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

**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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IEEE TRANSACTIONS ON NEURAL NETWORKS, VOL. 20, NO. 10, OCTOBER 20091679

Identifying the Topology of a Coupled FitzHugh–Nagumo

Neurobiological Network via a Pinning Mechanism

Jin Zhou, Wenwu Yu, Xiumin Li, Michael Small, and Jun-an Lu

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, al-

though 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.

Index Terms—Complex network, neural network, pinning, topology

identification, weight couplings.

I. INTRODUCTION

Dynamical neural networks, consisting of a large number of neu-

rons, have been a fascinating and important subject of research [1],

[2]. These neurons are connected to each other by synapses, which are

the specialized junctions where a neuron communicates with a target

cell [3]. The interactions among neurons, namely, the couplings, have

a great influence on the dynamical characteristics of neurobiological

network [3].

The properties of a neural network with a certain coupling config-

uration have been extensively investigated [1]–[6]. Network topology

is critical for the understanding of geometry characteristics, synchro-

nization,andapplicationofanetwork[7]–[14].Therefore,asaninverse

problem incomplex networkandneurobiology, topologyidentification

of a neural network is very important.

Inthehumanbrain,thereisalargenumberofneuronswhichinteract

mutually to represent and process information. When measuring brain

activity by electroencephalography (EEG) [15], magnetoencephalog-

raphy (MEG) [16], functional magnetic resonance imaging (fMRI)

[17], or positron emitted tomography (PET) [18], the sensor informa-

tion reflects the dynamics of neurons mediated as a local field potential

(LFP). By receiving this information, it is possible to estimate the

topology of the network. From signal processing point of view, there

Manuscript received April 22, 2008; revised June 01, 2009; accepted July 16,

2009. First published August 21, 2009; current version published October 07,

2009. This work was supported by the University Grants Council of the Hong

Kong Government under the Competitive Earmarked Research Grant PolyU

5269/06E.TheworkofJ.Zhou andJ.-anLuwassupportedbythe NationalNat-

uralScienceFoundationofChinaunderGrants70771084and60574045andthe

National Basic Research Program of China under Grant 2007CB310805.

J. Zhou is with the School of Mathematics and Statistics, Wuhan University,

Wuhan 430072, China, and also with the Department of Electronic and Infor-

mation Engineering, Hong Kong Polytechnic University, Hong Kong (e-mail:

enjzhou@gmail.com).

W. Yu is with the Department of Electronic Engineering, City University of

Hong Kong, Hong Kong (e-mail: wenwuyu@gmail.com; wwyu@ee.cityu.edu.

hk).

X. Li and M. Small are with the Department of Electronic and Informa-

tion Engineering, Hong Kong Polytechnic University, Hong Kong (e-mail:

07901216r@eie.polyu.edu.hk; small@ieee.org).

J. Lu is with the School of Mathematics and Statistics, Wuhan University,

Wuhan 430072, China (e-mail: jalu@whu.edu.cn).

Color versions of one or more of the figures in this brief are available online

at http://ieeexplore.ieee.org.

Digital Object Identifier 10.1109/TNN.2009.2029102

are many algorithms such as cross correlation and partial correlation

to identify the interactions among neurons in a network [19]–[21].

In addition, by exploiting information theory algorithms such as

transfer entropy [22] and mutual information [23], reconstructing the

functional connectivity among neurons is possible. While in the area

of mathematics and engineering, works on the rigorous theoretical

derivation for these techniques have been rare.

Very recently, topology identification of complex networks has been

intensively studied. First, Yu et al. estimated the adjacency matrix of a

linearly coupled complex network in 2006 [24]; then, in 2007, Tang et

al. modified Yu’s method and applied it into a neural network in which

the dynamics of each neuron is a Hindmarsh–Rose model [25]; in the

same year, Zhou and Lu recognized the topology of a general weighted

complex network even with different coupling nodes [26]; later, in

2008, Wu estimated the topology of a network with time-varying cou-

pling delay [27]. In the above methods, the states of all the nodes in the

network were monitored to achieve topology identification.

However, in many cases, only some of the couplings in several sub-

regions are unknown or uncertain, and the remainder is known [28].

Therefore, only some of the couplings need to be identified. The pin-

ning control mechanism, which was first applied to complex networks

by Wang et al. [30] and Li et al. [31], is a more economical and prac-

tical technique. Although the pinning mechanism requires more infor-

mation of the network to decide which nodes should be controlled, this

mechanism can reduce the number of controllers. Provided with some

known couplings, pinning mechanism is a possible way to estimate the

value of those which are uncertain.

