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What Can Trial-to-Trial Variability Tell Us? A Distribution-Based Approach to Spike Train Decoding in the Rat Hippocampus and Entorhinal Cortex

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Dynamic Neuroscience

Abstract

Is it possible to robustly encode information from a population of neurons that rarely provide information? Neurons within the rat hippocampus and entorhinal cortex (EC) have been shown to possess spatial receptive fields that modulate their firing properties under different sets of behavioral or experimental conditions, termed contexts. Recent studies have identified cells in these regions that show changes in trial-to-trial firing rate variability as a function of experimental context. How then, could changes in variability, not observable in a single trial be useful to information processing in the brain? We propose a scenario in which individual neurons with high trial-to-trial variability each provide substantive information over small subsets of trials, which are distributed so that a population of the cells could robustly encode context across all trials. In this chapter, we explore the nature of trial-to-trail variability, and seek to verify our hypothesis by developing a decoding algorithm that predicts context from spiking data using a model characterizing changes in the full distribution of firing rate structure across trials. We compare this algorithm to another decoding procedure that accounts for only changes in mean firing rate. We apply these decoding algorithms to and experimental spiking data, and show that information is combined from many sparsely encoding cells within CA1 and dcMEC ensembles to robustly encode context.

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References

  • Ainge, J. A., van der Meer, M. A. A., Langston, R. F., & Wood, E. R. (2007). Exploring the role of context-dependent hippocampal activity in spatial alternation behavior. Hippocampus, 17, 988–1002.

    Article  Google Scholar 

  • Barbieri, R., Frank, L. M., Nguyen, D. P., Quirk, M. C., Solo, V., Wilson, M. A., et al. (2004). Dynamic analyses of information encoding in neural ensembles. Neural Computation, 16(2), 277–307.

    Article  Google Scholar 

  • Barbieri, R., Quirk, M. C., Frank, L. M., Wilson, M. A., & Brown, E. N. (2001). Construction and analysis of non-Poisson stimulus-response models of neural spiking activity. Journal of Neuroscience Methods, 105, 25–37.

    Article  Google Scholar 

  • Brillinger, D. R. (1988). Maximum-likelihood analysis of spike trains of interacting nerve-cells. Biological Cybernetics, 59, 189–200.

    Article  Google Scholar 

  • Brillinger, D. R. (1992). Nerve-cell spike train data-analysis—A progression of technique. Journal of the American Statistical Association, 87, 260–271.

    Article  Google Scholar 

  • Brown, E. N., Barbieri, R., Eden, U. T., & Frank, L. M. (2003). Likelihood methods for neural data analysis. In J. Feng (Ed.), Computational neuroscience: A comprehensive approach (pp. 253–286). London: Chapman and Hall/CRC Press.

    Google Scholar 

  • Brown, E. N., Frank, L. M., Tang, D., Quirk, M. C., & Wilson, M. A. (1998). A statistical paradigm for neural spike train decoding applied to position prediction from ensemble firing patterns of rat hippocampal place cells. Journal of Neuroscience, 18(18), 7411–7425.

    Google Scholar 

  • Brown, E. N., Kass, R. E., & Mitra, P. P. (2004). Multiple neural spike train data analysis: State-of-the-art and future challenges. Nature Neuroscience, 7, 456–461.

    Article  Google Scholar 

  • Brown, E. N., Ngyuen, D. P., Frank, L. M., Wilson, M. A., & Solo, V. (2001). An analysis of neural receptive field plasticity by point process adaptive filtering. Proceedings of National Academy of Sciences USA, 98, 12261–12266.

    Google Scholar 

  • Churchland, M. M., Yu, B. M., Cunningham, J. P., Sugrue, L. P., Cohen, M. R., Corrado, G. S., et al. (2010). Stimulus onset quenches neural variability: A widespread cortical phenomenon. Nature Neuroscience, 13(3), 369–378. http://doi.org/10.1038/nn.2501.

    Article  Google Scholar 

  • Cohen, N. J., & Squire, L. R. (1980). Preserved learning and retention of pattern-analyzing skill in amnesia: Dissociation of knowing how and knowing that. Science, 210, 207–210.

    Article  Google Scholar 

  • Czanner, G., Eden, U. T., Wirth, S., Yanike, M., Suzuki, W. A., & Brown, E. N. (2008). Analysis of between-trial and within-trial neural spiking dynamics. Journal of Neurophysiology, 99(5), 2672–2693.

