A neural network model of the olfactory system of mice: computer simulation of the attention behavior of mice for some components in an odor

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1 Artif Life Rootics (28) 2:75 8 ISARB 28 DI.7/s RIGINAL ARTICLE Zu Soh Michiyo Suzuki Toshio Tsuji Nooru Tkiguchi Hiso htke A neurl network model of the olfctory system of mice: computer simultion of the ttention ehvior of mice for some components in n odor Received nd ccepted: July 3, 27 Astrct Recently, it ws oserved tht mice could identify n odor y pying ttention to only few of its components. Further, it hs een reported tht ech individul is ttrcted to different components of n odor. This ehvior is referred to s ttention ; however, its mechnism hs yet to e completely elucidted. In this pper, we first propose novel rtificil neurl network model sed on the iologicl structure of n olfctory system. Then series of computer simultions of odornt discrimintion re performed to evlute the ttention ility of the proposed model. Finlly, we chnged the connective weights etween the neurons to simulte individul differences. The simultion results indicte tht the inhiitory connections from the piriform cortex to the olfctory ul my contriute to the individul differences oserved in the ehviorl experiment. Key words lfctory system Attention Neurl network model Introduction In recent yers, the demnd for odor-processing pprtuses hs een incresing in the frgrnce nd entertin- Z. Soh (*) T. Tsuji Grdute School of Engineering, Hiroshim University, -4- Kgmiym, Higshi-hiroshim , Jpn Tel ; Fx e-mil: sozu@sys.hiroshim-u.c.jp M. Suzuki Microem Rdition Biology Group, Jpn Atomic Energy Agency, Tkski, Jpn N. Tkiguchi Grdute School of Advnced Sciences of Mtter, Hiroshim University, Hiroshim, Jpn H. htke Grdute School of Engineering, sk University, sk, Jpn This work ws presented in prt t the 2th Interntionl Symposium on Artificil Life nd Rootics, it, Jpn, Jnury 25 27, 27 ment industries. dornt informtion is difficult to process ecuse of its nture: it is high-dimensionl informtion, nd vst mounts of computtion re required to discriminte or clssify odors. This is ecuse odors re comintions of different (pproximtely 2 4 thousnd) kinds of odornt molecules. 2 Thus fr, in order to reduce the dimensions of odornt informtion, most odor-discriminting pprtuses hve een designed for prticulr odors, for exmple, n electronic nose for sensing nn ripeness developed y Lloet et l. 3 However, their odor discrimintion is not comprle to tht of living nose. Therefore, lerning from the olfctory system of living nose would e one of the most efficient prospective pproches. A numer of studies of the olfctory system of mice hve een reported. This system cn e considered to consist of three prts: olfctory receptors (Rs), which respond to odornt molecules; the olfctory ul (B), which integrtes the responses of the Rs; nd the piriform cortex (PC), which discrimintes the odornts sed on the informtion provided y the B 2 (Fig. 2). Herly 4 hs ntomiclly reveled the more detiled structure of the olfctory system. Through series of ehviorl experiments on odor discrimintion, Nkmur et l. reported tht mice could identify n odor y focusing on only few of its components. It ws lso oserved tht ech individul focuses on different components. This ehvior is termed s ttention (Fig. ). The mechnism of ttention ws reserched y Li 5 using computer model of the B; these ttention ws referred to s dpttion. Li suggested tht n inhiitory signl to the B might e one of the cuses of ttention. However, the origin of the inhiitory signl nd its control mechnism is still unknown; thus, the mechnism of ttention hs not een completely elucidted. In this reserch, we first constructed novel rtificil neurl network model of the olfctory system tht consisted of R, B, nd PC sed on iologicl insights. 4 In prticulr, the inhiitory connections from the PC to the B reported y Heimer 6 were lso tken into ccount for the proposed model. Then series of computer simultions we crried out with chnges in the inhiitory connections. In order to elucidte the mechnism of ttention, the simul-

