In this paper, we explore the mechanisms of central pattern generators (CPGs), circuits that can generate rhythmic patterns of motor activity without external input. We study the half-center oscillator, a simple form of CPG circuit consisting of neurons connected by reciprocally inhibitory synapses. We examine the role of asymmetric coupling factors in shaping rhythm activity and how different network topologies contribute to network efficiency. We have discovered that neurons with lower synaptic strength are more susceptible to noise that affects rhythm changes. Our research highlights the importance of asymmetric coupling factors, noise, and other synaptic parameters in shaping the broad regimes of CPG rhythm. Finally, we compare three topology types' regular regimes and provide insights on how to locate the rhythm activity.
Citation: Feibiao Zhan, Jian Song, Shenquan Liu. The influence of synaptic strength and noise on the robustness of central pattern generator[J]. Electronic Research Archive, 2024, 32(1): 686-706. doi: 10.3934/era.2024033
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In this paper, we explore the mechanisms of central pattern generators (CPGs), circuits that can generate rhythmic patterns of motor activity without external input. We study the half-center oscillator, a simple form of CPG circuit consisting of neurons connected by reciprocally inhibitory synapses. We examine the role of asymmetric coupling factors in shaping rhythm activity and how different network topologies contribute to network efficiency. We have discovered that neurons with lower synaptic strength are more susceptible to noise that affects rhythm changes. Our research highlights the importance of asymmetric coupling factors, noise, and other synaptic parameters in shaping the broad regimes of CPG rhythm. Finally, we compare three topology types' regular regimes and provide insights on how to locate the rhythm activity.
In the past years, many mathematicians were interested in finding the formula for the integer part of the reciprocal tails of the convergent series. That is, find the explicit value of ⌊(∑∞k=nak)−1⌋ when ∑∞k=1ak converges. The motivation of such research comes from the reciprocal sum of Fibonacci numbers. Let us recall that the Fibonacci sequence (Fn)n≥0 is defined by
Fn+2=Fn+1+Fn,F0=0,F1=1, |
where n≥0. In [1], Ohtsuka and Nakamura proved
⌊(∞∑k=n1Fk)−1⌋={Fn−2,if n≥2 is even;Fn−2−1,if n≥1 is odd, |
and
⌊(∞∑k=n1F2k)−1⌋={Fn−1Fn−1,if n≥2 is even;Fn−1Fn,if n≥1 is odd, |
where ⌊x⌋ denotes the greatest integer ≤x. In [2], Wang proved
⌊(∞∑k=n1F3k)−1⌋={FnF2n−1+Fn−2F2n+⌊111(14Fn−2−5Fn)⌋,if n≥2 is even;FnF2n−1+Fn−2F2n+⌊111(5Fn−14Fn−2)⌋,if n≥1 is odd, |
where F−1=F1=1. In [3], Hwang et al. provided the relevant formula for the reciprocal sum of the fourth power of the Fibonacci numbers, that is,
⌊(∞∑k=n1F4k)−1⌋=F4n−F4n−1+2(−1)n5F2n−1−{n+25}, |
where {x}=x−⌊x⌋. In addition, some mathematicians studied the reciprocal sums of Fibonacci, Lucas, and Pell sequences, such as [4,5,6,7], while some mathematicians studied the reciprocal sums of other types of sequences, such as [8,9,10].
Many mathematicians also considered the asymptotic behavior of these sequences. That is, find a suitable function gn such that
(∞∑k=nak)−1∼gn |
when ∑∞k=1ak converges. Here the notation An∼Bn means that
limn→∞(An−Bn)=0. |
In [11], Lee et al. proved that
(∞∑k=n1Fmk−l)−1∼Fmn−l−Fm(n−1)−l |
for any positive integer m and 0≤l≤m−1. In [12], Marques et al. proved that for any positive integer m there exists a positive constant Cm such that
(∞∑k=n1F2mk)−1∼F2mn−F2m(n−1)+(−1)mnCm. |
Moreover, they gave an explicit form for Cm as follows:
Cm={−2(L2m−2)25F2m√5,ifmis even,2(L2m+2)5L2m,ifmis odd, |
where Ln is the nth Lucas number. In [3], Hwang et al. studied the asymptotic behavior of the reciprocal sum of the fourth power of the Fibonacci numbers, and they proved that
(∞∑k=n1F4k)−1∼F4n−F4n−1+2(−1)n5F2n−1+2√575. |
In [13], Lee and Park studied the asymptotic behavior of the reciprocal sum of FkFk+m, and they proved that
(∞∑k=n1FkFk+2l)−1∼Fn+l−1Fn+l−(F2l+(−1)l)(−1)n3 |
and
(∞∑k=n1FkFk+2l−1)−1∼F2n+l−1−(Fl−1Fl+(−1)l)(−1)n3, |
where l is a positive integer. In addition, some mathematicians studied other types of asymptotic behavior, such as [14,15]. Inspired by the above results, we use the method of Yuan et al. [16] to study the asymptotic behavior of the sequences that are more general than the Fibonacci sequence. Let (un)n≥0 be the special Lucas u-sequence defined by
un+2=Aun+1−Bun,u0=0,u1=1, | (1.1) |
where n≥0, B=±1, and A is an integer such that A2−4B>0. The relevant properties of the Lucas u-sequence can be found in Sun's book [17]. We know that the Binet formula is related to the sequence (un)n≥0 has the form
un=αn−βnα−β,n≥0, | (1.2) |
where
α,β=A±√A2−4B2. |
Let
ak=1usmk,1umk+umk+l,1∑li=0umk+i,1umkumk+2l,1umkumk+2l−1,1umk+C, |
where m and l are positive integers, s=1,2,3,4, and C is any constant. The aim of this paper is to find a form gn such that
(∞∑k=nak)−1∼gn. |
The rest of this paper is organized as follows: in Section 2, we give our main results. In Section 3, we give the proof of our main results.
Let un be defined by the second-order linear recurrence sequence (1.1). In this paper, we shall prove the following eight theorems.
Theorem 2.1. For any positive integer m, we have
(∞∑k=n1umk)−1∼umn−um(n−1). |
Theorem 2.2. For any positive integer m, we have
(∞∑k=n1u2mk)−1∼u2mn−u2m(n−1)+BmnCm, |
where Cm=2(1−Bm)(α−β)2−2(α2m−1)2(α−β)2(α4m−Bm).
Theorem 2.3. For any positive integer m, we have
(∞∑k=n1u3mk)−1∼u3mn−u3m(n−1)+3BmnQm(um(n+2)−um(n−3)), |
where Qm=u2m(1−(Bα)5m)(1−(Bβ)5m).
Theorem 2.4. For any positive integer m, we have
(∞∑k=n1u4mk)−1∼u4mn−u4m(n−1)+4BmnUm(u2m(n+1)−Bmu2m(n−2))+Vm, |
where Um=u2m(1−Bmα6m)(1−Bmβ6m) and Vm=(α4m−1)2(α−β)4(16(α4m−1)(α6m−Bm)2−10α8m−1).
Theorem 2.5. For all positive integers m and l, we have
(∞∑k=n1umk+umk+l)−1∼umn+l−um(n−1)+l+umn−um(n−1). |
Theorem 2.6. For all positive integers m and l, we have
(∞∑k=n1∑li=0umk+i)−1∼1α−1(umn+l+1−um(n−1)+l+1−umn+um(n−1)). |
Remark 2.1. Note that when l=1, the two main terms of Theorems 2.5 and 2.6 are different. However, there is no contradiction since they are equivalent.
Theorem 2.7. For all positive integers m and l, we have
(ⅰ)
(∞∑k=n1umkumk+2l)−1∼u2mn+l−u2m(n−1)+l−Bmn(α2m−1)2α4m−BmCm,l, |
where Cm,l=u2l+2BlA2−4B(1−(1−Bm)(α4m−Bm)(α2m−1)2).
