Age-related Spike Timing Dependent Plasticity of Brain-inspired Model of Visual Information Processing with Reinforcement Learning

• Autorzy: Koprinkova-Hristova Petia , Bocheva Nadejda

Identyfikatory

DOI

10.15439/2020F141

• Konferencja: Federated Conference on Computer Science and Information Systems (15 ; 06-09.09.2020 ; Sofia, Bulgaria)
• Języki publikacji: EN

Abstrakty

EN

The paper summarizes our efforts to develop a spike timing neural network model of dynamic visual information processing and decision making inspired by the available knowledge about how the human brain performs this complicated task. It consists of multiple layers with functionality corresponding to the main visual information processing structures starting from the early level of the visual system up to the areas responsible for decision making based on accumulated sensory evidence as well as the basal ganglia modulation due to the feedback from the environment. In the present work, we investigated age-related changes in the spike timing dependent plastic synapses of the model as a result of reinforcement learning.

Słowa kluczowe

EN

visualization; decision making; information processing; reinforcement learning; visual systems; brain modeling; timing

PL

wizualizacja; podejmowanie decyzji; przetwarzanie informacji; uczenie wzmacniające; systemy wizualne; modelowanie mózgu; chronometraż

• Wydawca: Polskie Towarzystwo Informatyczne
• Czasopismo: Annals of Computer Science and Information Systems
• Rocznik: 2020
• Tom: Vol. 21
• Strony: 93--100
• Opis fizyczny: Bibliogr. 31 poz., rys., wz., tab.

Twórcy

autor

Koprinkova-Hristova Petia

• Institute of Information and Communication Technologies, Bulgarian Academy of Sciences Sofia, Bulgaria