Consider the original neural network as a drive network, and design

a response network which receives only the membrane potential evo-

lution of some of the neurons. Based on Schur complement [32], [33]

andLyapunovstabilitytheory[34],anovelcriterionispresentedtoesti-

mate the weight configuration matrix for a coupled FitzHugh–Nagumo

(FHN) neural network. In this paper, some simple adaptive feedback

controllers are used to identify the topology of this network by a pin-

ning mechanism.

The remainder of this brief is organized as follows. In Section II,

some preliminaries are briefly outlined. The mechanism of control-

ling the membrane potentials of a fraction of neurons in the response

network to reach topology estimation is discussed in Section III. In

Section IV, examples are simulated to illustrate the effectiveness of the

proposed approach. The main ideas and conclusions are summarized

in Section V.

II. PRELIMINARIES

A. FitzHugh–Nagumo Model

Since 1951, the quantitative study of electrically active cells has re-

ceived its principal impetus from the remarkable work by Hodgkin and

Huxley [35] on nerve conduction in the squid giant axon. The subject

of Hodgkin and Huxley’s work is the process by which the impulse

travelsalongtheaxoninthegiantaxonofthesquidusinga4-Dexpres-

sion [35]. Since the equations were too complicated to analyze com-

pletely, simpler systems were indeed necessary to aid in understanding

the properties of the Hodgkin–Huxley equations. Thus, by taking into

account the physiological background, FitzHugh (1961) and Nagumo

(1962), independently derived a 2-D system that provides a convenient

simplification of the 4-D Hodgkin–Huxley equations [35]–[38].

The FHN model is described by

?? ? ? ??

?? ? ??? ? ? ? ????

???? ? ? ???

Here, ? is the membrane potential, ? is the recovery variable, ??? is

the external stimulus current, and ????? are positive constants. Gener-

ally, ? ? ? ? ? makes ? as a fast variable and ? as a slow variable.

1045-9227/$26.00 © 2009 IEEE

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1680IEEE TRANSACTIONS ON NEURAL NETWORKS, VOL. 20, NO. 10, OCTOBER 2009

Fig. 1. Phase of ? versus phase of ? in FHN model with parameters ? ?

????, ? ? ???, ? ? ???, ?? ????????????, ? ??? ? ??, and ???? ? ?.

It is shown that the trajectories converge to a limit cycle. Thus, ? and ? are

bounded in a certain region ultimately and the ultimate boundary of ? can be

chosen as 2 here.

The parameter ? is one of the critical terms that can significantly in-

fluence the dynamics of the system. Choosing ? ? ????, ? ? ???,

? ? ???, ??? ? ????????????, ? ??? ? ??, and ???? ? ?, the state

variables of FHN model can be depicted by Fig. 1. Since the trajecto-

ries starting from arbitrary points will converge to a limit cycle, ? and

? are bounded in a certain region ultimately.

B. The Neurobiological Network Model

Ingeneral,consideraneuralnetworkwith? coupledneuronswhich

is described by FHN equations. The dynamics of the ?th neuron in the

network can then be formulated as

???? ????

???

?????????

?

????????

??????????????????

?

? ????????? ?

?

????????????

?

? ?????????

???? ????? ? ? ????

(1)

where ? ? ??? ? ?, ????????? ? ??? ???????

????????? ? ????? ? ? ????. As for couplings, ?????? is a non-

linearinner-couplingfunctionthatistheoutputofnode?,and???isthe

synaptic outer-coupling strength. The network topology is determined

by the weight configuration matrix ? ? ????????: if the ?th neuron

is a neighbor of the ?th neuron ?? ?? ??, then the weight ??? ?? ?;

otherwise, ??? ? ?. In addition, the matrix ? is diffusive satisfying

??? ? ?

For an FHN equation, the following facts are satisfied:

? ? ??? ???, and

???????????.

???? ????????????? ? ??????????

? ???? ??? ??? ????

????? ??? ??

?? ????? ??

?

? ???? ??????? ???

? ???? ????

? ??

?

??

?? ????? ??

?

? ???? ??????? ???

? ???

?

?

???? ????? ???? ??????? ???

and

???? ????????????? ? ??????????

? ????? ??????? ??? ? ?????? ????

? ????? ??????? ??? ? ?????? ????

for any two vectors ????????and ????????, where ? is a positive

constant representing the ultimate boundary of the first variable. For

example, when ? ? ????, ? ? ???, ? ? ???, ??? ? ????????????,

? ??? ? ??, and ???? ? ?, ? can be chosen as 2 from Fig. 1.