(ⅱ)
(∞∑k=n1umkumk+2l−1)−1∼α(1−β2m)u2mn+l−1−Bmn(α2m−1)2α4m−BmC′m,l, |
where C′m,l=ulul−1+Bl−1A2−4B(A−2(α−αβ2m)(α4m−Bm)(α2m−1)2).
Theorem 2.8. For any positive integer m and constant C, we have
(∞∑k=n1umk+C)−1∼umn−um(n−1)+Cαm−1αm+1. |
Let A=1 and B=−1 in (1.1). Then un becomes the nth Fibonacci number. So we can obtain some known results when we take some special values for m,A,B.
If we take A=1 and B=−1 in Theorem 2.2, then
Cm=2(1−Bm)(α−β)2−2(α2m−1)2(α−β)2(α4m−Bm)=2(1−(−1)m)5−2(α2m−1)25(α4m−(−1)m). | (2.1) |
If m is even, then it follows from (2.1) that
Cm=−2(α2m−1)25(α4m−(−1)m)=−2(α2m−αmβm)25(α4m−α2mβ2m)=−2(αm−βm)25(α2m−β2m)=−2(α2m+β2m−2)(α−β)5(α2m−β2m)(α−β)=−2(L2m−2)5√5u2m=−2(L2m−2)25u2m√5, |
where Ln is the nth Lucas number with L0=2,L1=1. If m is odd, then it follows from (2.1) that
Cm=45−2(α2m−1)25(α4m+1)=45−2(α2m+αmβm)25(α4m+α2mβ2m)=45−2(αm+βm)25(α2m+β2m)=45−2(α2m+β2m−2)5(α2m+β2m)=4L2m5L2m−2(L2m−2)5L2m=2(L2m+2)5L2m. |
So we have the following corollary, which is given by [12].
Corollary 2.1. Let Fn be the nth Fibonacci number with F0=0,F1=1 and let Ln be the nth Lucas number with L0=2,L1=1. Then for any positive integer m, we have
(∞∑k=n1F2mk)−1∼F2mn−F2m(n−1)+(−1)mnCm, |
where
Cm={−2(L2m−2)25F2m√5,ifmis even,2(L2m+2)5L2m,ifmis odd. |
If we take m=A=1 and B=−1 in Theorem 2.4, then
Um=u2m(1−Bmα6m)(1−Bmβ6m)=1(1+α6)(1+β6)=12+α6+β6=120 |
and
Vm=(α4m−1)2(α−β)4(16(α4m−1)(α6m−Bm)2−10α8m−1)=(α4−1)2(α−β)4(16(α4−1)(α6+1)2−10α8−1)=125(16(α4−1)3(α6+1)2−10(α4−1)α4+1)=125(4√5−10√53)=2√575, |
which imply that
u4mn−u4m(n−1)+4BmnUm(u2m(n+1)−Bmu2m(n−2))+Vm=u4n−u4n−1+4(−1)n⋅120⋅(u2n+1+u2n−2)+2√575=u4n−u4n−1+(−1)n5⋅(u2n+1+u2n−2)+2√575=u4n−u4n−1+(−1)n5⋅(u2n+1−u2n−1+u2n−1+u2n−2)+2√575=u4n−u4n−1+(−1)n5⋅(u2n+u2n−3)+2√575=u4n−u4n−1+2(−1)n5u2n−1+2√575. |
So we have the following corollary, which is given by [3, Corollary 4.3].
Corollary 2.2. Let Fn be the nth Fibonacci number with F0=0,F1=1. Then we have
(∞∑k=n1F4k)−1∼F4n−F4n−1+2(−1)n5F2n−1+2√575. |
If we take m=A=1 and B=−1 in Theorem 2.7, then
C1,l=u2l+2BlA2−4B(1−(1−Bm)(α4m−Bm)(α2m−1)2)=u2l+2(−1)l5(1−2(α4+1)(α2−1)2)=u2l+2(−1)l5(1−2(α2+β2)(α+β)2)=u2l−2(−1)l | (2.2) |
and
C′1,l=ulul−1+Bl−1A2−4B(A−2(α−αβ2m)(α4m−Bm)(α2m−1)2)=ulul−1+(−1)l−15(1−2(α−αβ2)(α4+1)(α2−1)2)=ulul−1+(−1)l−15(1−2(α4+1)(α2−1)2)=ulul−1+(−1)l. | (2.3) |
Note that
Bmn(α2m−1)2α4m−Bm=(−1)n(α2−1)2α4+1=(−1)n(α+β)2α2+β2=(−1)n3. | (2.4) |
Then it follows from (2.2)–(2.4) that
u2mn+l−u2m(n−1)+l−Bmn(α2m−1)2α4m−BmCm,l=u2n+l−u2n−1+l−(−1)n3C1,l=un+lun+l−1+(−1)n+l−1−(−1)n3(u2l−2(−1)l)=un+lun+l−1−(−1)n3(u2l−2(−1)l+3(−1)l)=un+lun+l−1−(−1)n3(u2l+(−1)l) |
and
α(1−β2m)u2mn+l−1−Bmn(α2m−1)2α4m−BmC′m,l=α(1−β2)u2n+l−1−(−1)n3C′1,l=u2n+l−1−(−1)n3(ulul−1+(−1)l). |
So we have the following corollary, which is given by [13, Sections 3 and 4].
Corollary 2.3. Let Fn be the nth Fibonacci number with F0=0,F1=1. Then for any positive integer l, we have
(∞∑k=n1FkFk+2l)−1∼Fn+l−1Fn+l−(F2l+(−1)l)(−1)n3 |
and
(∞∑k=n1FkFk+2l−1)−1∼F2n+l−1−(Fl−1Fl+(−1)l)(−1)n3. |
In this section, we give the proofs of the above eight theorems. We first give some identities that will be used in the proofs of our main results. Let m and k be positive integers. Then it follows from (1.2) that
1umk=α−βαmk−βmk=α−βαmk(1−Bmkα2mk)−1. | (3.1) |
Moreover, we have the following identities:
(1+x)−1=1−x+x2−x31+x, | (3.2a) |
(1−x)−1=1+x+x2+x31−x, | (3.2b) |
(1−x)−2=1+2x+3x2+x3(4−3x)(x−1)2, | (3.2c) |
(1−x)−3=1+3x+6x2+x3(10−15x+6x2)(1−x)3, | (3.2d) |
(1−x)−4=1+4x+10x2+x3(20−45x+36x2−10x3)(x−1)4. | (3.2e) |
To prove the above eight theorems, we may split the proofs into eight subsections as follows:
In this subsection, we will provide a proof of Theorem 2.1.