autor

Bocheva Nadejda

• Institute of Neurobiology, Bulgarian Academy of Sciences Sofia, Bulgaria

Bibliografia

• 1. A. G. Barto, R. S. Sutton and C. W. Anderson, C.W., "Neuronlike adaptive elements that can solve difficult learning control problems," IEEE Trans. on Systems, Man, and Cybernetics, vol. 13 (5), 1983, pp. 834-846. http://dx.doi.org/10.1109/TSMC.1983.6313077
• 2. M. N. Shadlen and W. T. Newsome, "Motion perception: seeing and deciding," Proc. Natl. Acad. Sci. USA, vol. 93 (2), pp. 628-633, 1996. http://dx.doi.org/10.1073/pnas.93.2.628
• 3. D. M. Herz, B. A. Zavala, R. Bogacz and P. Brown, "Neural correlates of decision thresholds in the human subthalamic nucleus," Current Biology, vol. 26 (7), pp. 916-920, 2016. http://dx.doi.org/10.1016/j.cub.2016.01.051
• 4. K. Dunovan, B. Lynch, T. Molesworth and T. Verstynen, T., "Competing basal-ganglia pathways determine the difference between stopping and deciding not to go," eLife, vol. 4, Article number e08723, 2015. DOI: 10.7554/eLife.08723
• 5. A. G. Barto, "Adaptive critics and the basal ganglia," in J. C. Houk, J. L. Davis and D. G. Beiser, Editors, Models of Information Processing in the Basal Ganglia, MIT Press, Cambridge, MA; 1995, pp. 215-232.
• 6. D. Joel, Y. Niv and E. Ruppin, "Actor-critic models of the basal ganglia: new anatomical and computational perspectives," Neural Networks, vol. 15, pp. 535-547, 2002. http://dx.doi.org/10.1016/S0893-6080(02)00047-3
• 7. M. J. Frank, L. C. Seeberger and R. C. O’Reilly, "By carrot or by stick: cognitive reinforcement learning in Parkinsonism," Science, vol. 306 (5703), pp. 1940-1943, 2004. http://dx.doi.org/10.1126/science.1102941
• 8. R. Bogacz and T. Larsen, T., "Integration of reinforcement learning and optimal decision-making theories of the basal ganglia,", Neural Computation, vol. 23 (4), pp. 817-851, 2011. http://dx.doi.org/10.1162/NECO_a_00103
• 9. K. Dunovan and T. Verstynen, "Believer-Skeptic meets actor-critic: Rethinking the role of basal ganglia pathways during decision-making and reinforcement learning,", Frontiers in Neuroscience, vol. 10, Article number 106, 2016. http://dx.doi.org/10.3389/fnins.2016.00106
• 10. P. Koprinkova-Hristova and N. Bocheva, "Spike timing neural model of eye movement motor response with reinforcement learning," Lecture Notes in Computer Science, in press.
• 11. J. Igarashi, O. Shounob, T. Fukai and H. Tsujino, "Real-time simulation of a spiking neural network model of the basal ganglia circuitry using general purpose computing on graphics processing units," Neural Networks, vol. 24, pp. 950-960, 2011. http://dx.doi.org/10.1016/j.neunet.2011.06.008
• 12. R. Krishnan, S. Ratnadurai, D. Subramanian, V. S. Chakravarthy and M. Rengaswamyd, "Modeling the role of basal ganglia in saccade generation: Is the indirect pathway the explorer?," Neural Networks, vol. 24, pp. 801-813, 2011. http://dx.doi.org/10.1016/j.neunet.2011.06.002
• 13. S. Kunkel et al.,"NEST 2.12.0," Zenodo, 2017. http://dx.doi.org/10.5281/zenodo.259534
• 14. P. Koprinkova-Hristova, N. Bocheva, S. Nedelcheva, M. Stefanova, B. Genova, R. Kraleva and V. Kralev, "STDP plasticity in TRN within hierarchical spike timing model of visual information processing," IFIP Advances in Information and Communication Technology, vol. 583 IFIP, pp. 279-290, 2020. http://dx.doi.org/10.1007/978-3-030-49161-1_24
• 15. P. Koprinkova-Hristova, N. Bocheva, S. Nedelcheva and M. Stefanova, "Spike timing neural model of motion perception and decision making," Frontiers in Computational Neuroscience, vol. 13, Article number 20, 2019. http://dx.doi.org/10.3389/fncom.2019.00020
• 16. P. Koprinkova-Hristova, N. Bocheva and S. Nedelcheva, "Investigation of feedback connections effect of a spike timing neural network model of early visual system, " in Innovations in Intelligent Systems and Applications (INISTA), Thessaloniki, Greece, 2018, http://dx.doi.org/10.1109/INISTA.2018.8466292
• 17. S. Nedelcheva and P. Koprinkova-Hristova, "Orientation selectivity tuning of a spike timing neural network model of the first layer of the human visual cortex," Studies in Computational Intelligence, vol. 793, pp. 291-303, 2019. http://dx.doi.org/10.1007/978-3-319-97277-0_24
• 18. T. W. Troyer, A. E. Krukowski, N. J. Priebe and K. D. Miller, "Contrast invariant orientation tuning in cat visual cortex: thalamocortical input tuning and correlation-based intracortical connectivity," J. Neurosci., vol. 18, pp. 5908-5927, 1998. http://dx.doi.org/10.1523/jneurosci.18-15-05908.1998
• 19. J. Kremkow, L. U. Perrinet, C. Monier, J.-M. Alonso, A. Aertsen, Y. Fregnac and G. S. Masson, "Push-pull receptive field organization and synaptic depression: Mechanisms for reliably encoding naturalistic stimuli in V1," Frontiers in Neural Circuits, vol. 10, Article number 37, 2016. http://dx.doi.org/10.3389/fncir.2016.00037
• 20. A. Casti, F. Hayot, Y. Xiao and E. Kaplan, "A simple model of retina-LGN transmission," J. Computational Neuroscience, vol. 24, pp. 235-252, 2008. http://dx.doi.org/10.1007/s10827-007-0053-7
• 21. M. Ghodratia, S.-M. Khaligh-Razavic and S. R. Lehky, "Towards building a more complex view of the lateral geniculate nucleus: Recent advances in understanding its role," Progress in Neurobiology, vol. 156, pp. 214-255, 2017. http://dx.doi.org/10.1016/j.pneurobio.2017.06.002
• 22. P. Gleeson, R. Martinez and A. Davison, "Network models of V1," Open Source Brain, http://www.opensourcebrain.org/projects/111.
• 23. S. Sadeh and S. Rotter, "Statistics and geometry of orientation selectivity in primary visual cortex," Biol. Cybern., vol. 108, pp. 631-653, 2014. http://dx.doi.org/10.1007/s00422-013-0576-0
• 24. M.-J. Escobar, G. S. Masson, T. Vieville and P. Kornprobst, "Action recognition using a bio-inspired feedforward spiking network," Int. J. Comput. Vis., vol. 82, pp. 284-301, 2009. http://dx.doi.org/10.1007/s11263-008-0201-1
• 25. O. W. Layton and B. R. Fajen, "Possible role for recurrent interactions between expansion and contraction cells in MSTd during self-motion perception in dynamic environments," Journal of Vision, vol. 17 (5), Article number 5, 2017. http://dx.doi.org/10.1167/17.5.5
• 26. W. Potjans, A. Morrison and M. Diesmann, "Enabling functional neural circuit simulations with distributed computing of neuromodulated plasticity," Front. in Comp. Neuroscience, vol. 4, 2010. http://dx.doi.org/10.3389/fncom.2010.00141
• 27. J. L. Plotkin and L. A. Goldberg, "Thinking outside the box (and arrow): Current themes in striatal dysfunction in movement disorders," The Neuroscientist, vol. 25 (4), pp. 359-379, 2019. http://dx.doi.org/10.1177/1073858418807887
• 28. W. Wei, J. E. Rubin and X.-J. Wang, "Role of the indirect pathway of the basal ganglia in perceptual decision making," The Journal of Neuroscience, vol. 35 (9), pp. 4052-4064, 2015. http://dx.doi.org/10.1523/JNEUROSCI.3611-14.2015
• 29. H. Yan and J. Wang, "Quantification of motor network dynamics in Parkinson’s disease by means of landscape and flux theory," PLoS ONE, vol. 12 (3), Article number e0174364, 2017. http://dx.doi.org/10.1371/journal.pone.0174364
• 30. M. Tsodyks, A. Uziel and H. Markram, "Synchrony generation in recurrent networks with frequency-dependent synapses," The Journal of Neuroscience, vol. 20 (1), pp. RC50, 2000. http://dx.doi.org/10.1523/jneurosci.20-01-j0003.2000
• 31. N. Bocheva, B. Genova and M. Stefanova, "Drift diffusion modeling of response time in heading estimation based on motion and form cues," Int. J. of Biology and Biomedical Engineering, vol. 12, pp. 75-83, 2018.

Uwagi

1. Track 1: Artificial Intelligence

2. Technical Session: 15th International Symposium Advances in Artificial Intelligence and Applications

3. Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).