2 76 An odor composed of 3 kinds of molecules A dor ox B H H c Emi Kosno Fig.. Concept of ttention ehvior. There re 2 4 thousnd kinds of odornt molecules. Mice only py ttention to prt of the molecules composing n odor C Fig. 3. Structure of the Y-mze nd the drinking ehvior of mouse (revised from the figure in Nkmur et l. ) dornt Mp Higher Brin Function Accurcy rte % 8 5 Ci E IA E IA Mouse A Mouse B E Ci IA Receptor Cells lfctory Bul Piriform Cortex Fig. 4. Results of the odor discrimintion experiment (unpulished dt) Fig. 2. Schemtic digrm of the olfctory system tion results were compred with the results of the ehviorl experiment performed y Nkmur et l. In this pper, we report tht the inhiitory connections from the PC to the B cn cuse ttention, nd differences in the inhiitory connections cn cuse individul differences mong mice. 2 Behviorl experiments on odor discrimintion in mice Nkmur et l. implemented series of ehviorl experiments on the odor discrimintion of mice to revel the ehvior of ttention. This section descries the experiments nd their results. The ehviorl experiments on the odor discrimintion of mice were crried out y Nkmur et l. using Y-shped pssge (Y-mze), s shown in Fig. 3. First, mouse deprived of wter is plced t the strting point (point C). It ws llowed to drink wter s rewrd only if it selected the correct point, A or B, from which the rewrded odor emnted. Further, one tril ws defined s the time tken from the mouse strting t point C to its rrivl t either point A or B. Twenty-four trils, which were defined s one session, were performed in dy for ech mouse. The ccurcy rte t which the mice selected the rewrded odor ws recorded. When the ccurcy rte surpssed 8%, it ws considered tht the mouse hd lernt to recognize the rewrded odor. Becuse the experiments require discriminting etween two kinds of odors, n ccurcy rte of 5% indicted tht the mouse ws unle to discriminte etween those odors. In the experiments, the mice were required to lern the rewrded odor [Ci : EB : IA] tht ws composed of three types of molecules: isomyl cette (IA), ethyl utyl (EB), nd citrl (Ci). Then they were required to discriminte etween the rewrded odor nd the other odors ([Ci], [EB], [IA], nd [EB : IA]) tht contined the sme types of molecules s the rewrded odor. 2. Methods 2.2 Results Figure 4 shows the experimentl results of two out of the eight individul mice on whom the experiment ws performed. The verticl nd horizontl xes show the ccurcy rte nd the odors for discrimintion, respectively. In the cse of individul A, we oserved tht the ccurcy rtes re pproximtely 5% for odors contining the molecule

3 77 EB ([EB], [EB : IA]). Becuse this molecule is common in the rewrded odor nd the unrewrded odors, this result implies tht individul A focused only on this molecule for odor discrimintion; consequently, it ws unle to discriminte the unrewrded odors [EB] nd [EB : IA] from the rewrded odor. The sme result ws oserved for two out of the eight individuls. In the cse of individul B, we oserved tht the ccurcy rte for odor [EB : IA] ws pproximtely 5%. In similr wy to individul A, this result indictes tht individul B pid ttention to the comintion of molecules EB nd IA. It ws oserved tht five out of eight individuls hd focused on the comintion of EB nd IA. From these results for odor discrimintion, Nkmur et l. suggested tht mice could focus on specific molecule or prticulr comintion of the molecules contined in the odor. Further, there re individul differences mong mice with respect to the molecules which they focus on. 3 Model of the olfctory system of mice In this section, we propose model of the olfctory system of mice sed on iologicl insights. An overview of the proposed model is shown in Fig. 5. In Fig. 5, the R lyer comprises olfctory receptors (Rs) tht respond to odornt molecules, the B lyer is the olfctory ul (B), the P lyer is the nterior piriform cortex, nd the Z lyer is the post-piriform cortex tht outputs the discrimintion results. Ech lyer consists of neuron model, nd the neuron models re connected sed on iologicl insights. 4,6 The connective weights re suject to chnge y the Hein lerning rule. The detils of ech lyer re given elow. 3. Receptor lyer There re pproximtely types of Rs in the nsl pssge of mice. Ech type of R responds to different kinds of molecules. 7 The proposed model is designed to discriminte odors tht consist of N kinds of molecules, S, S 2,..., S N ; thus, the R lyer is composed of N types of receptors. The concentrtion of ech molecule is expressed S S 2 S 3 S 4 S 5 dornt Inhiitory Excittory Confidence index C (t) R B P Z Receptor lfctory ul Piriform cortex Fig. 5. A model of the olfctory system in mouse s vlue in the intervl [, ]. The receptor model is defined y the following eqution ccording to the generl neuron model, 8 which is expressed s the sigmoid function URij (, )()= t () + exp { εrij (, ) ( urij (, ) () t θrij (, ) )} where is the grdient of the sigmoid function, u(t) is the internl stte of neuron t time-step t, nd θ is the firing threshold of the neuron. Ech receptor of the j-th column in the R lyer, which is shown in Fig. 5, responds to the sme molecule S j with different firing threshold θ. The internl stte of ech receptor in the R lyer is defined s u l (i,j)(t) = s i (t). The outputs of the receptors clculted y Eq. re input to the B lyer (B lyer). 