Abstract
We describe a biologically plausible learning model inspired by the structural and functional properties of the cortical columns present in the mammalian neocortex. The strength and robustness of our model is ascribed to its biologically plausible, uniformly structured, and hierarchically distributed processing units with their localized learning rules. By modeling cortical columns rather than individual neurons as our fundamental processing units, we get hierarchical learning networks that are computationally less demanding and better suited for studying higher cortical properties like independent feature detection, plasticity, etc. Another interesting attribute of our model is the use of feedback processing paths to generate invariant representation to robustly recognize variations of the same patterns and to determine the set of features sufficient for recognizing different patterns in the input dataset. We train and test our hierarchical networks using synthetic digit images as well as a subset of handwritten digit images obtained from the MNIST database. Our results show that our cortical networks use unsupervised feedforward processing as well as supervised feedback processing to robustly recognize handwritten digits.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Nicholls, J., Martin, A., Wallace, B., Fuchs, F.: From Neuron To Brain. Sinauer Associates Ins., 23 Plumtree Road, Sunderland, MA, USA (2001)
Hawkins, J., Blakeslee, S.: On Intelligence. Henry Holt & Company, Inc. (2005)
Hirsch, J., Martinez, L.: Laminar processing in the visual cortical column. Current Opinion in Neurobiology 16, 377–384 (2006)
Aimone, J., Wiles, J., Gage, F.: Computational influence of adult neurogenesis on memory encoding. Neuron 61, 187–2002 (2009)
Markram, H.: The blue brain project. In: SC 2006: Proceedings of the 2006 ACM/IEEE Conference on Supercomputing, vol. 53. ACM, New York (2006)
DARPA: Systems of neuromorphic adaptive plastic scalable electronics (synapse) (2008), http://www.darpa.mil/dso/thrusts/bio/biologically/synapse/
Clopath, C., Longtin, A., Gerstner, W.: An online hebbian learning rule that performs independent component analysis. In: Proceedings of Neural Information Processing Systems (2007)
Martinetz, T.: Competitive hebbian learning rule forms perfectly topology preserving maps. In: International Conference on Artificial Neural Networks, ICANN, pp. 427–434 (1993)
Arthur, J., Boahen, K.: Learning in silicon: Timing is everything. In: Proceedings of Advances in Neural Information Processing Systems. Advances in Neural Information Processing Systems, vol. 18, pp. 75–82 (2006)
Carpenter, G., Grossberg, S., Rosen, D.: Art2-a: An adaptive resonance algorithm for rapid category learning and recognition. Neural Networks 4, 493–504 (1991)
Hawkins, J., George, D.: Hierarchical temporal memory (2006), http://www.numenta.com/numenta_htm_concepts.pdf
George, D., Hawkins, J.: A hierarchical bayesian model of invariant pattern recognition in the visual cortex. In: Proceedings of International Joint Conference on Neural Networks. IEEE International Joint Conference on Neural Network, vol. 3, pp. 1812–1817 (2005)
Hinton, G.E., Osindero, S., Teh, Y.W.: A fast learning algorithm for deep belief nets. Neural Comput. 18, 1527–1554 (2006)
Mountcastle, V.: An organizing principle for cerebral function: The unit model and the distributed system. In: Edelman, G., Mountcastle, V. (eds.) The Mindful Brain. MIT Press, Cambridge (1978)
Maw, N., Pomplun, M.: Studying human face recognition with the gaze-contingent window technique. In: Proceedings of the Twenty-Sixth Annual Meeting of Cognitive Science Society, pp. 927–932 (2004)
Sigala, N., Logothetis, N.: Visual categorization shapes feature selectivity in the primate temporal cortex. Nature 415, 318–320 (2002)
Lecun, Y., Cortes, C.: The mnist database of handwritten digits (1998), http://yann.lecun.com/exdb/mnist/
Swanson, L.: Mapping the human brain: past, present, and future. Trends in Neurosciences 18, 471–474 (1995)
Mountcastle, V.: The columnar organization of the neocortex. Brain 120, 701–722 (1997)
Mountcastle, V.: Modality and topographic properties of single neurons of cat’s somatic sensory cortex. Journal of Neurophysiology 20, 408–434 (1957)
Hubel, D., Wiesel, T.: Receptive fields, binocular interactions and functional architecture in cat’s visual cortex. Journal of Physiology 160, 106–154 (1962)
Hubel, D., Wiesel, T.: Receptive fields and functional architecture of monkey striate cortex. Journal of Physiology 195, 215–243 (1968)
Peissig, J., Tarr, M.: Visual object recognition: do we know more now than we did 20 years ago? Annu. Rev. Psychol. 58, 75–96 (2007)
Grill-Spector, K., Kushnir, T., Hendler, T., Edelman, S., Itzchak, Y., Malach, R.: A sequence of object-processing stages revealed by fmri in the human occipital lobe. Hum. Brain Map. 6, 316–328 (1998)
Freeman, W.: Random activity at the microscopic neural level in cortex (”noise”) sustains and is regulated by low-dimensional dynamics of macroscopic activity (”chaos”). International Journal of Neural Systems 7, 473–480 (1996)
Rokni, U., Richardson, A., Bizzi, E., Seung, H.: Motor learning with unstable neural representations. Neuron 64, 653–666 (2007)
Seung, H.: Learning in spiking neural networks by reinforcement of stochastic synaptic transmission. Neuron 40, 1063–1073 (2003)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag GmbH Berlin Heidelberg
About this paper
Cite this paper
Hashmi, A., Lipasti, M. (2012). A Cortically Inspired Learning Model. In: Madani, K., Dourado Correia, A., Rosa, A., Filipe, J. (eds) Computational Intelligence. IJCCI 2010. Studies in Computational Intelligence, vol 399. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-27534-0_25
Download citation
DOI: https://doi.org/10.1007/978-3-642-27534-0_25
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-27533-3
Online ISBN: 978-3-642-27534-0
eBook Packages: EngineeringEngineering (R0)