| Title |
Symmetries Constrain Dynamics in a Family of Balanced Neural Networks
|
|---|---|
| Published in |
The Journal of Mathematical Neuroscience, October 2017
|
| DOI | 10.1186/s13408-017-0052-6 |
| Pubmed ID | |
| Authors |
Andrea K. Barreiro, J. Nathan Kutz, Eli Shlizerman |
| Abstract |
We examine a family of random firing-rate neural networks in which we enforce the neurobiological constraint of Dale's Law-each neuron makes either excitatory or inhibitory connections onto its post-synaptic targets. We find that this constrained system may be described as a perturbation from a system with nontrivial symmetries. We analyze the symmetric system using the tools of equivariant bifurcation theory and demonstrate that the symmetry-implied structures remain evident in the perturbed system. In comparison, spectral characteristics of the network coupling matrix are relatively uninformative about the behavior of the constrained system. |
X Demographics
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Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 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 31 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 31 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 11 | 35% |
| Student > Ph. D. Student | 7 | 23% |
| Student > Master | 4 | 13% |
| Student > Bachelor | 3 | 10% |
| Professor > Associate Professor | 3 | 10% |
| Other | 2 | 6% |
| Unknown | 1 | 3% |
| Readers by discipline | Count | As % |
|---|---|---|
| Mathematics | 8 | 26% |
| Physics and Astronomy | 5 | 16% |
| Engineering | 4 | 13% |
| Agricultural and Biological Sciences | 3 | 10% |
| Neuroscience | 3 | 10% |
| Other | 3 | 10% |
| Unknown | 5 | 16% |
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 12 October 2017.
All research outputs
#20,449,496
of 23,005,189 outputs
Outputs from The Journal of Mathematical Neuroscience
#70
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Outputs of similar age
#282,910
of 324,392 outputs
Outputs of similar age from The Journal of Mathematical Neuroscience
#2
of 2 outputs
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