Abstract
External information is encoded in spiking activities of neural population. The present study investigates the performance of population decoding in a short-time window. Two decoding strategies, namely, maximum likelihood inference and template-matching, are explored. We find that in a short-time window, two methods are not efficient and that their errors satisfy the Cauchy distributions. As expected, maximum likelihood inference outperforms template-matching asymptotically. However, in a very short time window, template-matching has smaller decoding errors than maximum likelihood inference. The implication of this result is discussed.
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Zhang, W., Wu, S. (2013). Neural Population Decoding in Short-Time Windows. In: Yang, J., Fang, F., Sun, C. (eds) Intelligent Science and Intelligent Data Engineering. IScIDE 2012. Lecture Notes in Computer Science, vol 7751. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-36669-7_8
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DOI: https://doi.org/10.1007/978-3-642-36669-7_8
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-36668-0
Online ISBN: 978-3-642-36669-7
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