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A CNN-based neuromorphic model for classification and decision control

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Abstract

In this paper, an insect brain-inspired computational structure was developed. The peculiarity of the core processing layer is the local connectivity among the spiking neurons, which allows for a representation under the cellular nonlinear network paradigm. Moreover, the processing layer works as a liquid state network with fixed internal connections and trainable output weights. Learning was accomplished by adopting a simple supervised, batch approach based on the calculation of the Moore–Penrose matrix. The architecture, taking inspiration from a specific neuropile of the insect brain, the mushroom bodies, is evaluated and compared with other standard and bio-inspired solutions present in the literature, referring to three different scenarios.

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References

  1. Agarap, A.F.M.: On breast cancer detection: an application of machine learning algorithms on the Wisconsin diagnostic dataset. In: Proceedings of the 2nd International Conference on Machine Learning and Soft Computing, ICMLSC ’18, pp. 5–9. ACM, New York (2018). https://doi.org/10.1145/3184066.3184080

  2. Aihan, T., Yalcin, M.E.: An application of small-world cellular neural networks on odor classification. Int. J. Bifurc. Chaos 22(01), 1250013 (2012). https://doi.org/10.1142/S0218127412500137

    Article  Google Scholar 

  3. Anderson, M.: Neural reuse: a fundamental organizational principle of the brain. Behav. Brain Sci. 33(4), 245–266 (2010)

    Article  Google Scholar 

  4. Arena, E., Arena, P., Strauss, R., Patané, L.: Motor-skill learning in an insect inspired neuro-computational control system. Front. Neurorobot. 11, 12 (2017). https://doi.org/10.3389/fnbot.2017.00012

    Article  Google Scholar 

  5. Arena, P., Berg, C., Patané, L., Strauss, R., Termini, P.S.: Aninsect brain computational model inspired by Drosophila melanogaster: architecture description. In: The 2010 International Joint Conference on Neural Networks (IJCNN), pp. 1–7 (2010). https://doi.org/10.1109/IJCNN.2010.5596974

  6. Arena, P., Caccamo, S., Patané, L., Strauss, R.: A computational model for motor learning in insects. In: IJCNN, Dallas, TX, pp. 1349–1356 (2013)

  7. Arena, P., Caccamo, S., Patané, L., Strauss, R.: A computational model for motor learning in insects. In: International Joint Conference on Neural Networks (IJCNN), Dallas, TX, Aug 4–9, pp. 1349–1356 (2013)

  8. Arena, P., Calí, M., Patané, L., Portera, A., Strauss, R.: Modeling the insect mushroom bodies: application to sequence learning. Neural Netw. 67, 37–53 (2015)

    Article  Google Scholar 

  9. Arena, P., Calí, M., Patané, L., Portera, A., Strauss, R.: A fly-inspired mushroom bodies model for sensory-motor control through sequence and subsequence learning. Int. J. Neural Syst. 26(6), 1650035 (2016)

    Article  Google Scholar 

  10. Arena, P., Castorina, S.M., Frasca, L.F., Ruta, M.: A CNN-based chip for robot locomotion control. ISCAS 3, 510–513 (2003)

    Google Scholar 

  11. Arena, P., Crucitti, P., Fortuna, L., Frasca, M., Lombardo, D., Patané, L.: Turing patterns in RD-CNNs for the emergence of perceptual states in roving robots. Bifurc. Chaos 17(1), 107–127 (2007)

    Article  MathSciNet  Google Scholar 

  12. Arena, P., Fiore, S.D., Patané, L., Pollino, M., Ventura, C.: Insect inspired unsupervised learning for tactic and phobic behavior enhancement in a hybrid robot. In: The 2010 International Joint Conference on Neural Networks (IJCNN), pp. 1–8 (2010). https://doi.org/10.1109/IJCNN.2010.5596542

  13. Arena, P., Fortuna, L., Branciforte, M.: Reaction–diffusion CNN algorithms to generate and control artificial locomotion. IEEE Trans. Circuits Syst. I 46(2), 253–260 (1999)

    Article  Google Scholar 

  14. Arena, P., Patané, L.: Spatial Temporal Patterns for Action-Oriented Perception in Roving Robots II: An Insect Brain Computational Model. Cognitive Systems Monographs, vol. 21. Springer, Berlin (2014)

    Book  Google Scholar 

  15. Arena, P., Patané, L., Strauss, R.: The insect mushroom bodies: a paradigm of neural reuse. In: ECAL, pp. 765–772. MIT Press, Taormina (2013)

  16. Aso, Y., et al.: The neuronal architecture of the mushroom body provides a logic for associative learning. eLife 3, e04577 (2014). https://doi.org/10.7554/eLife.04577

    Article  Google Scholar 

  17. Baek, W., Ignizio, J.P.: Pattern classification via linear programming. Comput. Ind. Eng. 25(1), 393–396 (1993). https://doi.org/10.1016/0360-8352(93)90304-G

    Article  Google Scholar 

  18. Bako, L.: Real-time classification of datasets with hardware embedded neuromorphic neural networks. Brief. Bioinform. 11(3), 348–363 (2010)

    Article  Google Scholar 

  19. Barata, J.C.A., Hussein, M.S.: The Moore–Penrose Pseudoinverse. A Tutorial Review of the Theory. John Hopkins University Press, Baltimore (2013)

    Google Scholar 

  20. Barnstedt, O., David, O., Felsenberg, J., Brain, R., Moszynski, J., Talbot, C., Perrat, P., Waddell, S.: Memory-relevant mushroom body output synapses are cholinergic. Neuron 89(6), 1237–1247 (2017). https://doi.org/10.1016/j.neuron.2016.02.015

    Article  Google Scholar 

  21. Bel haj ali, W., Piro, P., Giampaglia, D., Pourcher, T., Bar laud, M.: Biological cells classification using bio-inspired descriptor in a boosting k-NN framework. In: 2012 25th IEEE International Symposium on Computer-Based Med ical Systems (CBMS), pp. 1–6 (2012). https://doi.org/10.1109/CBMS.2012.6266359

  22. Chang, H., Astolfi, A.: Gaussian based classification with application to the Iris data set. IFAC Proc. 44(1), 14271–14276 (2011)

    Article  Google Scholar 

  23. Chua, L.O., Roska, T.: The CNN paradigm. IEEE Trans. Circuits Syst. I Fundam. Theory Appl. 40(3), 147–156 (1993). https://doi.org/10.1109/81.222795

    Article  MATH  Google Scholar 

  24. Dash, T., Sahu, S.R., Nayak, T., Mishra, G.: Neural network approach to control wall-following robot navigation. In: 2014 IEEE International Conference on Advanced Communications, Control and Computing Technologies, pp. 1072–1076 (2014). https://doi.org/10.1109/ICACCCT.2014.7019262

  25. Davis, R., Han, K.: Neuroanatomy: mushrooming mushroom bodies. Curr. Biol. 6, 146–148 (1996)

    Article  Google Scholar 

  26. Fisher, R.A.: The use of multiple measurement in taxonomic problems. Ann. Eugen. 7(2), 179–188 (1936). https://doi.org/10.1111/j.1469-1809.1936.tb02137.x

    Article  Google Scholar 

  27. Freire, A.L., Barreto, G.A., Veloso, M., Varela, A.T.: Short-term memory mechanisms in neural network learning of robot navigation tasks: a case study. In: 2009 6th Latin American Robotics Symposium (LARS 2009), pp. 1–6 (2009). https://doi.org/10.1109/LARS.2009.5418323

  28. Gerber, B., Tanimoto, H., Heisenberg, M.: An engram found? Evaluating the evidence from fruit flies. Curr. Opin. Neurobiol. 14, 737–744 (2004)

    Article  Google Scholar 

  29. Gorodkin, J.: Comparing two k-category assignments by a k-category correlation coefficient. Comput. Biol. Chem. 28(5–6), 367–374 (2004). https://doi.org/10.1016/j.compbiolchem.2004.09.006

    Article  MATH  Google Scholar 

  30. Gupta, N., Stopfer, M.: Functional analysis of a higher olfactory center, the lateral horn. J. Neurosci. 32(24), 8138–8148 (2012). https://doi.org/10.1523/JNEUROSCI.1066-12.2012

    Article  Google Scholar 

  31. Huerta, R., Nowotny, T., Garcia-Sanchez, M., Abarbanel, H., Rabinovich, M.: Learning classification in the olfactory system of insects. Neural Comput. 16(8), 1601–40 (2004)