    Article  Google Scholar 

  • Dayan, P., & Abbott, L. F. (2001). Theoretical neuroscience: Computational and mathematical modeling of neural systems. Cambridge, MA: MIT Press.

    MATH  Google Scholar 

  • Eden, U. T., Frank, L. M., Barbieri, R., Solo, V., & Brown, E. N. (2004). Dynamic analysis of neural encoding by point process adaptive filtering. Neural Computation, 16(5), 971–998.

    Article  Google Scholar 

  • Efron, B., & Gong, G. (1983). A leisurely look at the Bootstrap, the Jackknife, and cross-validation. American Statistician, 37, 36–48.

    MathSciNet  Google Scholar 

  • Eichenbaum, H., Dudchenko, P., Wood, E., Shapiro, M., & Tanila, H. (1999). The hippocampus, memory, and place cells: Is it spatial memory or a memory space? Neuron, 23, 209–226.

    Article  Google Scholar 

  • Eichenbaum, H., Otto, T., & Cohen, N. J. (1994). Functional components of the hippocampal memory system. Behavioral and Brain Sciences, 17, 449–472.

    Article  Google Scholar 

  • Fenton, A. A., & Muller, R. U. (1998). Place cell discharge is extremely variable during individual passes of the rat through the firing field. Proceedings of National Academy of Sciences USA, 95(6), 3182–3187.

    Article  Google Scholar 

  • Ferbinteanu, J., & Shapiro, M. L. (2003). Prospective and retrospective memory coding in the hippocampus. Neuron, 40, 1227–1239.

    Article  Google Scholar 

  • Frank, L. M., Brown, E. N., & Wilson, M. A. (2000). Trajectory encoding in the hippocampus and entorhinal cortex. Neuron, 27, 169–178.

    Article  Google Scholar 

  • Frank, L. M., Eden, U. T., Solo, V., Wilson, M. A., & Brown, E. N. (2002). Contrasting patterns of receptive field plasticity in the hippocampus and the entorhinal cortex: An adaptive filtering approach. Journal of Neuroscience, 22, 3817–3830.

    Google Scholar 

  • Fyhn, M., Molden, S., Witter, M. P., Moser, E. I., & Moser, M. B. (2004). Spatial representation in the entorhinal cortex. Science, 305, 1258–1264.

    Article  Google Scholar 

  • Griffin, A. L., Eichenbaum, H., & Hasselmo, M. E. (2007). Spatial representations of hippocampal CA1 neurons are modulated by behavioral context in a hippocampus-dependent memory task. Journal Neuroscience, 27, 2416–2423.

    Article  Google Scholar 

  • 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), 3305–3334.

    Article  MathSciNet  Google Scholar 

  • Jackson, J., & Redish, A. D. (2007). Network dynamics of hippocampal cell-assemblies resemble multiple spatial maps within single tasks. Hippocampus, 17, 1209–1229.

    Article  Google Scholar 

  • Johnson, A., & Redish, A. D. (2007). Neural ensembles in CA3 transiently encode paths forward of the animal at a decision point. Journal of Neuroscience, 27, 12176–12189.

    Article  Google Scholar 

  • Kass, R. E., & Ventura, V. (2001). A spike-train probability model. Neural Computation, 13(8), 1713–1720.

    Article  Google Scholar 

  • Kulkarni, J. E., & Paninski, L. (2008). State-space decoding of goal-directed movements. IEEE Signal Processing Magazine, 25, 78–86.

    Article  Google Scholar 

  • Lansky, P., Fenton, A. A., & Vaillant, J. (2001). The overdispersion in activity of place cells. Neurocomputing, 38, 1393–1399.

    Article  Google Scholar 

  • Latimer, K. W., Yates, J. L., Meister, M. L. R., Huk, A. C., & Pillow, J. W. (2015). Single-trial spike trains in parietal cortex reveal discrete steps during decision-making. Science, 349, 184–187.

    Article  Google Scholar 

  • Lee, I., Griffin, A. L., Zilli, E. A., Eichenbaum, H., & Hasselmo, M. E. (2006). Gradual translocation of spatial correlates of neuronal firing in the hippocampus toward prospective reward locations. Neuron, 51, 639–650.

