Nothing Special   »   [go: up one dir, main page]
Skip to main content
Springer Nature Link
Log in

Brain-like Combination of Feedforward and Recurrent Network Components Achieves Prototype Extraction and Robust Pattern Recognition

  • Conference paper
  • First Online:
Machine Learning, Optimization, and Data Science (LOD 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13811))

Abstract

Associative memory has been a prominent candidate for the computation performed by the massively recurrent neocortical networks. Attractor networks implementing associative memory have offered mechanistic explanation for many cognitive phenomena. However, attractor memory models are typically trained using orthogonal or random patterns to avoid interference between memories, which makes them unfeasible for naturally occurring complex correlated stimuli like images. We approach this problem by combining a recurrent attractor network with a feedforward network that learns distributed representations using an unsupervised Hebbian-Bayesian learning rule. The resulting network model incorporates many known biological properties: unsupervised learning, Hebbian plasticity, sparse distributed activations, sparse connectivity, columnar and laminar cortical architecture, etc. We evaluate the synergistic effects of the feedforward and recurrent network components in complex pattern recognition tasks on the MNIST handwritten digits dataset. We demonstrate that the recurrent attractor component implements associative memory when trained on the feedforward-driven internal (hidden) representations. The associative memory is also shown to perform prototype extraction from the training data and make the representations robust to severely distorted input. We argue that several aspects of the proposed integration of feedforward and recurrent computations are particularly attractive from a machine learning perspective.

Funding for the work is received from the Swedish e-Science Research Centre (SeRC), European Commission H2020 program. The authors gratefully acknowledge the HPC RIVR consortium (www.hpc-rivr.si) and EuroHPC JU (eurohpcju.europa.eu) for funding this research by providing computing resources of the HPC system Vega at the Institute of Information Science (www.izum.si).

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 79.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 99.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Douglas, R.J., Martin, K.A.C.: Recurrent neuronal circuits in the neocortex. Curr. Biol. 17, R496–R500 (2007). https://doi.org/10.1016/J.CUB.2007.04.024

    Article  Google Scholar 

  2. van Bergen, R.S., Kriegeskorte, N.: Going in circles is the way forward: the role of recurrence in visual inference. Curr. Opin. Neurobiol. 65, 176–193 (2020). https://doi.org/10.1016/J.CONB.2020.11.009

    Article  Google Scholar 

  3. Stepanyants, A., Martinez, L.M., Ferecskó, A.S., Kisvárday, Z.F.: The fractions of short- and long-range connections in the visual cortex. Proc. Natl. Acad. Sci. U. S. A. 106, 3555–3560 (2009). https://doi.org/10.1073/pnas.0810390106

    Article  Google Scholar 

  4. Hopfield, J.J.: Neural networks and physical systems with emergent collective computational abilities (associative memory/parallel processing/categorization/content-addressable memory/fail-soft devices). Proc. Natl. Acad. Sci. U. S. A. 79, 2554–2558 (1982)

    Article  MathSciNet  MATH  Google Scholar 

  5. Lansner, A.: Associative memory models: from the cell-assembly theory to biophysically detailed cortex simulations. Trends Neurosci. 32, 178–186 (2009). https://doi.org/10.1016/j.tins.2008.12.002

    Article  Google Scholar 

  6. Hebb, D.O.: The Organization of Behavior. Psychology Press (1949). https://doi.org/10.4324/9781410612403

  7. Lundqvist, M., Herman, P., Lansner, A.: Theta and gamma power increases and alpha/beta power decreases with memory load in an attractor network model. J. Cogn. Neurosci. 23, 3008–3020 (2011). https://doi.org/10.1162/jocn_a_00029

    Article  Google Scholar 

  8. Silverstein, D.N., Lansner, A.: Is attentional blink a byproduct of neocortical attractors? Front. Comput. Neurosci. 5, 13 (2011). https://doi.org/10.3389/FNCOM.2011.00013/BIBTEX