Proof. By (3.1) and (3.2b), we have
1umk=α−βαmk(1−Bmkα2mk)−1=α−βαmk(1+Bmkα2mk+1α4mk+Bmkα4mk(α2mk−Bmk))=(α−β)(1αmk+Bmkα3mk+1α5mk+Bmkα5mk(α2mk−Bmk)). | (3.3) |
Let n be a positive integer. Then it follows from (3.3) that
∞∑k=n1umk=(α−β)(∞∑k=n1αmk+∞∑k=nBmkα3mk+∞∑k=n1α5mk+∞∑k=nBmkα5mk(α2mk−Bmk))=(α−β)(αmαmn(αm−1)+Bmnα3mα3mn(α3m−Bm)+α5mα5mn(α5m−1))+(α−β)∞∑k=nBmkα5mk(α2mk−Bmk) =αm(α−β)αmn(αm−1)(1+Bmnα2m(αm−1)α2mn(α3m−Bm)+α4m(αm−1)α4mn(α5m−1)+O(1α6mn)). | (3.4) |
Here the notation f(x)=O(g(x)) means that there is a constant C such that |f(x)|≤Cg(x) for all large enough real numbers x. By (3.2a) and (3.4), we have
(∞∑k=n1umk)−1=αmn(αm−1)αm(α−β)(1+Bmnα2m(αm−1)α2mn(α3m−Bm)+α4m(αm−1)α4mn(α5m−1)+O(1α6mn))−1=αmn(αm−1)αm(α−β)(1−Bmnα2m(αm−1)α2mn(α3m−Bm)+O(1α4mn))=αmn(αm−1)αm(α−β)−Bmnαm(αm−1)2αmn(α−β)(α3m−Bm)+O(1α3mn)=αmnα−β−αm(n−1)α−β+O(1αmn)=αmn−βmn+βmnα−β−αm(n−1)−βm(n−1)+βm(n−1)α−β+O(1αmn)=umn−um(n−1)+βmn−βm(n−1)α−β+O(1αmn). |
Then
limn→∞((∞∑k=n1umk)−1−(umn−um(n−1)))=limn→∞(βmn−βm(n−1)α−β+O(1αmn))=0. |
So we have
(∞∑k=n1umk)−1∼umn−um(n−1). |
In this subsection, we will provide a proof of Theorem 2.2.
Proof. By (3.1) and (3.2c), we have
1u2mk=(α−β)2α2mk(1−Bmkα2mk)−2=(α−β)2α2mk(1+2Bmkα2mk+3α4mk+4Bmkα2mk−3α4mk(Bmkα2mk−1)2)=(α−β)2(1α2mk+2Bmkα4mk+3α6mk+4Bmkα2mk−3α6mk(Bmkα2mk−1)2)=(α−β)2(1α2mk+2Bmkα4mk+3α6mk+Rk), | (3.5) |
where
Rk=4Bmkα2mk−3α6mk(Bmkα2mk−1)2. |
Let n be a positive integer. Then it follows from (3.5) that
∞∑k=n1u2mk=(α−β)2(∞∑k=n1α2mk+∞∑k=n2Bmkα4mk+∞∑k=n3α6mk+∞∑k=nRk)=(α−β)2(α2mα2mn(α2m−1)+2Bmnα4mα4mn(α4m−Bm)+3α6mα6mn(α6m−1)+∞∑k=nRk)=(α−β)2α2mα2mn(α2m−1)(1+2Bmnα2m(α2m−1)α2mn(α4m−Bm)+3α4m(α2m−1)α4mn(α6m−1))+(α−β)2α2mα2mn(α2m−1)⋅α2mn(α2m−1)α2m∞∑k=nRk=(α−β)2α2mα2mn(α2m−1)(1+ω), | (3.6) |
where
ω=2Bmnα2mn⋅α2m(α2m−1)(α4m−Bm)+3α4mn⋅α4m(α4m+α2m+1)+α2mn(α2m−1)α2m∞∑k=nRk. |
Note that
ω=2Bmnα2mn⋅α2m(α2m−1)(α4m−Bm)+O(1α4mn). |
Then we have
ω2−ω31+ω=O(1α4mn). | (3.7) |
From (3.2a), (3.6), and (3.7), it follows that
(∞∑k=n1u2mk)−1=α2mn(α2m−1)(α−β)2α2m(1+ω)−1=α2mn(α2m−1)(α−β)2α2m(1−ω+ω2−ω31+ω)=α2mn(α2m−1)(α−β)2α2m(1−ω+O(1α4mn))=α2mn(α2m−1)(α−β)2α2m(1−2Bmnα2mn⋅α2m(α2m−1)(α4m−Bm)+O(1α4mn))=α2mn(α2m−1)(α−β)2α2m−2Bmn(α−β)2⋅(α2m−1)2(α4m−Bm)+O(1α2mn)=α2mn(α−β)2−α2m(n−1)(α−β)2−2Bmn(α−β)2⋅(α2m−1)2(α4m−Bm)+O(1α2mn)=(αmn−βmn+βmnα−β)2−(αm(n−1)−βm(n−1)+βm(n−1)α−β)2−2Bmn(α−β)2⋅(α2m−1)2(α4m−Bm)+O(1α2mn)=u2mn−u2m(n−1)+2Bmn(1−Bm)(α−β)2−2Bmn(α−β)2⋅(α2m−1)2(α4m−Bm)+β2mn(α2m−1)(α−β)2+O(1α2mn)=u2mn−u2m(n−1)+BmnCm+O(1α2mn), |
where
Cm=2(1−Bm)(α−β)2−2(α2m−1)2(α−β)2(α4m−Bm). |
Then
limn→∞((∞∑k=n1u2mk)−1−(u2mn−u2m(n−1)+BmnCm))=0. |
So we have
(∞∑k=n1u2mk)−1∼u2mn−u2m(n−1)+BmnCm, |
where Cm=2(1−Bm)(α−β)2−2(α2m−1)2(α−β)2(α4m−Bm).
In this subsection, we will provide a proof of Theorem 2.3.
Proof. By (3.1) and (3.2d), we have
1u3mk=(α−β)3α3mk(1−Bmkα2mk)−3=(α−β)3α3mk(1+3Bmkα2mk+6α4mk+10α4mk−15Bmkα2mk+6α4mk(Bmkα2mk−1)3)=(α−β)3(1α3mk+3Bmkα5mk+6α7mk+10α4mk−15Bmkα2mk+6α7mk(Bmkα2mk−1)3)=(α−β)3(1α3mk+3Bmkα5mk+6α7mk+Rk), | (3.8) |
where
Rk=10α4mk−15Bmkα2mk+6α7mk(Bmkα2mk−1)3. |
Let n be a positive integer. Then it follows from (3.8) that
∞∑k=n1u3mk=(α−β)3(∞∑k=n1α3mk+∞∑k=n3Bmkα5mk+∞∑k=n6α7mk+∞∑k=nRk)=(α−β)3(α3mα3mn(α3m−1)+3Bmnα5mα5mn(α5m−Bm)+6α7mα7mn(α7m−1)+∞∑k=nRk)=(α−β)3α3mα3mn(α3m−1)(1+3Bmnα2m(α3m−1)α2mn(α5m−Bm)+6α4m(α3m−1)α4mn(α7m−1))+(α−β)3α3mα3mn(α3m−1)⋅α3mn(α3m−1)α3m∞∑k=nRk=(α−β)3α3mα3mn(α3m−1)(1+ω), | (3.9) |
where
ω=3Bmnα2mn⋅α2m(α3m−1)(α5m−Bm)+6α4mn⋅α4m(α3m−1)(α7m−1)+α3mn(α3m−1)α3m∞∑k=nRk. |
Note that
ω=3Bmnα2mn⋅α2m(α3m−1)(α5m−Bm)+O(1α4mn). |
Then we have
ω2−ω31+ω=O(1α4mn). | (3.10) |
From (3.2a), (3.9), and (3.10), it follows that
(∞∑k=n1u3mk)−1=((α−β)3α3mα3mn(α3m−1))−1(1+ω)−1=α3mn(α3m−1)(α−β)3α3m(1−ω+ω2−ω31+ω)=α3mn(α3m−1)(α−β)3α3m(1−ω+O(1α4mn))=α3mn(α3m−1)(α−β)3α3m(1−3Bmnα2mn⋅α2m(α3m−1)(α5m−Bm)+O(1α4mn))=α3mn(α3m−1)(α−β)3α3m−3(Bα)mn(α−β)3⋅(α3m−1)2αm(α5m−Bm)+O(1αmn)=α3mn(α−β)3−α3m(n−1)(α−β)3−3(Bα)mn(α−β)3⋅(α3m−1)2αm(α5m−Bm)+O(1αmn)=(αmn−βmn+βmnα−β)3−(αm(n−1)−βm(n−1)+βm(n−1)α−β)3−3(Bα)mn(α−β)3⋅(α3m−1)2αm(α5m−Bm)+O(1αmn)=u3mn−u3m(n−1)+δ+O(1αmn), |
where
δ=3(Bα)mn(1−βm)+3(Bβ)mn(αm−1)+β3mn(1−(Bα)3m)(α−β)3−3(Bα)mn(α−β)3⋅(α3m−1)2αm(α5m−Bm)=3(Bα)mn(1−βm)(α−β)3−3(Bα)mn(α−β)3⋅(α3m−1)2αm(α5m−Bm)+O(1αmn)=3(Bα)mn(α−β)3(−(α3m−1)2+(1−βm)αm(α5m−Bm)αm(α5m−Bm))+O(1αmn)=−3(Bα)mn(α−β)3(α4m−2Bmα2m+B2m(Bα)5m−1)+O(1αmn)=3(Bα)mn(α−β)3⋅α2m(αm−βm)21−Bmα5m+O(1αmn)=(αm−βmα−β)2⋅3(Bα)m(n+2)α−β⋅11−(Bα)5m+O(1αmn). |
Let Qm=u2m(1−(Bα)5m)(1−(Bβ)5m). Then we can obtain
δ=3BmnQmαm(n+2)(1−(Bβ)5m)α−β+O(1αmn)=3BmnQm(αm(n+2)α−β−αm(n−3)α−β)+O(1αmn)=3BmnQm(αm(n+2)−βm(n+2)+βm(n+2)α−β−αm(n−3)−βm(n−3)+βm(n−3)α−β)+O(1αmn)=3BmnQm(um(n+2)−um(n−3)+βm(n+2)−βm(n−3)α−β)+O(1αmn). |
Then
limn→∞((∞∑k=n1u3mk)−1 −(u3mn−u3m(n−1)+3BmnQm(um(n+2)−um(n−3))))=limn→∞(3BmnQmβm(n+2)−βm(n−3)α−β+O(1αmn))=0. |
So we have
(∞∑k=n1u3mk)−1∼u3mn−u3m(n−1)+3BmnQm(um(n+2)−um(n−3)), |
where Qm=u2m(1−(Bα)5m)(1−(Bβ)5m).
