Information filtering by synchronous spikes in a neural population
Information about time-dependent sensory stimuli is encoded by the spike trains of neurons. Here we consider a population of uncoupled but noisy neurons (each subject to some intrinsic noise) that are driven by a common broadband signal. We ask specifically how much information is encoded in the syn...
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| Format: | Online |
| Language: | English |
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Springer US
2012
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| Online Access: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3605500/ |
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pubmed-3605500 |
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pubmed-36055002013-03-25 Information filtering by synchronous spikes in a neural population Sharafi, Nahal Benda, Jan Lindner, Benjamin Article Information about time-dependent sensory stimuli is encoded by the spike trains of neurons. Here we consider a population of uncoupled but noisy neurons (each subject to some intrinsic noise) that are driven by a common broadband signal. We ask specifically how much information is encoded in the synchronous activity of the population and how this information transfer is distributed with respect to frequency bands. In order to obtain some insight into the mechanism of information filtering effects found previously in the literature, we develop a mathematical framework to calculate the coherence of the synchronous output with the common stimulus for populations of simple neuron models. Within this frame, the synchronous activity is treated as the product of filtered versions of the spike trains of a subset of neurons. We compare our results for the simple cases of (1) a Poisson neuron with a rate modulation and (2) an LIF neuron with intrinsic white current noise and a current stimulus. For the Poisson neuron, formulas are particularly simple but show only a low-pass behavior of the coherence of synchronous activity. For the LIF model, in contrast, the coherence function of the synchronous activity shows a clear peak at high frequencies, comparable to recent experimental findings. We uncover the mechanism for this shift in the maximum of the coherence and discuss some biological implications of our findings. Springer US 2012-09-12 2013 /pmc/articles/PMC3605500/ /pubmed/22968549 http://dx.doi.org/10.1007/s10827-012-0421-9 Text en © The Author(s) 2012 |
| repository_type |
Open Access Journal |
| institution_category |
Foreign Institution |
| institution |
US National Center for Biotechnology Information |
| building |
NCBI PubMed |
| collection |
Online Access |
| language |
English |
| format |
Online |
| author |
Sharafi, Nahal Benda, Jan Lindner, Benjamin |
| spellingShingle |
Sharafi, Nahal Benda, Jan Lindner, Benjamin Information filtering by synchronous spikes in a neural population |
| author_facet |
Sharafi, Nahal Benda, Jan Lindner, Benjamin |
| author_sort |
Sharafi, Nahal |
| title |
Information filtering by synchronous spikes in a neural population |
| title_short |
Information filtering by synchronous spikes in a neural population |
| title_full |
Information filtering by synchronous spikes in a neural population |
| title_fullStr |
Information filtering by synchronous spikes in a neural population |
| title_full_unstemmed |
Information filtering by synchronous spikes in a neural population |
| title_sort |
information filtering by synchronous spikes in a neural population |
| description |
Information about time-dependent sensory stimuli is encoded by the spike trains of neurons. Here we consider a population of uncoupled but noisy neurons (each subject to some intrinsic noise) that are driven by a common broadband signal. We ask specifically how much information is encoded in the synchronous activity of the population and how this information transfer is distributed with respect to frequency bands. In order to obtain some insight into the mechanism of information filtering effects found previously in the literature, we develop a mathematical framework to calculate the coherence of the synchronous output with the common stimulus for populations of simple neuron models. Within this frame, the synchronous activity is treated as the product of filtered versions of the spike trains of a subset of neurons. We compare our results for the simple cases of (1) a Poisson neuron with a rate modulation and (2) an LIF neuron with intrinsic white current noise and a current stimulus. For the Poisson neuron, formulas are particularly simple but show only a low-pass behavior of the coherence of synchronous activity. For the LIF model, in contrast, the coherence function of the synchronous activity shows a clear peak at high frequencies, comparable to recent experimental findings. We uncover the mechanism for this shift in the maximum of the coherence and discuss some biological implications of our findings. |
| publisher |
Springer US |
| publishDate |
2012 |
| url |
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3605500/ |
| _version_ |
1611964640918503424 |