3.2 lfctory ul lyer The olfctory ul consists of glomeruli, excittory neurons known s mitrl cells, nd inhiitory neurons known s grnule cells. Since glomeruli re convergence of the nerve terminl extended from the olfctory receptors, they mp odornt molecules. There re round pirs of glomeruli on the olfctory ul surfce. A response pttern of the glomeruli is referred to s n odornt mp. 9 The outputs of the glomeruli re trnsmitted to the mitrl cells. Further, the mitrl cells receive n inhiitory input from the PC vi the grnule cells. 6 In the proposed model, the glomeruli re omitted for simplifiction, nd receptor R i,j tht responds to the sme molecule S j is directly connected to mitrl cell B i,j. Mitrl cell B i,j mps molecule S j, nd the firing pttern of the B lyer represents the odornt mp medited y mitrl cells. The neurons in the B lyer receive excittory input from the R lyer nd inhiitory input from the P lyer; thus, the internl sttes u B(i,j) (t) re s follows: ut () = wt () U w Bij (, ) Rmn (, ) RmnBij (, ), (, ) Rmn (, )( t) tu() t Pxy (, ), Bij (, ) Pxy (, ) Pxy (, ) (2) where w R(m,n),B(i,j) nd w P (m,n),b(i,j) re the connective weights from the R nd P lyers, respectively. The output of ech neuron, UB (i,j) (t), is expressed s sigmoid function in Eq.. The outputs of mitrl cells re trnsmitted to neurons in the PC. 3.3 Piriform cortex lyer The PC cn e divided into the nterior piriform cortex (APC) nd posterior piriform cortex (PPC). It is considered tht the APC processes the input from the B, while the PPC discrimintes the odor. 4 Therefore, in the proposed model, the PC lyer ws divided into the P nd Z lyers, corresponding to the APC nd PPC, respectively. The inhiitory connections which exist from the PC to the B 6 re lso tken into ccount. However, these connections re highly dependent on ech individul. Therefore, in the

4 78 proposed model, the connections etween the B nd P lyers re rndom. The internl stte u P(i,j)(t) of ech neuron in the P lyer is given y the eqution u ()= t w () t U () t Pij (, ) BmnPij (, ), (, ) Bmn (, ) Bmn (, ) where w B(m,n),P(i,j) (t) re the connective weights from the B lyer to the P lyer. n the other hnd, the internl stte u Z (t) of neuron in the Z lyer is given y the eqution: u ()= t w () t U () t Z Pmn (, ) (3) PmnZ (, ), Pmn (, ) (4) where w P(m,n),Z (t) re the connective weights from the P lyer to the Z lyer. Similr to the R nd B lyers, the output of ech neuron in the P nd Z lyers is defined s sigmoid function of Eq.. The odor discrimintion result ws determined y the output of the Z lyer: the output UZ <.5 indictes tht the model hs discriminted the odor s n unrewrded odor, while the output UZ >.5 indictes tht the model hs discriminted the rewrded odor. Thus, the Z lyer discrimintes etween the odors sed on the output of the P lyer. 3.4 Lerning lgorithm The connective weights etween the neurons in ech lyer re initilized y uniform rndom vlue of intervl [, ] nd re updted y the Hein lerning rule: wlmnkij (, ), (, )( t+ )= wlmnkij (, ), (, )()+ t δw() t lmnkij (, ), (, ) (5) δw lmnkij (, ), (, )()= t α{ U() t kij (, ) k }{ U() t lmn (, ) l } where α is the lerning rte nd l nd k re the thresholds of the chnge in the sign of δw l(m,n),k(i,j)(t). The connective weights etween the B nd P lyers nd those etween the P nd Z lyers re updted y the Hein rule. The connections etween the R nd B lyers re considered to e geneticlly determined; 9 therefore, the connective weights remined the sme. Lerning is ssumed to e controlled y higher rin function. For simplifiction, the higher rin function is modeled s the confidence index C(t) ( < C(t) < ). C(t) increses y /n when the discrimintion result is correct, while it decreses y /n for n incorrect result. Here, n is defined s constnt numer representing the voltility of C(t). The inhiitory connection from the P lyer to the B lyer is updted y the Hein lerning rule when C(t) > θ c, while it is decresed y constnt rte β ( < β < ) when C(t) < θ c. The lerning lgorithm is summrized in Fig Simultion In this section, series of simultions re performed to evlute the proposed model with regrd to its ility to simulte the ttention ehvior nd individul differences. Strt t = t mx End I. lfctory No system Input n odornt column (discriminte odors) Clculte the outputs of ech lyer No Decrese C(t) Is the output of Z lyer correct? 