Transient responses to rapid changes in mean and variance in spiking models.
The mean input and variance of the total synaptic input to a neuron can vary independently, suggesting two distinct information channels. Here we examine the impact of rapidly varying signals, delivered via these two information conduits, on the temporal dynamics of neuronal firing rate responses. W...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2008-01-01
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| Series: | PLoS ONE |
| Online Access: | http://europepmc.org/articles/PMC2582948?pdf=render |
| _version_ | 1811266785417101312 |
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| author | Peyman Khorsand Frances Chance |
| author_facet | Peyman Khorsand Frances Chance |
| author_sort | Peyman Khorsand |
| collection | DOAJ |
| description | The mean input and variance of the total synaptic input to a neuron can vary independently, suggesting two distinct information channels. Here we examine the impact of rapidly varying signals, delivered via these two information conduits, on the temporal dynamics of neuronal firing rate responses. We examine the responses of model neurons to step functions in either the mean or the variance of the input current. Our results show that the temporal dynamics governing response onset depends on the choice of model. Specifically, the existence of a hard threshold introduces an instantaneous component into the response onset of a leaky-integrate-and-fire model that is not present in other models studied here. Other response features, for example a decaying oscillatory approach to a new steady-state firing rate, appear to be more universal among neuronal models. The decay time constant of this approach is a power-law function of noise magnitude over a wide range of input parameters. Understanding how specific model properties underlie these response features is important for understanding how neurons will respond to rapidly varying signals, as the temporal dynamics of the response onset and response decay to new steady-state determine what range of signal frequencies a population of neurons can respond to and faithfully encode. |
| first_indexed | 2024-04-12T20:49:39Z |
| format | Article |
| id | doaj.art-d46203b4e7ae48e7abfcdca75dff5a93 |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-04-12T20:49:39Z |
| publishDate | 2008-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-d46203b4e7ae48e7abfcdca75dff5a932022-12-22T03:17:10ZengPublic Library of Science (PLoS)PLoS ONE1932-62032008-01-01311e378610.1371/journal.pone.0003786Transient responses to rapid changes in mean and variance in spiking models.Peyman KhorsandFrances ChanceThe mean input and variance of the total synaptic input to a neuron can vary independently, suggesting two distinct information channels. Here we examine the impact of rapidly varying signals, delivered via these two information conduits, on the temporal dynamics of neuronal firing rate responses. We examine the responses of model neurons to step functions in either the mean or the variance of the input current. Our results show that the temporal dynamics governing response onset depends on the choice of model. Specifically, the existence of a hard threshold introduces an instantaneous component into the response onset of a leaky-integrate-and-fire model that is not present in other models studied here. Other response features, for example a decaying oscillatory approach to a new steady-state firing rate, appear to be more universal among neuronal models. The decay time constant of this approach is a power-law function of noise magnitude over a wide range of input parameters. Understanding how specific model properties underlie these response features is important for understanding how neurons will respond to rapidly varying signals, as the temporal dynamics of the response onset and response decay to new steady-state determine what range of signal frequencies a population of neurons can respond to and faithfully encode.http://europepmc.org/articles/PMC2582948?pdf=render |
| spellingShingle | Peyman Khorsand Frances Chance Transient responses to rapid changes in mean and variance in spiking models. PLoS ONE |
| title | Transient responses to rapid changes in mean and variance in spiking models. |
| title_full | Transient responses to rapid changes in mean and variance in spiking models. |
| title_fullStr | Transient responses to rapid changes in mean and variance in spiking models. |
| title_full_unstemmed | Transient responses to rapid changes in mean and variance in spiking models. |
| title_short | Transient responses to rapid changes in mean and variance in spiking models. |
| title_sort | transient responses to rapid changes in mean and variance in spiking models |
| url | http://europepmc.org/articles/PMC2582948?pdf=render |
| work_keys_str_mv | AT peymankhorsand transientresponsestorapidchangesinmeanandvarianceinspikingmodels AT franceschance transientresponsestorapidchangesinmeanandvarianceinspikingmodels |