    Article  Google Scholar 

  32. Huerta, R., Vembu, S., Amigó, J.M., Nowotny, T., Elkan, C.: Inhibition in multiclass classification. Neural Comput. 24(9), 2473–2507 (2012)

    Article  MathSciNet  Google Scholar 

  33. Izhikevich, E.M.: Which model to use for cortical spiking neurons? IEEE Trans. Neural Netw. 15(5), 1063–1070 (2004)

    Article  Google Scholar 

  34. Jurman, G., Riccadonna, S., Furlanello, C.: A comparison of MCC and CEN error measures in multi-class prediction. PloS ONE 7(8), e41882 (2012). https://doi.org/10.1371/journal.pone.0041882

    Article  Google Scholar 

  35. Kholerdi, H.A., TaheriNejad, N., Jantsch, A.: Enhancement of classification of small data sets using self-awareness—an Iris flower case-study. In: 2018 IEEE International Symposium on Circuits and Systems (ISCAS), pp. 1–5 (2018)

  36. Leitch, B., Laurent, G.: Gabaergic synapses in the antennal lobe and mushroom body of the locust olfactory system. J. Comp. Neurol. 372(4), 487–514 (1996). https://doi.org/10.1002/(SICI)1096-9861(19960902)372:4<487::AID-CNE1>3.0.CO;2-0

  37. Leitch, B., Laurent, G.: Gabaergic synapses in the antennal lobe and mushroom body of the locust olfactory system. J. Comp. Neurol. 372(4), 487–514 (1996)

    Article  Google Scholar 

  38. Leroy, F., Brann, D.H., Meira, T., Siegelbaum, S.A.: Input-timing-dependent plasticity in the hippocampal CA2 region and its potential role in social memory. Neuron 95(5), 1089–1102.e5 (2017). https://doi.org/10.1016/j.neuron.2017.07.036

    Article  Google Scholar 

  39. Maass, W., Markram, H.: On the computational power of circuits of spiking neurons. J. Comput. Syst. Sci. 69(4), 593–616 (2004). https://doi.org/10.1016/j.jcss.2004.04.001

    Article  MathSciNet  MATH  Google Scholar 

  40. Martinez, T., Schuten, K.: A neural-gas network learns topologies. Artif. Neural Netw. 1, 397–402 (1991)

    Google Scholar 

  41. Matsumoto, K., Mori, H., Uehara, M.: Fault tolerance in small world cellular neural networks for image processing. In: 21st International Conference on Advanced Information Networking and Applications Workshops, 2007, AINAW ’07, vol. 1, pp. 835–839 (2007). https://doi.org/10.1109/AINAW.2007.183

  42. Navarro, R., Acevedo, E., Acevedo, A., Martínez, F.: Associative model for solving the wall-following problem. In: Carrasco-Ochoa, J.A., Martínez-Trinidad, J.F., Olvera López, J.A., Boyer, K.L. (eds.) Pattern Recognition, pp. 176–186. Springer, Berlin (2012)

    Chapter  Google Scholar 

  43. Nowotny, T., Rabinovich, M., Huerta, R., Abarbanel, H.: Decoding temporal information through slow lateral excitation in the olfactory system of insects. J. Comput. Neurosci. 15, 271–281 (2003)

    Article  Google Scholar 

  44. Ooi, S.Y., Tan, S.C., Cheah, W.P.: Experimental Study of Elman Network in Temporal Classification, pp. 245–254. Springer, Singapore (2017)

    Google Scholar 

  45. Papamakarios, G.: Comparison of Modern Stochastic Optimization Algorithms. University of Edinburgh, Edinburgh (2014)

    Google Scholar 

  46. Perez-Orive, J., Mazor, O., Turner, G., Cassenaer, S., Wilson, R., Laurent, G.: Oscillations and sparsening of odor representations in the mushroom body. Science 297, 359–365 (2002)

    Article  Google Scholar 

  47. Sachse, S., Galizia, C.: Role of inhibition for temporal and spatial odor representation in olfactory output neurons: a calcium imaging study. J. Neurophysiol. 87, 1106–1117 (2002)

