Noise as a resource for computation and learning in networks of spiking neurons

Proceedings of the IEEE

Volumn 102, Issue 5, 2014, Pages 860-880

  Maass, Wolfgang ^a  

^a GRAZ UNIVERSITY OF TECHNOLOGY   (Austria)

Author keywords

Computational power; neural networks; neuromorphic hardware; noise; self organization; spiking neurons; Stochastic computing

Indexed keywords

ENERGY EFFICIENCY; LEARNING SYSTEMS; NEURAL NETWORKS; NEURONS; PROBLEM SOLVING; STOCHASTIC SYSTEMS;

COMPUTATIONAL POWER; NEUROMORPHIC HARDWARES; NOISE; SELF ORGANIZATIONS; SPIKING NEURON; STOCHASTIC COMPUTING;

NOISE POLLUTION;

EID: 84900490680     PISSN: 00189219     EISSN: None     Source Type: Journal    
DOI: 10.1109/JPROC.2014.2310593     Document Type: Article

References (87)

1. M. McDonnell K. Boahen A. Ijspeert and T. Sejnowski "Engineering intelligent electronic systems based on computational neuroscience," Proc. IEEE 2014 to be published.
2. B. Benjamin P. Gao E. McQuinn S. Choudhary A. R. Chandrasekaran J. M. Bussat R. Alvarez-Icaza J. V. Arthur P. A. Merolla and K. Boahen "Neurogrid: A mixed-analog-digital multichip system for large-scale neural simulations," Proc. IEEE 2014 to be published.
3. T. Pfeil A. Grübl S. Jeltsch E. Müller P. Müller M. A. Petrovici M. Schmuker D. Brüderle J. Schemmel and K. Meier "Six networks on a universal neuromorphic computing substrate," Front. Neurosci. vol. 7 no. 11 2012 DOI: 10.3389/fnins.2013.00011.
4. T. J. Hamilton S. Afshar A. van Schaik and J. Tapson "Stochastic electronics: A neuro-inspired design paradigm for integrated circuits," Proc. IEEE 2014 DOI: 10.1109/JPROC.2014.2310713 to be published.
5. J. G. Borst "The low synaptic release probability in vivo," Trends Neurosci. vol. 33 no. 6 pp. 259-266 2010.
6. H. Markram and M. Tsodyks "Redistribution of synaptic efficacy between neocortical pyramidal neurons," Nature vol. 382 no. 6594 pp. 807-810 1996.
7. A. A. Faisal L. P. J. Selen and D. M. Wolpert "Noise in the nervous system," Nature Rev. Neurosci. vol. 9 no. 4 pp. 292-303 2008.
8. Y. Yarom and J. Hounsgaard "Voltage fluctuations in neurons: Signal or noise" Physiol. Rev. vol. 91 no. 3 pp. 917-929 2011.
9. P. G. Clarke "The limits of brain determinacy," Proc. Biol. Sci. vol. 279 no. 1734 pp. 1665-1674 2012.
10. M. D. McDonnell and L. M. Ward "The benefits of noise in neural systems: Bridging theory and experiment," Nature Rev. Neurosci. vol. 12 no. 7 pp. 415-426 2011.
11. A. Destexhe and M. Rudolph-Lilith Neuronal Noise. New York NY USA: Springer-Verlag 2012.
12. L. Buesing J. Bill B. Nessler and W. Maass "Neural dynamics as sampling: A model for stochastic computation in recurrent networks of spiking neurons," PLoS Comput. Biol. vol. 7 no. 11 2011 e1002211.
13. D. Pecevski L. Büsing and W. Maass "Probabilistic inference in general graphical models through sampling in stochastic networks of spiking neurons," PLoS Comput. Biol. vol. 7 no. 12 2011 e1002294.
14. S. Habenschuss H. Puhr and W. Maass "Emergence of optimal decoding of population codes through STDP," Neural Comput. vol. 25 no. 6 pp. 1371-1407 2013.
15. B. Nessler M. Pfeiffer L. Buesing and W. Maass "Bayesian computation emerges in generic cortical microcircuits through spike-timingdependent plasticity," PLOS Comput. Biol. vol. 9 no. 4 2013 e1003037.
16. S. Habenschuss Z. Jonke and W. Maass "Stochastic computations in cortical microcircuit models," PLoS Comput. Biol. vol. 9 no. 11 2013 e1003311.
17. M. Mitzenmacher and E. Upfal Probability and Computing: Randomized Algorithms and Probabilistic Analysis. Oxford U.K.: Oxford Univ. Press 2005.
18. A. Alaghi and J. P. Hayes "Survey of stochastic computing," ACM Trans. Embedded Comput. Syst. vol. 12 no. 2s 2013 DOI: 10.1145/2465787.2465794.
