Oğuztöreli, Mehmet Namik

Institute of Mathematics AS CR, v. v. i.

Archivum MathematicumMehmet Namik OğuztöreliActivities in a one-dimensional continuous neural networkArchivum Mathematicum, Vol. 14 (1978), No. 3, 161--169Persistent URL: http://dml.cz/dmlcz/107003Terms of use:© Masaryk University, 1978Institute of Mathematics of the Academy of Sciences of the Czech Republic provides access todigitized documents strictly for personal use. Each copy of any part of this document must containthese Terms of use.This paper has been digitized, optimized for electronic delivery and stampedwith digital signature within the project DML-CZ: The Czech Digital MathematicsLibrary http://project.dml.czARCH. MATH. 3, SCRIPTA FAC SCI. NAT. UJEP BRUNENSIS XIV: 161—170, 1978 ACTIVITIES IN A ONE-DIMENSIONAL CONTINUOUS NEURAL NETWORK MEHMET NAMIK OGUZTORELI (Received April 13, 1977) This paper is dedicated to the seventieth anniversary of Professor D. Mangeron I. INTRODUCTION A discrete neural model has been investigated from a mathematical, computational and physiological point of view in Refs [1]— [4]. In Ref [5] this model has been extended to a two-dimensional continuous neural network. In Ref [7] we studied the activity propagation in a special two-dimensional continuous neural model. In the present work we deal with the spatial and temporal propagation of nervous activities in a one-dimensional neural network with a special structure. The neurons of the neural network considered in this paper are supposed to be distributed over the half-axis R = {x | x > 0} and are connected to each other in a specific way which will be explained below. Let u -= u(t9 x) be the normalized and smoothed rate of generation of nerve impulses of the neuron P = P(x)eR at time t as described in Ref [5]. By this normalization we have 0 ^ u ^ 1. Let the rate of influence, the rate of excitation or inhibition, of a cell Q == Q(x) e R on the activity of the cell P be denoted by K(P; Q) = K(x; i). We assume that K(P; Q) is a Volterra kernel such that K(x; 0 = j j ^ * 0.D x.o-cr-"0 \PQeR and ° = ^x-l n otherwise where ii is a real constant and K0 is a given sufficiently smooth function defined on R with the property (1.2) K = ]\K0(S)\2dS<ao. o A neuron Q excites the neuron P if K(P; Q) > 0, inhibits P if K(P; Q) < 0, and it is not connected to P if K(P; Q) = 0. 161 Let a * a(x) be the rate of self-inhibition of the neuron P, and / = /(*\ *) be the external input at time t on P. We assume that (1.3) a0 = a(x) = aj Vx e J?, where a0 and ax are certain positive numbers with a0 > 1. Further, let A, c'ks and ^ be real numbers such that (1.4) 0 < yt < y2 < ... < ym, and consider the function (1.5) J K O - K ? ^ " * ' f ° r ' > 0 ' [o, for t S 0. The function H(t) will be taken as the self-regulation (adaptation) function of the neural network. Let / = {/11 > 0} and D = IxR. Consider the space U of all functions u = u(t, x) defined on D which are absolutely JL-integrable in xeR for fixed t (0 ^ t < < oo), and absolutely continuous, in tel for fixed xeR, and are such that 0 S u S 1 in />. Note that the function (1.6) S{g}(t,x) l l + exp{-g(í,x)} ' maps Lm(D) into itself; it is monotonically increasing with g and 0 < S{g} < 1 for any bounded element of Lco(D). It can be easily demonstrated as in Ref [5], with some minor modifications, that the nervous activities in the neural network under considerations are governed by the nonlinear integro-difFerential difference equation (u) ( 4 " + a ( x ) ) w ( r ' x ) = - S{f(t,x) + x\H(t - x)u(x,x)dx + II]K0(X - {)u(t - h, 0d{} 0 0 for (t, x) e D, subject to the initial condition (1.8) u(t, x) -a q>0(x) for t e /0 , x e R, where ^0(x) is the initial firing rate of the neuron P in the initial time-interval I0 = = {t | — h g t S 0}, and A is a small time-lag which occurs in the neural interactions. Here the initial firing rate is supposed to be independent of t for the sake of simplicity. For small A and fi Eq (1.7) can be approximated by the following linear inhomogene-ous integro-difFerential difference equation: 162 (1-9) (jj- + ot(x)\u(i,x) = t X - /oO> *) + fx(t, x) X f H(t - T) M(T, x) dt + /* J K0(x - 0 u(t - A, {) d{, o o where (i.io) / o - - m /i=/0-fo2. By making a few natural modifications in the proof given in § II of Ref [5] we can demonstrate that Eq (1.7) has a unique solution u = u(t, x) in U which satisfies the initial condition (1.8). The same result is also valid for the initial value problem (1-8)-(1-9). In the present paper we study the initial value problem (1.8)—(1.9) in the case m — 1 and (1.11) JT0(*) « « - ' * , where /? is a positive number. Further, to simplify our writing we shall denote cxy by X and yx by 7, and assume tha t / = /(*), that is the external force/is stationary, with respect to time so that f0 and fx are independent of time t: f0 = /0(x), A «/i(*). Note that the neurons in the network always excite (inhibit) each other if i* > 0 (/x < 0), by virtue of Eqs (1.1) and (1.11). The structure of the neural network with the above restrictions is surely not very realistic and oversimplified. Nevertheless it provides some insights on the nervous activities in the idealized conditions. II. ASSOCIATED PARTIAL DIFFERENTIAL DIFFERENCE EQUATION Consider the linearized neural network described by Eq (1.9) with / = f(x), /o =/o(*),/i =/i(*X H(t) = e~n and K0(x) = e~*\ Put v(t,x) = je-*x-Vu(t,Od/;, 0 a L 1 ) vj(t, x) - J e-**-{> <pj(0 d£ 0 = 0,1), o where <Po(x) is the given initial function in (1.8), and (II.2) <px(x) = fifx(x) v0(x) - a(x) <p0(x) [ » -7JM I • \ <" \teloJ Hence «t*i(*)(tvl--o-°) 5u (П-3) »| t6/0 = f0(x), -^ tel0 163 and (H.4) t;L=o = 0. Further 01.5) (~ + p\v(t,x) = u(t,x). Elimination of u(t, x) between Eqs (1.9) and (II. 5) yields the integrodifferential difference equation (11.6) (j- + a(x)\(JL + p\ v(ti x) _ fo(x) + ^ W v(t _ ht x) + + #i(x) j <T y(<~r) f-A- + p\ v(z, x) dx, and the differentiation of the two sides of Eq (II.6) with respect to t yields (11.7) ( | L + «(,) -1 - - V l W ) ( A + /») * , x) = - tf.to * * ' - » ' * > - xrfM / e - * - > ( A + /?)Kt, x) dr. Now we eliminate the terms that contain integrals, obtaining the following linear inhomogeneous third order partial differential difference equation in y: d3v(t, x) , , d2v(t, x) , . d2v(t, x) , . dv(t, x) , . 