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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Estimating the Information Extracted by a Single Spiking Neuron from a Continuous Input Time Series
|
|---|---|
| Published in |
Frontiers in Computational Neuroscience, June 2017
|
| DOI | 10.3389/fncom.2017.00049 |
| Pubmed ID | |
| Authors |
Fleur Zeldenrust, Sicco de Knecht, Wytse J. Wadman, Sophie Denève, Boris Gutkin |
| Abstract |
Understanding the relation between (sensory) stimuli and the activity of neurons (i.e., "the neural code") lies at heart of understanding the computational properties of the brain. However, quantifying the information between a stimulus and a spike train has proven to be challenging. We propose a new (in vitro) method to measure how much information a single neuron transfers from the input it receives to its output spike train. The input is generated by an artificial neural network that responds to a randomly appearing and disappearing "sensory stimulus": the hidden state. The sum of this network activity is injected as current input into the neuron under investigation. The mutual information between the hidden state on the one hand and spike trains of the artificial network or the recorded spike train on the other hand can easily be estimated due to the binary shape of the hidden state. The characteristics of the input current, such as the time constant as a result of the (dis)appearance rate of the hidden state or the amplitude of the input current (the firing frequency of the neurons in the artificial network), can independently be varied. As an example, we apply this method to pyramidal neurons in the CA1 of mouse hippocampi and compare the recorded spike trains to the optimal response of the "Bayesian neuron" (BN). We conclude that like in the BN, information transfer in hippocampal pyramidal cells is non-linear and amplifying: the information loss between the artificial input and the output spike train is high if the input to the neuron (the firing of the artificial network) is not very informative about the hidden state. If the input to the neuron does contain a lot of information about the hidden state, the information loss is low. Moreover, neurons increase their firing rates in case the (dis)appearance rate is high, so that the (relative) amount of transferred information stays constant. |
X Demographics
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 2 | 33% |
| Netherlands | 1 | 17% |
| Unknown | 3 | 50% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 5 | 83% |
| Scientists | 1 | 17% |
Mendeley readers
The data shown below were compiled from readership statistics for 70 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Netherlands | 1 | 1% |
| Brazil | 1 | 1% |
| Unknown | 68 | 97% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 21 | 30% |
| Student > Bachelor | 12 | 17% |
| Researcher | 10 | 14% |
| Student > Master | 5 | 7% |
| Student > Postgraduate | 3 | 4% |
| Other | 8 | 11% |
| Unknown | 11 | 16% |
| Readers by discipline | Count | As % |
|---|---|---|
| Neuroscience | 26 | 37% |
| Engineering | 7 | 10% |
| Physics and Astronomy | 6 | 9% |
| Computer Science | 6 | 9% |
| Agricultural and Biological Sciences | 4 | 6% |
| Other | 9 | 13% |
| Unknown | 12 | 17% |
Attention Score in Context
This research output has an Altmetric Attention Score of 3. 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 25 July 2017.
All research outputs
#13,318,285
of 22,973,051 outputs
Outputs from Frontiers in Computational Neuroscience
#520
of 1,348 outputs
Outputs of similar age
#156,518
of 317,069 outputs
Outputs of similar age from Frontiers in Computational Neuroscience
#20
of 41 outputs
Altmetric has tracked 22,973,051 research outputs across all sources so far. This one is in the 41st percentile – i.e., 41% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,348 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 6.1. This one has gotten more attention than average, scoring higher than 59% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 317,069 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 49th percentile – i.e., 49% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 41 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 51% of its contemporaries.