19. A. Litwin-Kumar and B. Doiron "Slow dynamics and high variability in balanced cortical networks with clustered connections," Nature Neurosci. vol. 15 no. 11 pp. 1498-1505 2012.
20. M. M. Churchland and L. F. Abbott "Two layers of neural variability," Nature Neurosci. vol. 15 no. 11 pp. 1472-1474 2012.
21. T. Tetzlaff M. Helias G. T. Einevoll and W. Diesmann "Decorrelation of neural-network activity by inhibitory feedback," PLoS Comput. Biol. vol. 8 no. 8 2012 e1002596.
22. M. A. Petrovici J. Bill I. Bytschok J. Schemmel and K. Meier "Stochastic inference with deterministic spiking neurons," 2013. [Online]. Available: arXiv: 1311.3211.
23. G. E. Hinton and T. J. Sejnowski "Optimal perceptual inference," in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. B. D. Ripley Ed. 1983 pp. 448-453.
24. D. H. Ackley G. E. Hinton and T. J. Sejnowski "A learning algorithm for Boltzmann machines," Cogn. Sci. vol. 9 no. 1 pp. 147-169 1985.
25. E. Aarts and J. Korst Simulated Annealing and Boltzmann Machines. New York NY USA: Wiley 1989.
26. J. J. Hopfield "Neural networks and physical systems with emergent collective computational abilities," Proc. Nat. Acad. Sci. USA vol. 79 no. 8 pp. 2554-2558 1982.
27. C. Andrieu N. D. Freitas A. Doucet and M. I. Jordan "An introduction to MCMC for machine learning," Mach. Learn. vol. 50 no. 1-2 pp. 5-43 2003.
28. S. Geman and D. Geman "Stochastic relaxation Gibbs distributions the Bayesian restoration of images," IEEE Trans. Pattern Anal. Mach. Intell. vol. PAMI-6 no. 6 pp. 721-741 Jun. 1984.
29. R. Salakhutdinov and G. Hinton "An efficient learning procedure for deep Boltzmann machines," Neural Comput. vol. 24 no. 8 pp. 1967-2006 2012.
30. R. Jolivet A. Rauch H. Lüscher and W. Gerstner "Predicting spike timing of neocortical pyramidal neurons by simple threshold models," J. Comput. Neurosci. vol. 21 no. 1 pp. 35-49 2006.
31. Z. Jonke S. Habenschuss and W. Maass "Noise enables networks of spiking neurons to carry out efficient stochastic search in high-dimensional spaces," 2014 to be published.
32. M. R. Garey and D. S. Johnson Computers and Intractability: A Guide to the Theory of NP-Completeness. New York NY USA: Freeman 1979.
33. E. Vul and H. Pashler "Measuring the crowd within probabilistic representations within individuals," Psychol. Sci. vol. 19 no. 7 pp. 645-647 2008.
34. J. B. Tenenbaum C. Kemp T. L. Griffiths and N. D. Goodman "How to grow a mind: Statistics structure abstraction," Science vol. 331 no. 6022 pp. 1279-1285 2011.
35. M. Oaksford and N. Chater Bayesian Rationality: The Probabilistic Approach to Human Reasoning. Oxford U.K.: Oxford Univ. Press 2007.
36. D. Koller and N. Friedman Probabilistic Graphical Models: Principles and Techniques (Adaptive Computation and Machine Learning). Cambridge MA USA: MIT Press 2009.
37. A. Steimer W. Maass and R. Douglas "Belief-propagation in networks of spiking neurons," Neural Comput. vol. 21 pp. 2502-2523 2009.
38. A. Pouget J. M. Beck W. J. Ma and P. E. Latham "Probabilistic brains: Knowns and unknowns," Nature Neurosci. vol. 16 pp. 1170-1178 2013.
39. P. Berkes G. Orban M. Lengyel and J. Fiser "Spontaneous cortical activity reveals hallmarks of an optimal internal model of the environment," Science vol. 331 pp. 83-87 2011.
40. A. Luczak P. Barthó S. L. Marguet G. Buszáki and K. D. Harris "Sequential structure of neocortical spontaneous activity in vivo," Proc. Nat. Acad. Sci. USA vol. 104 no. 1 pp. 347-352 2007.
41. G. Buzsáki "Neural syntax: Cell assemblies synapsembles readers," Neuron vol. 68 no. 3 pp. 362-385 2010.
42. S. Klampfl and W. Maass "Emergence of dynamic memory traces in cortical microcircuit models through STDP," J. Neurosci. vol. 33 no. 28 pp. 11515-11529 2013.