    Article  Google Scholar 

  • Lin, L., Osan, R., Shoham, S., Jin, W., Zuo, W., & Tsien, J. Z. (2005). Identification of network-level coding units for real-time representation of episodic experiences in the hippocampus. Proceedings of National Academy of Sciences USA, 102, 6125–6130.

    Google Scholar 

  • Lipton, P. A., White, J. A., & Eichenbaum, H. (2007). Disambiguation of overlapping experiences by neurons in the medial entorhinal cortex. Journal of Neuroscience, 27, 5785–5789.

    Article  Google Scholar 

  • McCullagh, P. (1984). Generalized linear-models. European Journal of Operation Research, 16, 285–292.

    Article  MathSciNet  Google Scholar 

  • Mizumori, S. J. Y., Ward, K. E., & Lavoie, A. M. (1992). Medial septal modulation of entorhinal single unit-activity in anesthetized and freely moving rats. Brain Research, 570, 188–197.

    Article  Google Scholar 

  • Moeliker, C. (2001). The first case of homosexual necrophilia in the mallard Anas platyrhynchos (Aves: Anatidae). Deinsea, 8, 243–247.

    Google Scholar 

  • Muller, R. (1996). A quarter of a century of place cells. Neuron, 17, 813–822.

    Article  Google Scholar 

  • O’Keefe, J. (1979). A review of the hippocampal place cells. Progress in Neurobiology, 13, 419–439.

    Article  Google Scholar 

  • O’Keefe, J., & Dostrovsky, J. (1971). The hippocampus as a spatial map. Preliminary evidence from unit activity in the freely-moving rat. Brain Research, 34(1), 171–175.

    Article  Google Scholar 

  • O’Keefe, J., & Nadel, L. (1978). The hippocampus as a cognitive map. London: Oxford University Press.

    Google Scholar 

  • Parzen, E. (1962). On estimation of a probability density function and mode. Annals of Mathematical Statistics, 33, 1065–1076.

    Article  MathSciNet  Google Scholar 

  • Prerau, M. J., & Eden, U. T. (2011). A general likelihood framework for characterizing the time course of neural activity. Neural Computation, 23(10), 2537–2566.

    Article  MathSciNet  Google Scholar 

  • Prerau, M. J., Lipton, P. A., Eichenbaum, H., & Eden, U. T. (2014). Characterizing context-dependent differential firing activity in the hippocampus and entorhinal cortex. Hippocampus, 24(4), 476–492.

    Article  Google Scholar 

  • Prerau, M. J., Smith, A. C., Eden, U. T., Kubota, Y., Yanike, M., Suzuki, W., et al. (2009). Characterizing learning by simultaneous analysis of continuous and binary measures of performance. Journal of Neurophysiology, 102(5), 3060–3072.

    Article  Google Scholar 

  • Prerau, M. J., Smith, A. C., Eden, U. T., Yanike, M., Suzuki, W., & Brown, E. N. (2008). A mixed filter algorithm for cognitive state estimation from simultaneously recorded continuous and binary measures of performance. Biological Cybernetics, 99, 1–14.

    Article  MathSciNet  Google Scholar 

  • Quirk, G. J., Muller, R. U., Kubie, J. L., & Ranck, J. B. (1992). The positional firing properties of medial entorhinal neurons—Description and comparison with hippocampal place cells. Journal of Neuroscience, 12, 1945–1963.

    Google Scholar 

  • Redish, A. D., & Touretzky, D. S. (1997). Cognitive maps beyond the hippocampus. Hippocampus, 7, 15–35.

    Article  Google Scholar 

  • Rieke, F., Warland, D., de Ruyter van Steveninck, R. R., & Bialek, W. (1997). Spikes: Exploring the neural code. Cambridge, MA: MIT Press.

    MATH  Google Scholar 

  • Scoville, W. B., & Milner, B. (1957). Loss of recent memory after bilateral hippocampal lesions. Journal of Neurology, Neurosurgery, and Psychiatry, 20, 11–21.

    Article  Google Scholar 

  • Serruya, M. D., Hatsopoulos, N. G., Paninski, L., Fellows, M. R., & Donoghue, J. P. (2002). Instant neural control of a movement signal. Nature, 416, 141–142.

    Article  Google Scholar 

  • Smith, A. C., Frank, L. M., Wirth, S., Yanike, M., Hu, D., Kubota, Y., et al. (2004). Dynamic analysis of learning in behavioral experiments. Journal of Neuroscience, 24(2), 447–461.

