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A pulsed neural network model of spectro-temporal receptive fields and population coding in auditory cortex

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Abstract

The existence of spectro-temporal receptive fields and evidence for population coding in auditory cortex motivate the development of such models, that explicitly operate in the time-frequency domain and are based on a pulsed neural network. In presenting such a model, a formal connection of the fields of Time Frequency Analysis and Pulsed Neural Networks is established. The resulting neural time-frequency signal representation is shown to be representable as a signal-dependent overcomplete dictionary. It is derived from neural population coding. Signal decomposition and filtering effects are presented, indicating obvious technical applications of the proposed model.

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References

  • Allen J (1977) Short term spectral analysis, synthesis, and modification by discrete fourier transform. IEEE Transactions on Acoustics, Speech, Signal Processing ASSP-25(3): 235–238

    Google Scholar 

  • Bohte SM (2003) Real neurons spike. In: Proceedings of the Workshop on the Future of Neural Networks, FUNN2003. Eindhoven, The Netherlands

  • Cohen L (1995) Time-Frequency Analysis, Signal Processing Series. Prentice-Hall

  • Cunningham GS and Williams WJ (1994) Fast implementations of generalized discrete time-frequency distributions. IEEE Transactions on Signal Processing 42: 1496–1508

    Article  Google Scholar 

  • de Charms, R. and Merzenich MM (1996) Primary cortical representation of sounds by the coordination of action-potential timing. Nature 381: 610–613

    Article  Google Scholar 

  • Depireux DA, Simon JZ, Klein DJ and Shamma SA (2001) Spectro-temporal response field characterization with dynamic ripples in ferret primary auditory cortex. Journal of Neurophysiology 85: 1220–1234

    Google Scholar 

  • DeWeese MR and Zador AM (2002) Binary coding in auditory cortex. In: Proceedings of the 16th Annual Conference on Neural Information Processing Systems (NIPS 2002), Vol. 15 of Advances in Neural Information Processing Systems. MIT Press

  • Eckhorn R, Reitboeck HJ, Arndt M and Dicke P (1990) Feature linking via synchronization among distributed assemblies: Simulations of results from cat visual cortex. Neural Computation 2: 293–307

    Google Scholar 

  • Flanagan JL (1972) Speech Analysis, Synthesis, and Perception. Springer-Verlag, New York

    Google Scholar 

  • Franosch J-MP, Kempter R, Fastl H and van Hemmen JL (2003) Zwicker tone illusion and noise reduction in the auditory system. Phys. Rev. Lett. 90: 178103

    Article  Google Scholar 

  • Gerstner W (1998) Populations of spiking neurons. In: Maass W and Bishop C (eds) Pulsed Neural Networks, pp. 261–295. MIT Press

  • Hahnloser RHR, Kozhevnikov AA and Fee MS (2002) An ultra-sparse code underlies the generation of neural sequences in a songbird. Nature 419: 65–70

    Article  Google Scholar 

  • Harris FJ (1978) On the use of windows for harmonic analysis with the discrete fourier transform. Proceedings of the IEEE 66(1): 51–84

    Article  Google Scholar 

  • Heil P (1997) Auditory onset responses revisited. I. First-spike timing. Journal of Neurophysiology 77(5): 2616–2641

    Google Scholar 

  • Hlawatsch F and Matz G (2002) Linear time-frequency filters. In: Boashash B (ed) Time-Frequency Signal Analysis and Processing. Prentice Hall, Englewood Cliffs (NJ)

    Google Scholar 

  • Hodgkin AL and Huxley AF (1952) A quantitative description of membrane current and its application to conduction and excitation in nerve. Journal of Physiology 117: 500–544

    Google Scholar 

  • Horn D and Opher I (1999) Collective excitation phenomena and their applications. In: Bishop CM (eds) (1999) Pulsed Neural Networks. MIT Press (Maass and Bishop, 1999), pp. 297–320

  • Johnson JL and Padgett ML (1999) PCNN models and applications. IEEE Transactions on Neural Networks 10(3): 480–498

    Article  Google Scholar 

  • Kistler W and Gerstner W (2002) Stable propagation of activity pulses in populations of spiking neurons. Neural Computation 14: 987–997

    Article  MATH  Google Scholar 

  • Klein D, König P and Körding KP (2003) Sparse spectrotemporal coding of sounds. EURASIP Journal on Applied Signal Processing 7: 659–667