43. S. Haeusler and W. Maass "A statistical analysis of information processing properties of lamina-specific cortical microcircuit models," Cereb. Cortex vol. 17 no. 1 pp. 149-162 2007.
44. M. E. Raichle "Two views of brain function," Trends Cogn. Sci. vol. 14 no. 4 pp. 180-190 2010.
45. M. H. Mohajerani A. W. Chan M. Mohsenvand J. LeDue R. Liu D. A. McVea J. D. Boyd Y. T. Wang M. Reimers and T. H. Murphy "Spontaneous cortical activity alternates between motifs defined by regional axonal projections," Nature Neurosci. vol. 16 no. 10 pp. 1426-1435 2013.
46. D. Pecevski D. Kappel and Z. Jonke "NEVESIM: Event-based neural simulation framework with a python interface," Front. Neuroinf. 2013 to be published.
47. D. L. Schacter D. R. Addis D. Hassabis V. C. Martin R. N. Spreng and K. K. Szpunar "The future of memory: Remembering imagining the brain," Neuron vol. 76 pp. 677-694 2012.
48. R. J. Douglas and K. A. Martin "Neuronal circuits of the neocortex," Annu. Rev. Neurosci. vol. 27 pp. 419-451 2004.
49. J. J. Hopfield "Searching for memories sudoku implicit check bits the iterative use of not-always-correct rapid neural computation," Neural Comput. vol. 20 pp. 1119-1164 2008.
50. M. Toussaint and C. Goerick "A Bayesian view on motor control and planning," in From Motor to Interaction Learning in Robots. Studies in Computational Intelligence O. Sigaud and J. Peters Eds. New York NY USA: Springer-Verlag 2010 pp. 227-252.
51. M. Botvinick and M. Toussaint "Planning as inference," Trends Cogn. Sci. vol. 16 no. 10 pp. 485-488 2012.
52. D. Kappel D. Pecevski S. Chase A. Whitford and W. Maass "A model for closed-loop probabilistic motor control through networks of spiking neurons with noise," 2014 to be published.
53. E. Rueckert D. Pecevski D. Kappel and W. Maass "An implementation of movement planning as probabilistic inference through networks of spiking neurons with noise," 2014 to be published.
54. N. Caporale and Y. Dan "Spike timing-dependent plasticity: A Hebbian learning rule," Annu. Rev. Neurosci. vol. 31 pp. 25-46 2008.
55. D. Kappel B. Nessler and W. Maass "STDP installs in winner-take-all circuits an online approximation to hidden Markov model learning," PLoS Comput. Biol. 2014.
56. A. Luczak and J. N. MacLean "Default activity patterns at the neocortical microcircuit level," Front. Integr. Neurosci. vol. 6 no. 30 2012 DOI: 10.3389/fnint.2012. 00030.
57. J. Bill L. Buesing S. Habenschuss B. Nessler R. Legenstein and W. Maass "Distributed Bayesian computation and self-organized learning in sheets of spiking neurons with local lateral inhibition," 2014 to be published.
58. S. Haeusler and W. Maass "Inhibitory networks orchestrate the self-organization of computational function in cortical microcircuit motifs through STDP," 2014 to be published.
59. Z. Jonke R. Legenstein S. Habenschuss and W. Maass "Emergent coding of features and Bayesian computation in ensembles of pyramidal cell with relaxed lateral inhibition," 2014 to be published.
60. D. Pecevski and W. Maass "Learning in an uncertain world: STDP installs in networks of spiking neurons complex memory structures that facilitate probabilistic inference," 2014 to be published.
61. R. Legenstein D. Pecevski and W. Maass "A learning theory for reward-modulated spike-timing-dependent plasticity with application to biofeedback," PLoS Comput. Biol. vol. 4 no. 10 2008 e1000180.
62. G. M. Hoerzer R. Legenstein and W. Maass "Emergence of complex computational structures from chaotic neural networks through reward-modulated Hebbian learning," Cereb. Cortex 2012 DOI: 10.1093/cercor/bhs348.
63. D. J. Herzfeld and R. Shadmehr "Motor variability is not noise but grist for the learning mill," Nature Neurosci. vol. 17 no. 2 pp. 149-150 2014.