C. The Lemmas

In order toderive the main results, the followinglemmas areneeded.

Lemma 1 (Schur complement [32], [33]): See Appendix I.

Lemma 2: Assume that ? is a diagonal matrix whose ?th ?? ?

????????diagonal elements are ? and the others are 0, where ? ? ? is a

properconstantwhichislargeenough.??istheminormatrixofasym-

metric matrix ? by removing all the ?th ?? ? ???????? row–column

pairs of ?. Then, ? ? ? ? ? is equivalent to ??? ?.

The proof of Lemma 2 is presented in Appendix II.

III. THE CONTROLLING MECHANISM

To identify the topology of a complex network, usually the orig-

inal network is served as a drive network. Design a response network

through receiving the evolution of nodes; it is possible to estimate the

weight configuration matrix. In previous literature, however, all the

nodesintheresponsenetworkshouldbecontrolledtoachievetopology

identification.

Inspired by pinning control and provided with some known weight

couplings, it is possible to identify the topology of the neurobiological

network without controlling all the neurons in the response network.

Without loss of generality, assume that in neural network (1) the

couplings ????? ? ? ? ???? ? ? ? ????? ? ??? are uncertain. To

realize weight configuration matrix estimation, the following response

network is designed:

???? ? ???????? ??? ?

?

???? ?????????? ? ?????

?? ??? ???????? ????

?? ???? ??????????????

? ? ? ? ?

? ? ? ? ???? ? ? ? ??

(2)

where????? ?? are tracing state variables,??? ???? ? ?? is the error

variable, ? ??? is the estimation of ??? for ? ? ? ? ??, ? ? ? ? ??,

? ??? ? ??? for others, ?? is positive constant, and ?? is the adaptive

feedback gain satisfying

???? ?????

??? ??

??? ? ? ? ??

otherwise

(3)

where ???? ? ? ? ??? is positive constant.

In order to propose the main results, the following hypothesis for

inner-coupling function is introduced.

Hypothesis 1 (H1): Assume that ??????? ? ? ? ??? are linearly

independent [39], and their differentials satisfy ?? ? ???

where ??and ??are positive constants.

Denote ??? ???????? as the symmetric part of the matrix ?.

Let??beamodifiedmatrixof??whichisobtainedbychangingthedi-

agonalelements???into??????????for? ? ? ? ?,???betheminor

matrix of?? by removing the first ?? row–column pairs, and ???????

????? ? ??,

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ZHOU et al.: IDENTIFYING THE TOPOLOGY OF A COUPLED FITZHUGH–NAGUMO NEUROBIOLOGICAL NETWORK VIA A PINNING MECHANISM1681

be the maximum eigenvalue of a symmetric matrix. Then, for identi-

fying topology of the coupled FHN network (1) in which the weight

couplings among a section of neurons are unknown, a criterion based

on pinning control is attained.

Theorem 1: Suppose that H1 holds. Then, the unknown elements

????? ? ? ? ???? ? ? ? ??? of the weight configuration matrix ?

in network (1) can be identified by the estimation ? ??? in the response

network(2)and(3),providedthat????????? ? ???????????????

??? ? ???????????.

Proof: Let? ??

?? ??? ??and ? ???

?. In addition, denote ?? as a variable between ?? and??? satisfying

??????? ? ?????? ? ??

didate as

?

?

? ? ??? ? ??? for ? ? ??? ?

?????????? ???. Then, consider a Lyapunov can-

? ??

?

?

???

???

? ?? ??

?

??

?

?

???

?

???

? ??

??

??

??

?

?

???

???? ???

??

where ? is a positive constant to be determined.

Taking the derivative of ? along the trajectories of (2) and (3) yields

?? ?

?

???

??? ???????? ??? ? ?????????

?? ?? ???????? ??? ? ?????????

?

?

???

?

???

??? ? ?????????? ? ????????? ?

?

???

?????

?

?

?

???

?

???

? ????????????? ?

?

???

???? ?????

?

?

?

???

? ???

?

???

? ? ?? ? ????????? ??? ? ??? ??

?

?

?

???

?

???

?????? ??????? ? ?????? ?

?

???

????

?

?

?

???

? ???

?

???

? ? ?? ? ????????? ??? ? ??? ??

?

?

?

???

????????

?????????

?

???

?

????????

????????

?????????

?

???

????

?

?

?

???

? ???

?

???

? ? ?? ? ????????? ??? ? ??? ??

