Adaptive Neuron Network Research Articles

What adaptive neuronal networks teach us about power grids.

Power grid networks, as well as neuronal networks with synaptic plasticity, describe real-world systems of tremendous importance for our daily life. The investigation of these seemingly unrelated types of dynamical networks has attracted increasing attention over the past decade. In this paper, we provide insight into the fundamental relation between these two types of networks. For this, we consider well-established models based on phase oscillators and show their intimate relation. In particular, we prove that phase oscillator models with inertia can be viewed as a particular class of adaptive networks. This relation holds even for more general classes of power grid models that include voltage dynamics. As an immediate consequence of this relation, we discover a plethora of multicluster states for phase oscillators with inertia. Moreover, the phenomenon of cascading line failure in power grids is translated into an adaptive neuronal network.

Chimeras in an adaptive neuronal network with burst-timing-dependent plasticity

The synchronized behavior of neurons depends on the structure and function of the synaptic connections between them. One of the activity-dependent synaptic modifications is the burst-timing-dependent plasticity, which relies on the latencies of the presynaptic and postsynaptic bursts. In this paper, we, therefore, study the collective behavior of a neuronal network with burst-timing-dependent plasticity, in particular, focusing on the emergence of chimera states. We consider separately non-local and global couplings, which have substantial effects on the collective dynamics. We show that the considered burst-timing-dependent plasticity leads to different behavior from static networks. The histogram of the synaptic strengths, in particular, reveals a different evolution of the chimera states in comparison to the development of synchronous and asynchronous states.

Effect of Channel Block on Multiple Coherence Resonance Induced by Time Delay in Adaptive Neuronal Networks with Spike-Timing-Dependent Plasticity

In this paper, we study effect of channel block (CB) on multiple coherence resonance (MCR) in adaptive scale-free Hodgkin–Huxley neuronal networks with spike-timing-dependent plasticity (STDP). It is found that potassium CB suppresses MCR, but sodium CB can enhance MCR, and there is optimal sodium CB level by which MCR becomes most pronounced. In addition, STDP has a significant influence on the effect of CB on MCR. As adjusting rate [Formula: see text] of STDP increases, for potassium CB there is proper [Formula: see text] by which MCR is most pronounced; however, for sodium CB MCR is reduced. These findings could provide a new insight into effect of CB on information processing in neural systems.

Channel Noise-Enhanced Synchronization Transitions Induced by Time Delay in Adaptive Neuronal Networks with Spike-Timing-Dependent Plasticity

In this paper, we numerically study the effect of channel noise on synchronization transitions induced by time delay in adaptive scale-free Hodgkin–Huxley neuronal networks with spike-timing-dependent plasticity (STDP). It is found that synchronization transitions by time delay vary as channel noise intensity is changed and become most pronounced when channel noise intensity is optimal. This phenomenon depends on STDP and network average degree, and it can be either enhanced or suppressed as network average degree increases depending on channel noise intensity. These results show that there are optimal channel noise and network average degree that can enhance the synchronization transitions by time delay in the adaptive neuronal networks. These findings could be helpful for better understanding of the regulation effect of channel noise on synchronization of neuronal networks. They could find potential implications for information transmission in neural systems.

Channel noise-induced temporal coherence transitions and synchronization transitions in adaptive neuronal networks with time delay

Using spike-timing-dependent plasticity (STDP), we study the effect of channel noise on temporal coherence and synchronization of adaptive scale-free Hodgkin-Huxley neuronal networks with time delay. It is found that the spiking regularity and spatial synchronization of the neurons intermittently increase and decrease as channel noise intensity is varied, exhibiting transitions of temporal coherence and synchronization. Moreover, this phenomenon depends on time delay, STDP, and network average degree. As time delay increases, the phenomenon is weakened, however, there are optimal STDP and network average degree by which the phenomenon becomes strongest. These results show that channel noise can intermittently enhance the temporal coherence and synchronization of the delayed adaptive neuronal networks. These findings provide a new insight into channel noise for the information processing and transmission in neural systems.