In this subsection, we will provide a proof of Theorem 2.4.
Proof. By (3.1) and (3.2e), we have
1u4mk=(α−β)4α4mk(1−Bmkα2mk)−4=(α−β)4α4mk(1+4Bmkα2mk+10α4mk+Rk)=(α−β)4(1α4mk+4Bmkα6mk+10α8mk+Rkα4mk), | (3.11) |
where
Rk=20Bmkα6mk−45α4mk+36Bmkα2mk−10α4mk(Bmkα2mk−1)4. |
Let n be a positive integer. Then it follows from (3.11) that
∞∑k=n1u4mk=(α−β)4(∞∑k=n1α4mk+∞∑k=n4Bmkα6mk+∞∑k=n10α8mk+∞∑k=nRkα4mk)=(α−β)4(α4mα4mn(α4m−1)+4Bmnα6mα6mn(α6m−Bm)+10α8mα8mn(α8m−1)+∞∑k=nRkα4mk)=(α−β)4α4mα4mn(α4m−1)(1+4Bmnα2m(α4m−1)α2mn(α6m−Bm)+10α4m(α4m−1)α4mn(α8m−1))+(α−β)4α4mα4mn(α4m−1)⋅α4mn(α4m−1)α4m∞∑k=nRkα4mk=(α−β)4α4mα4mn(α4m−1)(1+ω), | (3.12) |
where
ω=4Bmnα2m(α4m−1)α2mn(α6m−Bm)+10α4m(α4m−1)α4mn(α8m−1)+α4mn(α4m−1)α4m∞∑k=nRkα4mk. |
Note that
ω=4Bmnα2m(α4m−1)α2mn(α6m−Bm)+10α4m(α4m−1)α4mn(α8m−1)+O(1α6mn). |
Then we have
ω2−ω31+ω=16α4m(α4m−1)2α4mn(α6m−Bm)2+O(1α6mn). | (3.13) |
By (3.2a), (3.12), and (3.13), we have
(∞∑k=n1u4mk)−1=((α−β)4α4mα4mn(α4m−1))−1(1+ω)−1=α4mn(α4m−1)(α−β)4α4m(1−ω+ω2−ω31+ω)=α4mn(α4m−1)(α−β)4α4m(1−ω+16α4m(α4m−1)2α4mn(α6m−Bm)2+O(1α6mn))=α4mn(α4m−1)(α−β)4α4m(1−4Bmnα2m(α4m−1)α2mn(α6m−Bm)+1α4mnC′m+O(1α6mn))=α4mn(α4m−1)(α−β)4α4m−4Bmnα2mn(α4m−1)2(α−β)4α2m(α6m−Bm)+(α4m−1)(α−β)4α4mC′m+O(1α2mn)=α4mn(α−β)4−α4m(n−1)(α−β)4−4Bmnα2mn(α4m−1)2(α−β)4α2m(α6m−Bm)+(α4m−1)(α−β)4α4mC′m+O(1α2mn)=(αmn−βmn+βmnα−β)4−(αm(n−1)−βm(n−1)+βm(n−1)α−β)4−4Bmnα2mn(α4m−1)2(α−β)4α2m(α6m−Bm)+(α4m−1)(α−β)4α4mC′m+O(1α2mn)=u4mn−u4m(n−1)+δ+Vm+O(1α2mn), |
where
C′m=16α4m(α4m−1)2(α6m−Bm)2−10α4m(α4m−1)α8m−1, |
Vm=(α4m−1)(α−β)4α4mC′m=(α4m−1)(α−β)4α4m(16α4m(α4m−1)2(α6m−Bm)2−10α4m(α4m−1)α8m−1)=(α4m−1)2(α−β)4(16(α4m−1)(α6m−Bm)2−10α8m−1) |
and
δ=4Bmnα2mn(α−β)4((1−Bmβ2m)α2m(α6m−Bm)−(α4m−1)2α2m(α6m−Bm))=4Bmnα2mn(α−β)4(α4m−2Bmα2m+B2m1−Bmα6m)=4Bmnα2mn(α−β)4⋅α2m(αm−βm)21−Bmα6m=(αm−βmα−β)2⋅4Bmn(1−Bmα6m)(1−Bmβ6m)⋅α2m(n+1)(1−Bmβ6m)(α−β)2=4Bmnu2m(1−Bmα6m)(1−Bmβ6m)(α2m(n+1)(α−β)2−Bmα2m(n−2)(α−β)2). |
Let Um=u2m(1−Bmα6m)(1−Bmβ6m). Then we can obtain
δ=4BmnUm((αm(n+1)−βm(n+1)+βm(n+1)α−β)2−Bm(αm(n−2)−βm(n−2)+βm(n−2)α−β)2)=4BmnUm(u2m(n+1)−Bmu2m(n−2)+Bmβ2m(n−2)−β2m(n−2)(α−β)2)=4BmnUm(u2m(n+1)−Bmu2m(n−2))+O(1α2m(n−2)). |
Then
limn→∞((∞∑k=n1u4mk)−1−(u4mn−u4m(n−1)+4BmnUm(u2m(n+1)−Bmu2m(n−2))+Vm))=limn→∞(O(1α2m(n−2))+O(1α2mn))=0. |
So we have
(∞∑k=n1u4mk)−1∼u4mn−u4m(n−1)+4BmnUm(u2m(n+1)−Bmu2m(n−2))+Vm, |
where Um=u2m(1−Bmα6m)(1−Bmβ6m) and Vm=(α4m−1)2(α−β)4(16(α4m−1)(α6m−Bm)2−10α8m−1).