4. Simultion of lerning nd ttention First, the connective weights etween the B nd P lyers of the proposed neurl network model were initilized with rndom vlue in the intervl [,]. For this initil stte, the model ws leled M. ne step ws defined s providing input molecules to the R lyer nd otining output from the Z lyer. The rewrded odor A (N = 5, s = s 2 = s 3 =, nd s 4 = s 5 = ) ws repetedly input to the model in 5 steps. During these steps, the connective weights were updted y the lgorithm shown in Fig. 6 to mke the neuron in the Z lyer fire more strongly. The chnges in the outputs of M nd M2 re shown in Figs. 7 nd 8, respectively, where () nd () show the firing ptterns of ech lyer when t = nd t = 5, respectively. The output of ech neuron is represented y squre. The lrger the output of neuron, the whiter the corresponding squre. The chnges in the inhiitory weights from the P lyer to the B lyer nd the confidence indices re shown in Figs. 9 nd, respectively. From Fig. 7, it cn e oserved tht the neuron in the Z lyer fires wekly when Increse C(t) C(t) >.5 No C(t) >.5 No Decrese the inhiitory weights Updte ll weights y etween P nd B lyer. the Hein rule. Updte the other weights y the Hein rule. Fig. 6. Flowchrt of weight trining M M dor S S 2 S 3 S 4 S 5 S S 2 S 3 S 4 S 5 R B P Z t = t = 5 Fig. 7. Chnges in the output sttes of M II. Higher rin (judgement) III. Chnge connective weights.. Activtion Level

5 79 M2 M2 dor S S 2 S 3 S 4 S 5 S S 2 S 3 S 4 S 5 Fig. 8. Chnges in the output sttes of M2 R B P Z t = t = 5 M M2 S S 2 S 3 S 4 S 5 S S 2 S 3 S 4 S 5 Fig.. Sttes of M nd M2 when odor B is input. M. M2 Inhiitory weight Steps t 5 W P(4,3),B(3,2) Fig. 9. Chnges in the inhiitory weight from the P lyer to the B lyer Confidence index C(t) Steps t Fig.. Chnges in the confidence indices t = ; this indictes tht the model incorrectly discriminted the odor s the unrewrded odor. While the rewrded odor A ws repetedly input, the connective weights were updted y the lgorithm shown in Fig. 6; thus, M lernt odor A s the rewrded odor. This result cn e oserved in Fig. 7, in which the neuron in the Z lyer fires strongly t t = 5. At the sme time, the inhiitory connective weights from the P lyer to the B lyer increse, for exmple, the inhiitory weights from P 4, 3 to B 3,2 increse, s shown in Fig. 9. Hence, the neurons tht mp molecule S 3 were inhiited. 5 As result, M discrimintes odors only y molecules S nd S 2, for which its ttention ility hs een confirmed. Next, the proposed model ws initilized with different inhiitory connective weights etween the P nd B lyers; for this initil stte, the model ws leled M2. The sme simultion s descried ove ws gin crried out. This simultion result is shown in Fig.. In this figure, () nd () show the firing ptterns t t = nd t = 5, respectively. When t = 5, the neuron in the Z lyer fired strongly, indicting tht the model lernt odor A s the rewrded odor. It cn e lso e oserved tht M2 focused on molecules S nd S 3, while M focused on S nd S 2. These results suggest tht the difference in the initil weights of inhiitory connections from the P lyer to the B lyer could led the proposed model to focus on different molecules for odor discrimintion. 4.2 Individul differences in ttention As descried in Sect. 4., M nd M2 lernt to discriminte the rewrded odor A y utilizing few molecules. Furthermore, the simultion results indicted tht M nd M2 focused on different molecules. Here, in order to determine their discrimintion ilities, n unrewrded odor B, which contins some of the molecules tht comprise the rewrded odor A, is input to M nd M2. The result is shown in Fig.. Figure nd show the outputs of M nd M2, respectively, when the unrewrded odor B is input. It cn e oserved from Fig. tht the neuron in the Z lyer of M2 fired wekly; this indictes tht M2 hs successfully discriminted the unrewrded odor B. However, the result of M shows tht the neuron in its Z lyer fired strongly in the sme mnner s when the rewrded odor A ws input; this indictes tht M hs incorrectly discriminted the unrewrded odor B. The simultion descried in the previous section showed tht M pid ttention to molecules S nd S 2. Therefore, it cn e considered tht whenever the input odor contins oth molecules S nd S 2, M would discriminte it s the rewrded odor A.

