Spiking patterns of a minimal neuron to ELF sinusoidal electric field

Neuron as the main information carrier in neural systems is able to generate diverse spiking trains in response to different stimuli. Neuronal spiking patterns are related to the information processing in neural system. This paper investigates the dynamical behaviors of a two-dimensional minimal neuron model exposed to externally-applied extremely low frequency (ELF) sinusoidal electric field (EF). By numerical stimulation, it is found that neuron can exhibit different spiking patterns such as p:q mode-locking (i.e. a periodic oscillation defined as p action potentials generated by q cycle stimulations) and chaotic behaviors, depending on the values of stimulus frequencies. Transitions between different spiking patterns during exposure to the external EF are explored by interspike intervals (ISIs) and average firing rate. It is found that frequencies of the external EF can act as a bifurcation parameter, whose small change can cause the transition in neuronal behaviors. It is shown that a rich bifurcation structure including period-adding without chaos and mode-locking alternated with chaos suggests frequency discrimination of the neuronal firing patterns. Our results can provide a useful insight into the organization of similar bifurcation structures in excitable systems such as neurons under periodic forcing. Through detail analysis of the spiking patterns, we have explained how neuron’s information (spiking patterns) encodes the stimulus information (frequency), and vice versa.     

A Chaos Associative Model with a Sinusoidal Activation Function

In this paper, we shall propose a novel chaos neural network model applied to the chaotic autoassociation memory. The present artificial neuron model is properly characterized in terms of a time-dependent periodic activation function to involve chaotic dynamics as well as the energy steepest descent strategy. It is elucidated that the present neural network has a remarkable ability of dynamic memory retrieval beyond the conventional models with the nonmonotonous activation function as well as a monotonous activation function as the sigmoidal one. This advantage is found to result from the property of the analogue periodic mapping accompanied with a chaotic behaviour of the neurons. It is also concluded that the present analogue neuron model, with the periodicity control, has an apparently large memory capacity in comparison with the previously proposed association models.     

Chaotic invariant sets of a delayed discrete neural network of two non-identical neurons

In this paper, we show that a delayed discrete Hopfield neural network of two nonidentical neurons with no self-connections can demonstrate chaotic behavior in a region away from the origin. To this end, we first transform the model, by a novel way, into an equivalent system which enjoys some nice properties. Then, we identify a chaotic invariant set for this system and show that the system within this set is topologically conjugate to the full shift map on two symbols. This confirms chaos in the sense of Devaney. Our main result is complementary to the results in Kaslik and Balint (2008) and Huang and Zou (2005), where it was shown that chaos may occur in neighborhoods of the origin for the same system. We also present some numeric simulations to demonstrate our theoretical results.     

Cantorian Fractal Spacetime and Information in Neural Network of the Human Brain

The neural networks of the human brain act as very efficient parallel processing computers co-ordinating memory related responses to a multitude of input signals from sensory organs. Information storage, update and appropriate retrieval are controlled at the molecular level by the neuronal cytoskeleton which serves as the internal communication network within neurons. Information flow in the highly ordered parallel networks of the filamentous protein polymers which make up the cytoskeleton may be compared to atmospheric flows which exhibit long-range spatiotemporal correlations, i.e. long-term memory. Such long-range spatiotemporal correlations are ubiquitous to real world dynamical systems and is recently identified as signature of self-organized criticality or chaos. The signatures of self-organized criticality i.e. long-range temporal correlations have recently been identified in the electrical activity of the brain. The physics of self-organized criticality or chaos is not yet identified. A recently developed non-deterministic cell dynamical system model for atmospheric flows predicts the observed long-range spatiotemporal correlations as intrinsic to quantum-like mechanics governing flow dynamics. The model visualises large scale circulations to form as the result of spatial integration of enclosed small scale perturbations with intrinsic two-way ordered energy flow between the scales. Such a concept maybe applied for the collection and integration of a multitude of signals at the cytoskeletal level and manifested in activation of neurons in the macroscale. The cytoskeleton networks inside neurons may be the elementary units of a unified dynamic memory circulation network with intrinsic global response to local stimuli. A cell dynamical system model for human memory circulation network analogous to atmospheric circulations network is presented in this paper. The model like the analysis of Koruga et al. make use of certain connections to the concept of Cantorian-Fractal spacetime.     

