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. 2014 Aug 21;10(8):e1003793. doi: 10.1371/journal.pcbi.1003793

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© 2014 Barranca et al

This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.

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Characteristics of optimal CS networks examined using the dependence of relative reconstruction error on network features: a convergence of the sensory-cell (sensory-neuron) to receptor connection matrix , b stimulus strength , c convergence of the sensory-cell connection matrix , d connection strength , e simulation time over which spikes are recorded, f variance of Gaussian noise added to stimulus components, g number of sensory-cells, and h level of randomness in the sensory-cell to receptor connection matrix . In panel a, we normalize the stimulus strength by , the number of nonzero entries in the receptor-to-sensory-neuron connectivity matrix , to keep the amount of drive to a sensory neuron approximately constant when changing convergence. The minimal relative error at the optimal convergence in panel a is approximately equal to 0.35.

Inline graphic — Characteristics of optimal CS networks examined using the dependence of relative reconstruction error on network features: a convergence of the sensory-cell (sensory-neuron) to receptor connection matrix , b stimulus strength , c convergence of the sensory-cell connection matrix , d connection strength , e simulation time over which spikes are recorded, f variance of Gaussian noise added to stimulus components, g number of sensory-cells, and h level of randomness in the sensory-cell to receptor connection matrix . In panel a, we normalize the stimulus strength by , the number of nonzero entries in the receptor-to-sensory-neuron connectivity matrix , to keep the amount of drive to a sensory neuron approximately constant when changing convergence. The minimal relative error at the optimal convergence in panel a is approximately equal to 0.35.