6 8 These simultion results re consistent with the ehviorl experiment crried out y Nkmur et l., which ws descried in Sect. 2. Further, the results suggest tht the individul differences in odor discrimintion could e cused y the individul differences in the inhiitory connections from the nterior piriform cortex (P lyer) to the olfctory ul (B lyer). As mentioned in Sect., Li 5 suggested tht the ttention is cused y n inhiitory signl. ur simultion results support this hypothesis, nd imply tht the unspecified origin of the inhiitory connection might e the nterior piriform cortex. 5 Conclusion In this pper, we focused on the ttention ehvior of mice oserved in the ehviorl experiments, nd proposed neurl network model of their olfctory system sed on iologicl insights. A series of simultions of the proposed model ws crried out so tht the ttention ehvior ws oserved. Further, y chnging the inhiitory connection from the piriform cortex to the olfctory ul, possile cuse of the individul differences in ttention ws discussed. Although the proposed model is mcroscopic, the simultion results cptured the feture oserved in the odornt discrimintion experiment on mice. Further studies re required to improve the cpility of this model so tht it cn process more kinds of odornt molecules. As next step, we re plnning to increse the scle of the receptor model nd olfctory ul model. Acknowledgments This work ws prtilly supported y the 2st Century CE Progrm of JSPS (Jpn Society for the Promotion of Science) on Hyper-Humn Technology towrd the 2st Century Industril Revolution. Michiyo Suzuki ws supported y Reserch Fellowship from the Jpn Society for the Promotion of Science for Young Scientists. The uthors would like to thnk Dr. kuhr for his dvice on the iologicl spects of this study, nd Mr. Hirno for editing skills nd support. References. Nkmur T, kuhr K, Tkiguchi N (25) Explore lgorithms in olfctory system of mice (in Jpnese). Softwre Biol 4: Shiuy T, Sotoike M (22) Reception of odors (in Jpnese). Frgrnce : Lloet E, Hines EL, Grdner JW, et l. (999) Non-destructive nn ripeness determintion using neurl network-sed electronic nose. Mesure Sci Technol : Herly LB (2) Prllel-distriuted processing in olfctory cortex. New insights from morphologicl nd physiologicl nlysis of neuronl circuitry. Chem. Senses 26: Li Z (99) A model of olfctory dpttion nd sensitivity enhncement in the olfctory ul. Biol Cyern 62: Heimer L (968) Synptic distriutions of centripetl nd centrifugl nerve fires in the olfctory system of the rt. An experimentl ntomicl study. J Ant 3: Buck L, Axel R (99) A novel multigene fmily my encode odornt receptors: moleculr sis for odor recognition, Cell 65: Smolensky P (986) Neurl nd conceptul interprettion on PDP models. Prllel distriuted processing: explortions in the microstructure, vol. 2. MIT Press, Cmridge, pp Mori K, Yoshihr Y (995) Moleculr recognition nd olfctory processing in the mmmlin olfctory system, Prog Neuroiol 45: He D (949) The orgniztion of ehvior. Wiley, New York