A novel method of control using chaotic dynamics in systems having many degrees-of-freedom

Chaotic dynamics in systems having many degrees of freedom are investigated from the viewpoint of harnessing chaos and is applied to complex control problems to indicate that chaotic dynamics has potential capabilities for complex control functions by simple rule(s). An important idea is that chaotic dynamics generated in these systems give us autonomous complex pattern dynamics itinerating through intermediate state points between embedded designed attractors in high-dimensional state space. A key point is that, with the use of simple adaptive switching between a weakly chaotic regime and a strongly chaotic regime, complex problems can be solved. As an actual example, a two-dimensional maze, where it should be noted that the set context is one of typical ill-posed problems, is solved with the use of chaos in a recurrent neural network model. Our computer experiments show that the success rate over several hundreds trials is much better, at least, than that of a random number generator. Our functional simulations indicate that harnessing of chaos is one of essential ideas to approach mechanisms of brain functions. (© 2008 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim)     

Phase-locking and chaos in a silent Hodgkin–Huxley neuron exposed to sinusoidal electric field

Neuronal firing patterns are related to the information processing in neural system. This paper investigates the response characteristics of a silent Hodgkin–Huxley neuron to the stimulation of externally-applied sinusoidal electric field. The neuron exhibits both p:q phase-locked (i.e. a periodic oscillation defined as p action potentials generated by q cycle stimulations) and chaotic behaviors, depending on the values of stimulus frequencies and amplitudes. In one-parameter space, a rich bifurcation structure including period-adding without chaos and phase-locking alternated with chaos suggests frequency discrimination of the neuronal firing patterns. Furthermore, by mapping out Arnold tongues, we partition the amplitude–frequency parameter space in terms of the qualitative behaviors of the neuron. Thus the neuron’s information (firing patterns) encodes the stimulus information (amplitude and frequency), and vice versa.     

Sinusoidal modulation control method in a chaotic neural network

Chaotic neural networks (CNNs) have chaotic dynamic associative memory properties: The memory states appear non-periodically, and cannot be converged to a stored pattern. Thus, it is necessary to control chaos in a CNN in order to recognize associative memory. In this paper, a novel control method, the sinusoidal modulation control method, has been proposed to control chaos in a CNN. In this method, a sinusoidal wave simplified from brain waves is used as a control signal to modulate a parameter of the CNN. The simulation results demonstrate the effectiveness of this control method. The controlled CNN can be applied to information processing. Moreover, the method provides a way to associate brain waves by controlling CNNs.     

A novel GCM chaotic neural network for information processing

A new chaotic neural network named “globally coupled map using sine map(SI-GCM)”, which is a modified Kaneko’s globally coupled map model, is proposed. With the introduction of sine map and chaotic neurons’ different way of coupling, it exhibits rich dynamic behaviors. By adopting a variable threshold parameter control method, it can be controlled to specified-period orbit. Furthermore, the controlled SI-GCM has excellent associative memory performance. It can not only output unique fixed pattern, but also output periodic patterns which contain the stored pattern closest to the initial pattern. Simulation results suggest that SI-GCM is fit for information processing.     

Nonlinear data analysis of experimental (EEG) data and comparison with theoretical (ANN) data

In this article, nonlinear dynamical tools such as largest Lyapunov exponents (LE), fractal dimension, correlation dimension, pointwise correlation dimension will be used to analyze electroencephalogram (EEG) data obtained from healthy young subjects with eyes open and eyes closed condition with the view to compare brain complexity under this two condition. Results of similar calculations from some earlier works will be produced for comparison with present results. Also, a brief report on difference of opinion among coworkers regarding such tools will be reported; particularly applicability of LE will be reviewed. The issue of nonlinearity will be addressed by using surrogate data technique. We have extracted another data set that represented chaotic state of the system considered in our earlier work of mathematical modeling of artificial neural network. We further attempt to compare results to find nature of chaos arising from such theoretical models. © 2002 Wiley Periodicals, Inc.     

