| Title |
Sparse Functional Identification of Complex Cells from Spike Times and the Decoding of Visual Stimuli
|
|---|---|
| Published in |
The Journal of Mathematical Neuroscience, January 2018
|
| DOI | 10.1186/s13408-017-0057-1 |
| Pubmed ID | |
| Authors |
Aurel A. Lazar, Nikul H. Ukani, Yiyin Zhou |
| Abstract |
We investigate the sparse functional identification of complex cells and the decoding of spatio-temporal visual stimuli encoded by an ensemble of complex cells. The reconstruction algorithm is formulated as a rank minimization problem that significantly reduces the number of sampling measurements (spikes) required for decoding. We also establish the duality between sparse decoding and functional identification and provide algorithms for identification of low-rank dendritic stimulus processors. The duality enables us to efficiently evaluate our functional identification algorithms by reconstructing novel stimuli in the input space. Finally, we demonstrate that our identification algorithms substantially outperform the generalized quadratic model, the nonlinear input model, and the widely used spike-triggered covariance algorithm. |
X Demographics
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Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 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 16 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Greece | 1 | 6% |
| Unknown | 15 | 94% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 4 | 25% |
| Researcher | 3 | 19% |
| Student > Postgraduate | 2 | 13% |
| Student > Bachelor | 1 | 6% |
| Lecturer | 1 | 6% |
| Other | 2 | 13% |
| Unknown | 3 | 19% |
| Readers by discipline | Count | As % |
|---|---|---|
| Engineering | 3 | 19% |
| Computer Science | 3 | 19% |
| Agricultural and Biological Sciences | 2 | 13% |
| Neuroscience | 2 | 13% |
| Linguistics | 1 | 6% |
| Other | 1 | 6% |
| Unknown | 4 | 25% |
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 04 June 2021.
All research outputs
#15,488,947
of 23,016,919 outputs
Outputs from The Journal of Mathematical Neuroscience
#36
of 80 outputs
Outputs of similar age
#270,343
of 441,922 outputs
Outputs of similar age from The Journal of Mathematical Neuroscience
#2
of 4 outputs
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