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Exploring the role of structure in a time constrained decision task

Published 19 Jan 2024 in cs.NE and q-bio.NC | (2401.10849v1)

Abstract: The structure of the basal ganglia is remarkably similar across a number of species (often described in terms of direct, indirect and hyperdirect pathways) and is deeply involved in decision making and action selection. In this article, we are interested in exploring the role of structure when solving a decision task while avoiding to make any strong assumption regarding the actual structure. To do so, we exploit the echo state network paradigm that allows to solve complex task based on a random architecture. Considering a temporal decision task, the question is whether a specific structure allows for better performance and if so, whether this structure shares some similarity with the basal ganglia. Our results highlight the advantage of having a slow (direct) and a fast (hyperdirect) pathway that allows to deal with late information during a decision making task.

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References (31)
  1. T. Boraud, A. Leblois, and N. P. Rougier, “A natural history of skills,” Progress in Neurobiology, vol. 171, dec 2018. [Online]. Available: https://doi.org/10.1016%2Fj.pneurobio.2018.08.003
  2. H. Jaeger, “Echo state network,” scholarpedia, vol. 2, no. 9, 2007.
  3. R. Schmidt, D. K. Leventhal, N. Mallet, F. Chen, and J. D. Berke, “Canceling actions involves a race between basal ganglia pathways,” Nature neuroscience, vol. 16, no. 8, 2013.
  4. M. Dale, S. O’Keefe, A. Sebald, S. Stepney, and M. A. Trefzer, “Reservoir computing quality: connectivity and topology,” Natural Computing, vol. 20, 2021.
  5. D. J. Watts and S. H. Strogatz, “Collective dynamics of ‘small-world’networks,” nature, vol. 393, no. 6684, 1998.
  6. D. S. Bassett and E. Bullmore, “Small-world brain networks,” The neuroscientist, vol. 12, no. 6, 2006.
  7. Z. Cheng, Z. Deng, X. Hu, B. Zhang, and T. Yang, “Efficient reinforcement learning of a reservoir network model of parametric working memory achieved with a cluster population winner-take-all readout mechanism,” Journal of Neurophysiology, vol. 114, no. 6, 2015.
  8. K. Bai, F. Liao, and X. Hu, “Reservoir computing with a small-world network for discriminating two sequential stimuli,” in Advances in Neural Networks-ISNN 2017: 14th International Symposium, ISNN 2017, Sapporo, Hakodate, and Muroran, Hokkaido, Japan, June 21–26, 2017, Proceedings, Part I 14.   Springer, 2017.
  9. Z. Deng and Y. Zhang, “Collective behavior of a small-world recurrent neural system with scale-free distribution,” IEEE Transactions on neural networks, vol. 18, no. 5, 2007.
  10. Y. Kawai, J. Park, and M. Asada, “A small-world topology enhances the echo state property and signal propagation in reservoir computing,” Neural Networks, vol. 112, 2019.
  11. K.-i. Kitayama, “Guiding principle of reservoir computing based on “small-world” network,” Scientific reports, vol. 12, no. 1, 2022.
  12. N. Rodriguez, E. Izquierdo, and Y.-Y. Ahn, “Optimal modularity and memory capacity of neural reservoirs,” Network Neuroscience, vol. 3, no. 2, 2019.
  13. P. F. Dominey, T. M. Ellmore, and J. Ventre-Dominey, “Effects of connectivity on narrative temporal processing in structured reservoir computing,” in 2022 International Joint Conference on Neural Networks (IJCNN).   IEEE, 2022.
  14. S. Jarvis, S. Rotter, and U. Egert, “Extending stability through hierarchical clusters in echo state networks,” Frontiers in neuroinformatics, vol. 4, 2010.
  15. C. Gallicchio, A. Micheli, and L. Pedrelli, “Deep reservoir computing: A critical experimental analysis,” Neurocomputing, vol. 268, 2017.
  16. J. Moon, Y. Wu, and W. D. Lu, “Hierarchical architectures in reservoir computing systems,” Neuromorphic Computing and Engineering, vol. 1, no. 1, 2021.
  17. Y. Xue, L. Yang, and S. Haykin, “Decoupled echo state networks with lateral inhibition,” Neural Networks, vol. 20, no. 3, 2007.
  18. J. D. Davidson and A. El Hady, “Foraging as an evidence accumulation process,” PLOS Computational Biology, vol. 15, no. 7, 07 2019. [Online]. Available: https://doi.org/10.1371/journal.pcbi.1007060
  19. R. Ratcliff and G. McKoon, “The diffusion decision model: Theory and data for two-choice decision tasks,” Neural Computation, vol. 20, 4 2008. [Online]. Available: https://direct.mit.edu/neco/article/20/4/873-922/7299
  20. S. D. Brown and A. Heathcote, “The simplest complete model of choice response time: Linear ballistic accumulation,” Cognitive Psychology, vol. 57, 11 2008. [Online]. Available: https://linkinghub.elsevier.com/retrieve/pii/S0010028507000722
  21. M. A. Pisauro, E. Fouragnan, C. Retzler, and M. G. Philiastides, “Neural correlates of evidence accumulation during value-based decisions revealed via simultaneous eeg-fmri,” Nature Communications, vol. 8, 6 2017.
  22. P. Cisek, G. A. Puskas, and S. El-Murr, “Decisions in changing conditions: The urgency-gating model,” Journal of Neuroscience, vol. 29, 2009.
  23. M. Usher and J. L. McClelland, “The time course of perceptual choice: The leaky, competing accumulator model,” 2001.
  24. D. Thura, J. Beauregard-Racine, C. W. Fradet, and P. Cisek, “Decision making by urgency gating: Theory and experimental support,” Journal of Neurophysiology, vol. 108, 2012.
  25. M. Lukoševičius, “A practical guide to applying echo state networks,” in Neural Networks: Tricks of the Trade: Second Edition.   Springer, 2012.
  26. N. Trouvain, L. Pedrelli, T. T. Dinh, and X. Hinaut, “Reservoirpy: an efficient and user-friendly library to design echo state networks,” in International Conference on Artificial Neural Networks.   Springer, 2020.
  27. A. R. Aron and R. A. Poldrack, “Cortical and subcortical contributions to stop signal response inhibition: role of the subthalamic nucleus,” Journal of Neuroscience, vol. 26, no. 9, 2006.
  28. N. P. Rougier, “A density-driven method for the placement of biological cells over two-dimensional manifolds,” Frontiers in Neuroinformatics, vol. 12, 3 2018. [Online]. Available: http://journal.frontiersin.org/article/10.3389/fninf.2018.00012/full
  29. I. Bar-Gad, G. Morris, and H. Bergman, “Information processing, dimensionality reduction and reinforcement learning in the basal ganglia,” Progress in neurobiology, vol. 71, no. 6, 2003.
  30. T. Akiba, S. Sano, T. Yanase, T. Ohta, and M. Koyama, “Optuna: A next-generation hyperparameter optimization framework,” in Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery & data mining, 2019.
  31. J. Bergstra, R. Bardenet, Y. Bengio, and B. Kégl, “Algorithms for hyper-parameter optimization,” Advances in neural information processing systems, vol. 24, 2011.

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