Learning of Chua's circuit attractors by locally recurrent neural networks

Many practical applications of neural networks require the identification of strongly non-linear (e.g., chaotic) systems. In this paper, locally recurrent neural networks (LRNNs) are used to learn the attractors of Chua's circuit, a paradigm for studying chaos. LRNNs are characterized by a feed-forward structure whose synapses between adjacent layers have taps and feedback connections. In general, the learning procedures of LRNNs are computationally simpler than those of globally recurrent networks. Results show that LRNNs can be trained to identify the underlying link among Chua's circuit state variables, and exhibit chaotic attractors under autonomous working conditions.     

瞬时混沌神经网络的混沌动力学

首先利用"不可分意味着混沌"从理论上证明了一维瞬时混沌神经网络在一定的条件下按Li-Yorke意义是混沌的;特别地,进一步推出了混沌神经网络按Li-Yorke意义是混沌的充分条件,而这将从理论上证明Aihara等人通过数值计算所得结论;最后,为说明前面的结论给出了一个例子及其数值计算的结果。     

Chaotic synchronization of a fractional-order system based on washout filter control

In this paper, chaos in a fractional-order neural network system with varying time delays is presented, and chaotic synchronization system with varying time delays is constructed. The stability of constructed synchronization system is analyzed by Laplace transformation theory. In addition, the bifurcation graph of the chaotic system is illustrated. The study results show that the chaos in such fractional-order neural networks with varying time delay can be synchronized, and Washout filter control can be used to reduce the range of coupled parameter.     

A template for the exploration of chaotic locomotive patterns

Inverted pendulum and spring-mass models have been successfully used to explore the dynamics of the lower extremity for animal and human locomotion. These models have been classified as templates that describe the biomechanics of locomotion. A template is a simple model with all the joint complexities, muscles and neurons of the locomotor system removed. Such templates relate well to the observed locomotive patterns and provide reference points for the development of more elaborate dynamical systems. In this investigation, we explored if a passive dynamic double pendulum walking model, that walks down a slightly sloped surface (γ<0.0189 rad), can be used as a template for exploring chaotic locomotion. Simulations of the model indicated that as γ was increased, a cascade of bifurcations were present in the model's locomotive pattern that lead to a chaotic attractor. Positive Lyapunov exponents were present from 0.01839 rad <γ<0.0189 rad (Lyapunov exponent range=+0.002 to +0.158). Hurst exponents for the respective γ confirmed the presence of chaos in the model's locomotive pattern. These results provide evidence that a passive dynamic double pendulum walking model can be used as a template for exploring the biomechanical control parameters responsible for chaos in human locomotion.     

广义神经网络及其稳定性,容错性分析

本文建立了一个广义神经网络模型，并研究了它的渐近稳定性和指数稳定性，由这些结果我们可以估计各记忆模式的吸引域及其中每一点趋向记忆模式的指数收敛速度，以此来评价网络的容错能力．     

Towards a unified recurrent neural network theory: The uniformly pseudo-projection-anti-monotone net

In the past decades, various neural network models have been developed for modeling the behavior of human brain or performing problem-solving through simulating the behavior of human brain. The recurrent neural networks are the type of neural networks to model or simulate associative memory behavior of human being. A recurrent neural network (RNN) can be generally formalized as a dynamic system associated with two fundamental operators: one is the nonlinear activation operator deduced from the input-output properties of the involved neurons, and the other is the synaptic connections (a matrix) among the neurons. Through carefully examining properties of various activation functions used, we introduce a novel type of monotone operators, the uniformly pseudo-projectionanti-monotone (UPPAM) operators, to unify the various RNN models appeared in the literature. We develop a unified encoding and stability theory for the UPPAM network model when the time is discrete. The established model and theory not only unify but also jointly generalize the most known results of RNNs. The approach has lunched a visible step towards establishment of a unified mathematical theory of recurrent neural networks.     

