Abstract
Different varieties of artificial neural networks have proved their power in several pattern recognition problems, particularly feed-forward neural networks. Nevertheless, these kinds of neural networks require of several neurons and layers in order to success when they are applied to solve non-linear problems. In this paper is shown how a spiking neuron can be applied to solve different linear and non-linear pattern recognition problems. A spiking neuron is stimulated during T ms with an input signal and fires when its membrane potential reaches a specific value generating an action potential (spike) or a train of spikes. Given a set of input patterns belonging to K classes, each input pattern is transformed into an input signal, then the spiking neuron is stimulated during T ms and finally the firing rate is computed. After adjusting the synaptic weights of the neuron model, we expect that input patterns belonging to the same class generate almost the same firing rate and input patterns belonging to different classes generate firing rates different enough to discriminate among the different classes. At last, a comparison between a feed-forward neural network and a spiking neuron is presented when they are applied to solve non-linear and real object recognition problems.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Anderson, J.A.: Introduction to Neural Networks. MIT Press, Cambridge (1995)
Werbos, P.J.: Backpropagation through time: What it does and how to do it. Proc. IEEE 78, 1550–1560 (1990)
Garro, B.A., Sossa, H., Vazquez, R.A.: Design of Artificial Neural Networks using a Modified Particle Swarm Optimization Algorithm. IJCNN, 938–945 (2009)
Maass, W.: Networks of spiking neurons: the third generation of neural network models. Neural Networks 10(9), 1659–1671 (1997)
Rieke, F., et al.: Spikes: Exploring the Neural Code. Bradford Book (1997)
Hasselmo, M.E., Bodelon, C., et al.: A Proposed Function for Hippo-campal Theta Rhythm: Separate Phases of Encoding and Retrieval Enhance Re-versal of Prior Learning. Neural Computation 14, 793–817 (2002)
Hopfield, J.J., Brody, C.D.: What is a moment? Cortical sensory integration over a brief interval. PNAS 97(25), 13919–13924 (2000)
Loiselle, S., Rouat, J., Pressnitzer, D., Thorpe, S.: Exploration of rank order coding with spiking neural networks for speech recognition. IJCNN 4, 2076–2080 (2005)
Azhar, H., Iftekharuddin, K., et al.: A chaos synchronization-based dynamic vision model for image segmentation. IJCNN 5, 3075–3080 (2005)
Thorpe, S.J., Guyonneau, R., et al.: SpikeNet: Real-time visual processing with one spike per neuron. Neurocomputing 58(60), 857–864 (2004)
Di Paolo, E.A.: Spike-timing dependent plasticity for evolved robots. Adaptive Behavior 10(3), 243–263 (2002)
Floreano, D., Zufferey, J., et al.: From wheels to wings with evolutionary spiking neurons. Artificial Life 11(1-2), 121–138 (2005)
Izhikevich, E.M.: Simple model of spiking neurons. IEEE Trans. on Neural Networks 14(6), 1569–1572 (2003)
Izhikevich, E.M.: Which model to use for cortical spiking neurons? IEEE Trans. on Neural Networks 15(5), 1063–1070 (2004)
Izhikevich, E.M.: Dynamical Systems in Neuroscience: The Geometry of Excitability and Bursting. The MIT Press, Cambridge (2007)
Gerstner, W., et al.: Spiking Neuron Models. Cambridge University Press, Cambridge (2002)
Frias-Martinez, E., Gobet, F.: Automatic generation of cognitive theories using genetic programming. Minds and Machines 17(3), 287–309 (2007)
Hendrickson, E., et al.: Converting a globus pallidus neuron model from 585 to 6 compartments using an evolutionary algorithm. BMC Neurosci. 8(s2), P122 (2007)
Price, K., Storn, R.M., Lampinen, J.A.: Diffentential evolution: a practical ap-proach to global optimization. Springer, Heidelberg (2005)
Murphy, P.M., Aha, D.W.: UCI repository of machine learning databases. Dept. Inf. Comput. Sci., Univ. California, Irvine, CA (1994)
Vazquez, R.A., Sossa, H.: A new associative model with dynamical synapses. Neural Processing Letters 28(3), 189–207 (2008)
Otsu, N.: A threshold selection method from gray-level histograms. IEEE Trans. on SMC 9(1), 62–66 (1979)
Jain, R., et al.: Machine Vision. McGraw-Hill, New York (1995)
Hu, M.K.: Visual pattern recognition by moment invariants. IRE Trans. on Information Theory 8, 179–187 (1962)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Vázquez, R.A. (2010). Pattern Recognition Using Spiking Neurons and Firing Rates. In: Kuri-Morales, A., Simari, G.R. (eds) Advances in Artificial Intelligence – IBERAMIA 2010. IBERAMIA 2010. Lecture Notes in Computer Science(), vol 6433. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-16952-6_43
Download citation
DOI: https://doi.org/10.1007/978-3-642-16952-6_43
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-16951-9
Online ISBN: 978-3-642-16952-6
eBook Packages: Computer ScienceComputer Science (R0)