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State Space Models for Spike Data

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Handbook of Neuroengineering

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Abstract

State-space methods provide powerful tools to solve a variety of neural data analysis problems. For spike train data, they are used to decode signals from population spiking activity, to track changes in neural coding properties due to learning and adaptation, to infer biophysical mechanisms underlying spike generation, and many other applications. At the heart of these methods is a set of two model components: a state model that defines how a signal or collection of signals can change through time, and a point process observation model that describes how those signals influence neural spiking. In this chapter, we explain how to define these models to address neural coding problems. We derive point process filter and smoother algorithms to estimate signals from spiking data, Monte Carlo methods to improve the computational efficiency of these algorithms, and an approximate expectation-maximization (E-M) algorithm to estimate the model parameters. We demonstrate the application of these methods to the problem for estimating a rat’s movement on a circular maze from a population of simulated place cells.

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Abbreviations

E-M:

Expectation-Maximization

E-step:

Expectation Step

HPD:

Highest Posterior Density

ML:

Maximum Likelihood

M-step:

Maximization Step

rMSE:

Root-Mean-Squared Error

SMC:

Sequential Monte Carlo

SSPPF:

Stochastic State Point Process Filter

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Author information

Authors and Affiliations

  1. Department of Computer Science, Worcester Polytechnic Institute, Boston, MA, USA

    Ali Yousefi

  2. Department of Mathematics and Statistics, Boston University, Boston, MA, USA

    Uri T. Eden

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  1. Ali Yousefi
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  2. Uri T. Eden
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Correspondence to Uri T. Eden .

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Editors and Affiliations

  1. Neuroengineering and Biomedical Instrumentation Laboratory Department of Biomedical Engineering and Department of Electrical and Computer Engineering, Neurology, Johns Hopkins University, Baltimore, MD, USA

    Nitish V. Thakor

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Yousefi, A., Eden, U.T. (2023). State Space Models for Spike Data. In: Thakor, N.V. (eds) Handbook of Neuroengineering. Springer, Singapore. https://doi.org/10.1007/978-981-16-5540-1_109

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