    Article  Google Scholar 

  9. Fiebig, F., Lansner, A.: A spiking working memory model based on Hebbian short-term potentiation. J. Neurosci. 37, 83–96 (2017). https://doi.org/10.1523/JNEUROSCI.1989-16.2016

    Article  Google Scholar 

  10. MacGregor, R.J., Gerstein, G.L.: Cross-talk theory of memory capacity in neural networks. Biol. Cybern. 65, 351–355 (1991). https://doi.org/10.1007/BF00216968

    Article  Google Scholar 

  11. LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436–444 (2015). https://doi.org/10.1038/nature14539

    Article  Google Scholar 

  12. Mattar, M.G., Daw, N.D.: Prioritized memory access explains planning and hippocampal replay. Nat. Neurosci. 21, 1609–1617 (2018). https://doi.org/10.1038/S41593-018-0232-Z

    Article  Google Scholar 

  13. Krotov, D., Hopfield, J.J.: Unsupervised learning by competing hidden units. Proc. Natl. Acad. Sci. U. S. A. 116, 7723–7731 (2019). https://doi.org/10.1073/pnas.1820458116

    Article  MathSciNet  MATH  Google Scholar 

  14. Bartunov, S., Santoro, A., Hinton, G.E., Richards, B.A., Marris, L., Lillicrap, T.P.: Assessing the scalability of biologically-motivated deep learning algorithms and architectures. In: Advances in Neural Information Processing Systems, pp. 9368–9378 (2018)

    Google Scholar 

  15. Illing, B., Gerstner, W., Brea, J.: Biologically plausible deep learning—but how far can we go with shallow networks? Neural Netw. 118, 90–101 (2019). https://doi.org/10.1016/j.neunet.2019.06.001

    Article  Google Scholar 

  16. Ravichandran, N.B., Lansner, A., Herman, P.: Learning representations in Bayesian confidence propagation neural networks. In: Proceedings of the International Joint Conference on Neural Networks. (2020). https://doi.org/10.1109/IJCNN48605.2020.9207061

  17. Ravichandran, N.B., Lansner, A., Herman, P.: Brain-like approaches to unsupervised learning of hidden representations - a comparative study. In: Farkaš, I., Masulli, P., Otte, S., Wermter, S. (eds.) ICANN 2021. LNCS, vol. 12895, pp. 162–173. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-86383-8_13

    Chapter  Google Scholar 

  18. Pulvermüller, F., Tomasello, R., Henningsen-Schomers, M.R., Wennekers, T.: Biological constraints on neural network models of cognitive function. Nat. Rev. Neurosci. 228(22), 488–502 (2021). https://doi.org/10.1038/s41583-021-00473-5

    Article  Google Scholar 

  19. Mountcastle, V.B.: The columnar organization of the neocortex (1997). https://academic.oup.com/brain/article/120/4/701/372118. https://doi.org/10.1093/brain/120.4.701

  20. Douglas, R.J., Martin, K.A.C.: Neuronal circuits of the neocortex (2004). www.annualreviews.org. https://doi.org/10.1146/annurev.neuro.27.070203.144152

  21. Buxhoeveden, D.P., Casanova, M.F.: The minicolumn hypothesis in neuroscience. Brain 125, 935–951 (2002). https://doi.org/10.1093/BRAIN/AWF110

    Article  Google Scholar 

  22. Carandini, M., Heeger, D.J.: Normalization as a canonical neural computation. Nat. Rev. Neurosci. 131(13), 51–62 (2011). https://doi.org/10.1038/nrn3136

    Article  Google Scholar 

  23. Fransen, E., Lansner, A.: A model of cortical associative memory based on a horizontal network of connected columns. Netw. Comput. Neural Syst. 9, 235–264 (1998). https://doi.org/10.1088/0954-898X_9_2_006

    Article  MATH  Google Scholar 

  24. Lansner, A., Ekeberg, Ö.: A one-layer feedback artificial neural network with a Bayesian learning rule. Int. J. Neural Syst. 01, 77–87 (1989). https://doi.org/10.1142/S0129065789000499

