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Efficient Source Detection Using Integrate-and-Fire Neurons

  • Laurent Perrinet
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3696)

Abstract

Sensory data extracted by neurons is often noisy or ambiguous and a goal of low-level cortical areas is to build an efficient strategy extracting the relevant information. It is believed that this is implemented in cortical areas by elementary inferential computations dynamically extracting the most likely parameters corresponding to the sensory signal. We explore here a neuro-mimetic model of the feed-forward connections in the primary visual area (V1) solving this problem in the case where the signal may be idealized by a linear generative model using an over-complete dictionary of primitives. Relying on an efficiency criterion, we derive an algorithm as an approximate solution which provides a distributed probabilistic representation of input features and uses incremental greedy inference processes. This algorithm is similar to Matching Pursuit and mimics the parallel and event-based nature of neural computations. We show a simple implementation using a network of integrate-and-fire neurons using fast lateral interactions which transforms an analog signal into a list of spikes. Though simplistic, numerical simulations show that this Sparse Spike Coding strategy provides an efficient representation of natural images compared to classical computational methods.

Keywords

Conjugate Gradient Method Natural Image Lateral Interaction Neural Computation Source Detection 
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.

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References

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    Olshausen, B., Field, D.J.: Sparse coding with an overcomplete basis set: A strategy employed by V1? Vision Research 37, 3311–3325 (1998)CrossRefGoogle Scholar
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    Perrinet, L., Samuelides, M., Thorpe, S.: Coding static natural images using spiking event times: do neurons cooperate? IEEE Transactions on Neural Networks, Special Issue on Temporal Coding for Neural Information Processing 15, 1164–1175 (2004), http://incm.cnrs-mrs.fr/perrinet/publi/perrinet03ieee.pdf Google Scholar
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    Lapicque, L.: Recherches quantitatives sur l’excitation électrique des nerfs traitée comme une polarisation. Journal of Physiology, Paris 9, 620–635 (1907)Google Scholar
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    Perrinet, L.: Feature detection using spikes: the greedy approach. Journal of Physiology, Paris (2004), http://incm.cnrs-mrs.fr/perrinet/publi/perrinet04tauc.pdf

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Laurent Perrinet
    • 1
  1. 1.INCM/CNRSMarseille Cedex 20France

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