DISCOVERING CORTICAL ALGORITHMS

Atif G. Hashmi, Mikko H. Lipasti

Abstract

We describe a cortical architecture inspired by the structural and functional properties of the cortical columns distributed and hierarchically organized throughout the mammalian neocortex. This results in a model which is both computationally efficient and biologically plausible. The strength and robustness of our cortical architecture is ascribed to its distributed and uniformly structured processing units and their local update rules. Since our architecture avoids complexities involved in modeling individual neurons and their synaptic connections, we can study other interesting neocortical properties like independent feature detection, feedback, plasticity, invariant representation, etc. with ease. Using feedback, plasticity, object permanence, and temporal associations, our architecture creates invariant representations for various similar patterns occurring within its receptive field. We trained and tested our cortical architecture using a subset of handwritten digit images obtained from the MNIST database. Our initial results show that our architecture uses unsupervised feedforward processing as well as supervised feedback processing to differentiate handwritten digits from one another and at the same time pools variations of the same digit together to generate invariant representations.

References

  1. Aimone, J., Wiles, J., and Gage, F. (2009). Computational influence of adult neurogenesis on memory encoding.
  2. Calvin, W. (1998). Cortical columns, modules, and hebbian cell assemblies. In Arbib, M. A., editor, The Handbook of Brain Theory and Neural Networks, pages 269-272. MIT Press, Cambridge, MA.
  3. Carpenter, G., Grossberg, S., and Rosen, D. (1991). Art2- a: An adaptive resonance algorithm for rapid category learning and recognition. Neural Networks, 4:493- 504.
  4. Clopath, C., Longtin, A., , and Gerstner, W. (2007). An online hebbian learning rule that performs independent component analysis. In Proceedings of Neural Information Processing Systems. Neural Information Processing Systems.
  5. DARPA (2008). Systems of neuromorphic adaptive plastic scalable electronics (synapse). http://www.darpa.mil/dso/thrusts/bio/biologically/ synapse/.
  6. Freeman, W. (1996). 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(4):473-480.
  7. George, D. and Hawkins., J. (2005). A hierarchical bayesian model of invariant pattern recognition in the visual cortex. In Proceedings of International Joint Conference on Neural Networks, volume 3, pages 1812- 1817. IEEE International Joint Conference on Neural Network.
  8. Grill-Spector, K., Kushnir, T., Hendler, T., Edelman, S., Itzchak, Y., and Malach, R. (1998). A sequence of object-processing stages revealed by fmri in the human occipital lobe. Hum. Brain Map., 6:316-328.
  9. Hawkins, J. and Blakeslee, S. (2005). Henry Holt & Company, Inc.
  10. Hawkins, J. and George, D. (2006). Hierarchical temporal memory. www.numenta.com/Numenta HTM Conce pts.pdf.
  11. Hinton, G. E., Osindero, S., and Teh, Y.-W. (2006). A fast learning algorithm for deep belief nets. Neural Comput., 18(7):1527-1554.
  12. Hirsch, J. and Martinez, L. (2006). Laminar processing in the visual cortical column. Current Opinion in Neurobiology, 16:377-384.
  13. Hubel, D. and Wiesel, T. (1962). Receptive fields, binocular interactions and functional architecture in cat's visual cortex. Journal of Physiology, 160:106-154.
  14. Hubel, D. and Wiesel, T. (1968). Receptive fields and functional architecture of monkey striate cortex. Journal of Physiology, 195:215-243.
  15. Johansson, C. and Lansner, A. (2004). Towards cortex sized artificial nervous systems. Lecture Notes in Computer Science: Knowledge-Based Intelligent Information and Engineering Systems, 3213:959-966.
  16. Kalisman N, Silberberg G, M. H. (2005). The neocortical microcircuit as a tabula rasa. Proc. Natl. Acad. Sci. USA, 102, 880-885.
  17. Lecun, Y. and Cortes, C. (1998). The mnist database of handwritten digits. http://yann.lecun.com/exdb/ mnist/.
  18. Markram, H. (2006). The blue brain project. In SC 7806: Proceedings of the 2006 ACM/IEEE conference on Supercomputing, page 53, New York, NY, USA. ACM.
  19. Martinetz, T. (1993). Competitive hebbian learning rule forms perfectly topology preserving maps. In International Conference on Artificial Neural Networks, ICANN, pages 427 -434.
  20. Mountcastle, V. (1978). An organizing principle for cerebral function: The unit model and the distributed system. In Edelman, G. and Mountcastle, V., editors, The Mindful Brain. MIT Press, Cambridge, Mass.
  21. Mountcastle, V. (1997). The columnar organization of the neocortex. Brain, 120:701-722.
  22. Nicholls, J., Martin, A., Wallace, B., and Fuchs, F. (2001). From Neuron To Brain. Sinauer Associates Ins, 23 Plumtree Road, Sunderland, MA, USA.
  23. Peissig, J. and Tarr, M. (2007). Visual object recognition: do we know more now than we did 20 years ago? Annu. Rev. Psychol., 58:75-96.
  24. Ringach, D. (2004). Haphazard wiring of simple receptive fields and orientation columns in visual cortex. J. Neurophysiol., 92(1):468-476.
  25. Rokni, U., Richardson, A., Bizzi, E., and Seung, H. (2007). Motor learning with unstable neural representations. Neuron, 64:653-666.
  26. Roth, G. and Dicke, U. (2005). Evolution of brain and intelligence. TRENDS in Cognitive Sciences, 5:250-257.
  27. Seung, H. (2003). Learning in spiking neural networks by reinforcement of stochastic synaptic transmission. Neuron, 40:1063-1073.
  28. Sillito, A., Cudeiro, J., and Jones, H. (2006). Always returning: feedback and sensory processing in visual cortex and thalamus. Trends Neurosci., 29(6):307-316.
  29. Swanson, L. (1995). Mapping the human brain: past, present, and future. Trends in Neurosciences, 18(11):471 -474.
  30. Weng, C., Yeh, C., Stoelzel, C., and Alonso, J. (2006). Receptive field size and response latency are correlated within the cat visual thalamus. Journal of Neurophysiology, 93:3537 -3547.
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Paper Citation


in Harvard Style

G. Hashmi A. and H. Lipasti M. (2010). DISCOVERING CORTICAL ALGORITHMS . In Proceedings of the International Conference on Fuzzy Computation and 2nd International Conference on Neural Computation - Volume 1: ICNC, (IJCCI 2010) ISBN 978-989-8425-32-4, pages 196-204. DOI: 10.5220/0003079301960204


in Bibtex Style

@conference{icnc10,
author={Atif G. Hashmi and Mikko H. Lipasti},
title={DISCOVERING CORTICAL ALGORITHMS},
booktitle={Proceedings of the International Conference on Fuzzy Computation and 2nd International Conference on Neural Computation - Volume 1: ICNC, (IJCCI 2010)},
year={2010},
pages={196-204},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003079301960204},
isbn={978-989-8425-32-4},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Fuzzy Computation and 2nd International Conference on Neural Computation - Volume 1: ICNC, (IJCCI 2010)
TI - DISCOVERING CORTICAL ALGORITHMS
SN - 978-989-8425-32-4
AU - G. Hashmi A.
AU - H. Lipasti M.
PY - 2010
SP - 196
EP - 204
DO - 10.5220/0003079301960204