Multiple coherence resonances and synchronization transitions by time delay in adaptive scale-free neuronal networks with spike-timing-dependent plasticity

In this paper, we numerically study the effect of spike-timing-dependent plasticity (STDP) on multiple coherence resonances (MCR) and synchronization transitions (ST) induced by time delay in adaptive scale-free Hodgkin–Huxley neuronal networks. It is found that STDP has a big influence on MCR and ST induced by time delay and on the effect of network average degree on the MCR and ST. MCR is enhanced or suppressed as the adjusting rate Ap of STDP decreases or increases, and there is optimal Ap by which ST becomes strongest. As network average degree 〈k〉 increases, ST is enhanced and there is optimal 〈k〉 at which MCR becomes strongest. Moreover, for a larger Ap value, ST is enhanced more rapidly with increasing 〈k〉 and the optimal 〈k〉 for MCR increases. These results show that STDP can either enhance or suppress MCR, and there is optimal STDP that can most strongly enhance ST induced by time delay in the adaptive neuronal networks. These findings could find potential implication for the information processing and transmission in neural systems.

Spike-timing-dependent plasticity enhanced synchronization transitions induced by autapses in adaptive Newman-Watts neuronal networks

In this paper, we numerically study the effect of spike-timing-dependent plasticity (STDP) on synchronization transitions induced by autaptic activity in adaptive Newman–Watts Hodgkin–Huxley neuron networks. It is found that synchronization transitions induced by autaptic delay vary with the adjusting rate Ap of STDP and become strongest at a certain Ap value, and the Ap value increases when network randomness or network size increases. It is also found that the synchronization transitions induced by autaptic delay become strongest at a certain network randomness and network size, and the values increase and related synchronization transitions are enhanced when Ap increases. These results show that there is optimal STDP that can enhance the synchronization transitions induced by autaptic delay in the adaptive neuronal networks. These findings provide a new insight into the roles of STDP and autapses for the information transmission in neural systems.

Effect of spike-timing-dependent plasticity on coherence resonance and synchronization transitions by time delay in adaptive neuronal networks

In this paper, we numerically study how time delay induces multiple coherence resonance (MCR) and synchronization transitions (ST) in adaptive Hodgkin-Huxley neuronal networks with spike-timing dependent plasticity (STDP). It is found that MCR induced by time delay STDP can be either enhanced or suppressed as the adjusting rate A p of STDP changes, and ST by time delay varies with the increase of A p , and there is optimal A p by which the ST becomes strongest. It is also found that there are optimal network randomness and network size by which ST by time delay becomes strongest, and when A p increases, the optimal network randomness and optimal network size increase and related ST is enhanced. These results show that STDP can either enhance or suppress MCR and optimal STDP can enhance ST induced by time delay in the adaptive neuronal networks. These findings provide a new insight into STDP’s role for the information processing and transmission in neural systems.

Psychic Neuronal Hypersynchronies: A New Psychiatric Paradigm?

This paper deals with certain psychic automatisms that appear suddenly in the consciousness of the subject in a passive or automatic way and that usually are diagnosed as psychiatric symptoms or manifestations of an epileptic partial seizure. These mental automatisms are described in many writings by mystics, philosophers, literates, composers, and many different great artists and creators from human history, but they did not confer any pathological value on these experiences. The analysis of the epileptogenic activity gives us arguments to propose that these automatic experiences are due to neuronal circuits physiologically adapted to fire in a hypersynchronous way. With these data we enunciate an innovative hypothesis: these psychic experiences are manifestations of an adaptive neuronal network for which we propose the term psychic hypersynchrony. Finally, we discuss the consequences of this hypothesis, especially in the epileptic, psychiatric, and neuropsychopharmacologic fields.

Adaptive Neural Network Motion Control of Manipulators with Experimental Evaluations

A nonlinear proportional-derivative controller plus adaptive neuronal network compensation is proposed. With the aim of estimating the desired torque, a two-layer neural network is used. Then, adaptation laws for the neural network weights are derived. Asymptotic convergence of the position and velocity tracking errors is proven, while the neural network weights are shown to be uniformly bounded. The proposed scheme has been experimentally validated in real time. These experimental evaluations were carried in two different mechanical systems: a horizontal two degrees-of-freedom robot and a vertical one degree-of-freedom arm which is affected by the gravitational force. In each one of the two experimental set-ups, the proposed scheme was implemented without and with adaptive neural network compensation. Experimental results confirmed the tracking accuracy of the proposed adaptive neural network-based controller.

