The Experts below are selected from a list of 219 Experts worldwide ranked by ideXlab platform

Daniel Gardner - One of the best experts on this subject based on the ideXlab platform.

  • Noise modulation of synaptic weights in a Biological Neural Network
    Neural Networks, 2003
    Co-Authors: Daniel Gardner
    Abstract:

    Abstract In order to link Neural Network theory and neurobiology, a hypothesis for modulation of synaptic weights is proposed which is consistent with synaptic biophysics. The hypothesis states that membrane potential variance is an adaptive variable for neurons, and predicts that injecting noise into neurons will alter synaptic weights calculated from recorded syaptic currents. Tests in neurons of Aplysia provide no evidence for associative or nonassociative modulation over a 1–2 h term, but suggest the use of time integral of conductance as a Biological measure of synaptic weight.

  • Static determinants of synaptic strength
    1993
    Co-Authors: Daniel Gardner
    Abstract:

    This chapter contains sections titled: The Neurophysiology Of Neural Network Models, The Neurobiology Of A Biological Neural Network, Buccal Ganglia Synaptic Strengths Differ Both Statically And Dynamically, Cell And Network Properties Of The Buccal Ganglia Transcend Simplifying Assumptions Of Network Models, Backpropagation Imposes A Requirement For Retrograde Information Transfer, Buccal Ganglia Synaptic Strengths Are Specified By Postsynaptic Neurons, Postsynaptic Neurons Specify Presynaptic Quantal Release, Aplysia NeuroBiological. Mechanisms Consistent With Retrosynaptic Information Transfer, Static And Dynamic Retrosynaptic Plasticity In Neurobiology, Retrosynaptic Mechanisms For Network Learning Rules, Acknowledgment

Rose P Ignatius - One of the best experts on this subject based on the ideXlab platform.

  • Lévy noise-induced near-death spikes and phase transitions of a Biological Neural Network
    Nonlinear Dynamics, 2020
    Co-Authors: K K Mineeja, Rose P Ignatius
    Abstract:

    Near-death spikes or near-death surges are a sudden increase in neuron activity in the human brain before neurons end their firings. Just before a person is clinically dead, such spikes are observed in certain cases, so the name is near-death spikes. The reason for this behavior is the lack of oxygen in brain (Chawla et al. in J Palliat Med 12(12):1095–1100, 2009). In this study, it is demonstrated that a particular type of noise called Lévy noise can generate such activity in the Neural Network of the worm Caenorhabditis elegans . The study identified different parameter regions of noise at which the Network makes transitions from one synchronous state to another and the mechanism behind them. Such transitions are already reported in cortical regions of brain (Canavero et al. in Surg Neurol Int 7(Suppl 24):S623–S625, 2016). During the transition period between asynchronous and synchronous firing states, Network is more susceptible to changes in firing pattern of individual neurons (Zandt et al. in PLoS ONE 6(7):e22127, 2011; Uzuntarla et al. Neural Netw 110:131–140, 2019). In this work, it is demonstrated that the recognized parameter regions can be used to control the Network dynamics. The study also identified Lévy noise values at which the Network displays generation of waves of different frequencies. This result suggests a new method for neurostimulation in the case of traumatic brain injury. The study reveals that the characteristic exponent ( $$\alpha $$ α ) of the noise has better influence on the Network dynamics than the scale parameter of noise ( D ) and the synaptic coupling constant (Gsyn) of the Network. The neuronal Network even displayed Gamma oscillations for large values of $$\alpha $$ α . If the parameters of the neurons are made chaotic, the Network firing rate is diminished and it displayed Delta and Theta oscillations.

  • spatiotemporal activities of a pulse coupled Biological Neural Network
    Nonlinear Dynamics, 2018
    Co-Authors: K K Mineeja, Rose P Ignatius
    Abstract:

    The present work is on the spatiotemporal activities and effects of chaotic neurons in a pulse-coupled Biological Neural Network. The Biological Neural Network used is that of Caenorhabditis elegans. Because of its similarity to human Neural Network, it can be used to understand the simple dynamics of human brain. Within the Network the neurons are found to exhibit chaotic nature, even though their parameters are that of normal neurons. It is observed that when the strength of synaptic conductance is increased, initially the bursting synchronization, entropy of the Network and the average firing rate decrease slightly and then increase. Since chaotic dynamics of neuron plays an important role in human brain functions, the neurons of the Network are intentionally made chaotic and the dynamics is studied. As the neurons of the Network are made chaotic, ‘near-death’-like surges of neuron activity before ending firing is observed throughout the Network. Also, the brain dynamics changes from alert to rest state. When most of the neurons of the Network are made chaotic, their activities become independent of the coupling strength.