  25. Sandberg, A., Lansner, A., Petersson, K.M., Ekeberg, Ö.: A Bayesian attractor network with incremental learning. Netw. Comput. Neural Syst. 13, 179–194 (2002). https://doi.org/10.1080/net.13.2.179.194

    Article  MATH  Google Scholar 

  26. Lansner, A., Holst, A.: A higher order Bayesian neural network with spiking units (1996). https://doi.org/10.1142/S0129065796000816

  27. Tully, P.J., Hennig, M.H., Lansner, A.: Synaptic and nonsynaptic plasticity approximating probabilistic inference. Front. Synaptic Neurosci. 6, 8 (2014). https://doi.org/10.3389/FNSYN.2014.00008/ABSTRACT

    Article  Google Scholar 

  28. Johansson, C., Sandberg, A., Lansner, A.: A capacity study of a Bayesian neural network with hypercolumns. Rep. Stud. Artif. Neural Syst. (2001)

    Google Scholar 

  29. George, D., et al.: A generative vision model that trains with high data efficiency and breaks text-based CAPTCHAs. Science (80), 358 (2017). https://doi.org/10.1126/SCIENCE.AAG2612

  30. Yamins, D.L.K., DiCarlo, J.J.: Using goal-driven deep learning models to understand sensory cortex. Nat. Neurosci. 193(19), 356–365 (2016). https://doi.org/10.1038/nn.4244

    Article  Google Scholar 

  31. Felleman, D.J., Van Essen, D.C.: Distributed hierarchical processing in the primate cerebral cortex. Cereb. Cortex. 1, 1–47 (1991). https://doi.org/10.1093/cercor/1.1.1

    Article  Google Scholar 

  32. Tang, H., et al.: Recurrent computations for visual pattern completion. Proc. Natl. Acad. Sci. U. S. A. 115, 8835–8840 (2018). https://doi.org/10.1073/PNAS.1719397115/SUPPL_FILE/PNAS.1719397115.SAPP.PDF

    Article  Google Scholar 

  33. Roelfsema, P.R.: Cortical algorithms for perceptual grouping (2006). https://doi.org/10.1146/annurev.neuro.29.051605.112939

  34. Wyatte, D., Curran, T., O’Reilly, R.: The limits of feedforward vision: recurrent processing promotes robust object recognition when objects are degraded. J. Cogn. Neurosci. 24, 2248–2261 (2012). https://doi.org/10.1162/jocn_a_00282

    Article  Google Scholar 

  35. Fyall, A.M., El-Shamayleh, Y., Choi, H., Shea-Brown, E., Pasupathy, A.: Dynamic representation of partially occluded objects in primate prefrontal and visual cortex. Elife 6, (2017). https://doi.org/10.7554/eLife.25784

  36. Li, W., Piëch, V., Gilbert, C.D.: Learning to link visual contours. Neuron 57, 442–451 (2008). https://doi.org/10.1016/J.NEURON.2007.12.011

    Article  Google Scholar 

  37. Li, W., Gilbert, C.D.: Global contour saliency and local colinear interactions. J. Neurophysiol. 88, 2846–2856 (2002). https://doi.org/10.1152/JN.00289.2002

    Article  Google Scholar 

  38. Lamme, V.A.F., Roelfsema, P.R.: The distinct modes of vision offered by feedforward and recurrent processing. Trends Neurosci. 23, 571–579 (2000). https://doi.org/10.1016/S0166-2236(00)01657-X

    Article  Google Scholar 

  39. Grossberg, S.: Competitive learning: from interactive activation to adaptive resonance. Cogn. Sci. 11, 23–63 (1987). https://doi.org/10.1016/S0364-0213(87)80025-3

    Article  Google Scholar 

  40. Rumelhart, D.E., Zipser, D.: Feature discovery by competitive learning. Cogn. Sci. 9, 75–112 (1985). https://doi.org/10.1016/S0364-0213(85)80010-0

