| Title |
A Simple Algorithm for Averaging Spike Trains
|
|---|---|
| Published in |
The Journal of Mathematical Neuroscience, February 2013
|
| DOI | 10.1186/2190-8567-3-3 |
| Pubmed ID | |
| Authors |
Hannah Julienne, Conor Houghton |
| Abstract |
Although spike trains are the principal channel of communication between neurons, a single stimulus will elicit different spike trains from trial to trial. This variability, in both spike timings and spike number can obscure the temporal structure of spike trains and often means that computations need to be run on numerous spike trains in order to extract features common across all the responses to a particular stimulus. This can increase the computational burden and obscure analytical results. As a consequence, it is useful to consider how to calculate a central spike train that summarizes a set of trials. Indeed, averaging responses over trials is routine for other signal types. Here, a simple method for finding a central spike train is described. The spike trains are first mapped to functions, these functions are averaged, and a greedy algorithm is then used to map the average function back to a spike train. The central spike trains are tested for a large data set. Their performance on a classification-based test is considerably better than the performance of the medoid spike trains. |
X Demographics
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Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 1 | 33% |
| Unknown | 2 | 67% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 3 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 25 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Germany | 2 | 8% |
| Unknown | 23 | 92% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 6 | 24% |
| Student > Ph. D. Student | 5 | 20% |
| Student > Doctoral Student | 3 | 12% |
| Professor > Associate Professor | 3 | 12% |
| Other | 2 | 8% |
| Other | 4 | 16% |
| Unknown | 2 | 8% |
| Readers by discipline | Count | As % |
|---|---|---|
| Neuroscience | 6 | 24% |
| Computer Science | 5 | 20% |
| Agricultural and Biological Sciences | 4 | 16% |
| Engineering | 4 | 16% |
| Psychology | 1 | 4% |
| Other | 2 | 8% |
| Unknown | 3 | 12% |
Attention Score in Context
This research output has an Altmetric Attention Score of 2. 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 07 March 2013.
All research outputs
#13,884,212
of 22,699,621 outputs
Outputs from The Journal of Mathematical Neuroscience
#27
of 80 outputs
Outputs of similar age
#108,256
of 193,194 outputs
Outputs of similar age from The Journal of Mathematical Neuroscience
#1
of 3 outputs
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So far Altmetric has tracked 80 research outputs from this source. They receive a mean Attention Score of 2.6. This one has gotten more attention than average, scoring higher than 65% of its peers.
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We're also able to compare this research output to 3 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them