Functional network reorganization in motor cortex can be explained by reward-modulated Hebbian learning

Advances in Neural Information Processing Systems 22 - Proceedings of the 2009 Conference

Volumn , Issue , 2009, Pages 1105-1113

  Legenstein, Robert ^a   Chase, Steven M ^b,c,d   Schwartz, Andrew B ^b,c   Maass, Wolfgang ^a  

^a GRAZ UNIVERSITY OF TECHNOLOGY   (Austria)

^b UNIVERSITY OF PITTSBURGH SCHOOL OF MEDICINE   (United States)

^c CENTER FOR THE NEURAL BASIS OF COGNITION   (United States)

^d CARNEGIE MELLON UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

TUNING;

CONTROL TASK; FUNCTIONAL NETWORK; HEBBIAN LEARNING; LEARNING EFFECTS; LEARNING RULES; MOTOR-CORTEX; NETWORK REORGANIZATION; NEURONAL NOISE; NEUROPROSTHETIC; TUNING PROPERTIES;

NEURONS;

EID: 84858713861     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (24)

1. B. Jarosiewicz, S. M. Chase, G. W. Fraser, M. Velliste, R. E. Kass, and A. B. Schwartz. Functional network reorganization during learning in a brain-computer interface paradigm. Proc. Nat. Acad. Sci. USA, 105(49):19486-91, 2008.
2. A. P. Georgopoulos, R. E. Ketner, and A. B. Schwartz. Primate motor cortex and free arm movements to visual targets in three- dimensional space. ii. coding of the direction of movement by a neuronal population. J. Neurosci., 8:2928-2937, 1988.
3. A. B. Schwartz. Useful signals from motor cortex. J. Physiology, 579:581-601, 2007.
4. Y. Loewenstein and H. S. Seung. Operant matching is a generic outcome of synaptic plasticity based on the covariance between reward and neural activity. Proc. Nat. Acad. Sci. USA, 103(41):15224-15229, 2006.
5. A. G. Barto, R. S. Sutton, and C. W. Anderson. Neuronlike adaptive elements that can solve difficult learning control problems. IEEE Trans. Syst. Man Cybern., SMC-13(5):834-846, 1983.
6. P. Mazzoni, R. A. Andersen, and M. I. Jordan. A more biologically plausible learning rule for neural networks. Proc. Nat. Acad. Sci. USA, 88(10):4433-4437, 1991.
7. R. J. Williams. Simple statistical gradient-following algorithms for connectionist reinforcement learning. Machine Learning, 8:229-256, 1992.
8. J. Baxter and P. L. Bartlett. Direct gradient-based reinforcement learning: I. gradient estimation algorithms. Technical report, Research School of Information Sciences and Engineering, Australian National University, 1999.
9. X. Xie and H. S. Seung. Learning in neural networks by reinforcement of irregular spiking. Phys. Rev. E, 69(041909), 2004.
10. I. R. Fiete and H. S. Seung. Gradient learning in spiking neural networks by dynamic perturbation of conductances. Phys. Rev. Lett., 97(4):048104-1 to 048104-4, 2006.
11. J.-P. Pfister, T. Toyoizumi, D. Barber, and W. Gerstner. Optimal spike-timing-dependent plasticity for precise action potential firing in supervised learning. Neural Computation, 18(6):1318-1348, 2006.
12. E. M. Izhikevich. Solving the distal reward problem through linkage of STDP and dopamine signaling. Cerebral Cortex, 17:2443-2452, 2007.
13. D. Baras and R. Meir. Reinforcement learning, spike-time-dependent plasticity, and the bcm rule. Neural Computation, 19(8):2245-2279, 2007.
14. R. V. Florian. Reinforcement learning through modulation of spike-timing-dependent synaptic plasticity. Neural Computation, 6:1468-1502, 2007.
15. M. A. Farries and A. L. Fairhall. Reinforcement learning with modulated spike timing-dependent synaptic plasticity. J. Neurophys., 98:3648-3665, 2007.
16. R. Legenstein, D. Pecevski, and W. Maass. A learning theory for reward-modulated spike-timing-dependent plasticity with application to biofeedback. PLoS Computational Biology, 4(10):1-27, 2008.
17. C. H. Bailey, M. Giustetto, Y.-Y. Huang, R. D. Hawkins, and E. R. Kandel. Is heterosynaptic modulation essential for stabilizing Hebbian plasticity and memory? Nat. Rev. Neurosci., 1:11-20, 2000.
18. Q. Gu. Neuromodulatory transmitter systems in the cortex and their role in cortical plasticity. Neuro-science, 111(4):815-835, 2002.
19. Samuel J. Sober, Melville J. Wohlgemuth, and Michael S. Brainard. Central contributions to acoustic variation in birdsong. J. Neurosci., 28(41):10370-9, 2008.
20. E. C. Tumer and M. S. Brainard. Performance variability enables adaptive plasticity of 'crystallized' adult birdsong. Nature, 250(7173):1240-1244, 2007.
21. A. P. Georgopoulos, A. P. Schwartz, and R. E. Ketner. Neuronal population coding of movement direction. Science, 233:1416-1419, 1986.
22. J. Baxter and P. L. Bartlett. Infinite-horizon policy-gradient estimation. J. Artif. Intell. Res., 15:319-350, 2001.
23. R. Legenstein, S. M. Chase, A. B. Schwartz, and W. Maass. A reward-modulated hebbian learning rule can explain experimentally observed network reorganization in a brain control task. Submitted for publication, 2009.
24. U. Rokni, A G. Richardson, E. Bizzi, and H. S. Seung. Motor learning with unstable neural representations. Neuron, 54:653-666, 2007.