    Article  Google Scholar 

  41. Földiák, P.: Forming sparse representations by local anti-Hebbian learning. Biol. Cybern. 64, 165–170 (1990). https://doi.org/10.1007/BF02331346

    Article  Google Scholar 

  42. Szegedy, C., et al.: Intriguing properties of neural networks. In: International Conference on Learning Representations, ICLR (2014)

    Google Scholar 

  43. Lake, B.M., Ullman, T.D., Tenenbaum, J.B., Gershman, S.J.: Building machines that learn and think like people. Behav. Brain Sci. 40 (2017). https://doi.org/10.1017/S0140525X16001837

  44. Kietzmann, T.C., Spoerer, C.J., Sörensen, L.K.A., Cichy, R.M., Hauk, O., Kriegeskorte, N.: Recurrence is required to capture the representational dynamics of the human visual system. 116 (2019). https://doi.org/10.1073/pnas.1905544116

  45. Rao, R.P.N., Ballard, D.H.: Predictive coding in the visual cortex: a functional interpretation of some extra-classical receptive-field effects. Nat. Neurosci. 21(2), 79–87 (1999). https://doi.org/10.1038/4580

    Article  Google Scholar 

  46. Bastos, A.M., Usrey, W.M., Adams, R.A., Mangun, G.R., Fries, P., Friston, K.J.: Canonical microcircuits for predictive coding. Neuron 76, 695–711 (2012). https://doi.org/10.1016/J.NEURON.2012.10.038

    Article  Google Scholar 

  47. Tully, P.J., Lindén, H., Hennig, M.H., Lansner, A.: Spike-based Bayesian-Hebbian learning of temporal sequences. PLOS Comput. Biol. 12, e1004954 (2016). https://doi.org/10.1371/JOURNAL.PCBI.1004954

    Article  Google Scholar 

  48. Martinez, R.H., Lansner, A., Herman, P.: Probabilistic associative learning suffices for learning the temporal structure of multiple sequences. PLoS One 14, e0220161 (2019). https://doi.org/10.1371/JOURNAL.PONE.0220161

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Division of Computational Science and Technology, School of Electrical Engineering and Computer Science, KTH Royal Institute of Technology, Stockholm, Sweden

    Naresh Balaji Ravichandran, Anders Lansner & Pawel Herman

  2. Department of Mathematics, Stockholm University, Stockholm, Sweden

    Anders Lansner

  3. Digital Futures, KTH Royal Institute of Technology, Stockholm, Sweden

    Pawel Herman

Authors
  1. Naresh Balaji Ravichandran
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Anders Lansner
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Pawel Herman
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Naresh Balaji Ravichandran .

Editor information

Editors and Affiliations

  1. University of Catania, Catania, Italy

    Giuseppe Nicosia

  2. University of Reading, Reading, UK

    Varun Ojha

  3. University of Oxford, Oxford, UK

    Emanuele La Malfa

  4. University of Cambridge, Cambridge, UK

    Gabriele La Malfa

  5. University of Florida, Gainesville, FL, USA

    Panos Pardalos

  6. Free University of Bozen-Bolzano, Bolzano, Italy

    Giuseppe Di Fatta

  7. University of Catania, Catania, Italy

    Giovanni Giuffrida

  8. Dana-Farber Cancer Institute, Boston, MA, USA

    Renato Umeton

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Ravichandran, N.B., Lansner, A., Herman, P. (2023). Brain-like Combination of Feedforward and Recurrent Network Components Achieves Prototype Extraction and Robust Pattern Recognition. In: Nicosia, G., et al. Machine Learning, Optimization, and Data Science. LOD 2022. Lecture Notes in Computer Science, vol 13811. Springer, Cham. https://doi.org/10.1007/978-3-031-25891-6_37

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-25891-6_37

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-25890-9

  • Online ISBN: 978-3-031-25891-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation