Learning to discriminate through long-term changes of dynamical synaptic transmission

Neural Computation

Volumn 21, Issue 12, 2009, Pages 3408-3428

  Leibold, Christian ^a,b   Bendels, Michael H K ^a,b  

^a UNIVERSITY OF MUNICH   (Germany)

^b Bernstein Center for Computational Neuroscience Munich   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

ANIMAL; BIOLOGICAL MODEL; HUMAN; LEARNING; LETTER; METHODOLOGY; NERVE CELL INHIBITION; NERVE CELL NETWORK; NERVE CELL PLASTICITY; NERVE TRACT; NONLINEAR SYSTEM; PERCEPTIVE DISCRIMINATION; PHOTOSTIMULATION; PHYSIOLOGY; PRINCIPAL COMPONENT ANALYSIS; SYNAPSE; SYNAPTIC TRANSMISSION; VISUAL FIELD;

ANIMALS; DISCRIMINATION (PSYCHOLOGY); HUMANS; LEARNING; MODELS, NEUROLOGICAL; NERVE NET; NEURAL INHIBITION; NEURAL PATHWAYS; NEURONAL PLASTICITY; NONLINEAR DYNAMICS; PHOTIC STIMULATION; PRINCIPAL COMPONENT ANALYSIS; SYNAPSES; SYNAPTIC TRANSMISSION; VISUAL FIELDS;

EID: 72249120385     PISSN: 08997667     EISSN: 1530888X     Source Type: Journal    
DOI: 10.1162/neco.2009.12-08-929     Document Type: Letter

References (35)

