Reconstructing stimuli from the spike-times of leaky integrate and fire neurons
Reconstructing stimuli from the spike-trains of neurons is an important approach for understanding the neural code. One of the difficulties associated with this task is that signals which are varying continuously in time are encoded into sequences of discrete events or spikes. An important problem i...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2011-02-01
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| Series: | Frontiers in Neuroscience |
| Subjects: | |
| Online Access: | http://journal.frontiersin.org/Journal/10.3389/fnins.2011.00001/full |
| _version_ | 1819107439311585280 |
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| author | Sebastian eGerwinn Sebastian eGerwinn Sebastian eGerwinn Jakob H Macke Matthias eBethge Matthias eBethge Matthias eBethge |
| author_facet | Sebastian eGerwinn Sebastian eGerwinn Sebastian eGerwinn Jakob H Macke Matthias eBethge Matthias eBethge Matthias eBethge |
| author_sort | Sebastian eGerwinn |
| collection | DOAJ |
| description | Reconstructing stimuli from the spike-trains of neurons is an important approach for understanding the neural code. One of the difficulties associated with this task is that signals which are varying continuously in time are encoded into sequences of discrete events or spikes. An important problem is to determine how much information about the continuously varying stimulus can be extracted from the time-points at which spikes were observed, especially if these time-points are subject to some sort of randomness. For the special case of spike trains generated by leaky integrate and fire neurons, noise can be introduced by allowing variations in the threshold every time a spike is released. A simple decoding algorithm previously derived for the noiseless case can be extended to the stochastic case, but turns out to be biased. Here, we review a solution to this problem, by presenting a simple yet efficient algorithm which greatly reduces the bias, and therefore leads to better decoding performance in the stochastic case. |
| first_indexed | 2024-12-22T02:54:03Z |
| format | Article |
| id | doaj.art-16b4017b63b041b19bcd087327641e13 |
| institution | Directory Open Access Journal |
| issn | 1662-453X |
| language | English |
| last_indexed | 2024-12-22T02:54:03Z |
| publishDate | 2011-02-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Neuroscience |
| spelling | doaj.art-16b4017b63b041b19bcd087327641e132022-12-21T18:41:19ZengFrontiers Media S.A.Frontiers in Neuroscience1662-453X2011-02-01510.3389/fnins.2011.000017096Reconstructing stimuli from the spike-times of leaky integrate and fire neuronsSebastian eGerwinn0Sebastian eGerwinn1Sebastian eGerwinn2Jakob H Macke3Matthias eBethge4Matthias eBethge5Matthias eBethge6University of TübingenMax Planck Institute for Biological CyberneticsBernstein Center for Computational NeuroscienceUniversity College LondonUniversity of TübingenMax Planck Institute for Biological CyberneticsBernstein Center for Computational NeuroscienceReconstructing stimuli from the spike-trains of neurons is an important approach for understanding the neural code. One of the difficulties associated with this task is that signals which are varying continuously in time are encoded into sequences of discrete events or spikes. An important problem is to determine how much information about the continuously varying stimulus can be extracted from the time-points at which spikes were observed, especially if these time-points are subject to some sort of randomness. For the special case of spike trains generated by leaky integrate and fire neurons, noise can be introduced by allowing variations in the threshold every time a spike is released. A simple decoding algorithm previously derived for the noiseless case can be extended to the stochastic case, but turns out to be biased. Here, we review a solution to this problem, by presenting a simple yet efficient algorithm which greatly reduces the bias, and therefore leads to better decoding performance in the stochastic case.http://journal.frontiersin.org/Journal/10.3389/fnins.2011.00001/fullDecodingspiking neuronspopulation codingBayesian inferenceleaky integrate and fire neuronstimulus reconstruction |
| spellingShingle | Sebastian eGerwinn Sebastian eGerwinn Sebastian eGerwinn Jakob H Macke Matthias eBethge Matthias eBethge Matthias eBethge Reconstructing stimuli from the spike-times of leaky integrate and fire neurons Frontiers in Neuroscience Decoding spiking neurons population coding Bayesian inference leaky integrate and fire neuron stimulus reconstruction |
| title | Reconstructing stimuli from the spike-times of leaky integrate and fire neurons |
| title_full | Reconstructing stimuli from the spike-times of leaky integrate and fire neurons |
| title_fullStr | Reconstructing stimuli from the spike-times of leaky integrate and fire neurons |
| title_full_unstemmed | Reconstructing stimuli from the spike-times of leaky integrate and fire neurons |
| title_short | Reconstructing stimuli from the spike-times of leaky integrate and fire neurons |
| title_sort | reconstructing stimuli from the spike times of leaky integrate and fire neurons |
| topic | Decoding spiking neurons population coding Bayesian inference leaky integrate and fire neuron stimulus reconstruction |
| url | http://journal.frontiersin.org/Journal/10.3389/fnins.2011.00001/full |
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