Abstract
Neural network models for unsupervised pattern recognition learning are challenged when the difference between the patterns of the training set is small. The standard neural network architecture for pattern recognition learning consists of adaptive forward connections and lateral inhibition, which provides competition between output neurons. We propose an additional adaptive inhibitory feedback mechanism, to emphasize the difference between training patterns and improve learning. We present an implementation of adaptive feedback inhibition for spiking neural network models, based on spike timing dependent plasticity (STDP). When the inhibitory feedback connections are adjusted using an anti-Hebbian learning rule, feedback inhibition suppresses the redundant activity of input units which code the overlap between similar stimuli. We show, that learning speed and pattern discriminatability can be increased by adding this mechanism to the standard architecture.
Chapter PDF
References
Beierlein, M., Gibson, J.R., Connors, B.W.: Two dynamically distinct inhibitory networks in layer 4 of the neocortex. Journal of Neurophysiology 90, 2987–3000 (2003)
Bi, G., Poo, M.: Synaptic modifications in cultured hippocampal neurons: Dependence on spike timing, synaptic strength, and postsynaptic cell type. The Journal of Neuroscience 18(24), 10464–10472 (1998)
Callaway, E.M.: Local circuits in primary visual cortex of the macaque monkey. Annual Review of Neuroscience 21, 47–74 (1998)
Eckhorn, R.: Neural mechanisms of scene segmentation: Recordins from the visual cortex suggest basic circuits for linking field models. IEEE Transactions on Neural Networks 10(3), 464–479 (1999)
Eckhorn, R., Bruns, A., Gabriel, A., Al-Shaikhli, B., Saam, M.: Different types of signal coupling in the visual cortex related to neural mechanisms of associative processing and perception. IEEE Transactions on Neural Networks 15(5), 1039–1052 (2004)
Fukushima, K.: Cognitron: A self-organizing multilayered neural network. Biological Cybernetics 20, 121–136 (1975)
Földiák, P.: Forming sparse representations by local anti-hebbian learning. Biological Cybernetics 64, 165–170 (1990)
Grossberg, S.: Linking the laminar circuits of visual cortex to visual perception: Development, grouping and attention. Neuroscience and Biobeavioral Revies 25, 513–526 (2001)
Izhikevich, E.M.: Simple model of spiking neurons. IEEE Transactions on Neural Networks 14(6), 1569–1572 (2003)
Izhikevich, E.M.: Which model to use for cortical spiking neurons? IEEE Transactions on Neural Networks 15(5), 1063–1070 (2004)
Miyake, S., Fukushima, K.: A neural network model for the mechanism of feature-extraction. A self-organizing network with feedback inhibition. Biological Cybernetics 50, 377–384 (1984)
Rao, R.P.N., Ballard, D.H.: Dynamic model of visual recognition predicts neural response properties in the visual cortex. Neural Computation 9, 721–763 (1997)
Royer, S., Paré, D.: Conservation of total synaptic weight through balanced synaptic depression and potentiation. Nature 422, 518–522 (2003)
Spratling, M.W.: Pre-synaptic lateral inhibition provides a better arcitecture for self-organizing neural networks. Network: Computation in Neural Systems 10, 285–301 (1999)
Spratling, M.W., Johnson, M.H.: Pre-integration lateral inhibition enhances unsupervised learning. Neural Computation 14(9), 2157–2179 (2002)
van Ooyen, A., Nienhuis, B.: Pattern recognition in the neocognitron is improved by neuronal adaptation. Biological Cybernetics 70, 47–53 (1993)
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
Michler, F., Wachtler, T., Eckhorn, R. (2006). Adaptive Feedback Inhibition Improves Pattern Discrimination Learning. 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_3
Download citation
DOI: https://doi.org/10.1007/11829898_3
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)