Abstract
Neurophysiological investigations suggest that presynaptic ionotropic receptors are important mechanism for controlling synaptic transmission. In this paper, presynaptic kainate receptors are incorporated in a feedforward inhibitory neural network in order to investigate their role in the cortical information processing. Computer simulations showed that the proposed mechanism is able to compute the function maximum by disinhibiting the cell with the maximal amplitude. The maximum is computed with high precision even in the case where inhibitory synaptic weights are weak and (or) asymmetric. Moreover, the network is able to track time-varying input and to select multiple winners. These capabilities do not depend on the dimensionality of the network. Also, the model is able to implement the winner-take-all behaviour.
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.
References
Alger, B.E., Pitler, T.A.: Retrograde signaling at GABAA-receptor synapses in the mammalian CNS. Trends Neurosci. 18, 333–340 (1995)
Binns, K.E., Turner, J.P., Salt, T.E.: Kainate receptor (GluR5)-mediated disinhibition of responses in rat ventrobasal thalamus allows a novel sensory processing mechanism. J. Physiol. 551, 525–537 (2003)
Ermentrout, B.: Complex dynamics in winner-take-all neural nets with slow inhibition. Neural Netw. 5, 415–431 (1992)
Fukai, T., Tanaka, S.: A simple neural network exhibiting selective activation of neuronal ensembles: from winner-take-all to winners-share-all. Neural Comput. 9, 77–97 (1997)
Gawne, T.J., Martin, J.M.: Responses of primate visual cortical V4 neurons to simultaneously presented stimuli. J. Neurophysiol. 88, 1128–1135 (2002)
Grossberg, S.: Nonlinear neural networks: Principles, mechanisms, and architectures. Neural Netw. 1, 17–61 (1988)
Huettner, J.E.: Kainate receptors and synaptic transmission. Prog. Neurobiol. 70, 387–407 (2001)
Kaski, S., Kohonen, T.: Winner-take-all networks for physiological models of competitive learning. Neural Netw. 7, 973–984 (1994)
Kreitzer, A.C., Regehr, W.G.: Retrograde signalling by endocannabinoids. Curr. Opin. Neurobiol. 12, 324–330 (2002)
MacDermott, A.B., Role, L.W., Siegelbaum, S.A.: Presynaptic ionotropic receptors and the control of transmitter release. Annu. Rev. Neurosci. 22, 443–485 (1999)
Riesenhuber, M., Poggio, T.A.: Hierarchical models of object recognition in cortex. Nat. Neurosci. 2, 1019–1025 (1999)
Sato, T.: Interactions of visual stimuli in the receptive fields of inferior temporal neurons in awake macaques. Exp. Brain Res. 77, 23–30 (1989)
Tsotsos, J., Culhane, S., Wai, W., Lai, Y., Davis, N., Nuflo, F.: Modeling visual attention via selective tuning. Artif. Intel. 78, 507–545 (1995)
Wang, D.L.: Object selection based on oscillatory correlations. Neural Netw. 12, 579–592 (1999)
Yu, A.J., Giese, M.A., Poggio, T.A.: Biophysically plausible implementations of the maximum operation. Neural Comput. 14, 2857–2881 (2002)
Yuille, A.L., Grzywacz, N.M.: A winner-take-all mechanism based on presynaptic inhibition feedback. Neural Comput. 1, 334–347 (1989)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Domijan, D., Šetić, M. (2007). Computing the Maximum Using Presynaptic Inhibition with Glutamate Receptors. In: Mele, F., Ramella, G., Santillo, S., Ventriglia, F. (eds) Advances in Brain, Vision, and Artificial Intelligence. BVAI 2007. Lecture Notes in Computer Science, vol 4729. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-75555-5_40
Download citation
DOI: https://doi.org/10.1007/978-3-540-75555-5_40
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-75554-8
Online ISBN: 978-3-540-75555-5
eBook Packages: Computer ScienceComputer Science (R0)