Identifying the Topology of a Coupled FitzHugh–Nagumo Neurobiological Network via a Pinning Mechanism

Sch. of Math. & Stat., Wuhan Univ., Wuhan, China
IEEE Transactions on Neural Networks (Impact Factor: 2.95). 11/2009; 20(10):1679 - 1684. DOI: 10.1109/TNN.2009.2029102
Source: PubMed

ABSTRACT Topology identification of a network has received great interest for the reason that the study on many key properties of a network assumes a special known topology. Different from recent similar works in which the evolution of all the nodes in a complex network need to be received, this brief presents a novel criterion to identify the topology of a coupled FitzHugh-Nagumo (FHN) neurobiological network by receiving the membrane potentials of only a fraction of the neurons. Meanwhile, although incomplete information is received, the evolution of all the neurons including membrane potentials and recovery variables are traced. Based on Schur complement and Lyapunov stability theory, the exact weight configuration matrix can be estimated by a simple adaptive feedback control. The effectiveness of the proposed approach is successfully verified by neural networks with fixed and switching topologies.

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Available from: Wenwu Yu, Sep 27, 2015
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    • "Recently, reconfigurable digital platforms have been used to performed nervous system models [1]–[7]. Field-programmable gate arrays (FPGAs) are generic, programmable digital devices that can perform complex logical operations. "
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    ABSTRACT: The implementation of biological neural networks is a key objective of the neuromorphic research field. Astrocytes are the largest cell population in the brain. With the discovery of calcium wave propagation through astrocyte networks, now it is more evident that neuronal networks alone may not explain function-ality of the strongest natural computer, the brain. Models of cor-tical function must now account for astrocyte activities as well as their relationships with neurons in encoding and manipulation of sensory information. From an engineering viewpoint, astrocytes provide feedback to both presynaptic and postsynaptic neurons to regulate their signaling behaviors. This paper presents a modified neural glial interaction model that allows a convenient digital implementation. This model can reproduce relevant biological as-trocyte behaviors, which provide appropriate feedback control in regulating neuronal activities in the central nervous system (CNS). Accordingly, we investigate the feasibility of a digital implementation for a single astrocyte constructed by connecting a two coupled FitzHugh Nagumo (FHN) neuron model to an implementation of the proposed astrocyte model using neuron-astrocyte interactions. Hardware synthesis, physical implementation on FPGA, and theoretical analysis confirm that the proposed neuron astro-cyte model, with significantly low hardware cost, can mimic biological behavior such as the regulation of postsynaptic neuron activity and the synaptic transmission mechanisms. Index Terms—Astrocyte, field programmable gate array (FPGA), neural glial interaction, neuron.
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    • "However, there is often various unknown or uncertain information in complex networks of the real world. This information including the topology connection of networks, and dynamical parameters of nodes, is always partially known and also changes continuously in many real complex networks such as gene networks, protein- DNA structure network, power grid networks, and biological neural networks [1] [2] [3] [4]. Knowledge about the identification of the topology of complex networks is the prerequisite to analyze, control, and predict their dynamical behaviors. "
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    Mathematical Problems in Engineering 10/2013; 2013. DOI:10.1155/2013/401983 · 0.76 Impact Factor
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    • "Very recently, a permutation-based measure named inner composition alignment was introduced to identify relations between subsystems [25]. However, in the synchronization-based methods, the interacting systems and observed data have to be noise free, which usually does not conform to practical cases [10] [11] [12] [13] [14] [15]. The correlation-based methods are incapable of distinguishing between direct and indirect interactions, which in many situations do not provide very satisfactory results [18]. "
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