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Decoding movement trajectories through a T-maze using point process filters applied to place field data from rat hippocampal region CA1.
Neural Comput. 2009 Dec; 21(12):3305-34.NC

Abstract

Firing activity from neural ensembles in rat hippocampus has been previously used to determine an animal's position in an open environment and separately to predict future behavioral decisions. However, a unified statistical procedure to combine information about position and behavior in environments with complex topological features from ensemble hippocampal activity has yet to be described. Here we present a two-stage computational framework that uses point process filters to simultaneously estimate the animal's location and predict future behavior from ensemble neural spiking activity. First, in the encoding stage, we linearized a two-dimensional T-maze, and used spline-based generalized linear models to characterize the place-field structure of different neurons. All of these neurons displayed highly specific position-dependent firing, which frequently had several peaks at multiple locations along the maze. When the rat was at the stem of the T-maze, the firing activity of several of these neurons also varied significantly as a function of the direction it would turn at the decision point, as detected by ANOVA. Second, in the decoding stage, we developed a state-space model for the animal's movement along a T-maze and used point process filters to accurately reconstruct both the location of the animal and the probability of the next decision. The filter yielded exact full posterior densities that were highly nongaussian and often multimodal. Our computational framework provides a reliable approach for characterizing and extracting information from ensembles of neurons with spatially specific context or task-dependent firing activity.

Authors+Show Affiliations

Department of Mathematics and Statistics, Boston University, Boston, MA 02215, USA. yifei@bu.eduNo affiliation info availableNo affiliation info availableNo affiliation info availableNo affiliation info available

Pub Type(s)

Journal Article
Research Support, N.I.H., Extramural
Research Support, U.S. Gov't, Non-P.H.S.

Language

eng

PubMed ID

19764871

Citation

Huang, Yifei, et al. "Decoding Movement Trajectories Through a T-maze Using Point Process Filters Applied to Place Field Data From Rat Hippocampal Region CA1." Neural Computation, vol. 21, no. 12, 2009, pp. 3305-34.
Huang Y, Brandon MP, Griffin AL, et al. Decoding movement trajectories through a T-maze using point process filters applied to place field data from rat hippocampal region CA1. Neural Comput. 2009;21(12):3305-34.
Huang, Y., Brandon, M. P., Griffin, A. L., Hasselmo, M. E., & Eden, U. T. (2009). Decoding movement trajectories through a T-maze using point process filters applied to place field data from rat hippocampal region CA1. Neural Computation, 21(12), 3305-34. https://doi.org/10.1162/neco.2009.10-08-893
Huang Y, et al. Decoding Movement Trajectories Through a T-maze Using Point Process Filters Applied to Place Field Data From Rat Hippocampal Region CA1. Neural Comput. 2009;21(12):3305-34. PubMed PMID: 19764871.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Decoding movement trajectories through a T-maze using point process filters applied to place field data from rat hippocampal region CA1. AU - Huang,Yifei, AU - Brandon,Mark P, AU - Griffin,Amy L, AU - Hasselmo,Michael E, AU - Eden,Uri T, PY - 2009/9/22/entrez PY - 2009/9/22/pubmed PY - 2009/12/30/medline SP - 3305 EP - 34 JF - Neural computation JO - Neural Comput VL - 21 IS - 12 N2 - Firing activity from neural ensembles in rat hippocampus has been previously used to determine an animal's position in an open environment and separately to predict future behavioral decisions. However, a unified statistical procedure to combine information about position and behavior in environments with complex topological features from ensemble hippocampal activity has yet to be described. Here we present a two-stage computational framework that uses point process filters to simultaneously estimate the animal's location and predict future behavior from ensemble neural spiking activity. First, in the encoding stage, we linearized a two-dimensional T-maze, and used spline-based generalized linear models to characterize the place-field structure of different neurons. All of these neurons displayed highly specific position-dependent firing, which frequently had several peaks at multiple locations along the maze. When the rat was at the stem of the T-maze, the firing activity of several of these neurons also varied significantly as a function of the direction it would turn at the decision point, as detected by ANOVA. Second, in the decoding stage, we developed a state-space model for the animal's movement along a T-maze and used point process filters to accurately reconstruct both the location of the animal and the probability of the next decision. The filter yielded exact full posterior densities that were highly nongaussian and often multimodal. Our computational framework provides a reliable approach for characterizing and extracting information from ensembles of neurons with spatially specific context or task-dependent firing activity. SN - 0899-7667 UR - https://www.unboundmedicine.com/medline/citation/19764871/Decoding_movement_trajectories_through_a_T_maze_using_point_process_filters_applied_to_place_field_data_from_rat_hippocampal_region_CA1_ L2 - https://www.mitpressjournals.org/doi/10.1162/neco.2009.10-08-893?url_ver=Z39.88-2003&rfr_id=ori:rid:crossref.org&rfr_dat=cr_pub=pubmed DB - PRIME DP - Unbound Medicine ER -