    Article  Google Scholar 

  48. Salama, G.I., Abdelhalim, M.B., Zeid, M.A.: Experimental comparison of classifiers for breast cancer diagnosis. In: 2012 Seventh International Conference on Computer Engineering Systems (ICCES), pp. 180–185 (2012). https://doi.org/10.1109/ICCES.2012.6408508

  49. Sathya, S., Joshi, S., Padmavathi, S.: Classification of breast cancer dataset by different classification algorithms. In: 2017 4th International Conference on Advanced Computing and Communication Systems (ICACCS), pp. 1–4 (2017). https://doi.org/10.1109/ICACCS.2017.8014573

  50. Sboev, A., Vlasov, D., Rybka, R., Serenko, A.: Solving a classification task by spiking neurons with STDP and temporal coding. Procedia Comput. Sci. 123, 494–500 (2018)

    Article  Google Scholar 

  51. Schmuker, M., Pfeil, T., Nawrot, M.P.: A neuromorphic network for generic multivariate data classification. Proc. Natl. Acad. Sci. 111(6), 2081–2086 (2014). https://doi.org/10.1073/pnas.1303053111

    Article  Google Scholar 

  52. Schmuker, M., Schneider, G.: Processing and classification of chemical data inspired by insect olfaction. Proc. Natl. Acad. Sci. 104(51), 20285–20289 (2007). https://doi.org/10.1073/pnas.0705683104

    Article  Google Scholar 

  53. Shim, Y., Philippides, A., Staras, K., Husbands, P.: Unsupervised learning in an ensemble of spiking neural networks mediated by ITDP. PLOS Comput. Biol. 12(10), 1–41 (2016). https://doi.org/10.1371/journal.pcbi.1005137

    Article  Google Scholar 

  54. Vogt, K., Aso, Y., Hige, T., Knapek, S., Ichinose, T., Friedrich, A.B., Turner, G.C., Rubin, G.M., Tanimoto, H.: Direct neural pathways convey distinct visual information to Drosophila mushroom bodies. eLife 5, e14009 (2016). https://doi.org/10.7554/eLife.14009

    Article  Google Scholar 

  55. Wang, J., Guo, P., Xin, X.: Review of pseudoinverse learning algorithm for multilayer neural networks and applications. In: Huang, T., Lv, J., Sun, C., Tuzikov, A.V. (eds.) Advances in Neural Networks—ISNN 2018, pp. 99–106. Springer, Cham (2018)

    Chapter  Google Scholar 

  56. Wolberg, W.H., Mangasarian, O.L.: Multisurface method of pattern separation for medical diagnosis applied to breast cytology. Proc. Natl. Acad. Sci. 87(23), 9193–9196 (1990)

    Article  Google Scholar 

  57. Yang, J., Zhang, P., Liu, Y.: Robustness of classification ability of spiking neural networks. Nonlinear Dyn. 82(1), 723–730 (2015). https://doi.org/10.1007/s11071-015-2190-2

    Article  MathSciNet  Google Scholar 

  58. Yavuz, E., Eyupoglu, C., Sanver, U., Yazici, R.: An ensemble of neural networks for breast cancer diagnosis. In: 2017 International Conference on Computer Science and Engineering (UBMK), pp. 538–543 (2017). https://doi.org/10.1109/UBMK.2017.8093456

  59. Zhou, L.: Global asymptotic stability of cellular neural networks with proportional delays. Nonlinear Dyn. 77(1), 41–47 (2014). https://doi.org/10.1007/s11071-014-1271-y

    Article  MathSciNet  MATH  Google Scholar 

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Funding

This study was funded by MIUR Project CLARA—Cloud platform for LAndslide Risk Assessment (Grant Number SNC_00451).

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Authors and Affiliations

  1. DIEEI, University of Catania, Viale A. Doria 6, 95100, Catania, Italy

    Paolo Arena, Marco Calí, Luca Patané, Agnese Portera & Angelo G. Spinosa

  2. National Institute of Biostructures and Biosystems (INBB), Viale delle Medaglie d’Oro 305, 00136, Rome, Italy

    Paolo Arena

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  1. Paolo Arena
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  2. Marco Calí
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  3. Luca Patané
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Correspondence to Luca Patané.

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Arena, P., Calí, M., Patané, L. et al. A CNN-based neuromorphic model for classification and decision control. Nonlinear Dyn 95, 1999–2017 (2019). https://doi.org/10.1007/s11071-018-4673-4

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