Abstract
Feedback is the fundamental property of neural circuits in the cerebral cortex. If cortical area A projects to cortical area B, then area B invariably sends feedback connections to area A. Similarly, within a given cortical area, there exists massive recurrent excitatory feedback between pyramidal neurons due to local horizontal connections.In cortical information processing feeback plays vital role. In this paper we reviewed the neural coding strategies and learning methods based on the idea of feedback connections between cortical areas instantiate statistical generative models of cortical inputs.
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References
Rao, R., Olshausen, B., Lewicki, M.: Predictive Coding, Cortical Feedback, and Spike-Timing Dependent Plasticity. Statistical Theroies of the Brain (2000)
Girard, P., Hupe, J., Bullier, J.: Feedforward and Feedback Connections Between Areas V1 and V2 of the Monkey Have Similar Rapid Conduction Velocities. Journal of Neurophysiology (2000)
Lee, T.S., Mumford, D.: Hierarchical Bayesian inference in the visual cortex. Journal of Optical Society of America (2003)
Ponulak, F., Kasinski, A.: Introduction to spiking neural networks: Information processing, learning and applications. Acta Neurobiology (2011)
Pasternak, T., James, W.B., Calkins, D.: Visual Information Processing in the Primate Brain. Biological Psychology 3 (2003)
Wysoski, S.G., Benuskova, L., Kasabov, N.: Fast and adaptive network of spiking neurons for multi-view visual pattern recognition. Elsevier Journal of Neurocomputing (2008)
Adrian, Zotterman, Y.: The impulses produced by sensory nerve-endings: Part II. The response of a single end-organ. Journal Physiology (1926)
Johansson, S., Birznieks, I.: First spikes in ensembles of human tactile afferents code complex spatial fingertip events. National Neuroscience (2004)
Saal, H., Vijayakumar, S., Johansson, S.: Information about complex fingertip parameters in individual human tactile afferent neurons. Journal of Neuroscience (2009)
Thorpe, S.J., Delorme, A., VanRullen, R.: Spike-based strategies for rapid processing. Neural Networks (2001)
Lestienne, R.: Spike timing, synchronization and information processing on the sensory side of the central nervous system. Journal of Neurobiology (2001)
Ponulak, F., Kasinski, A.: Supervised learning in spiking neural networks with ReSuMe: sequence learning, classification and spike-shifting. Neural Computing (2010)
Izhikevich, E.: Resonance and selective communication via bursts in neurons having subthreshold oscillations. BioSystems (2002)
Shin, J., Smith, D., Swiercz, W.: Recognition of partially occluded and rotated images with a network of spiking neurons. IEEE Transcation on Neural Networks (2010)
Singer, W.: Neuronal synchrony:a versatile code for the definition of relations. Journal of Neuron (1999)
Hopfield, J.: Pattern recognition computation using action potential timing for stimulus Representation. Journal of Nature (1995)
Buzsaki, G.: Rhythms of the Brain. Oxford University Press, New York (2006)
Chen, H., Bermak, A., Law, M., Martinez, D.: Spike latency coding in a biologically inspired microelectronic nose. IEEE Transaction on Biomedical Circuits Systems (2011)
Kiss, T., Orban, G., Erdi, P.: Modeling hippocampal theta oscillation: Applications in neuropharmacology and robot navigation. International Journal of Intelligent Systems (2006)
Azvan, R., Florian, V.: A reinforcement learning algorithm for spiking neural networks. In: IEEE Intenational Symbosim on Symbolic and Numeric Algorithms (2005)
Mongillo, G., Barak, O., Tsodyks, M.: Synaptic theory of working memory. Science (2008)
Ito, M.: Control of mental activities by internal models in the cerebellum. Journal of Nature (2008)
Carey, M., Medina, J., Lisberger, S.: Instructive signals for motor learning from visual cortical area MT. Journal of Neuroscience (2005)
Long, L.N.: An Adaptive Spiking Neural Network with Hebbian Learning. In: IEEE Workshop on Adaptive Intelligent System (2011)
Sima, J.: Gradient Learning in Networks of Smoothly Spiking Neurons. Advances in Neuro Information Processing (2009)
Xin, J., Embrechts, M.J.: Supervised learning with spiking neuron networks. In: Proceedings IEEE International Joint Conference on Neural Networks (2001)
Kasinski, A., Ponulak, F.: Experimental Demonstration of Learning Properties of a New Supervised Learning Method for the Spiking Neural Network. In: Biological Inspirations, ICANN 2006 (2006)
Ponulak, F., Kasinski, A.: ReSuMe learning method for Spiking Neural Networks dedicated to neuroprostheses control. Journal of Neural Computation (2010)
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Diana Andrushia, A., Thangarajan, R. (2012). Survey on Information Processing in Visual Cortex: Cortical Feedback and Spiking Neural Network. In: Kim, Th., Ko, Ds., Vasilakos, T., Stoica, A., Abawajy, J. (eds) Computer Applications for Communication, Networking, and Digital Contents. FGCN 2012. Communications in Computer and Information Science, vol 350. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35594-3_44
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DOI: https://doi.org/10.1007/978-3-642-35594-3_44
Publisher Name: Springer, Berlin, Heidelberg
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