Abstract
To adapt to the environment and survive, most animals can control their behaviors by making decisions. The process of decision making and responding according to changes in the environment is stable, sustainable, and learnable. Understanding how behaviors are regulated by neural circuits, and the encoding and decoding mechanisms from stimuli to responses, are important goals in neuroscience. A biologically plausible decision circuit consisting of computational neuron and synapse models and its learning mechanism are designed in this paper. The learning mechanism is based on two parts: first, effect of the punishment from the environment on the temporal correlations of neuron firings; second, spike timing dependent plasticity (STDP) of synapse. The decision circuit was used successfully to simulate the behavior of Drosophila exhibited in real experiments. In this paper, we place focus on the connections and interactions among excitatory and inhibitory neurons and try to give an explanation at a micro level (i.e. neurons and neural circuit) of how the observable decision making behavior is acquired and achieved.
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References
Ananthanarayanan, R., Modha, D.S.: Anatomy of a cortical simulator. In: Proceedings of the 2007 ACM/IEEE Conference on Supercomputing, p. 3. ACM (2007)
Bell, C.C., Han, V.Z., Sugawara, Y., Grant, K.: Synaptic plasticity in a cerebellum-like structure depends on temporal order. Nature 387(6630), 278–281 (1997)
Bi, G.-Q., Poo, M.-M.: Synaptic modifications in cultured hippocampal neurons: dependence on spike timing, synaptic strength, and postsynaptic cell type. J. Neurosci. 18(24), 10464–10472 (1998)
Bohte, S.M., Poutré, H.L., Kok, J.N.: Unsupervised clustering with spiking neurons by sparse temporal coding and multilayer RBF networks. IEEE Trans. Neural Netw. 13(2), 426–435 (2002)
Carandini, M.: From circuits to behavior: a bridge too far? Nat. Neurosci. 15(4), 507–509 (2012)
Eliasmith, C., Stewart, T.C., Choo, X., Bekolay, T., DeWolf, T., Tang, Y., Rasmussen, D.: A large-scale model of the functioning brain. Science 338(6111), 1202–1205 (2012)
Foderaro, G., Henriquez, C., Ferrari, S.: Indirect training of a spiking neural network for flight control via spike-timing-dependent synaptic plasticity. In: 2010 49th IEEE Conference on Decision and Control (CDC), pp. 911–917. IEEE (2010)
Gerstner, W., Kempter, R., van Hemmen, J.L., Wagner, H.: A neuronal learning rule for sub-millisecond temporal coding. Nature 383, 76–78 (1996). (LCN-ARTICLE-1996-002)
Izhikevich, E.M., Edelman, G.M.: Large-scale model of mammalian thalamocortical systems. Proc. Nat. Acad. Sci. 105(9), 3593–3598 (2008)
Izhikevich, E.M., et al.: Simple model of spiking neurons. IEEE Trans. Neural Netw. 14(6), 1569–1572 (2003)
Markram, H.: The blue brain project. Nat. Rev. Neurosci. 7(2), 153–160 (2006)
Markram, H., Lübke, J., Frotscher, M., Sakmann, B.: Regulation of synaptic efficacy by coincidence of postsynaptic APS and EPSPS. Science 275(5297), 213–215 (1997)
Natschläger, T., Ruf, B.: Spatial and temporal pattern analysis via spiking neurons. Netw. Comput. Neural Syst. 9(3), 319–332 (1998)
Nishiyama, M., Hong, K., Mikoshiba, K., Poo, M.-M., Kato, K.: Calcium stores regulate the polarity and input specificity of synaptic modification. Nature 408(6812), 584–588 (2000)
Tang, S., Guo, A.: Choice behavior of Drosophila facing contradictory visual cues. Science 294(5546), 1543–1547 (2001)
Waldrop, M.M.: Computer modelling: brain in a box. Nature 482(7386), 456–458 (2012)
Wang, X.-J.: Probabilistic decision making by slow reverberation in cortical circuits. Neuron 36(5), 955–968 (2002)
Wittenberg, G.M., Wang, S.S.-H.: Malleability of spike-timing-dependent plasticity at the ca3-ca1 synapse. J. Neurosci. 26(24), 6610–6617 (2006)
Zhang, K., Guo, J.Z., Peng, Y., Xi, W., Guo, A.: Dopamine-mushroom body circuit regulates saliency-based decision-making in Drosophila. Science 316(5833), 1901–1904 (2007)
Zhang, X., Xu, Z., Henriquez, C., Ferrari, S.: Spike-based indirect training of a spiking neural network-controlled virtual insect. In: 2013 IEEE 52nd Annual Conference on Decision and Control (CDC), pp. 6798–6805. IEEE (2013)
Acknowledgments
This work was supported by NSFC project (Project No.61375122), and in part by Shanghai Science and Technology Development Funds (13dz2260200, 13511504300). We thank LetPub (www.letpub.com) for its linguistic assistance during the preparation of this manuscript.
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Wei, H., Bu, Y., Dai, D. (2016). A Possible Neural Circuit for Decision Making and Its Learning Process. In: Liu, CL., Hussain, A., Luo, B., Tan, K., Zeng, Y., Zhang, Z. (eds) Advances in Brain Inspired Cognitive Systems. BICS 2016. Lecture Notes in Computer Science(), vol 10023. Springer, Cham. https://doi.org/10.1007/978-3-319-49685-6_18
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DOI: https://doi.org/10.1007/978-3-319-49685-6_18
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