Intrinsic adaptation in autonomous recurrent neural networks

Neural Computation

Volumn 24, Issue 2, 2012, Pages 523-540

  Marković, Dimitrije ^a   Gros, Claudius ^a  

^a GOETHE UNIVERSITY   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

ADAPTATION; BIOLOGICAL MODEL; BRAIN; ENTROPY; LEARNING; LETTER; NERVE CELL; NERVE CELL NETWORK; NONLINEAR SYSTEM; PHYSIOLOGY; SYNAPSE; TIME;

ADAPTATION, PHYSIOLOGICAL; BRAIN; ENTROPY; LEARNING; MODELS, NEUROLOGICAL; NERVE NET; NEURONS; NONLINEAR DYNAMICS; SYNAPSES; TIME FACTORS;

BRAIN; RECURRENT NEURAL NETWORKS;

ACTIVITY PATTERNS; ADAPTATION PROCESS; CHAOTICS; DYNAMICAL REGIME; DYNAMICAL STATE; EXTERNAL STIMULUS; INFORMATION ENTROPY; OPTIMISATIONS; SENSORY INPUT; STATE DYNAMICS;

NEURONS;

EID: 84856403606     PISSN: 08997667     EISSN: 1530888X     Source Type: Journal    
DOI: 10.1162/NECO_a_00232     Document Type: Letter

References (34)

1. Adami, C. (1995). Self-organized criticality in living systems. Physics Letters A, 203, 29-32.
2. Amari, S. I. (1998).Natural gradient works efficiently in learning. NeuralComputation, 10, 251-276.
3. Ashby, W. (1962). Principles of the self-organizing system. In Principles of selforganization: Transactions of the University of Illinois Symposium (pp. 255-278).
4. Bak, P., & Paczuski, M. (1995). Complexity, contingency, and criticality. Proceedings of the National Academy of Sciences of the United States of America, 92, 6689-6696.
5. Bak, P., Tang, C., & Wiesenfeld, K. (1987) Self-organized criticality: An explanation of the 1/f noise. Physical Review Letters, 59, 381-384.
6. Bak, P., Tang, C., & Wiesenfeld, K. (1998). Self-organized criticality. Physical Review A, 38, 364-374.
7. Bekkers, J. M. (2003). Synaptic transmission: Functional autapses in the cortex. Current Biology, 13, 433-435.
8. Bertschinger, N., & Natschläger, T. (2004). Real-time computation at the edge of chaos in recurrent neural networks. Neural Computation, 16, 1413-1436.
9. Boffetta, G., Giuliani, P., Paladin, G., & Vulpiani, A. (1998). An extension of the Lyapunov analysis for the predictability problem. Journal of the Atmospheric Science, 55, 3409-3416.
10. Bottou, L. (2004). Stochastic learning. Berlin: Springer-Verlag.
11. Camazine, S., Deneubourg, J.-L., Franks, N. R., Sneyd, J., & Theraula, G. (2003). Self-organization in biological systems. Princeton, NJ: Princeton University Press.
12. Chialvo, D. R. (2010). Emergent complex neural dynamics. Nature Physics, 6, 744-750.
13. Dauce, E., Quoy, M., Cessac, B., Doyon, B., & Samuelides, M. (1998). Self-organization and dynamics reduction in recurrent networks: Stimulus presentation and learning. Neural Networks, 11, 521-533.
14. Der, R., Hesse, F., & Martius, G. (2006). Rocking stamper and jumping snakes from a dynamical systems approach to artificial life. Adaptive Behavior, 14, 105-115.
15. Ding, R., & Li, J. (2007).Nonlinear finite-time Lyapunov exponent and predictability. Physics Letters A, 364, 396-400.
16. Gros, C. (2007). Neural networks with transient state dynamics. New Journal of Physics, 9, 109.
17. Gros, C. (2009). Cognitive computation with autonomously active neural networks: An emerging field. Cognitive Computation, 1, 77-90.
18. Gros, C. (2010). Complex and adaptive dynamical systems: A primer (2nd ed., pp. 145-175). Berlin: Springer.
19. Herrmann, C. S., & Klaus, A. (2004). Autapse turns neuron into oscillator. International Journal of Bifurcation and Chaos, 14, 623-633.
20. Kinouchi, O., & Copelli, M. (2006). Optimal dynamical range of excitable networks at criticality. Nature Physics, 2, 348-351.
21. Legenstein, R., & Maass, W. (2006). Edge of chaos and prediction of computational performance for neural circuit models. Neural Networks, 20, 323-334.
22. Levina, A., Herrmann, J. M., & Geisel, T. (2007). Dynamical synapses causing selforganized criticality in neural networks. Nature Physics, 3, 857-860.
23. Levina, A., Herrmann, J. M., & Geisel, T. (2009). Phase transitions towards criticality in a neural system with adaptive interactions. Physical Review Letters, 102, 1-4.
24. Marković, D., & Gros, C. (2010). Self-organized chaos through polyhomeostatic optimization. Physical Review Letters, 105, 068702.
25. Mozzachiodi, R., & Byrne, J. H. (2010). More than synaptic plasticity: Role of nonsynaptic plasticity in learning and memory. Trends in Neurosciences, 33, 17-26.
26. Siri, B., Berry, H., Cessac, B., Delord, B., & Quoy, M. (2008). A mathematical analysis of the effects of Hebbian learning rules on the dynamics and structure of discretetime random recurrent neural networks. Neural Computation, 20, 2937-2966.
27. Siri, B., Quoy, M., Delord, B., Cessac, B., & Berry, H. (2007). Effects of Hebbian learning on the dynamics and structure of random networks with inhibitory and excitatory neurons. Journal of Physiology, Paris, 101, 136-148.
28. Solé, R. V., & Miramontes, O. (1995). Information at the edge of chaos in fluid neural networks. Physica D: Nonlinear Phenomena, 80, 171-180.
29. Sompolinsky, H., Crisanti, A., & Sommers, H. J. (1998). Chaos in random neural networks. Physical Review Letters, 61, 259-262.
30. Spall, J. C. (2005). Introduction to stochastic search and optimization: Estimation, simulation, and control (2nd ed.). Hoboken, NJ: Wiley.
31. Stemmler, M., & Koch, C. (1999). How voltage-dependent conductances can adapt to maximize the information encoded by neuronal firing rate. Nature Neuroscience, 2, 521-527.
32. Triesch, J. (2005). A gradient rule for the plasticity of a neuronâs intrinsic excitability. In Proceedings of Artificial Neural Networks: Biological Inspirations ICANN 2005 (pp. 65-70). Berlin: Springer-Verlag.
33. Triesch, J. (2007). Synergies between intrinsic and synaptic plasticity mechanisms. Neural Computation, 909, 885-909.
34. Zimmer, C. (1999). Life after chaos. Science, 284, 83-86.