In this subsection, we will provide a proof of Theorem 2.5.
Proof. By (1.2) and (3.2b), we have
1umk+umk+l=(αmk−βmkα−β+αmk+l−βmk+lα−β)−1=(αmk(1+αl)α−β)−1(1−Bmk(1+βl)α2mk(1+αl))−1=α−βαmk(1+αl)(1+Bmk(1+βl)α2mk(1+αl)+(1+βl)2α4mk(1+αl)2+Rk)=α−β1+αl(1αmk+Bmk(1+βl)α3mk(1+αl)+(1+βl)2α5mk(1+αl)2+Rkαmk), | (3.14) |
where
Rk=Bmk(1+βl)α4mk(1+αl)(α2mk(1+αl)−Bmk(1+βl)). |
Let n be a positive integer. Then it follows from (3.14) that
∞∑k=n1umk+umk+l=α−β1+αl(∞∑k=n1αmk+∞∑k=nBmk(1+βl)α3mk(1+αl)+∞∑k=n(1+βl)2α5mk(1+αl)2+∞∑k=nRkαmk)=α−β1+αl(αmαmn(αm−1)+Cm)=αm(α−β)αmn(αm−1)(1+αl)(1+αmn(αm−1)αmCm), | (3.15) |
where
Cm=Bmnα3m(1+βl)α3mn(α3m−Bm)(1+αl)+α5m(1+βl)2α5mn(α5m−1)(1+αl)2+∞∑k=nRkαmk. |
Then we have
αmn(αm−1)αmCm=O(1α2mn). | (3.16) |
By (3.2a), (3.15), and (3.16), we have
(∞∑k=n1umk+umk+l)−1=(αm(α−β)αmn(1+αl)(αm−1))−1(1+O(1α2mn))−1=αmn(1+αl)(αm−1)αm(α−β)(1+O(1α2mn))=αmn+αmn+l−αm(n−1)−αm(n−1)+lα−β+O(1αmn)=αmn+l−βmn+l+βmn+lα−β−αm(n−1)+l−βm(n−1)+l+βm(n−1)+lα−β+αmn−βmn+βmnα−β−αm(n−1)−βm(n−1)+βm(n−1)α−β+O(1αmn)=umn+l−um(n−1)+l+umn−um(n−1)+βmn+βmn+l−βm(n−1)−βm(n−1)+lα−β+O(1αmn). |
Then
limn→∞((∞∑k=n1umk+umk+l)−1−(umn+l−um(n−1)+l+umn−um(n−1)))=limn→∞(βmn+βmn+l−βm(n−1)−βm(n−1)+lα−β+O(1αmn))=0. |
So we have
(∞∑k=n1umk+umk+l)−1∼umn+l−um(n−1)+l+umn−um(n−1). |
In this subsection, we will provide a proof of Theorem 2.6.
Proof. By (1.2), we have
l∑i=0umk+i=1α−β(αmkl∑i=0αi−βmkl∑i=0βi)=1α−β(αmk(1−αl+1)1−α−βmk(1−βl+1)1−β)=αmk(1−αl+1)(α−β)(1−α)(1−Bmk(1−α)(1−βl+1)α2mk(1−β)(1−αl+1)). | (3.17) |
From (3.2b) and (3.17), it follows that
1l∑i=0umk+i=(αmk(1−αl+1)(α−β)(1−α))−1(1−Bmk(1−α)(1−βl+1)α2mk(1−β)(1−αl+1))−1=(α−β)(1−α)αmk(1−αl+1)(1+Bmk(1−α)(1−βl+1)α2mk(1−β)(1−αl+1)+(1−α)2(1−βl+1)2α4mk(1−β)2(1−αl+1)2+Rk)=(α−β)(1−α)(1−αl+1)(1αmk+Bmk(1−α)(1−βl+1)α3mk(1−β)(1−αl+1)+(1−α)2(1−βl+1)2α5mk(1−β)2(1−αl+1)2)+(α−β)(1−α)(1−αl+1)⋅Rkαmk, | (3.18) |
where
Rk=(1−βl+1)3(1−α)3Bmk(4αmk(1−β)(1−αl+1)−3Bmk(1−α)(1−βl+1))α4mk(1−β)2(1−αl+1)2(α2mk(1−β)(1−αl+1)−Bmk(1−α)(1−βl+1)). |
Let n be a positive integer. Then it follows from (3.18) that
∞∑k=n1∑li=0umk+i=(α−β)(1−α)(1−αl+1)(∞∑k=n1αmk+Cm)=(α−β)(1−α)(1−αl+1)(αmαmn(αm−1)+Cm)=αm(α−β)(1−α)αmn(αm−1)(1−αl+1)(1+αmn(αm−1)αmCm), | (3.19) |
where
Cm=∞∑k=nBmk(1−α)(1−βl+1)α3mk(1−β)(1−αl+1)+∞∑k=n(1−α)2(1−βl+1)2α5mk(1−β)2(1−αl+1)2+∞∑k=nRkαmk=Bmnα3m(1−α)(1−βl+1))α3mn(α3m−Bm)(1−β)(1−αl+1)+α5m(1−α)2(1−βl+1)2α5mn(α5m−1)(1−β)2(1−αl+1)2+∞∑k=nRkαmk=O(1α3mn). |
Then we have
αmn(αm−1)αmCm=O(1α2mn). | (3.20) |
By (3.2a), (3.19), and (3.20), we have
(∞∑k=n1l∑i=0umk+i)−1=(αm(α−1)(α−β)αmn(αl+1−1)(αm−1))−1(1+O(1α2mn))−1=αmn(αl+1−1)(αm−1)αm(α−1)(α−β)(1+O(1α2mn))=αmn+l+1−αm(n−1)+l+1−αmn+αm(n−1)(α−1)(α−β)+O(1αmn)=αm(n−1)−βm(n−1)+βm(n−1)(α−1)(α−β)−αm(n−1)+l+1−βm(n−1)+l+1+βm(n−1)+l+1(α−1)(α−β)−αmn−βmn+βmn(α−1)(α−β)+αmn+l+1−βmn+l+1+βmn+l+1(α−1)(α−β)+O(1αmn)=1α−1(umn+l+1−um(n−1)+l+1−umn+um(n−1))+βmn+l+1−βmn+βm(n−1)−βm(n−1)+l+1(α−1)(α−β)+O(1αmn). |
Then
limn→∞((∞∑k=n1∑li=0umk+i)−1−1α−1(umn+l+1−um(n−1)+l+1−umn+um(n−1)))=limn→∞(βmn+l+1−βmn+βm(n−1)−βm(n−1)+l+1(α−1)(α−β)+O(1αmn))=0. |
So we have
(∞∑k=n1∑li=0umk+i)−1∼1α−1(umn+l+1−um(n−1)+l+1−umn+um(n−1)). |
In this subsection, we will provide a proof of Theorem 2.7.