64. H. G. Wu Y. R. Miyamoto L. N. G. Castro B. P. Ölveczky and M. A. Smith "Temporal structure of motor variability is dynamically regulated and predicts motor learning ability," Nature Neurosci. vol. 17 no. 2 pp. 312-321 2014.
65. G. Dragoi and G. Buzsaki "Temporal encoding of place sequences by hippocampal cell assemblies," Neuron vol. 50 no. 1 pp. 145-157 2006.
66. M. Siegel M. R. Warden and E. K. Miller "Phase-dependent neuronal coding of objects in short-term memory," Proc. Nat. Acad. Sci. USA vol. 106 no. 50 pp. 21341-21346 2009.
67. W. Singer "Cortical dynamics revisited," Trends Cogn. Sci. vol. 17 no. 12 pp. 616-626 2013.
68. T. D. Sanger "Neural population codes," Current Opinion Neurobiol. vol. 13 pp. 238-249 2003.
69. Y. Huang and R. P. Rao "Predictive coding," Wiley Interdisciplinary Rev. Cogn. Sci. vol. 2 no. 5 pp. 580-593 2011.
70. M. Boerlin C. K. Machens and S. Dene ve "Predictive coding of dynamical variables in balanced spiking networks.," PLoS Comput. Biol. vol. 9 no. 11 2013 e1003258.
71. T. Lochmann and S. Deneve "Neural processing as causal inference," Current Opinion Neurobiol. vol. 21 no. 5 pp. 774-781 2011.
72. J. Schemmel D. Bruderle A. Grubl M. Hock K. Meier and S. Millner "A wafer-scale neuromorphic hardware system for large-scale neural modeling," in Proc. IEEE Int. Symp. Circuits Syst. 2010 pp. 1947-1950.
73. J. Fiser P. Berkes G. Orban and M. Lengyel "Probabilistic computation: A possible functional role for spontaneous activity in the cortex," Perception vol. 40 pp. 53-53 2011.
74. T. L. Griffiths N. Chater D. Norris and A. Pouget "How the Bayesians got their beliefs (and what those beliefs actually are): Comment on Bowers and Davis," Psychol. Bull. vol. 138 pp. 415-422 2012.
75. J. S. Bowers and C. J. Davis "Bayesian just-so stories in psychology and neuroscience," Psychol. Bull. vol. 138 no. 3 pp. 389-414 2012.
76. M. Gur A. Beylin and D. M. Snodderly "Response variability of neurons in primary visual cortex (v1) of alert monkeys," J. Neurosci. vol. 17 pp. 2914-2920 1997.
77. M. Gur and D. M. Snodderly "Response variability of neurons in primary visual cortex (v1) of alert monkeys," J. Neurosci. vol. 17 no. 8 pp. 2914-2920 2006.
78. R. P. N. Rao "Bayesian computation in recurrent neural circuits," Neural Comput. vol. 16 pp. 1-38 2004.
79. S. Deneve "Bayesian spiking neurons I: Inference," Neural Comput. vol. 20 no. 1 pp. 91-117 2008.
80. W. Maass "Lower bounds for the computational power of networks of spiking neurons," Neural Comput. vol. 8 no. 1 pp. 1-40 1996.
81. J. S. Anderson I. Lampl D. C. Gillespie and D. Ferster "The contribution of noise to contrast invariance of orientation tuning in cat visual cortex," Science vol. 290 no. 5498 pp. 1968-1972 2000.
82. S. J. Gershman E. Vul and J. B. Tenenbaum "Multistabilits and perceptual inference," Neural Comput. vol. 24 no. 1 pp. 1-24 2012.
83. B. E. Pfeiffer and D. J. Foster "Hippocampal place-cell sequences depict future paths to remembered goals," Nature vol. 497 no. 7447 pp. 74-79 2013.
84. M. Rigotti O. Barak M. R. Warden X. J. Wang N. D. Daw E. K. Miller and S. Fusi "The importance of mixed selectivity in complex cognitive tasks," Nature vol. 497 no. 7451 pp. 585-590 2013.
85. V. Mante D. Sussillo K. V. Shenoy and W. T. Newsome "Context- dependent computation by recurrent dynamics in prefrontal cortex," Nature vol. 503 no. 7474 pp. 78-84 2013.
86. M. Okun P. Yger S. L. Marguet F. Gerard-Mercier A. Benucci S. Katzner and K. D. Harris "Population rate dynamics and multineuron firing patterns in sensory cortex," J. Neurosci. vol. 32 no. 48 pp. 17 108-17 119 2012.
87. J. Fiser M. Lengyel C. Savin G. Orbn and P. Berkes "How (not) to assess the importance of correlations for the matching of spontaneous and evoked activity," 2013. [Online]. Available: arXiv:1301.6554.