Xianlin Zeng - One of the best experts on this subject based on the ideXlab platform.

  • synchronization of Biological Neural Network systems with stochastic perturbations and time delays
    Journal of The Franklin Institute-engineering and Applied Mathematics, 2014
    Co-Authors: Xianlin Zeng, Wassim M. Haddad, Tomohisa Hayakawa, Qing Hui, James Bailey
    Abstract:

    Abstract With advances in biochemistry, molecular biology, and neurochemistry there has been impressive progress in the understanding of the molecular properties of anesthetic agents. However, despite these advances, we still do not understand how anesthetic agents affect the properties of neurons that translate into the induction of general anesthesia at the macroscopic level. There is extensive experimental verification that collections of neurons may function as oscillators and the synchronization of oscillators may play a key role in the transmission of information within the central nervous system. This may be particularly relevant to understand the mechanism of action for general anesthesia. In this paper, we develop a stochastic synaptic drive firing rate model for an excitatory and inhibitory cortical neuronal Network in the face of system time delays and stochastic input disturbances. In addition, we provide sufficient conditions for global asymptotic and exponential mean-square synchronization for this model.

  • Synchronization of Biological Neural Network systems with stochastic perturbations and time delays
    2012 IEEE 51st IEEE Conference on Decision and Control (CDC), 2012
    Co-Authors: Xianlin Zeng, Wassim M. Haddad, Tomohisa Hayakawa, James M. Bailey
    Abstract:

    With advances in biochemistry, molecular biology, and neurochemistry there has been impressive progress in the understanding of the molecular properties of anesthetic agents. However, despite these advances, we still do not understand how anesthetic agents affect the properties of neurons that translate into the induction of general anesthesia at the macroscopic level. There is extensive experimental verification that collections of neurons may function as oscillators and the synchronization of oscillators may play a key role in the transmission of information within the central nervous system. This may be particularly relevant to understanding the mechanism of action for general anesthesia. In this paper, we develop a stochastic synaptic drive firing rate model for an excitatory and inhibitory cortical neuronal Network in the face of system time delays. In addition, we provide sufficient conditions for global asymptotic mean-square synchronization for this model.

Chun Sing Lai - One of the best experts on this subject based on the ideXlab platform.

  • a general memristor based pulse coupled Neural Network with variable linking coefficient for multi focus image fusion
    Neurocomputing, 2018
    Co-Authors: Zhekang Dong, Chun Sing Lai, Shukai Duan
    Abstract:

    Abstract Pulse coupled Neural Network (PCNN) is a kind of visual cortex-inspired Biological Neural Network, which has been proved a powerful candidate in the field of digital image processing due to its unique characteristics of global coupling and pulse synchronization. Notably, the inherent parameters estimation issue emerging in the entire system greatly affects the overall Network performance. In this paper, a novel memristor crossbar array with its corresponding peripheral circuits is proposed, which is able to construct a general memristor-based PCNN (MPCNN) with variable linking coefficient. In order to verify the effectiveness and generality of the presented Network, the single-channel MPCNN is further applied into the multi-focus image fusion problem with an improved multi-channel configuration. Correspondingly, a new type of MPCNN-based image fusion algorithm is put forward along with the design of an appropriate mapping function based on the image orientation information measure. Finally, a series of contrast experiments with comprehensive analysis demonstrate that the proposed fusion method has superior performances in terms of image quality and fusion effect compared to several existing algorithms.

Zhekang Dong - One of the best experts on this subject based on the ideXlab platform.

  • a general memristor based pulse coupled Neural Network with variable linking coefficient for multi focus image fusion
    Neurocomputing, 2018
    Co-Authors: Zhekang Dong, Chun Sing Lai, Shukai Duan
    Abstract:

    Abstract Pulse coupled Neural Network (PCNN) is a kind of visual cortex-inspired Biological Neural Network, which has been proved a powerful candidate in the field of digital image processing due to its unique characteristics of global coupling and pulse synchronization. Notably, the inherent parameters estimation issue emerging in the entire system greatly affects the overall Network performance. In this paper, a novel memristor crossbar array with its corresponding peripheral circuits is proposed, which is able to construct a general memristor-based PCNN (MPCNN) with variable linking coefficient. In order to verify the effectiveness and generality of the presented Network, the single-channel MPCNN is further applied into the multi-focus image fusion problem with an improved multi-channel configuration. Correspondingly, a new type of MPCNN-based image fusion algorithm is put forward along with the design of an appropriate mapping function based on the image orientation information measure. Finally, a series of contrast experiments with comprehensive analysis demonstrate that the proposed fusion method has superior performances in terms of image quality and fusion effect compared to several existing algorithms.