Proof. Let h be a positive integer. By (1.2), we have
1umkumk+h=(α−β)2((αmk−βmk)(αmk+h−βmk+h))−1=(α−β)2α2mk+h((1−Bmkα2mk)(1−Bmk+hα2mk+2h))−1=(α−β)2α2mk+h(1−Bmkα2mk−Bmk+hα2mk+2h+Bhα4mk+2h)−1=(α−β)2α2mk+h(1−η)−1, | (3.21) |
where
η=Bmkα2mk+Bmk+hα2mk+2h−Bhα4mk+2h=Bmkα2mk+Bmk+hα2mk+2h+O(1α4mk). |
Then we have
η2+η31−η=O(1α4mk). | (3.22) |
By (3.2b), (3.21), and (3.22), we have
1umkumk+h=(α−β)2α2mk+h(1+η+η2+η31−η)=(α−β)2α2mk+h(1+η+O(1α4mk))=(α−β)2α2mk+h(1+Bmkα2mk(1+Bhα2h)+O(1α4mk))=(α−β)2αh(1α2mk+Bmkα4mk(1+Bhα2h)+O(1α6mk)). | (3.23) |
Let n be a positive integer. Then it follows from (3.23) that
∞∑k=n1umkumk+h=(α−β)2αh(∞∑k=n1α2mk+(1+Bhα2h)∞∑k=nBmkα4mk)+O(1α6mn)=(α−β)2αh(α2mα2mn(α2m−1)+(1+Bhα2h)Bmnα4mα4mn(α4m−Bm))+O(1α6mn)=(α−β)2α2mα2mn+h(α2m−1)(1+(1+Bhα2h)Bmnα2m(α2m−1)α2mn(α4m−Bm)+O(1α4mn))=(α−β)2α2mα2mn+h(α2m−1)(1+ω), | (3.24) |
where
ω=(1+Bhα2h)Bmnα2m(α2m−1)α2mn(α4m−Bm)+O(1α4mn). |
Then we have
ω2−ω31+ω=O(1α4mn). | (3.25) |
By (3.2a), (3.24), and (3.25), we have
(∞∑k=n1umkumk+h)−1=((α−β)2α2mα2mn+h(α2m−1))−1(1+ω)−1=α2mn+h(α2m−1)(α−β)2α2m(1−ω+ω2−ω31+ω)=α2mn+h(α2m−1)(α−β)2α2m(1−ω+O(1α4mn))=α2mn+h(α2m−1)(α−β)2α2m(1−(1+Bhα2h)Bmnα2m(α2m−1)α2mn(α4m−Bm)+O(1α4mn))=α2mn+h−α2m(n−1)+h(α−β)2−(1+Bhα2h)Bmn(α2m−1)2αh(α−β)2(α4m−Bm)+O(1α2mn). | (3.26) |
(ⅰ) If we take h=2l, then it follows from (3.26) that
(∞∑k=n1umkumk+2l)−1=(αmn+l−βmn+l+βmn+lα−β)2−(αm(n−1)+l−βm(n−1)+l+βm(n−1)+lα−β)2−(1+1α4l)Bmn(α2m−1)2α2l(α−β)2(α4m−Bm)+O(1α2mn)=u2mn+l−u2m(n−1)+l+δ+O(1α2mn), |
where
δ=2(Bmn+l−Bm(n−1)+l)(α−β)2−(1+1α4l)Bmn(α2m−1)2α2l(α−β)2(α4m−Bm)=−Bmn(α2m−1)2α4m−Bm(α2l+β2l(α−β)2−2Bl(1−Bm)(α4m−Bm)(α−β)2(α2m−1)2)=−Bmn(α2m−1)2α4m−Bm(u2l+2Bl(α−β)2−2Bl(1−Bm)(α4m−Bm)(α−β)2(α2m−1)2)=−Bmn(α2m−1)2α4m−Bm(u2l+2Bl(α−β)2(1−(1−Bm)(α4m−Bm)(α2m−1)2)). |
Let Cm,l=u2l+2Bl(α−β)2(1−(1−Bm)(α4m−Bm)(α2m−1)2). Then
limn→∞((∞∑k=n1umkumk+2l)−1−(u2mn+l−u2m(n−1)+l−Bmn(α2m−1)2α4m−BmCm,l))=0. |
So we have
(∞∑k=n1umkumk+2l)−1∼u2mn+l−u2m(n−1)+l−Bmn(α2m−1)2α4m−BmCm,l, |
where Cm,l=u2l+2BlA2−4B(1−(1−Bm)(α4m−Bm)(α2m−1)2).
(ⅱ) If we take h=2l−1, then it follows from (3.26) that
(∞∑k=n1umkumk+2l−1)−1=α2mn+2l−1−α2m(n−1)+2l−1(α−β)2−(1+Bα4l−2)Bmn(α2m−1)2α2l−1(α−β)2(α4m−Bm)+O(1α2mn)=(α−αβ2m)α2mn+2l−2(α−β)2−(1+Bα4l−2)Bmn(α2m−1)2α2l−1(α−β)2(α4m−Bm)+O(1α2mn)=(α−αβ2m)(α2mn+2l−2−β2mn+2l−2+β2mn+2l−2(α−β)2)−(1+Bα4l−2)Bmn(α2m−1)2α2l−1(α−β)2(α4m−Bm)+O(1α2mn)=(α−αβ2m)u2mn+l−1+δ+O(1α2mn), |
where
δ=2Bmn+l−1(α−αβ2m)(α−β)2−(1+B2l−1α4l−2)Bmn(α2m−1)2α2l−1(α−β)2(α4m−Bm)=−Bmn(α2m−1)2α4m−Bm(αlαl−1+β2l−1(α−β)2−2Bl−1(α−αβ2m)(α4m−Bm)(α−β)2(α2m−1)2)=−Bmn(α2m−1)2α4m−Bm(ulul−1+Bl−1(α+β)(α−β)2−2Bl−1(α−αβ2m)(α4m−Bm)(α−β)2(α2m−1)2)=−Bmn(α2m−1)2α4m−Bm(ulul−1+Bl−1(α−β)2((α+β)−2(α−αβ2m)(α4m−Bm)(α2m−1)2)). |
Let C′m,l=ulul−1+Bl−1(α−β)2((α+β)−2(α−αβ2m)(α4m−Bm)(α2m−1)2). Then
So we have
where .
In this subsection, we will provide a proof of Theorem 2.8.
Proof. By (1.2), we have
(3.27) |
where
Then we have
(3.28) |
From (3.2a), (3.27), and (3.28), it follows that
(3.29) |
Let be a positive integer. Then it follows from (3.29) that
(3.30) |
where
Then we have
(3.31) |
By (3.2b), (3.30), and (3.31), we have
Then
So we have
Let be the special Lucas -sequence defined by , where , , and is an integer such that . In this paper, we study the asymptotic behavior of the sequences involving . In Section 1, we give the definition of the asymptotic behavior and introduce the asymptotic behavior of some sequences. In Section 2, we give the asymptotic formulas for , where
are positive integers, , and is any constant. In Section 3, we give the proof of these results.
The authors declare they have not used Artificial Intelligence (AI) tools in the creation of this article.
Hongjian Li was supported by the Project of Guangdong University of Foreign Studies (Grant No. 2024RC063). Pingzhi Yuan was supported by the National Natural Science Foundation of China (Grant No. 12171163) and the Basic and Applied Basic Research Foundation of Guangdong Province (Grant No. 2024A1515010589).
The authors declare there are no conflicts of interest.
