Abstract
In this paper, we compare two models biologically inspired and gathering spatio-temporal data coding, representation and processing. These models are based on Self-Organizing Map (SOM) yielding to a Spatio-Temporel Organization Map (STOM). More precisely, the map is trained using two different spatio-temporal algorithms taking their roots in biological researches: The ST-Kohonen and the Time-Organized Map (TOM). These algorithms use two kinds of spatio-temporal data coding. The first one is based on the domain of complex numbers, while the second is based on the ISI (Inter Spike Interval). STOM is experimented in the field of speech recognition in order to evaluate its performance for such time variable application and to prove that biological models are capable of giving good results as stochastic and hybrid ones.
Chapter PDF
Similar content being viewed by others
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
References
Agmon-Snir, H., Segev, I.: Signal Delay and iInput Synchronization in Passive Dendritic Structures. Journal of Neurophysiology 70(5) (1973)
Cariani, P.: As If Time Really Mattered: Temporal Strategies for Neural Coding of Sensory Information. CC-AI 12(1-2) (1995)
Spengler, F., Hilger, T., Wang, X., Merzenich, M.: Learning induced formation of cortical populations involved in tactile object recognition. Social Neurosciences 22, 105–110 (1999)
Wilson, H., Cowan, J.: A mathematical theory of the functional dynamics of cortical and thalamic nervous tissue. Biology Cybernetic 13, 55–80 (1973)
Amari, S.: Topographic organization of nerve fields. Bull Math Biology 42, 339–364 (1980)
Szentagothai, J.: The module concept in cerebral cortex architecture. Brain research, 47–496 (1995)
Vinh ho, H.: Un reseau de neurons a decharge pour la reconnaissance des processus spatio-temporels. PhD thesis, Genie Electric Department, Monreal University (1992)
Wiemer, J., Spengler, F., Joublin, F., Wacquant, S.: Learning cortical topography from spatiotemporal stimuli. Biology cybernetic 82, 173–187 (2000)
Casti, A.R.R., Omurtag, A., Sornborger, A., Aplan, E., Knight, B., Victor, J., Sirovich, L.: A population study of integrate and fire or burst neuron. Neural Computation 14(5) (2002)
Laurence, S., Tsoi, A.C., Back, A.D.: The gamma MLP for speech phoneme recognition. Advances in Neural Information Processing System 8, 785–791 (1996)
Wiemer, J.C.: The Time-Organized Map (TOM) algorithm: extending the self-organizing map (SOM) to spatiotemporal signals. Neural Networks 15 (2003)
Vaucher, G.: A la recherche d’une algerbre neuronale spatio-temporal. P.hD thesis. Nancy University (1996)
Mozayyani, N., Alanou, V., Derfus, J., Vaucher, G.: A spatio-temporal data coding applied to kohonen maps. In: Inter. conf. on Artificial Neural Network, pp. 75–79 (1995)
Baig, A.B.: Une approche methodologique de l’utilisation des STAN applique a la reconnaissance visuelle de la parole. PhD thesis, Suplec, campus universitaire de rennes (2000)
Vaucher, G.: A Complex-Valued spiking machine. In: Kaynak, O., Alpaydın, E., Oja, E., Xu, L. (eds.) ICANN 2003 and ICONIP 2003. LNCS, vol. 2714, pp. 967–976. Springer, Heidelberg (2003)
Thorpe, S.: Spiking arrival times: A highly efficient coding scheme for neural networks. In: Parallel Processing in Neural System, Elseiver Press, Amsterdam (1990)
Rall, W.: Core conductor theory and cable properties. In: Handbook of physiology: the nervous system, Americain physiology society (1977)
Calliope. La parole et son traitement automatique. Masson, Paris, Milan, Barcelone (1989)
Durand, S.: Learning speech as acoustic sequences with the unsupervised model TOM. In: NEURAP, 8th international conference on neural networks and their applications, Marseille french (1995)
Béroulle, D.: Un modèle de mémoire adaptative, dynamique et associative, pour le traitement automatique de la parole. Thèse de l’université de Paris 11 (1985)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Salem, Z.N.B., Bougrain, L., Alexandre, F. (2006). Comparison Between Two Spatio-Temporal Organization Maps for Speech Recognition. In: Schwenker, F., Marinai, S. (eds) Artificial Neural Networks in Pattern Recognition. ANNPR 2006. Lecture Notes in Computer Science(), vol 4087. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11829898_2
Download citation
DOI: https://doi.org/10.1007/11829898_2
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-37951-5
Online ISBN: 978-3-540-37952-2
eBook Packages: Computer ScienceComputer Science (R0)