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| Title |
Coding Properties of Mouse Retinal Ganglion Cells with Dual-Peak Patterns with Respect to Stimulus Intervals
|
|---|---|
| Published in |
Frontiers in Computational Neuroscience, July 2016
|
| DOI | 10.3389/fncom.2016.00075 |
| Pubmed ID | |
| Authors |
Ru-Jia Yan, Hai-Qing Gong, Pu-Ming Zhang, Pei-Ji Liang |
| Abstract |
How visual information is encoded in spikes of retinal ganglion cells (RGCs) is essential in visual neuroscience. In the present study, we investigated the coding properties of mouse RGCs with dual-peak patterns with respect to visual stimulus intervals. We first analyzed the response properties, and observed that the latencies and spike counts of the two response peaks in the dual-peak pattern exhibited systematic changes with the preceding light-OFF interval. We then applied linear discriminant analysis (LDA) to assess the relative contributions of response characteristics of both peaks in information coding regarding the preceding stimulus interval. It was found that for each peak, the discrimination results were far better than chance level based on either latency or spike count, and were further improved by using the combination of the two parameters. Furthermore, the best discrimination results were obtained when latencies and spike counts of both peaks were considered in combination. In addition, the correct rate for stimulation discrimination was higher when RGC population activity was considered as compare to single neuron's activity, and the correct rate was increased with the group size. These results suggest that rate coding, temporal coding, and population coding are all involved in encoding the different stimulus-interval patterns, and the two response peaks in the dual-peak pattern carry complementary information about stimulus interval. |
X Demographics
The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Switzerland | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 14 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 14 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 4 | 29% |
| Student > Ph. D. Student | 3 | 21% |
| Student > Bachelor | 2 | 14% |
| Student > Master | 2 | 14% |
| Student > Doctoral Student | 1 | 7% |
| Other | 0 | 0% |
| Unknown | 2 | 14% |
| Readers by discipline | Count | As % |
|---|---|---|
| Neuroscience | 8 | 57% |
| Agricultural and Biological Sciences | 1 | 7% |
| Business, Management and Accounting | 1 | 7% |
| Medicine and Dentistry | 1 | 7% |
| Engineering | 1 | 7% |
| Other | 0 | 0% |
| Unknown | 2 | 14% |
Attention Score in Context
This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 19 July 2016.
All research outputs
#20,335,770
of 22,880,691 outputs
Outputs from Frontiers in Computational Neuroscience
#1,160
of 1,345 outputs
Outputs of similar age
#317,189
of 363,111 outputs
Outputs of similar age from Frontiers in Computational Neuroscience
#38
of 40 outputs
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