    Article  MATH  Google Scholar 

  • Klein DJ, Depireux DA, Simon JZ and Shamma SA (2000) Robust spectrotemporal reverse correlation for the auditory system: optimizing stimulus design. Journal of Computational Neuroscience 9(1): 85–111

    Article  MATH  Google Scholar 

  • Körding KP, König P and Klein D (2002) Learning of sparse auditory receptive fields. In: Proceedings of the International Joint Conference on Neural Networks (IJCNN 2002). Honolulu, Hawaii

  • Lewicki M and Sejnowski T (2000) Learning overcomplete representations. Neural Computation 12(2): 337–365

    Article  Google Scholar 

  • Maass W (1999) Computing with spiking neurons. In: Maass W and Bishop CM (eds) Pulsed Neural Networks, pp. 55–85. MIT Press

  • Maass W and Bishop CM (eds) (1999) Pulsed Neural Networks. MIT Press

  • Machens CK, Wehr M and Zador AM (2002) Spectro-temporal receptive fields of subthreshold responses in auditory cortex. In: Proceedings of the 16th Annual Conference on Neural Information Processing Systems (NIPS 2002), Vol. 15 of Advances in Neural Information Processing Systems. MIT Press

  • Mallat S and Zhang Z (1993) Matching pursuit with time-frequency dictionaries. IEEE Transactions on Signal Processing 41(12): 3397–3415

    Article  MATH  Google Scholar 

  • Olshausen B and Field D (1997) Sparse coding with an overcomplete basis set: a strategy employed by V1?. Vision Research 37: 3311–3325

    Article  Google Scholar 

  • Perrinet L and Samuelides M (2002) Sparse image coding using an asynchronous spiking neural network. In: Proceedings of the European Symposium on Artificial Neural Networks, ESANN'2002, pp. 313–318. Bruges, Belgium

  • Perrinet L, Samuelides M and Thorpe S (2003) Emergence of filters from natural scenes in a sparse spike coding scheme. In: Proceedings of the Workshop on the Future of Neural Networks, FUNN2003. Eindhoven, The Netherlands

  • Portnoff MR (1980) Time-frequency representation of digital signals and systems based on short-time fourier analysis. IEEE Transactions on Acoustics, Speech, Signal Processing ASSP-28: 55–69

    Google Scholar 

  • Qian S (1999) Joint time-frequency analysis. IEEE Signal Processing Magazine 16(2): 52–67

    Article  Google Scholar 

  • Qian S and Chen D (1993) Signal representation in adaptive Gaussian functions and adaptive spectrogram. In: Proceedings of the 27th Annual Conference on Information Sciences and Systems, pp. 59–65

  • Schamschula MP, Johnson JL and Inguva R (2000) Image processing with pulse coupled neural networks. In: Proceedings of the 2nd International Forum on Multimedia and Image Processing, World Automation Congress. Maui, Hawaii

  • Shamma SA (1995) The Handbook of Brain Theory and Neural Networks, Chapt. Auditory Cortex, pp. 110–115. MIT Press

  • Taylor JG (ed) (1998) Image Processing using Pulse-Coupled Neural Networks, Perspectives in Neural Computing. Springer-Verlag

  • van Son RJJH, Binnenpoorte D, van den Heuvel H and Pols LCW (2001) The IFA corpus: a phonemically segmented Dutch ‘open source’ speech database. In: Proceedings of Eurospeech 2001. Aalborg, Denmark

  • Volkmer M (2003) Neural speech enhancement in the time-frequency domain. In: Proceedings of the 2003 IEEE Signal Processing Society International Workshop on Neural Networks for Signal Processing, NNSP2003, pp. 617–627. Toulouse, France

  • Xia X (1998) A quantitative analysis of SNR in the short-time Fourier transform domain for multicomponent signals. IEEE Transactions on Signal Processing 46: 200–203

    Article  Google Scholar 

  • Zaruda JM (ed) (1999) Special Issue on Pulse Coupled Neural Networks, Vol. 10 of IEEE Transactions on Neural Networks. IEEE Neural Networks Council

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  1. Department of Computer Engineering VI, Technical University Hamburg-Harburg, D-21073, Hamburg, Germany (

    Markus Volkmer

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  1. Markus Volkmer
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Volkmer, M. A pulsed neural network model of spectro-temporal receptive fields and population coding in auditory cortex. Natural Computing 3, 177–193 (2004). https://doi.org/10.1023/B:NACO.0000027754.87207.18

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  • DOI: https://doi.org/10.1023/B:NACO.0000027754.87207.18

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