3t>(r, x) , . , + a3(x) ^ + a4(*) ^ + as(x) v(t, x) = = r/o(*) + y«AW t>(' - it, *) + MtiW g" ( f ~ h — , where (II.9) *,(*) = -ft a2(x) = a(x) + y, n3(x) = 0[a(x) + y], a4(x) = ycc(x) - Xf(x), a5(x) = j8[ya(x) - #i(x)]. The functions u and i; determine each other uniquely by virtue of Eqs (II. I) and (H.5), respectively. In the next section the solution v(t, x) of Eq (II.8) satisfying the conditions (II.3) and (II.4) will be constructed by the methods of integral equa-tions. HI. ASSOCIATED INTEGRAL EQUATION To establish the integral equation which is satisfied by the function v(t, x), we integrate successively both sides of Eq (II.8) twice with respect to t between 0 and t, 164 once with respect to x between 0 and .v. Then, taking into account the conditions (II.3) and (II.4), and putting A(t,x) = -\a2(x) + ta4(x)~], (111.1) B(t,x) = -fl, C(t, x) = a'2(x) - a3(x) + t[a4(x) - a'5(x)], D(t, x) = (1 + yt)ft(x) and (111.2) g(t, x) = v0(x) + tv{(x) + + I | y t2fo(S) + [/? + t{a^) - nf0(0)l v0(S) + [fi + a2(0] tvtf)] df, we obtain the following integro-difference equation (III .3) v(t, x) = g(t, x) + // J f D(t - x - h, £) v(x, 0 dx d£ + (Tv) (t, x), - f t o where (Tv) (t, x) = f A(t~ T, x) V(T, X) dx + J B(t, 0 v(t, £) d£ + f f C(t - T, Q <T, {) dT d£. o o o o (IH.4) Note that the double integral on the right-hand side of Eq (III.3) involve only the values of the function v(T, £) for ~h f^x -£ t — h. Put (111.5) /„ = {t | (n - 1) h = t = «h}, * = 0, 1, 2,... and (111.6) ww(t, x) = v(t, x) for t 6 /„. Then we have (111.7) wn(t, x) = gn(t, x) + (Twn) (t, x) for t e In, where n - 2 fcft x (111.8) g„(t,*)=g(t, *) + /<£ 1 jD( t -T - l . ^ )w t (T ,OdTd^ + fc=l(*-l)ft 0 t-h x + /x f ^(t-x-KOw^^Odxdl (n-2)h 0 Thus, if the functions w0(t, x), wt(t, x),..., wrt(t, x) are known, the function gn(tt x) is completely determined on Dn = I„x D, and Eq (III.8) is a pure integral equation. Although the operator T involves different simple and double integral operators, Eq (III.7) can be solved uniquely and the solution wn(t) is smooth on Dn sinc^ the functions A, B, C, D and g are sufficiently smooth by virtue of the hypotheses of §1. We omit the details here. 165 Integral equations involving different simple and double integral operators first investigated by M. Picone in his great work on partial differential equations (cf. Ref [6]). We recently dealt with such equations in several occasions (cf. Ref [7] - [9]). Before closing this section we note that w0(t9 x) = v0(x) by virtue of Eq (II.3), and (m.8) gi(t, x) = g(t9 x) + ft + -J- A } v0(O d«, (r, x)eDi. Hence wt(t9 x) is well determined on Dt. Using w0 and wx we can construct w2 on D2, and so on. This stepwise continuation of the solution in the forward direction of time is well known in the theory of differential difference equations (cf. [10] — [11]). IV. A SPECIAL CASE In this section we shall briefly investigate the specil case a(x, y) = a, f(t, x) s / and h = 0 where a and / are constants, a > 1. In this case / 0 , fx and a^s (k == =-1, 2, 3, 4, 5) are also constant, and Eq (II.8) is a pure partial differential equation of the form ,*xr ix d*v . d2v . d2v - dv f dv . . aWA) W^ + b l ^ + bl^x- + b3nr + Ki* + bsV = yfo subjected to the conditions (II.3) and (II.4), where (IV.2) bt=ftt * 2 = a + y, b3=fi(* + y)~itfl9 b4 = ay - Xfl9 b5 =^(ay - kfx - uft). Although v(t9 x) can be constructed by the integral equation method described in § III, we shall try to construct it analytically by means of Laplace transformation. For this purpose, put 00 (IV.3) V = V(t; s) = | e'sxv(t, x) dx o and (IV.4) XK(S) = J e-~iKx) dx (k = 0,1). 