?

?

?

???

????????

? ?

?

???

?

????????

??????????????? ?

?

???

????

?

???????

???

?

???? ? ? ?

? ???

?

???

? ? ?

?

???

? ? ?

?

??

?

?

???

??????

??

?

?

?

???????

????

where?? ? ??????????????????,?

? ? ??? ?????????? ?????

? ?

??

? ? ?

?

???

? ? ?

?

???

??????

?? ? ???? ? ? ? ?? ? ??????????, ??? is the ? ? ? identity

matrix, and ? is the diagonal matrix whose ?th ?? ? ? ? ??? elements

are?andothersare0.Duetothat????????? ? ???????????????

??????????????,one can choose an appropriate constant ? ? ? such

that ???????? ? ???? ? ???????? according to Lemma 2. It grows

??? ???? ? ????? ? ??? ? ??? ? ??? ? ????????? ? ?. As a result,

since ?????? ? ?, ? is negative definite according to Lemma 1.

The largest invariant set of ??? ? ?? is ? ? ? ? ???? ? ? and? ?? ?

??? ? ? ? ??. According to LaSalle’s invariance principle [34], all

the trajectories of systems (1)–(3) will converge to ? ? ? asymptotically

for any initial values. In this set, it is obvious that???? ? ?, and fur-

ther,

constants ???? ? ? ? ???, such that

have ? ??? ? ? for ? ? ? ? ???? ? ? ? ?? in the set ? ? ?. Therefore, we

get ???

? ?? ? ?for? ? ? ? ?.Asaconclusion,theunknowncoupling

strength in neural network (1) can be identified using the response net-

work (2) and (3). Thus, the proof is completed.

From this theorem, it is shown that using the pinning adaptive feed-

back control approach, the exact topology of model (1) can be esti-

mated. At the same time, it is obvious that although just the mem-

brane potentials of some of the neurons are received, all the evolution

of the neurons including membrane potentials and recovery variables

are traced. As there are many circumstances in which the connections

among some of nodes in a complex network are unknown, this mech-

anism is of great practical significance.

Remark 1: As a special case, if the couplings of the entire net-

work (1) are unknown, the proposed mechanism (2) and (3) guarantees

topology identification provided only with H1 holding.

Remark 2: For neurobiological networks whose weight configu-

ration matrix is switching, our method works well too as shown in

Section IV.

Remark 3: The proposed mechanism can also be applied to the

neural network in which dynamics of each neuron is something other

than FHN equations, and even to neural networks with different types

of neurons (dynamics of each neuron need not to be identical). Similar

mathematical or engineering conclusions can be derived.

?

???? ?????????? ? ?. Since H1 holds, there do not exist nonzero

?

???????????? ? ?. Thus, we

????? ??? ? ? for ? ? ? ? ??, ? ? ? ? ??, and ???

???

????

????

??? ? ?,

IV. NUMERICAL SIMULATIONS

Here, 100 neurons are considered to form a Barabási–Albert scale-

free (see Appendix III) neurobiological network ??? ? ? ? ?? in

whichthedynamicsofeachneuronisanFHNequationwithparameters

? ? ????, ? ? ???, ? ? ???, and ??? ? ????????????. Assume

that the adjacent matrix of this network is ?, the common weight is

? ? ??. Suppose that the weight coupling matrix is ? ? ??, where

??? ? ????, ??? ? ??, ??? ? ??, ??? ? ?, ??? ? ??, ??? ? ????,

??? ? ??, and ??? ? ? are unknown. In addition, ????? ? ? ?

??????? ? ? ? ???? are assumed to be inner-coupling function. It is

obvious that ?? ? ?? ? ?. Then, we have?? ? ??.

In view of ????? ? ? ? ??? ? ? ? ?? to be identified, con-

sider the minor matrix???which is obtained by removing the first

four row–column pairs of??. The maximum eigenvalue of???satis-

fies ????????? ? ???????? ? ??????? ? ??? ? ?????? ? ??? ?

????????? ? ???? ? ??????.

Choose the initial values as ?? ? ?, ?? ? ???, ????? ? ?, ????? ?