Classes of network connectivity and dynamics

Many kinds of complex systems exhibit characteristic patterns of temporal correlations that emerge as the result of functional interactions within a structured network. One such complex system is the brain, composed of numerous neuronal units linked by synaptic connections. The activity of these neuronal units gives rise to dynamic states that are characterized by specific patterns of neuronal activation and co‐activation. These patterns, called functional connectivity, are possible neural correlates of perceptual and cognitive processes. Which functional connectivity patterns arise depends on the anatomical structure of the underlying network, which in turn is modified by a broad range of activity‐dependent processes. Given this intricate relationship between structure and function, the question of how patterns of anatomical connectivity constrain or determine dynamical patterns is of considerable theoretical importance. The present study develops computational tools to analyze networks in terms of their structure and dynamics. We identify different classes of network, including networks that are characterized by high complexity. These highly complex networks have distinct structural characteristics such as clustered connectivity and short wiring length similar to those of large‐scale networks of the cerebral cortex. © 2002 Wiley Periodicals, Inc.     

Economic intermittency in a two-country model of business cycles coupled by investment

Intermittent behavior of economic dynamics is investigated by a two-country model of Keynes-Goodwin type business cycles. Numerical simulations show that after an economic system evolves from weak chaos to strong chaos the system keeps its memory before the transition and its time series alternates episodically between periods of weakly and strongly chaotic fluctuations. In addition, we examine the intermittent phenomena from the view point of business cycle patterns near the crisis point.     

A novel chaotic search for quadratic assignment problems

We propose a novel method for solving the quadratic assignment problems. First, we realize the conventional tabu search on a neural network, and modify it to a chaotic version. Our novel method includes both effects of chaotic dynamics and tabu search. We compare the performance of the novel chaotic search with the conventional tabu search and an exponential tabu search whose memory effect for tabu (forbidding previous moves) decays exponentially. We show that the exponential tabu search has higher performance than the conventional tabu search, and further that the novel method with a chaotic neural network exhibits the best performance. We also propose a controlling method of the chaotic neural network for realizing easy and robust applications of our method. Then, better performance can be realized without manual parameter setting for various problems.     

Chaotic synchronization of nearest-neighbor diffusive coupling Hindmarsh–Rose neural networks in noisy environments

The chaotic synchronization of Hindmarsh–Rose neural networks linked by a nonlinear coupling function is discussed. The HR neural networks with nearest-neighbor diffusive coupling form are treated as numerical examples. By the construction of a special nonlinear-coupled term, the chaotic system is coupled symmetrically. For three and four neurons network, a certain region of coupling strength corresponding to full synchronization is given, and the effect of network structure and noise position are analyzed. For five and more neurons network, the full synchronization is very difficult to realize. All the results have been proved by the calculation of the maximum conditional Lyapunov exponent.     

Storage and retrieval of ultrametric patterns in a network of CA1 neurons of the hippocampus

We explore the possibility of storage and retrieval of ultrametrically organized patterns in hippocampus, the part of the brain devoted to the memory processes. The ultrametric structure has been chosen for having a good representation of the categories of memory. The storage and retrieval process is the one typical of the hippocampus and it is based on the dynamic of the CA1 neurons under the input from the neurons of the Enthorinal cortex and the Ca3 system. We explore if this real system of neurons exhibits the property of associative memory introduced since a long time in the artificial neural networks. We study how the performance is dependent on the deviation of the system of patterns from ultrametricity. The evolution of the system is simulated by means of a parallel computer and the statistics of storage and retrieval is investigated.