Summary
The Korean Brain Neuroinformatics Research Program has dual goals, i.e., to understand the information processing mechanism in the brain and to develop intelligent machine based on the mechanism. The basic form of the intelligent machine is called Artificial Brain, which is capable of conducting essential human functions such as vision, auditory, inference, and emergent behavior. By the proactive learning from human and environments the Artificial Brain may develop oneself to become more sophisticated entity. The OfficeMate will be the first demonstration of these intelligent entities, and will help human workers at offices for scheduling, telephone reception, document preparation, etc. The research scopes for the Artificial Brain and OfficeMate are presented with some recent results.
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
Lee, S.Y.: Korean Brain Neuroinformatics Research Program: The 3rd Phase. International Joint Conference on Neural Networks, Budapest, Hungary (2004).
Itti L., Koch, C.: Computational model of visual attention. Nature Reviews Neuroscience 2 (2001) 194-203.
Haxby, J.V., Hoffman, E.A., Gobbini, M.I.: The distributed human neural system for face perception. Trends in Cognitive Sciences 4 (2000) 223-233.
Jeong, S.Y., Lee, S.Y.: Adaptive learning algorithm to incorporate additional functional constraints into neural networks. Neurocomputing 35(2000)73-90.
Olshausen, B., Field, D.: Emergence of simple-cell receptive field properties by learning a sparse code for natural images. Nature 381 (1996) 607-609.
Clement, R.S., Witte, R.S., Rousche, P.J., Kipke, D.R.: Functional connectivity in auditory cortex using chronic, multichannel unit recordings. Neurocomputing 26 (1999) 347-354.
Lee, J.H., Lee, T.W., Jung, H.Y., Lee, S.Y.: On the Efficient Speech Feature Extraction Based on Independent Component Analysis. Neural Processing Letters 15 (2002) 235-245.
Hyvarinen, A., Hoyer, P.O., Inki, M.: Topographic independent component analysis. Neural Computation 13 (2001) 1527-1558.
Jeon, H.B., Lee, J.H., Lee, S.Y.: On the center-frequency ordered speech feature extraction based on independent component analysis. International Conference on Neural Information Processing, Shanghai, China (2001)1199-1203.
Kim, T., Lee, S.Y.: Learning self-organized topology-preserving complex speech features at primary auditory cortex. Neurocomputing 65-66 (2005) 793-800.
Eggermont, J.J.: Between sound and perception: reviewing the search for a neural code. Hearing Research 157 (2001) 1-42.
Park, K.Y., Lee, S.Y.: An engineering model of the masking for the noiserobust speech recognition. Neurocomputing 52-54 (2003) 615-620.
Yost, W.A.: Fundamentals of hearing - An introduction. Academic Press (2000).
Torkkola, T.: Blind separation of convolved sources based on information maximization. In Proc. IEEE Workshop on Neural Networks for Signal Processing, Kyoto (1996) 423-432.
Park, H.M., Jeong, H.Y., Lee, T.W., Lee, S.Y.: Subband-based blind signal separation for noisy speech recognition. Electronics Letters 35 (1999) 2011-2012.
Dhir, C.S., Park, H.M., Lee, S.Y.: Permutation Correction of Filter Bank ICA Using Static Channel Characteristics. Proc. International Conf. Neural Information Processing, Calcutta, India (2004) 1076-1081.
Lee, S.Y., Mozer, M.C.: Robust Recognition of Noisy and Superimposed Patterns via Selective Attention. Neural Information Processing Systems 12 (1999) MIT Press 31-37.
Park, K.Y., and Lee, S.Y.: Out-of-Vocabulary Rejection based on Selective Attention Model. Neural Processing Letters 12 (2000) 41-48.
Kim, B.T., and Lee, S.Y.: Sequential Recognition of Superimposed Patterns with Top-Down Selective Attention. Neurocomputing 58-60 (2004) 633-640.
Bae, U.M., Park, H.M., Lee, S.Y.: Top-Down Attention to Complement Independent Component Analysis for Blind Signal Separation. Neuro-computing 49 (2002) 315-327.
Lee, M., and Lee, S.Y.: Unsupervised Extraction of Multi-Frame Features for Lip-Reading. Neural Information Processing - Letters and Reviews 10 (2006)97-104.
Kim, C.M., Park, H.M., Kim, T., Lee, S.Y., Choi, Y.K.: FPGA Implementation of ICA Algorithm for Blind Signal Separation and Active Noise Canceling. IEEE Transactions on Neural Networks 14 (2003) 1038-1046.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2007 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Lee, SY. (2007). Artificial Brain and OfficeMate TR based on Brain Information Processing Mechanism. In: Duch, W., Mańdziuk, J. (eds) Challenges for Computational Intelligence. Studies in Computational Intelligence, vol 63. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-71984-7_6
Download citation
DOI: https://doi.org/10.1007/978-3-540-71984-7_6
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-71983-0
Online ISBN: 978-3-540-71984-7
eBook Packages: EngineeringEngineering (R0)