Delay-induced firing behavior and transitions in adaptive neuronal networks with two types of synapses

In this paper, we study delay-induced firing behavior and transitions in adaptive Newman-Watts networks of thermosensitive neurons with electrical or chemical synapses. It is found that electrical and chemical synapse time delay-induced firing behavior and transitions differ significantly. In the case of electrical synapses, the bursts for a fixed delay involve equal number of spikes in each burst, and for certain time delays the firing can be inhibited. However, in the case of chemical synapses the bursts for a fixed delay involve different numbers of spikes in each burst, and no firing inhibition is observed. It is also shown that larger growth rates of adaptive coupling strength or larger network randomness can enhance the synchronization of bursting in the case of electrical synapses but reduce it in the case of chemical synapses. These results show that electrical and chemical synapses have different effects on delay-induced firing behavior and dynamical evolution. Compared to electrical synapses, chemical synapses might be more beneficial to the generation of firing and abundant firing transitions in adaptive and delayed neuronal networks. These findings can help to better understand different firing behaviors in neuronal networks with electrical and chemical synapses.

Delay-induced diversity of firing behavior and ordered chaotic firing in adaptive neuronal networks

In this paper, we study the effect of time delay on the firing behavior and temporal coherence and synchronization in Newman–Watts thermosensitive neuron networks with adaptive coupling. At beginning, the firing exhibit disordered spiking in absence of time delay. As time delay is increased, the neurons exhibit diversity of firing behaviors including bursting with multiple spikes in a burst, spiking, bursting with four, three and two spikes, firing death, and bursting with increasing amplitude. The spiking is the most ordered, exhibiting coherence resonance (CR)-like behavior, and the firing synchronization becomes enhanced with the increase of time delay. As growth rate of coupling strength or network randomness increases, CR-like behavior shifts to smaller time delay and the synchronization of firing increases. These results show that time delay can induce diversity of firing behaviors in adaptive neuronal networks, and can order the chaotic firing by enhancing and optimizing the temporal coherence and enhancing the synchronization of firing. However, the phenomenon of firing death shows that time delay may inhibit the firing of adaptive neuronal networks. These findings provide new insight into the role of time delay in the firing activity of adaptive neuronal networks, and can help to better understand the complex firing phenomena in neural networks.

SU-GG-T-01: Performance Study of Novel Acceleration-Enhanced Filters in the Prediction of Normal and Irregular Respiration Motion

Purpose: To study the performance of the novel acceleration‐enhanced(AE) filters in the prediction of normal and irregular respiration motion and compare them with those of traditional adaptive normalized least mean squares (nLMS) and adaptive neuron network (ANN) filters. This facilitates the real‐time tumor tracking and dynamic delivery of radiation dose to the tumor.Method and Materials:Respiration signals are collected from volunteers under IRB approved protocol by respiration sensor, which is able to sense the expansion and contraction of the rib cage. One‐dimensional respiration signal is used in this work for the evaluation of the performances from different prediction filters. The respiration signals are captured at a frequency of 256 Hz and sampled at 32 Hz. The prediction performances of the filters in the prediction of normal and irregular respiration are compared, including adaptive nLMS and adaptive ANN filters, as well as their AE versions, which use the accelerations derived from the predicted velocities for further corrections to the predicted positions. Results: The adaptive ANN filter performs better than the adaptive nLMS filter in the prediction of normal respiration whereas the latter excels in predicting irregular respiration signals. The implementation of AE method prompts the performances of the traditional filters. For example, with a prediction time fixed at 250 ms, the ANN and nLMS filters gave percentage root mean square (RMS) prediction errors of 3.51% and 4.25% for normal respiration, and 8.85% and 6.17% for irregular respiration, respectively. AE‐ANN filter gives a percentage RMS error of 3.45% in normal respiration prediction and AE‐nLMS has a 5.87% RMS error in irregular respiration prediction. Conclusion: The Acceleration‐enhanced method is able to improve the performance of the traditional filters. The AE‐ANN and AE‐nLMS filter give the minimum error in the prediction of the normal and irregular respiration, respectively. Supported by Elekta.

Associative sequential recall in a synaptic matrix

An adaptive neuronal network (synaptic matrix) was simulated. After learning a number of randomly generated scenes, the network was tested for associative sequential recall in a stimulus-bound mode and in an image-bound mode. It is demonstrated that neuronal mechanisms of this kind can exhibit orderliness or looseness of associative response suggestive of human recall behavior.