[1] |
E. Marder, D. Bucher, Central pattern generators and the control of rhythmic movements, Curr. Biol., 11 (2001), R986–R996. https://doi.org/10.1016/S0960-9822(01)00581-4 doi: 10.1016/S0960-9822(01)00581-4
![]() |
[2] |
E. Marder, R. L. Calabrese, Principles of rhythmic motor pattern generation, Physiol. Rev., 76 (1996), 687–717. https://doi.org/10.1152/physrev.1996.76.3.687 doi: 10.1152/physrev.1996.76.3.687
![]() |
[3] |
A. Sakurai, C. A. Gunaratne, P. S. Katz, Two interconnected kernels of reciprocally inhibitory interneurons underlie alternating left-right swim motor pattern generation in the mollusk Melibe leonina, J. Neurophysiol., 112 (2014), 1317–1328. https://doi.org/10.1152/jn.00261.2014 doi: 10.1152/jn.00261.2014
![]() |
[4] |
D. Alaçam, A. Shilnikov, Making a swim central pattern generator out of latent parabolic bursters, Int. J. Bifurcation Chaos, 25 (2015), 1540003. https://doi.org/10.1142/S0218127415400039 doi: 10.1142/S0218127415400039
![]() |
[5] | A. I. Selverston, Model Neural Networks and Behavior, New York, 1985. https://doi.org/10.1007/978-1-4757-5858-0 |
[6] |
W. N. Frost, P. S. Katz, Single neuron control over a complex motor program, PNAS, 93 (1996), 422–426. https://doi.org/10.1073/pnas.93.1.422 doi: 10.1073/pnas.93.1.422
![]() |
[7] | P. S. Katz, S. L. Hooper, Invertebrate central pattern generators, Cold Spring Harbor Monogr. Ser., 49 (2007), 251. |
[8] |
E. Marder, S. Kedia, E. O. Morozova, New insights from small rhythmic circuits, Curr. Opin. Neurobiol., 76 (2022), 102610. https://doi.org/10.1016/j.conb.2022.102610 doi: 10.1016/j.conb.2022.102610
![]() |
[9] |
E. Marder, Neuromodulation of neuronal circuits: back to the future, Neuron, 76 (2012), 1–11. https://doi.org/10.1016/j.neuron.2012.09.010 doi: 10.1016/j.neuron.2012.09.010
![]() |
[10] |
I. Belykh, A. Shilnikov, When weak inhibition synchronizes strongly desynchronizing networks of bursting neurons, Phys. Rev. Lett., 101 (2008), 078102. https://doi.org/10.1103/PhysRevLett.101.078102 doi: 10.1103/PhysRevLett.101.078102
![]() |
[11] |
T. Nowotny, M. I. Rabinovich, Dynamical origin of independent spiking and bursting activity in neural microcircuits, Phys. Rev. Lett., 98 (2007), 128106. https://doi.org/10.1103/PhysRevLett.98.128106 doi: 10.1103/PhysRevLett.98.128106
![]() |
[12] |
A. I. Selverston, Invertebrate central pattern generator circuits, Phil. Trans. R. Soc. B, 365 (2010), 2329–2345. https://doi.org/10.1098/rstb.2009.0270 doi: 10.1098/rstb.2009.0270
![]() |
[13] |
A. I. Selverston, M. I. Rabinovich, H. D Abarbanel, R. Elson, A. Szücs, R. D. Pinto, et al., Reliable circuits from irregular neurons: a dynamical approach to understanding central pattern generators, J. Physiol.-Paris, 94 (2000), 357–374. https://doi.org/10.1016/S0928-4257(00)01101-3 doi: 10.1016/S0928-4257(00)01101-3
![]() |
[14] |
R. Huerta, M. A. Sánchez-Montañés, F. Corbacho, J. A. Sigüenza, A central pattern generator to control a pyloric-based system, Biol. Cybern., 82 (2000), 85–94. https://doi.org/10.1007/PL00007963 doi: 10.1007/PL00007963
![]() |
[15] |
M. Lodi, A. L. Shilnikov, M. Storace, Design principles for central pattern generators with preset rhythms, IEEE Trans. Neural Networks Learn. Syst., 31 (2019), 3658–3669. https://doi.org/10.1109/TNNLS.2019.2945637 doi: 10.1109/TNNLS.2019.2945637
![]() |
[16] |
S. Chen, Y. Liu, T. Chen, J. Lou, Rhythm motion control in bio-inspired fishtail based on central pattern generator, IET Cyber-Syst. Robot., 3 (2021), 53–67. https://doi.org/10.1049/csy2.12007 doi: 10.1049/csy2.12007
![]() |
[17] |
J. Wojcik, J. Schwabedal, R. Clewley, A. L. Shilnikov, Key bifurcations of bursting polyrhythms in 3-cell central pattern generators, PLoS One, 9 (2014), e92918. https://doi.org/10.1371/journal.pone.0092918 doi: 10.1371/journal.pone.0092918
![]() |
[18] |
J. T. C. Schwabedal, A. B. Neiman, A. L. Shilnikov, Robust design of polyrhythmic neural circuits, Phys. Rev. E, 90 (2014), 022715. https://doi.org/10.1103/PhysRevE.90.022715 doi: 10.1103/PhysRevE.90.022715
![]() |
[19] | R. Azodi-Avval, F. Bahrami, A mathematical model of arm movement during rhythmic motor activity, in 2011 18th Iranian Conference of Biomedical Engineering (ICBME), (2011), 304–308. https://doi.org/10.1109/ICBME.2011.6168578 |
[20] |
M. B. Reyes, P. V. Carelli, J. C. Sartorelli, R. D. Pinto, A modeling approach on why simple central pattern generators are built of irregular neurons, PLoS One, 10 (2015), e0120314. https://doi.org/10.1371/journal.pone.0120314 doi: 10.1371/journal.pone.0120314
![]() |
[21] |
J. Dethier, G. Drion, A. Franci, R. Sepulchre, A positive feedback at the cellular level promotes robustness and modulation at the circuit level, J. Neurophysiol., 114 (2015), 2472–2484. https://doi.org/10.1152/jn.00471.2015 doi: 10.1152/jn.00471.2015
![]() |
[22] |
J. Collens, K. Pusuluri, A. Kelley, D. Knapper, T. Xing, S. Basodi, et al., Dynamics and bifurcations in multistable 3-cell neural networks, Chaos, 30 (2020), 072101. https://doi.org/10.1063/5.0011374 doi: 10.1063/5.0011374
![]() |
[23] |
Q. Lu, X. Wang, J. Tian, A new biological central pattern generator model and its relationship with the motor units, Cognit. Neurodyn., 16 (2022), 135–147. https://doi.org/10.1007/s11571-021-09710-0 doi: 10.1007/s11571-021-09710-0
![]() |
[24] |
Q. Lu, J. Tian, Synchronization and stochastic resonance of the small-world neural network based on the CPG, Cognit. Neurodyn., 8 (2014), 217–226. https://doi.org/10.1007/s11571-013-9275-8 doi: 10.1007/s11571-013-9275-8
![]() |
[25] |
Y. Zang, E. Marder, Neuronal morphology enhances robustness to perturbations of channel densities, PNAS, 120 (2023), e2219049120. https://doi.org/10.1073/pnas.2219049120 doi: 10.1073/pnas.2219049120
![]() |
[26] |
E. M. Izhikevich, Neural excitability, spiking, and bursting, Int. J. Bifurcation Chaos, 10 (2000), 1171–1266. https://doi.org/10.1142/S0218127400000840 doi: 10.1142/S0218127400000840
![]() |
[27] |
B. Lu, X. Jiang, Reduced and bifurcation analysis of intrinsically bursting neuron model, Electron. Res. Arch., 31 (2023), 5928–5945. https://doi.org/10.3934/era.2023301 doi: 10.3934/era.2023301
![]() |
[28] |
F. Zhan, S. Liu, X. Zhang, J. Wang, B. Lu, Mixed-mode oscillations and bifurcation analysis in a pituitary model, Nonlinear Dyn., 94 (2018), 807–826. https://doi.org/10.1007/s11071-018-4395-7 doi: 10.1007/s11071-018-4395-7
![]() |
[29] |
W. B. Kristan, Neuronal decision-making circuits, Curr. Biol., 18 (2008), R928–R932. https://doi.org/10.1016/j.cub.2008.07.081 doi: 10.1016/j.cub.2008.07.081
![]() |
