Same as Fig 1B and 1C for the axolotl and mouse data, but showing Poisson explained variance (exp var), as measured on the frozen-noise sections of the data.

Same as Fig 1B and 1C for the axolotl and mouse data, but showing Poisson explained variance (exp var), as measured on the frozen-noise sections of the data.

<p><b>A)</b> Comparison of the Poisson explained variance of each model to the explained variance of the LN model. Each data point represents one cell. <b>B)</b> <i>Left</i>: The excess explained variance (computed as explained variance of suppressive model...

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Bibliographic Details
Main Author:	Neda Shahidi (21238796) (author)
Other Authors:	Fernando Rozenblit (21238799) (author), Mohammad H. Khani (19533176) (author), Helene M. Schreyer (19533179) (author), Matthias Mietsch (11412713) (author), Dario A. Protti (21238802) (author), Tim Gollisch (70921) (author)
Published:	2025
Subjects:	Neuroscience Science Policy Environmental Sciences not elsewhere classified Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified single neurons aim neuron &# 8217 canonical mechanisms explaining driven feedback module different temporal regimes implemented via subtractive retinal ganglion cells generalization across species slow axolotl cells stimuli </ p divisive model depends subtractive model capturing ganglion cells feedback model sensory stimuli different types capturing interactions subtractive models xlink "> visual contrast stimulus filters stimulus components spiking responses perform best particular responses neural activity marmoset retina interaction affects divisive interactions cell type based stimulus
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