2015/9 Vol. J98 D No. 9 Shidara [7] t s t V (s t)=e[r t+1 + γr t+2 + γ 2 r t+3 + ] (1) r t t E γ 0 1 V (s t) TD V new(s t 1) V
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1 a) b) Modeling the Function of the Ventral Striatum in Reinforcement Learning Based on the Analysis of Neuronal Activity Masanari SHINOTSUKA a), Masahiko MORITA b), and Munetaka SHIDARA TD striosome striosome 1. [1] Schultz TD Graduate School of Systems and Information Engineering, University of Tsukuba, Tennodai, Tsukuba-shi, Japan Faculty of Engineering, Information and Systems, University of Tsukuba, Tennodai, Tsukuba-shi, Japan Faculty of Medicine, University of Tsukuba, Tennodai, Tsukuba-shi, Japan a) m.shinotsuka2@gmail.com b) mor@bcl.esys.tsukuba.ac.jp DOI: /transinfj.2014JDP7137 [2] Barto [3] Doya [4] striosome V (s) striosome striosome [5], [6] D Vol. J98 D No. 9 pp c
2 2015/9 Vol. J98 D No. 9 Shidara [7] t s t V (s t)=e[r t+1 + γr t+2 + γ 2 r t+3 + ] (1) r t t E γ 0 1 V (s t) TD V new(s t 1) V old (s t 1)+αδ t 1 (2) δ t 1 δ t 1 = r t + γv (s t) V (s t 1) (3) TD temporal differencetd 1 Fig. 1 Neural circuits of the basal ganglia. (1) (3) 0 TD basal ganglia cerebral cortex 1 (striatum) striosome matrix striosome (DA cell) matrix (internal segment of globus pallidus, GPi) (substantia nigra pars reticulata, SNr) GPi/SNr (thalamus) Schultz [2] 1278
3 TD striosome matrix striosome matrix striosome [8] [9] Hebb TD matrix striosome actor critic Barto [3] matrix Q(s, a) Doya [4] striosome 2 striosome s t V (s t) striosome TD striosome TD striosome 2 Fig. 2 Structure common to conventional reinforcement learning models of the basal ganglia. striosome striosome [10], [11] Cromwell [11] Shidara [7] [12] Goldstein [5] Kim [6] 1279
![2015/9 Vol. J98 D No. 9 striosome 2. 2 Shidara [7] 2. 2. 1 3A Wait Go OK 1 1 3 3B 1 3 1 2 3 1/2 2 1 2/3 3 2 3B 3C 1/1, 1/2, 2/2, 1/3, 2/3, 3/3 1/1, 2/2, 3/3 1 1/2 1/2, 1/3, 2/3 1/6 1/2 2. 2. 2 100 200 100 200 1 5 3 Shidara [7] Fig.](/docs-images/92/107764717/images/4-0.jpg "3 Multiple trial reward schedule task (adapted from Shidara et al. [7]). 1 Shidara [7] Table 1 Response in the cue condition (adapted from Shidara et al. [7]). 1/3 1/2 2/3 3/3 2/2 1/1 n (1) 16 (2) 13 (3) 6 (4) 3 (5) 3 1280")
4 2015/9 Vol. J98 D No. 9 striosome 2. 2 Shidara [7] A Wait Go OK B / / B 3C 1/1, 1/2, 2/2, 1/3, 2/3, 3/3 1/1, 2/2, 3/3 1 1/2 1/2, 1/3, 2/3 1/6 1/ Shidara [7] Fig. 3 Multiple trial reward schedule task (adapted from Shidara et al. [7]). 1 Shidara [7] Table 1 Response in the cue condition (adapted from Shidara et al. [7]). 1/3 1/2 2/3 3/3 2/2 1/1 n (1) 16 (2) 13 (3) 6 (4) 3 (5)
5 [12] 3. 1 (1) (2) (1) (2) 2/3, 3/3, 2/2 1/3, 1/2, 1/1 Shidara σ = ms 200 ms 1000 ms 0 90% Fig. 4 Response period. 5 Fig. 5 Histogram of the response onset time /26 [13], [14] 100 ms ms
6 2015/9 Vol. J98 D No. 9 6 Fig. 6 Classification diagram of history dependence for the ventral striatum neurons. 1 1/2 1/3 2/ % n = n 1 (1) (5) (1) (2) (1) 8 (2) 7 (3) (5) ms Shidara striosome
7 Fig. 7 7 Structure of the proposed model. 8 Fig. 8 Network output to the test sequence. Elman [15] t cue t cue t r t+1 1 1/2 1/3 2/ Elman TD δ t 1 = r t + γo t O t 1 (4) 0 O t t r t 0 1 TD r t O t γ /2 2/2 1/3 2/3 3/
8 2015/9 Vol. J98 D No (a) 2 F (1, 190) = 34.1 p <0.01; 2 F (1, 190) = 19.1 p < Fig. 9 Classification diagram of history dependence for the middle elements of the model (b) F (1, 190) = 4.99 p< (a): F (1, 145) = 15.9 p<0.01; 2 F (1, 145) = 4.21 p <0.05, (b): F (1, 227) = 4.36 p < Fig Example of the response of middle elements to a random sequence. Fig Example of the response of ventral striatum neurons in the random condition. 1284
9 Fig Correspondence of the proposed model to the brain structure. 13 Fig. 13 State values estimated from the internal state F (5, 193) = 3.53 p <0.01 2/2 3/3 2/3 3/3 vs 2/3 t(70) = 4.41 p <0.01 2/2 vs 2/3 t(60) = 2.6 p<0.011/1, 1/2, 1/3 [10], [11] 1 1 V V
10 2015/9 Vol. J98 D No. 9 [12] TD V V Q matrix [16] TD 1 1 TD (B) , 1286
![22300138, 25282246 [1] R.S. Sutton and A.G. Barto, Reinforcement Learning, MIT Press, 1998. [2] W. Schultz, P. Dayan, and P.R. Montague, A neural substrate of prediction and reward, Science, vol.](/docs-images/92/107764717/images/11-0.jpg "275, pp.1593 1599, 1997 [3] A.G. Barto, Adaptive critics and the basal ganglia, in Models of Information Processing in the Basal Ganglia, ed. J.C. Houk, J.L. Davis, and D.G. Beiser, pp.")
