Online neural sequence detection with hierarchical dirichlet point process
AUTHORs: 
Weihan Li, Yu Qi, Gang PanAuthors Info & Claims
Article No.: 482, Pages 6654 - 6665
Abstract
Neural sequence detection plays a vital role in neuroscience research. Recent impressive works utilize convolutive nonnegative matrix factorization and NeymanScott process to solve this problem. However, they still face two limitations. Firstly, they accommodate the entire dataset into memory and perform iterative updates of multiple passes, which can be inefficient when the dataset is large or grows frequently. Secondly, they rely on the prior knowledge of the number of sequence types, which can be impractical with data when the future situation is unknown. To tackle these limitations, we propose a hierarchical Dirichlet point process model for efficient neural sequence detection. Instead of computing the entire data, our model can sequentially detect sequences in an online unsupervised manner with Particle filters. Besides, the Dirichlet prior enables our model to automatically introduce new sequence types on the fly as needed, thus avoiding specifying the number of types in advance. We manifest these advantages on synthetic data and neural recordings from songbird higher vocal center and rodent hippocampus.
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References
[1]
Kevin Canini, Lei Shi, and Thomas Griffiths. Online inference of topics with latent dirichlet allocation. In Artificial Intelligence and Statistics, pages 65-72. PMLR, 2009.
[2]
Olivier Cappé, Simon J Godsill, and Eric Moulines. An overview of existing methods and recent advances in sequential monte carlo. Proceedings of the IEEE, 95(5):899-924, 2007.
[3]
Zhe Chen, Andres D Grosmark, Hector Penagos, and Matthew A Wilson. Uncovering representations of sleep-associated hippocampal ensemble spike activity. Scientific reports, 6(l):1-13, 2016.
[4]
Thomas J Davidson, Fabian Kloosterman, and Matthew A Wilson. Hippocampal replay of extended experience. Neuron, 63(4):497-507, 2009.
[5]
Nan Du, Mehrdad Farajtabar, Amr Ahmed, Alexander J Smola, and Le Song. Dirichlet-hawkes processes with applications to clustering continuous-time document streams. In Proceedings of the 21th ACM SIGKDD international conference on knowledge discovery and data mining, pages 219-228, 2015.
[6]
Mark S Goldman. Memory without feedback in a neural network. Neuron, 61(4):621-634, 2009.
[7]
AD Grosmark, J Long, and G Buzsáki. Recordings from hippocampal area ca1, pre, during and post novel spatial learning. CRCNS. org, 10:K0862DC5, 2016.
[8]
Andres D Grosmark and György Buzsáki. Diversity in neural firing dynamics supports both rigid and learned hippocampal sequences. Science, 351(6280):1440-1443, 2016.
[9]
Richard HR Hahnloser, Alexay A Kozhevnikov, and Michale S Fee. An ultra-sparse code underliesthe generation of neural sequences in a songbird. Nature, 419(6902):65-70, 2002.
[10]
Christopher D Harvey, Philip Coen, and David W Tank. Choice-specific sequences in parietal cortex during a virtual-navigation decision task. Nature, 484(7392):62-68, 2012.
[11]
Alan G Hawkes. Spectra of some self-exciting and mutually exciting point processes. Biometrika, 58(1):83-90, 1971.
[12]
Daoyun Ji and Matthew A Wilson. Coordinated memory replay in the visual cortex and hippocampus during sleep. Nature neuroscience, 10(1):100-107, 2007.
[13]
Dahua Lin. Online learning of nonparametric mixture models via sequential variational approximation. Advances in Neural Information Processing Systems, 26, 2013.
[14]
Kourosh Maboudi, Etienne Ackermann, Laurel Watkins de Jong, Brad E Pfeiffer, David Foster, Kamran Diba, and Caleb Kemere. Uncovering temporal structure in hippocampal output patterns. Elife, 7:e34467, 2018.
[15]
Emily L Mackevicius, Andrew H Bahle, Alex H Williams, Shijie Gu, Natalia I Denisenko, Mark S Goldman, and Michale S Fee. Unsupervised discovery of temporal sequences in high-dimensional datasets, with applications to neuroscience. Elife, 8:e38471, 2019.
[16]
Charalampos Mavroforakis, Isabel Valera, and Manuel Gomez-Rodriguez. Modeling the dynamics of learning activity on the web. In Proceedings of the 26th International Conference on World Wide Web, pages 1421-1430, 2017.
