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Input-Modulation as an Alternative to Conventional Learning Strategies

  • Esin Yavuz
  • Thomas Nowotny
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9886)

Abstract

Animals use various strategies for learning stimulus-reward associations. Computational methods that mimic animal behaviour most commonly interpret learning as a high level phenomenon, in which the pairing of stimulus and reward leads to plastic changes in the final output layers where action selection takes place. Here, we present an alternative input-modulation strategy for forming simple stimulus-response associations based on reward. Our model is motivated by experimental evidence on modulation of early brain regions by reward signalling in the honeybee. The model can successfully discriminate dissimilar odours and generalise across similar odours, like bees do. In the most simplified connectionist description, the new input-modulation learning is shown to be asymptotically equivalent to the standard perceptron.

Keywords

Reinforcement learning Olfactory system Spiking neural network 

Notes

Acknowledgments

This work is supported by the EPSRC (Green Brain Project, grant number EP/J019690/1) and Human Frontiers Science Program, grant number RGP0053/2015.

References

  1. 1.
    Cassenaer, S., Laurent, G.: Hebbian STDP in mushroom bodies facilitates the synchronous flow of olfactory information in locusts. Nature 448(7154), 709–713 (2007)CrossRefGoogle Scholar
  2. 2.
    Denker, M., Finke, R., Schaupp, F., Grün, S., Menzel, R.: Neural correlates of odor learning in the honeybee antennal lobe. Eur. J. Neurosci. 31(1), 119–133 (2010)CrossRefGoogle Scholar
  3. 3.
    Devaud, J.M., Blunk, A., Podufall, J., Giurfa, M., Grünewald, B.: Using local anaesthetics to block neuronal activity and map specific learning tasks to the mushroom bodies of an insect brain. Eur. J. Neurosci. 26(11), 3193–3206 (2007)CrossRefGoogle Scholar
  4. 4.
    Ditzen, M.: Odor concentration and identity coding in the antennal lobe of the honeybee Apis Mellifera. Ph.D. thesis, Freie Universität Berlin (2005)Google Scholar
  5. 5.
    Faber, T., Joerges, J., Menzel, R.: Associative learning modifies neural representations of odors in the insect brain. Nat. Neurosci. 2(1), 74–78 (1999)CrossRefGoogle Scholar
  6. 6.
    Guerrieri, F., Schubert, M., Sandoz, J.C., Giurfa, M.: Perceptual and neural olfactory similarity in honeybees. PLoS Biol. 3(4), e60 (2005)CrossRefGoogle Scholar
  7. 7.
    Hammer, M., Menzel, R.: Multiple sites of associative odor learning as revealed by local brain microinjections of octopamine in honeybees. Learn Mem. 5(1), 146–156 (1998)Google Scholar
  8. 8.
    Huerta, R., Nowotny, T., Garcia-Sanchez, M., Abarbanel, H.D.I., Rabinovich, M.I.: Learning classification in the olfactory system of insects. Neural Comput. 16, 1601–1640 (2004)CrossRefzbMATHGoogle Scholar
  9. 9.
    Kemenes, I., Straub, V.A., Nikitin, E.S., Staras, K., O’Shea, M., Kemenes, G., Benjamin, P.R.: Role of delayed nonsynaptic neuronal plasticity in long-term associative memory. Curr. Biol. 16(13), 1269–1279 (2006)CrossRefGoogle Scholar
  10. 10.
    Krofczik, S., Menzel, R., Nawrot, M.P.: Rapid odor processing in the honeybee antennal lobe network. Front Comput. Neurosci. 2, 1–13 (2008)CrossRefGoogle Scholar
  11. 11.
    Malun, D., Giurfa, M., Galizia, C.G., Plath, N., Brandt, R., Gerber, B., Eisermann, B.: Hydroxyurea-induced partial mushroom body ablation does not affect acquisition and retention of olfactory differential conditioning in honeybees. J. Neurobiol. 53(3), 343–360 (2002)CrossRefGoogle Scholar
  12. 12.
    Menzel, R.: The honeybee as a model for understanding the basis of cognition. Nat. Rev. Neurosci. 13(11), 758–768 (2012)CrossRefGoogle Scholar
  13. 13.
    Münch, D., Schmeichel, B., Silbering, A.F., Galizia, C.G.: Weaker ligands can dominate an odor blend due to syntopic interactions. Chem. Sens. 38, 293–304 (2013). bjs138CrossRefGoogle Scholar
  14. 14.
    Nowotny, T., Stierle, J.S., Galizia, C.G., Szyszka, P.: Data-driven honeybee antennal lobe model suggests how stimulus-onset asynchrony can aid odour segregation. Brain Res. 1536, 119–134 (2013)CrossRefGoogle Scholar
  15. 15.
    Rath, L., Galizia, C.G., Szyszka, P.: Multiple memory traces after associative learning in the honey bee antennal lobe. Eur. J. Neurosci. 34(2), 352–360 (2011)CrossRefGoogle Scholar
  16. 16.
    Sutton, R.S., Barto, A.G.: Reinforcement Learning: An Introduction. MIT press, Cambridge (1998)Google Scholar
  17. 17.
    Traub, R.D., Miles, R.: Neuronal Networks of the Hippocampus, vol. 777. Cambridge University Press, Cambridge (1991)CrossRefGoogle Scholar
  18. 18.
    Yavuz, E., Turner, J., Nowotny, T.: GeNN: a code generation framework for accelerated brain simulations. Sci. Rep. 6, 18854 (2016)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.School of Engineering and InformaticsUniversity of SussexBrightonUK

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