![215 232, MIT Press, 1995. [4] K. Doya, Complementary roles of basal ganglia and cerebellum in learning and motor control, Current Opinion in Neurobiology, vol.10, no.6, pp.732 739, 2000. [5] B.L.](/docs-images/92/107764717/images/11-1.jpg "Goldstein, B.R. Barnett, G. Vasquez, S.C. Tobia, V. Kashtelyan, A.C. Burton, D.W. Bryden, and M.R. Roesch, Ventral striatum encodes past and predicted value independent of motor contingencies, Journal of Neuroscience, vol.")
![32, pp.2027 2036, 2012. [6] Y.B. Kim, N. Huh, H. Lee, E.H. Baeg, D. Lee, and M.W. Jung, Encoding of action history in the rat ventral striatum, J. Neurophysiology, vol.98, pp.3548 3556, 2007. [7] M.](/docs-images/92/107764717/images/11-2.jpg "Shidara, T.G. Aiger, and B.J. Richmond, Neuronal signals in the monkey ventral striatum related to progress through a predictable series of trials, J. Neuroscience, vol.18, pp.2613 2625, 1998. [8] C.")
11 , [1] R.S. Sutton and A.G. Barto, Reinforcement Learning, MIT Press, [2] W. Schultz, P. Dayan, and P.R. Montague, A neural substrate of prediction and reward, Science, vol.275, pp , 1997 [3] A.G. Barto, Adaptive critics and the basal ganglia, in Models of Information Processing in the Basal Ganglia, ed. J.C. Houk, J.L. Davis, and D.G. Beiser, pp , MIT Press, [4] K. Doya, Complementary roles of basal ganglia and cerebellum in learning and motor control, Current Opinion in Neurobiology, vol.10, no.6, pp , [5] B.L. Goldstein, B.R. Barnett, G. Vasquez, S.C. Tobia, V. Kashtelyan, A.C. Burton, D.W. Bryden, and M.R. Roesch, Ventral striatum encodes past and predicted value independent of motor contingencies, Journal of Neuroscience, vol.32, pp , [6] Y.B. Kim, N. Huh, H. Lee, E.H. Baeg, D. Lee, and M.W. Jung, Encoding of action history in the rat ventral striatum, J. Neurophysiology, vol.98, pp , [7] M. Shidara, T.G. Aiger, and B.J. Richmond, Neuronal signals in the monkey ventral striatum related to progress through a predictable series of trials, J. Neuroscience, vol.18, pp , [8] C.R. Gerfen, The neostriatal mosaic: Multiple levels of compartmemtal organization in the basal ganglia, Annual Review of Neuroscience, vol.15, pp , [9] J.N.J. Reynolds, B.I. Hyland, and J.R. Wickens, A cellular mechanism of reward-related learning, Nature, vol.413, pp.67 70, [10] W. Schultz, P. Apicella, E. Scarnati, and T. Ljungberg, Neuronal activity in monkey ventral striatum related to the expectation of reward, Journal of Neuroscience, vol.12, pp , [11] H.C. Cromwell and W. Schultz, Effects of expectations for different reward magnitudes on neuronal activity in primate striatum, J. Neurophysiology, vol.89, pp , [12] vol.25, no.4, pp , [13] Z. Liu and B.J. Richmond, Response differences in monkey TE and perirhinal cortex: Stimulus association related to reward schedules, J. Neurophysiology, vol.83, pp , [14] Y. Naya, M. Yoshida, and Y. Miyashita, Forward processing of long-term associative memory in monkey inferotemporal cortex, J. Neuroscience, vol.23, pp , [15] J.L. Elman, Finding structure in time, Cognitive Science, vol.14, pp , [16] Y. Sawatsubashi, M.F.B. Samusudin, and K. Shibata, Emergence of discrete and abstract state representation in continuous input task through reinforcement learning, Advances in Intelligent Systems and Computing, vol.208, pp.13 22,
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