1. Abbott, L., & Regehr, W. (2004). Synaptic computation. Nature, 431, 796-803.
2. Abbott, L., Varela, J., Sen, K., & Nelson, S. (1997). Synaptic depression and cortical gain control. Science, 275, 220-224.
3. Amit, D., Campbell, C., & Wong, K. (1989). The interaction space of neural networks with sign-constrained synapses. J.Phys.A:Math.Gen.,22, 4687-4693.
4. Bingham, E., & Hyvärinen, A. (2000). A fast fixed-point algorithm for independent component analysis of complex valued signals. Int. J. Neural. Syst., 10, 1-8.
5. Brunel, N., Hakim, V., Isope, P., Nadal, J., & Barbour, B. (2004). Optimal information storage and the distribution of synaptic weights: Perceptron versus Purkinje cell. Neuron, 43, 745-757.
6. Felmy, F., Neher, E., & Schneggenburger, R. (2003). Probing the intracellular calcium sensitivity of transmitter release during synaptic facilitation. Neuron, 37, 801-811.
7. Fortune, E., & Rose, G. (2001). Short-term synaptic plasticity as a temporal filter. Trends Neurosci., 24, 381-385.
8. Fried, H., Fuss, S., & Korsching, S. (2002). Selective imaging of presynaptic activity in the mouse olfactory bulb shows concentration and structure dependence of odor responses in identified glomeruli. Proc. Natl. Acad. Sci. U.S.A., 99, 3222-3227.
9. Gardner, E. (1987). Maximum storage capacity in neural networks. Europhys. Lett., 4, 481-485.
10. Gerstner, W. (2000). Population dynamics of spiking neurons: Fast transients, asynchronous states, and locking. Neural Comput., 12, 43-89.
11. Gundlfinger, A., Leibold, C., Gebert, K., Moisel, M., Schmitz, D., & Kempter, R. (2007). Differential modulation of short-term synaptic dynamics by long-term potentiation at mouse hippocampal mossy fibre synapses. J. Physiol. (Lond.), 585, 853-865.
12. Gütig, R., & Sompolinsky, H. (2006). The tempotron: A neuron that learns spike timing-based decisions. Nat. Neurosci., 9, 420-428.
13. Hanse, E., & Gustafsson, B. (2001). Vesicle release probability and pre-primed pool at glutamatergic synapses in area CA1 of the rat neonatal hippocampus. J. Physiol. (Lond.), 531, 481-493.
14. Haeusler, S., & Maass, W. (2007). A statistical analysis of information-processing properties of lamina-specific cortical microcircuit models. Cereb. Cortex., 17, 149- 162.
15. Hertz, J., Krogh, A., & Palmer, R. (1991). Introduction to the theory of neural computation. Boulder, CO: Westview Press.
16. Jaeger, H., & Haas, H. (2004). Harnessing nonlinearity: Predicting chaotic systems and saving energy in wireless communication. Science, 304, 78-80.
17. Ketchum, K., & Haberly, L. (1993). Membrane currents evoked by afferent fiber stimulation in rat piriform cortex. I. Current source-density analysis. J. Neurophysiol., 69, 248-260.
18. Köhler, H., & Widmaier, D. (1991). Sign-constrained linear learning and diluting in neural networks. J.Phys.A:Math.Gen.,24, L495-L502.
19. Le Bè, J., Silberberg, G., Wang, Y., & Markram, H. (2007). Morphological, electrophysiological, and synaptic properties of corticocallosal pyramidal cells in the neonatal rat neocortex. Cereb. Cortex, 17, 2204-2213.
20. Legenstein, R., & Maass, W. (2008). On the classification capability of sign- constrained perceptrons. Neural Comput., 20, 288-309.
21. Leibold, C. (2004). Stability analysis of asynchronous states in neuronal networks with conductance-based inhibition. Phys.Rev.Lett.,93, 208104.
22. Leibold, C., Gundlfinger, A., Schmidt, R., Thurley, K., Schmitz, D., & Kempter, R. (2008). Temporal compression mediated by short-term synaptic plasticity. Proc. Natl. Acad. Sci. U.S.A., 105, 4417-4422.
23. Lund, J., Angelucci, A., & Bressloff, P. (2003). Anatomical substrates for functional columns in macaque monkey primary visual cortex. Cereb. Cortex., 13, 15-24.
24. Maass, W., Natschläger, T., & Markram, H. (2002). Real-time computing without stable states: A new framework for neural computation based on perturbations. Neural Comput., 14, 2531-2560.
25. Markram, H., & Tsodyks, M. (1996). Redistribution of synaptic efficacy between neocortical pyramidal neurons. Nature, 382, 807-810.
26. Moore, D., Rosenberg, J., & Coleman, J. (2005). Discrimination training of phonemic contrasts enhances phonological processing in mainstream school children. Brain Lang., 94, 72-85.
27. Olshausen, B., & Field, D. (1997). Sparse coding with an overcomplete basis set: A strategy employed by V1? Vision Res., 37, 3311-3325.
28. Riesenhuber, M., & Poggio, T. (2002). Neural mechanisms of object recognition. Curr. Opin. Neurobiol., 12, 162-168.
29. Shinzato, T., & Kabashima, Y. (2008). Perceptron capacity revisited: Classification ability for correlated patterns. J.Phys.A:Math.Theor.,41, 324013.
30. Tsodyks, M., & Markram, H. (1997). The neural code between neocortical pyramidal neurons depends on neurotransmitter release probability. Proc. Natl. Acad. Sci. U.S.A., 94, 719-723.
31. van Hateren, J., & van der Schaaf, A. (1998). Independent component filters of natural images compared with simple cells in primary visual cortex. Proc. Biol. Sci., 265, 359-366.
32. Vapnik, V. (1998). Statistical learning theory. New York: Wiley.
33. Wang, Y., Markram, H., Goodman, P., Berger, T., Ma, J., & Goldman-Rakic, P. S. (2006). Heterogeneity in the pyramidal network of the medial prefrontal cortex. Nat. Neurosci., 9, 534-542.
34. Wilson, D., & Stevenson, R. (2003). The fundamental role of memory in olfactory perception. Trends Neurosci., 26, 243-247.
35. Zucker, R., & Regehr, W. (2002). Short-term synaptic plasticity. Annu. Rev. Physiol., 64, 355-405.