0 Then multiplying both sides of Eqs (IV. 1), (II.3) and (II.4) by e~sx and integrating with respect to x over the interval (0, oo), we find the ordinary differential equation (TV.5) (5 + b1)^- + (b2s + b3)^- + (fc4s + bs) V = ^ -dt & s subjected to the initial conditions dV (IV.6) V 166 !ř=-o = Xo(s), дt = Zi(s). íí = 0 The associated characteristic equation is (IV.7) (s + bt) r2 + (b2s + *3) r + (bAs + b5) = 0, and its roots are - Í.\ -(b2s + b3) + jA(s) r i-2( s ) 2(7TbO (IV.8) where (TV.9) _l(s) = (b\ - 4b4) s2 + 2(b2b3 - 2b.b4 - b5) s + (bf - 4b.b5). Hence, if _I(J) ^ 0 we have r.(x) ^ r2(s) and y/o (IV.10) where (IV.ll) V(t; s) = s(b4s + b5) + C1(s)er,(l)' + C2(s)eГl(,)' r M >*2(s)Xo(s)-Xi(s) yfpr2(s)  l W r^-r^) s(bAs + b5)(r2(s) - rt(s)) ' I r w = * l ( s ) - rt(s) Xo(s) y/ori(i)  [ í W r2(í)-r.(s) + s(b4s + h5)(r_(_) - -,.(_)) ' Accordingly, we have i c + ioo (IV.12) i<t,x) = | - - ( 1 - «-•-) + -si- f (C.(s) e r , ( 5 )' + C2(s) er j (s )'} e"dx, &5 Z7tt c _ j o o where 0 = b5/b4 and c is a suitably chosen positive number. Further, if A(s) = 0, then r^s) = r2(s) = b* + *\ = r(s) and 2(s + b:) (TV. 13) where (IV.14) /o „-<>)' F ( í ; S ) = rfl, . 4 . /, 1 + - C l ( S > ' + C 2 ( S ) - e" s(oAs + b5) r <v. - v řĉ  , í 6 ! , h-bJh"] v r . y/o(b2s +• t>3) C_(.) - *<-) + [ " f + 2(5 + 6.) J X o ( 5 ) " 2s(s+b.)(b,S + b5) C 2(S) = Xo(s) - Уfo s(b^s + b5) Accordingly, we find (IV.15) where Y(x) = „ l(x) + -?f v0(x) + -_________L j . - - t c - o ^ o d£ _ p(f,x) = ţ-(í - e~ x) + {Y(x) t + Z(x)] R(t,x) (ГV.lб) ___-[_____ 4- b 3 ~ bíb2 -bix • ^3^4 ~ 62^5 g - g - l 2 |_b.b5 + btfibi -b5)e + b5(b5 - btb<) • J У/o \Z(x) = v0(x)-f.(l-e-*x) 167 and < i v . l w , , ) . .-«•>•{. + £[.-» J^g-J^w*. - OA,.*)]} where /i(z) is the Bessel function of the first kind of order 1. Finally we note that A(s) = 0 if and only if (TV. 18) b\ - 4Z>4 = 0, b2b3 - 2b,bA -b5=0, b\- 4btb5 = 0. The first and second equations in (IV. 18) yield respectively the relationships nv 19) x - - ( a "y)1 u - A" + y)2 (IV.19) X- —^—. P- 4 a / i and the third equation yields the relationships (IV.20) y = a [ - 2 + V ^ l I ] , a > -^~ since y > 0. Note that 0 <fx ^ —. Hence if the structural parameters a, fi, y,fi9 A and /i satisfy the conditions (IV.19) and (IV.20), then the activities in the neural model described by Eqs (II. 5) and (IV. 1) show oscillations according to the formulas (II.5)and(IV.15)-(IV.17). Further, since 0 ;g u(t, x) <£ 1 always in D, the functions v0(x) and vx(x) are bounded on R. We can easily verify that (IV.21) lim v(t, x) = lim — ; = 0 for fixed x e R, r-+ + oo *-> + » # t lim v(l, x) = lim - ^ - ^ - = 0 for fixed t e I, dt x-+ + oo x-* + oo whenever the conditions (IVA9)—(IV.20) are satisfied. Hence, under these condi-tions, we have the limits (ГV.22) lim u(t, x) = 0 for fixed x e R, t-* + co lim u(t, x) = 0 for fixed t e I X-+ + 00 by virtue of Eq (II.5). The physiological significance of these limits are obvious. Acknowledgements. It is a very pleasant duty to the author to express his sincere thanks to Professor D. Mangeron and Professor R. B. Stein, the University of Alberta, for their very constructive comments. R E F E R E N C E S [1] M. N. Oguztoreli: On the neural equations of Cowan and Stein (1). Utilitas Mathematica 2, 305—317 (1972). 