[30] |
K. L. Briggman, W. B. Kristan, Multifunctional pattern-generating circuits, Annu. Rev. Neurosci., 31 (2008), 271–294. https://doi.org/10.1146/annurev.neuro.31.060407.125552 doi: 10.1146/annurev.neuro.31.060407.125552
![]() |
[31] |
A. A. A. Hill, J. Lu, M. A. Masino, O. H. Olsen, R. L. Calabrese, A model of a segmental oscillator in the leech heartbeat neuronal network, J. Comput. Neurosci., 10 (2001), 281–302. https://doi.org/10.1023/A:1011216131638 doi: 10.1023/A:1011216131638
![]() |
[32] | R. L. Calabrese, Half-center oscillators underlying rhythmic movements, in The Handbook of Brain Theory and Neural Networks, (1998), 444–447. |
[33] |
Y. Zang, S. Hong, E. D. Schutter, Firing rate-dependent phase responses of Purkinje cells support transient oscillations, eLife, 9 (2020), e60692. https://doi.org/10.7554/eLife.60692 doi: 10.7554/eLife.60692
![]() |
[34] |
M. Liu, L. Duan, In-phase and anti-phase spikes synchronization within mixed Bursters of the pre-Bözinger complex, Electron. Res. Arch., 30 (2022), 961–977. https://doi.org/10.3934/era.2022050 doi: 10.3934/era.2022050
![]() |
[35] |
S. Li, G. Zhang, J. Wang, Y. Chen, B. Deng, Emergent central pattern generator behavior in chemical coupled two-compartment models with time delay, Physica A, 491 (2018), 177–187. https://doi.org/10.1016/j.physa.2017.08.121 doi: 10.1016/j.physa.2017.08.121
![]() |
[36] |
A. Doloc-Mihu, R. L. Calabrese, A database of computational models of a half-center oscillator for analyzing how neuronal parameters influence network activity, J. Biol. Phys., 37 (2011), 263–283. https://doi.org/10.1007/s10867-011-9215-y doi: 10.1007/s10867-011-9215-y
![]() |
[37] | A. Doloc-Mihu, R. L. Calabrese, Analysis of family structures reveals robustness or sensitivity of bursting activity to parameter variations in a half-center oscillator (HCO) model, eNeuro, 3 (2016). https://doi.org/10.1523/ENEURO.0015-16.2016 |
[38] |
I. Elices, P. Varona, Asymmetry factors shaping regular and irregular bursting rhythms in central pattern generators, Front. Comput. Neurosci., 11 (2017), 9. https://doi.org/10.3389/fncom.2017.00009 doi: 10.3389/fncom.2017.00009
![]() |
[39] |
A. J. White, Sensory feedback expands dynamic complexity and aids in robustness against noise, Biol. Cybern., 116 (2022), 267–269. https://doi.org/10.1007/s00422-021-00917-2 doi: 10.1007/s00422-021-00917-2
![]() |
[40] |
Z. Yu, P. J. Thomas, Dynamical consequences of sensory feedback in a half-center oscillator coupled to a simple motor system, Biol. Cybern., 115 (2021), 135–160. https://doi.org/10.1007/s00422-021-00864-y doi: 10.1007/s00422-021-00864-y
![]() |
[41] |
R. Huerta, P. Varona, M. I. Rabinovich, H. D. I. Abarbanel, Topology selection by chaotic neurons of a pyloric central pattern generator, Biol. Cybern., 84 (2001), L1–L8. https://doi.org/10.1007/PL00007976 doi: 10.1007/PL00007976
![]() |
[42] |
V. In, A. Kho, P. Longhini, J. D. Neff, A. Palacios, P. L. Buono, Meet ANIBOT: the first biologically-inspired animal robot, Int. J. Bifurcation Chaos, 32 (2022), 2230001. https://doi.org/10.1142/S0218127422300014 doi: 10.1142/S0218127422300014
![]() |
[43] |
A. S. Lele, Y. Fang, J. Ting, A. Raychowdhury, Learning to walk: bio-mimetic hexapod locomotion via reinforcement-based spiking central pattern generation, IEEE J. Emerging Sel. Top. Circuits Syst., 10 (2020), 536–545. https://doi.org/10.1109/JETCAS.2020.3033135 doi: 10.1109/JETCAS.2020.3033135
![]() |
[44] |
T. Sun, Z. Dai, P. Manoonpong, Distributed-force-feedback-based reflex with online learning for adaptive quadruped motor control, Neural Networks, 142 (2021), 410–427. https://doi.org/10.1016/j.neunet.2021.06.001 doi: 10.1016/j.neunet.2021.06.001
![]() |
[45] |
A. Espinal, H. Rostro-Gonzalez, M. Carpio, E. I. Guerra-Hernandez, M. Ornelas-Rodriguez, M. Sotelo-Figueroa, Design of spiking central pattern generators for multiple locomotion gaits in hexapod robots by christiansen grammar evolution, Front. Neurorobot., 10 (2016), 6. https://doi.org/10.3389/fnbot.2016.00006 doi: 10.3389/fnbot.2016.00006
![]() |
[46] |
F. Zhan, S. Liu, Response of electrical activity in an improved neuron model under electromagnetic radiation and noise, Front. Comput. Neurosci., 11 (2017), 107. https://doi.org/10.3389/fncom.2017.00107 doi: 10.3389/fncom.2017.00107
![]() |
[47] |
F. Zhan, S. Liu, J. Wang, B. Lu, Bursting patterns and mixed-mode oscillations in reduced Purkinje model, Int. J. Mod. Phys. B, 32 (2018), 1850043. https://doi.org/10.1142/S0217979218500431 doi: 10.1142/S0217979218500431
![]() |
[48] |
D. Terman, J. E. Rubin, A. C. Yew, C. J. Wilson, Activity patterns in a model for the subthalamopallidal network of the basal ganglia, J. Neurosci., 22 (2002), 2963–2976. https://doi.org/10.1523/JNEUROSCI.22-07-02963.2002 doi: 10.1523/JNEUROSCI.22-07-02963.2002
![]() |
[49] |
F. Su, J. Wang, S. Niu, H. Li, B. Deng, C. Liu, et al., Nonlinear predictive control for adaptive adjustments of deep brain stimulation parameters in basal ganglia–thalamic network, Neural Networks, 98 (2018), 283–295. https://doi.org/10.1016/j.neunet.2017.12.001 doi: 10.1016/j.neunet.2017.12.001
![]() |
[50] |
J. Song, S. Liu, H. Lin, Model-based quantitative optimization of deep brain stimulation and prediction of Parkinson's states, Neuroscience, 498 (2022), 105–124. https://doi.org/10.1016/j.neuroscience.2022.05.019 doi: 10.1016/j.neuroscience.2022.05.019
![]() |
[51] |
J. Song, H. Lin, S. Liu, Basal ganglia network dynamics and function: role of direct, indirect and hyper-direct pathways in action selection, Network: Comput. Neural Syst., 34 (2023), 84–121. https://doi.org/10.1080/0954898X.2023.2173816 doi: 10.1080/0954898X.2023.2173816
![]() |
[52] |
M. Valero, I. Zutshi, E. Yoon, G. Buzsáki, Probing subthreshold dynamics of hippocampal neurons by pulsed optogenetics, Science, 375 (2022), 570–574. https://doi.org/10.1126/science.abm1891 doi: 10.1126/science.abm1891
![]() |
[53] |
C. A. Tassinari, G. Cantalupo, B. Hoegl, P. Cortelli, L. Tassi, S. Francione, et al., Neuroethological approach to frontolimbic epileptic seizures and parasomnias: the same central pattern generators for the same behaviours, Rev. Neurol., 165 (2009), 762–768. https://doi.org/10.1016/j.neurol.2009.08.002 doi: 10.1016/j.neurol.2009.08.002
![]() |
[54] |
C. A. Tassinari, E. Gardella, G. Cantalupo, G. Rubboli, Relationship of central pattern generators with parasomnias and sleep-related epileptic seizures, Sleep Med. Clin., 7 (2012), 125–134. https://doi.org/10.1016/j.jsmc.2012.01.003 doi: 10.1016/j.jsmc.2012.01.003
![]() |