    Article  Google Scholar 

  • Smith, A. C., Stefani, M. R., Moghaddam, B., & Brown, E. N. (2005). Analysis and design of behavioral experiments to characterize population learning. Journal of Neurophysiology, 93, 1776–1792.

    Article  Google Scholar 

  • Smith, A. C., Wirth, S., Suzuki, W. A., & Brown, E. N. (2007). Bayesian analysis of interleaved learning and response bias in behavioral experiments. Journal of Neurophysiology, 97, 2516–2524.

    Article  Google Scholar 

  • Smith, D. M., & Mizumori, S. J. (2006). Hippocampal place cells, context, and episodic memory. Hippocampus, 16, 716–729.

    Article  Google Scholar 

  • Snyder, D. L., & Miller, M. I. (1991). Random point processes in time and space. New York: Springer.

    Book  Google Scholar 

  • Tolman, E. C. (1948). Cognitive maps in rats and men. Psychological Review, 55, 189–208.

    Article  Google Scholar 

  • Touretzky, D., & Muller, R. (2006). Place field dissociation and multiple maps in hippocampus. Neurocomputing, 69, 1260–1263.

    Article  Google Scholar 

  • Touretzky, D., & Redish, A. D. (1996). Theory of rodent navigation based on interacting representations of space. Hippocampus, 6, 247–270.

    Article  Google Scholar 

  • Truccolo, W., Eden, U. T., Fellows, M. R., Donoghue, J. P., & Brown, E. N. (2005). A point process framework for relating neural spiking activity to spiking history, neural ensemble, and extrinsic covariate effects. Journal of Neurophysiology, 93(2), 1074–1089.

    Article  Google Scholar 

  • Turlach, B. A. (1993). Bandwidth Selection in Kernel Density Estimation: A Review. Technical report. Institut de Statistique, Louvain-la-Neuve, Belgium. Discussion Paper 9317.

    Google Scholar 

  • Ventura, V., Cai, C., & Kass, R. E. (2005). Trial-to-trial variability and its effect on time-varying dependency between two neurons. Journal of Neurophysiology, 94, 2928–2939.

    Article  Google Scholar 

  • Wessberg, J., Stambaugh, C. R., Kralik, J. D., Beck, P. D., Laubach, M., Chapin, J. K., et al. (2000). Real-time prediction of hand trajectory by ensembles of cortical neurons in primates. Nature, 408, 361–365.

    Article  Google Scholar 

  • Wiener, M. C., & Richmond, B. J. (2003). Decoding spike trains instant by instant using order statistics and the mixture-of-Poissons model. Journal of Neuroscience, 23, 2394–2406.

    Google Scholar 

  • Wilson, M. A., & McNaughton, B. L. (1993). Dynamics of the hippocampal ensemble code for space. Science, 261, 1055–1058.

    Article  Google Scholar 

  • Wood, E. R., Dudchenko, P. A., Robitsek, R. J., & Eichenbaum, H. (2000). Hippocampal neurons encode information about different types of memory episodes occurring in the same location. Neuron, 27, 623–633.

    Article  Google Scholar 

  • Zhang, K., Ginzburg, I., McNaughton, B. L., & Sejnowski, T. J. (1998). Interpreting neuronal population activity by reconstruction: Unified framework with application to hippocampal place cells. Journal of Neurophysiology, 79(2), 1017–1044.

    Article  Google Scholar 

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Authors and Affiliations

  1. Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA

    Michael J. Prerau

  2. Boston University, Boston, MA, USA

    Uri T. Eden

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  1. Michael J. Prerau
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  2. Uri T. Eden
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Correspondence to Michael J. Prerau .

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Editors and Affiliations

  1. School of Medicine, New York University, New York, NY, USA

    Zhe Chen

  2. Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD, USA

    Sridevi V. Sarma

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Prerau, M.J., Eden, U.T. (2018). What Can Trial-to-Trial Variability Tell Us? A Distribution-Based Approach to Spike Train Decoding in the Rat Hippocampus and Entorhinal Cortex. In: Chen, Z., Sarma, S.V. (eds) Dynamic Neuroscience. Springer, Cham. https://doi.org/10.1007/978-3-319-71976-4_4

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  • DOI: https://doi.org/10.1007/978-3-319-71976-4_4

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  • Online ISBN: 978-3-319-71976-4

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