168 [2] K. V. Leung, D. Mangeron, M. N. Oguztoreli , R. B. Stein: a) On a class of nonlinear integro-differential equations. I. Rend. Accad. Naz Lincei 54, 522—528 (1973). b) On a class of nonlinear integrodifferential equations. II. Rend. Accad. Naz. Lincei 54, 699—705 (1973). c) On a class of nonlinear integrodifferential equations. III. Bull. Acad. Roy. Sci. Begique. Serie 5, 59, 492—499 (1973). d) On the stability and numerical solutions of two neural models. Utilitas Mathematica 5, 167—212 (1974). [3] R. B. Stein, K. V. Leung, M. N. Oguztoreli , D. W. Williams: Properties of small neural networks. Kybernetik 14, 223—230 (1974). [4] R. B. Stein, K. V. Leung, D. Mangeron, M. N. Oguztorel i : Improved neuronal models for studying neural networks. Kybernetik 15, 1—9 (1974). [5] M. N. Oguztorel i : On the activities in a continuous neural network. Biol. Cybernetics 18, 41__48 (1975). [6] M. Pic one: a) Sopra unproblema dei valor i a I con tor no nelle equazioni iperboliche alle derivate parziali delsecond'ordine e sopra una classe di equazioni integrali che a quello si riconnettono, "Rend. Circolo Mat. Palermo", 31, 133—190 (1911); b) Su alcuni problemi di determinazione delle soluzioni di equazioni lineari a derivate parziali del second'ordine di tipo iperbolico, "Atti Accad. Istituto di Bologna, CI. sci. fis.'\ Anno 258, ser XII, 8, 60—71 (1910); c) Duodecim Doctorum Virorum Vitqe et Operum Notitia, "Pontificia Academia Scientiarum", A.D. MCMLXX, 117—146. [7] M. N. Oguztorel i : Activity propagation in a continuous neural network, (in press). [8] M. N. Oguztorel i : A G our sat problem for the fourth moment equation. J. Math. Phys. 16, 1244—1246 (1975). [9] M. N. Oguztorel i : On a pseudoparabolic differential equation. Utilitas Mathematica 7, 121—133 (1975). [10] L. E. ETsgoPts, S. B. Nork in : Introduction to the Theory and Application of Differential Equations with Deviating Arguments. Academic Press. New York and London 1973. [11] M. N. Oguztorel i : Time-lag control system. Academic Press. New York and London 1966. [12] D. V. Widder: The Laplace Transform. Princeton University Press. Princeton 1946. [13] A. Erdelyi, W. Magnus, F. Oberhettinger, F. G. Tricomi: Tables of Integral Transforms. Vol. II. McGraw-Hill. New York, Toronto, London 1954. [14] G. E. Roberts, H. Kaufman: Table of Laplace Transforms. W. B. Saunders, Philadelphia and London, 1966. M. N. Oguztoreli Department of Mathematics University of Alberta Edmonton, Alberta CANADA T6G 2GI 169 

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Activities in a one-dimensional continuous neural network

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Activities in a one-dimensional continuous neural network

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Institute of Mathematics AS CR, v. v. i.

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