Discovering excitatory relationships using dynamic Bayesian networks

Knowledge and Information Systems

Volumn 29, Issue 2, 2011, Pages 273-303

  Patnaik, Debprakash ^a   Laxman, Srivatsan ^b   Ramakrishnan, Naren ^a  

^a Virginia Polytechnic Institute and State University   (United States)

^b MICROSOFT RESEARCH   (United States)

Author keywords

Computational neuroscience; Dynamic Bayesian networks; Frequent episodes; Spike train analysis; Temporal data mining

Indexed keywords

EID: 80053902649     PISSN: 02191377     EISSN: 02193116     Source Type: Journal    
DOI: 10.1007/s10115-010-0344-6     Document Type: Article

References (29)

1. Bromberg F, Margaritis D, Honavar V (2009) Efficient markov network structure discovery using independence tests. J Artif Int Res 35(1): 449-484.
2. Chickering DM (2003) Optimal structure identification with greedy search. J Mach Learn Res 3: 507-554.
3. Chow C, Liu C (1968) Approximating discrete probability distributions with dependence trees. IEEE Trans Inf Theory 14(3): 462-467.
4. Cooper GF, Herskovits E (1992) A bayesian method for the induction of probabilistic networks from data. Mach Learn 9: 309-347.
5. Czanner G, Eden UT, Wirth S, Yanike M, Suzuki WA, Brown EN (2008) Analysis of between-trial and within-trial neural spiking dynamics. J Neurophysiol 99(5): 2672-2693.
6. Eldawlatly S, Zhou Y, Jin R, Oweiss KG (2010) On the use of dynamic bayesian networks in reconstructing functional neuronal networks from spike train ensembles. Neural Comput 22(1): 158-189.
7. Friedman N, Murphy K, Russell S (1998) Learning the structure of dynamic probabilistic networks. In: Proceedings of the UAI'98. Morgan Kaufmann, pp 139-147.
8. Friedman N, Nachman I, Pe'er D (1999) Learning bayesian network structure from massive datasets: the "Sparse Candidate" algorithm. In: 5th conference on uncertainty in artificial intelligence UAI (1999). pp 206-215.
9. Izhikevich EM (2006) Polychronization: computation with spikes. Neural Comput 18(2): 245-282.
10. Jordan, MI (ed) (1998) Learning in graphical models. MIT Press, Cambridge.
11. Laxman S (2006) Discovering frequent episodes: fast algorithms, Connections with HMMs and generalizations. PhD thesis, IISc, Bangalore, India.
12. Laxman S, Sastry PS, Unnikrishnan KP (2005) Discovering frequent episodes and learning hidden markov models: a formal connection. IEEE TKDE 17(11): 1505-1517.
13. Mannila H, Toivonen H, Verkamo A (1997) Discovery of frequent episodes in event sequences. Data Min Knowl Discov 1(3): 259-289.
14. Meila M (1999) An accelerated chow and liu algorithm: fitting tree distributions to high-dimensional sparse data. In: Proceedings of the ICML'99. pp 249-257.
15. Murphy K (2002) Dynamic Bayesian Networks: representation, inference and learning. PhD thesis, University of California, Berkeley, CA, USA.
16. Papapetrou P et al (2009) Mining frequent arrangements of temporal intervals. Knowl Inf Syst 21(2): 133-171.
17. Patnaik D, Sastry PS, Unnikrishnan KP (2007) Inferring neuronal network connectivity from spike data: a temporal data mining approach. Sci Program 16(1): 49-77.
18. Pavlov D, Mannila H, Smyth P (2003) Beyond independence: probabilistic models for query approximation on binary transaction data. IEEE TKDE 15(6): 1409-1421.
19. Raajay V (2009) Frequent episode mining and multi-neuronal spike train data analysis. Master's thesis, IISc, Bangalore.
20. Rieke F, Warland D, Steveninck R, Bialek W (1999) Spikes: exploring the neural code. MIT Press, Cambridge.
21. Sastry PS, Unnikrishnan KP (2010) Conditional probability based significance tests for sequential patterns in multi-neuronal spike trains. Neural Comput 22(2): 1025-1059.
22. Seppanen JK (2006) Using and extending itemsets in data mining: query approximation, dense itemsets and tiles. PhD thesis, Helsinki University of Technology.
23. Sprekeler H, Michaelis C, Wiskott L (2007) Slowness: an objective for spike-timing-dependent plasticity? PLoS Comput Biol 3(6): e112. doi: 10. 1371/journal. pcbi. 0030112.
24. Takahashi N et al (2007) Watching neuronal circuit dynamics through functional multineuron calcium imaging (fmci). Neurosci Res 58(3): 219-225.
25. Wagenaar DA, Pine J, Potter SM (2006) An extremely rich repertoire of bursting patterns during the development of cortical cultures. BMC Neurosci 7(1): 11 doi: 10. 1186/1471-2202-7-11.
26. Wang C, Parthasarathy S (2006) Summarizing itemset patterns using probabilistic models. In: Proceedings of the KDD'06. ACM, New York, NY, USA, pp 730-735.
27. Wang K, Zhang J, Shen F, Shi L (2008) Adaptive learning of dynamic bayesian networks with changing structures by detecting geometric structures of time series. Knowl Inf Syst 17(1): 121-133.
28. Wang T, Yang J (2010) A heuristic method for learning bayesian networks using discrete particle swarm optimization. Knowl Inf Syst 24(2): 269-281 doi: 10. 1007/s10115-009-0239-6.
29. Williamson J (2000) Approximating discrete probability distributions with bayesian networks. In: Proceedings of the international conference on AI in science & technology, Tasmania. pp 16-20.