[17]
Kevin P Murphy. Machine learning: a probabilistic perspective. MIT press, 2012.
[18]
Gang Pan, Jia-Jun Li, Yu Qi, Hang Yu, Jun-Ming Zhu, Xiao-Xiang Zheng, Yue-Ming Wang, and Shao-Min Zhang. Rapid decoding of hand gestures in electrocorticography using recurrent neural networks. Frontiers in neuroscience, 12:555, 2018.
[19]
Eva Pastalkova, Vladimir Itskov, Asohan Amarasingham, and Gyorgy Buzsaki. Internally generated cell assembly sequences in the rat hippocampus. Science, 321(5894):1322-1327, 2008.
[20]
Sven Peter, Elke Kirschbaum, Martin Both, Lee Campbell, Brandon Harvey, Conor Heins, Daniel Durstewitz, Ferran Diego, and Fred A Hamprecht. Sparse convolutional coding for neuronal assembly detection. Advances in Neural Information Processing Systems, 30, 2017.
[21]
Yu Qi, Bin Liu, Yueming Wang, and Gang Pan. Dynamic ensemble modeling approach to nonstationary neural decoding in brain-computer interfaces. Advances in Neural Information Processing Systems, 32, 2019.
[22]
Yu Qi, Xinyun Zhu, Kedi Xu, Feixiao Ren, Hongjie Jiang, Junming Zhu, Jianmin Zhang, Gang Pan, and Yueming Wang. Dynamic ensemble bayesian filter for robust control of a human brain-machine interface. arXiv preprint arXiv:2204.11840, 2022.
[23]
Pietro Quaglio, Vahid Rostami, Emiliano Torre, and Sonja Grün. Methods for identification of spike patterns in massively parallel spike trains. Biological cybernetics, 112(1):57-80, 2018.
[24]
Eleonora Russo and Daniel Durstewitz. Cell assemblies at multiple time scales with arbitrary lag constellations. Elife, 6:e19428, 2017.
[25]
Yee Teh, Michael Jordan, Matthew Beal, and David Blei. Sharing clusters among related groups: Hierarchical dirichlet processes. Advances in neural information processing systems, 17, 2004.
[26]
Emiliano Torre, Carlos Canova, Michael Denker, George Gerstein, Moritz Helias, and Sonja Grün. Asset: analysis of sequences of synchronous events in massively parallel spike trains. PLoS computational biology, 12(7):e1004939, 2016.
[27]
Chong Wang and David Blei. Truncation-free online variational inference for bayesian nonpara- metric models. Advances in neural information processing systems, 25, 2012.
[28]
Alex Williams, Anthony Degleris, Yixin Wang, and Scott Linderman. Point process models for sequence detection in high-dimensional neural spike trains. Advances in neural information processing systems, 33:14350-14361, 2020.
[29]
Alex H Williams, Tony Hyun Kim, Forea Wang, Saurabh Vyas, Stephen I Ryu, Krishna V Shenoy, Mark Schnitzer, Tamara G Kolda, and Surya Ganguli. Unsupervised discovery of demixed, low-dimensional neural dynamics across multiple timescales through tensor component analysis. Neuron, 98(6):1099-1115, 2018.
[30]
Alex H Williams and Scott W Linderman. Statistical neuroscience in the single trial limit. Current opinion in neurobiology, 70:193-205, 2021.
[31]
Alex H Williams, Ben Poole, Niru Maheswaranathan, Ashesh K Dhawale, Tucker Fisher, Christopher D Wilson, David H Brann, Eric M Trautmann, Stephen Ryu, Roman Shusterman, et al. Discovering precise temporal patterns in large-scale neural recordings through robust and interpretable time warping. Neuron, 105(2):246-259, 2020.
[32]
Alexander Henry Williams. Unsupervised Methods for Neural Data Analysis with Single Trial Resolution. Stanford University, 2019.
[33]
Svante Wold. Cross-validatory estimation of the number of components in factor and principal components models. Technometrics, 20(4):397-405, 1978.
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Information
Published In
November 2022
39114 pages
ISBN:9781713871088
- Editors:
- S. Koyejo,
- S. Mohamed,
- A. Agarwal,
- D. Belgrave,
- K. Cho,
- A. Oh
Copyright © 2022 Neural Information Processing Systems Foundation, Inc.
Publisher
Curran Associates Inc.
Red Hook, NY, United States
Publication History
Published: 03 April 2024
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