neurophysiological experiments, such as stimulus-dependent synchronization of fast oscillations, synchronization on different time scales, ongoing activity, and attention-dependent neural activity.     

The dynamics of memory retrieval in hierarchical networks

Memory retrieval is of central importance to a wide variety of brain functions. To understand the dynamic nature of memory retrieval and its underlying neurophysiological mechanisms, we develop a biologically plausible spiking neural circuit model, and demonstrate that free memory retrieval of sequences of events naturally arises from the model under the condition of excitation-inhibition (E/I) balance. Using the mean-field model of the spiking circuit, we gain further theoretical insights into how such memory retrieval emerges. We show that the spiking neural circuit model quantitatively reproduces several salient features of free memory retrieval, including its semantic proximity effect and log-normal distributions of inter-retrieval intervals. In addition, we demonstrate that our model can serve as a platform to examine memory retrieval deficits observed in neuropsychiatric diseases such as Parkinson’s and Alzheimer’s diseases. Furthermore, our model allows us to make novel and experimentally testable predictions, such as the prediction that there are long-range correlations in the sequences of retrieved items.     

Hindmarsh-Rose 神经网络的混沌同步

研究了通过特殊构造的非线性函数耦合连接的神经网络的混沌同步问题。在发展基于稳定性准则的混沌同步方法的基础上,给出了计算同步稳定性的误差发展方程,当耦合强度取参考值时,可实现稳定的混沌同步而不需要计算最大条件Lyapunov指数去判定是否稳定。通过对按照完全连接形式构成的Hindmarsh-Rose神经网络的数值模拟,显示可仅从两个耦合神经的耦合强度的稳定性范围预期到许多耦合神经实现同步的稳定性范围。该方法在噪声影响下,对实现神经元的混沌同步仍具有较强的鲁棒性。此外发现随着耦合神经数的增加,满足同步稳定性的耦合强度减小,与耦合神经的数量成反比。     

Broadband Criticality of Human Brain Network Synchronization

Self-organized criticality is an attractive model for human brain dynamics, but there has been little direct evidence for its existence in large-scale systems measured by neuroimaging. In general, critical systems are associated with fractal or power law scaling, long-range correlations in space and time, and rapid reconfiguration in response to external inputs. Here, we consider two measures of phase synchronization: the phase-lock interval, or duration of coupling between a pair of (neurophysiological) processes, and the lability of global synchronization of a (brain functional) network. Using computational simulations of two mechanistically distinct systems displaying complex dynamics, the Ising model and the Kuramoto model, we show that both synchronization metrics have power law probability distributions specifically when these systems are in a critical state. We then demonstrate power law scaling of both pairwise and global synchronization metrics in functional MRI and magnetoencephalographic data recorded from normal volunteers under resting conditions. These results strongly suggest that human brain functional systems exist in an endogenous state of dynamical criticality, characterized by a greater than random probability of both prolonged periods of phase-locking and occurrence of large rapid changes in the state of global synchronization, analogous to the neuronal “avalanches” previously described in cellular systems. Moreover, evidence for critical dynamics was identified consistently in neurophysiological systems operating at frequency intervals ranging from 0.05–0.11 to 62.5–125 Hz, confirming that criticality is a property of human brain functional network organization at all frequency intervals in the brain's physiological bandwidth.     

A model of cortical memory processing based on columnar organization

Recent neurophysiological experiments using mammalian brains indicated that some cortical neurons exhibit oscillatory activities which can be of functional importance in visual perception. These findings suggest that the oscillation is an ubiquitous feature of cortical information processing carried out by columns which are receiving growing attention as functional subdivisions of cortical circuitry. On the assumption that a basic functional unit is a column comprising excitatory and inhibitory neurons, a network model of cortical memory processing which can account for these oscillations is proposed. Numerical simulations revealed that for appropriately determined parameters the network can attain memory-pattern retrieval resulting from fixed-point behaviour despite the fact that columns have the characteristic of oscillators. Received: 19 March 1993/Accepted in revised form: 23 September 1993     

Oscillatory binocular system and temporal segmentation of stereoscopic depth surfaces

A dynamical neural network model of binocular stereopsis is proposed to solve the problem of segmentation which remains ambiguous even when the problem of binocular correspondence is solved. Being compatible with the recent neurophysiological findings (Engel et al. 1991), the model assumes that neural cells show oscillatory activities and that segmentation into a coherent depth surface is coded by synchronization of activities. Employing appropriate constraints for segmentation, the present model shows proper segmentation of depth surfaces and also solves segmentational ambiguity caused by a gap. It is newly shown that binocularly-unmatched monocular cells are discriminated in temporal segmentation of monocular cells caused by recurrent interactions between monocular and binocular cells. Integrative interactions with the other visual components through temporal segmentation are also discussed.     

Fast and robust image segmentation by small-world neural oscillator networks

Inspired by the temporal correlation theory of brain functions, researchers have presented a number of neural oscillator networks to implement visual scene segmentation problems. Recently, it is shown that many biological neural networks are typical small-world networks. In this paper, we propose and investigate two small-world models derived from the well-known LEGION (locally excitatory and globally inhibitory oscillator network) model. To form a small-world network, we add a proper proportion of unidirectional shortcuts (random long-range connections) to the original LEGION model. With local connections and shortcuts, the neural oscillators can not only communicate with neighbors but also exchange phase information with remote partners. Model 1 introduces excitatory shortcuts to enhance the synchronization within an oscillator group representing the same object. Model 2 goes further to replace the global inhibitor with a sparse set of inhibitory shortcuts. Simulation results indicate that the proposed small-world models could achieve synchronization faster than the original LEGION model and are more likely to bind disconnected image regions belonging together. In addition, we argue that these two models are more biologically plausible.     

A thalamo-cortical neural mass model for the simulation of brain rhythms during sleep

Cortico-thalamic interactions are known to play a pivotal role in many brain phenomena, including sleep, attention, memory consolidation and rhythm generation. Hence, simple mathematical models that can simulate the dialogue between the cortex and the thalamus, at a mesoscopic level, have a great cognitive value. In the present work we describe a neural mass model of a cortico-thalamic module, based on neurophysiological mechanisms. The model includes two thalamic populations (a thalamo-cortical relay cell population, TCR, and its related thalamic reticular nucleus, TRN), and a cortical column consisting of four connected populations (pyramidal neurons, excitatory interneurons, inhibitory interneurons with slow and fast kinetics). Moreover, thalamic neurons exhibit two firing modes: bursting and tonic. Finally, cortical synapses among pyramidal neurons incorporate a disfacilitation mechanism following prolonged activity. Simulations show that the model is able to mimic the different patterns of rhythmic activity in cortical and thalamic neurons (beta and alpha waves, spindles, delta waves, K-complexes, slow sleep waves) and their progressive changes from wakefulness to deep sleep, by just acting on modulatory inputs. Moreover, simulations performed by providing short sensory inputs to the TCR show that brain rhythms during sleep preserve the cortex from external perturbations, still allowing a high cortical activity necessary to drive synaptic plasticity and memory consolidation. In perspective, the present model may be used within larger cortico-thalamic networks, to gain a deeper understanding of mechanisms beneath synaptic changes during sleep, to investigate the specific role of brain rhythms, and to explore cortical synchronization achieved via thalamic influences.