?????????,?????? ? ????????,????? ? ??????,? ????? ? ???????

for ? ? ? ? ???, and ? ?????? ? ? with ? ? ? ? ??? ? ? ? ? in the

numerical simulation. Fig. 2(a) shows the evolution of inner-coupling

functions ??????? and ???????. From Fig. 2(a), it is easy to see that

??????? and ??????? are linearly independent, for example, choosing

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1682IEEE TRANSACTIONS ON NEURAL NETWORKS, VOL. 20, NO. 10, OCTOBER 2009

Fig. 2. (a) Evolution of inner-coupling functions ? ??? ? ?? ? ????. It is obvious that ? ??? ? and ? ??? ? are linearly independent. (b) Tracing errors?? and

? ? ?? ? ? ? ???? versus time ?. It is seen that all the state variables can be traced.

Fig. 3. (a) Estimations of the unknown coupling set ??

time ?. It is shown that the estimations approach to the corresponding weight values perfectly.

??????

? versus time ?. (b) Estimations of the unknown coupling set ??

?? ????

? versus

two different times ??? ? and ??? ?, where ? is a large constant, it

is seen that

???????????

???????????

???????????

???????????

?? ??

The synchronous errors??? and? ???? ? ? ? ???? are plotted in

Fig. 2(b), which shows that all the state variables have been traced.

The topology estimations are illustrated in Fig. 3. It is found that the

weights have been estimated precisely.

If attime

??

? ????

????????????????????????????????? switches from ????????????

???????????????

to

???????????????????????????,

proposed topology identification approach also performs well,

which is exhibited in Figs. 4 and 5. Besides the successful topology

identification, all the state variables have been traced.

theunknowncouplingset

the

V. CONCLUSION

In this brief, a criterion has been presented for identifying the uncer-

tain topology of a neurobiological network by using an adaptive feed-

back controlling method. Unlike similar approaches which monitor all

the states of all the nodes to reconstruct network topology, we have

presented a different mechanism. By receiving the membrane poten-

tials of only a fraction of the neurons, an estimated model is designed

to identify the unknown weight couplings in the original neural net-

work. Simulated examples are shown to illustrate the effectiveness of

the proposed approach. In addition to the application in neurobiology,

this technology is expected to be implemented on many other fields in

whichthedynamicsofeachagentscanbemonitoredandreceived,such

as remote control and diagnostics, disease transmission, management,

and administration of Internet cafe, and so on.

APPENDIX I

Lemma 1 (Schur complement [32], [33]): The following linear ma-

trix inequality (LMI):

????

?????

????

????

? ?

where ?????? ???? and ?????? ????, is equivalent to one of the

following conditions:

a) ???? ? ? and ???? ? ??????????????? ? ?;

b) ???? ? ? and ???? ? ???????????????? ?.

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ZHOU et al.: IDENTIFYING THE TOPOLOGY OF A COUPLED FITZHUGH–NAGUMO NEUROBIOLOGICAL NETWORK VIA A PINNING MECHANISM1683

Fig. 4. Evolution of inner-coupling functions and variable tracing of the neural network whose topology switches at ? ? ????. (a) Evolution of inner-coupling

functions ? ??? ? ?? ? ????. (b) Tracing errors?? and? ? ?? ? ? ? ???? versus time ?. It is seen that all the state variables can be traced.

Fig.5. Topologyidentificationfortheneuralnetworkwhosetopologyswitchesat? ? ????.(a)Theestimationsoftheunknowncouplingset??

vs. time ?. (b) The estimations of the unknown coupling set ??

?? ??

values perfectly.

???? ??

?

??

? vs. time ?. It is shown that the estimations approach to the corresponding weight

APPENDIX II

The Proof of Lemma 2: On the one hand, if ? ? ? ? ?, one has

??? ? clearly.

On the other hand, we will prove that if ??? ?, then ? ? ? ? ?.

It is obvious that

? ? ????

? ? ? ?

??

??

??

?

where ??? represents the ? ? ? identity matrix, and ?? is the cor-

responding matrix with compatible dimension. Since ? ? ???? ?

????????

? ? ? when ? ? ? is a sufficiently large constant, we have

? ? ????

??

??

??

?

? ?

if??? ?accordingtoLemma1.Thatis,??? ?leadsto??? ? ?.

Thus, the proof is completed.

APPENDIX III

The Barabási–Albert model (widely known as the BA model) [40]

introduced in 1998 explains the power-law degree distribution of net-

works by considering two main ingredients: growth and preferential

attachment. The algorithm used in the BA model is as follows.

• Growth: Starting with ?? fully connected nodes, at every time

step, a new node is introduced and connected to ??? ??? ex-

isting nodes in the network.

• Preferential attachment: Assume that the probability ? that a new

node is connected to node ? depends on the degree ?? of node ?,

such that ? ? ????

Numerical simulations and analytic results indicate that this algorithm

evolves a scale-free network.

????.

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