We have investigated the consequences of including artificial astrocytes, which present the biologically defined properties involved in astrocyte-neuron communication, on artificial neural network performance.
Consequently, good performance on artificial data does not guarantee good performance on real biological data.
After introducing the method, we test its performance on artificial benchmark graphs, comparing it with the performances of the best algorithms currently available.
Consistent with this, interspecific variation in aphid performance on artificial diet provides strong evidence that, in a range of aphid species, host plant adapted lineages vary in their amino acid biosynthetic abilities [e.g. [ 12, 22- 24].
It follows the CBR-cycle with its adaptation tool based on Artificial Neural Networks and its performances are evaluated and discussed.
When I meet with my family, I tell them clearly that I want photos to be taken, and they're alerted to watch out for moments to capture and are happy to do so, yet I can't help but feel all we catch is something very artificial as I put on a performance, aware of the camera, careful not to be caught mid sentence with distorted mouth or mid blink with eyes half shut.
Below, we outline the ENIGMA algorithm, test our methodology on artificial expression data and compare its performance to other methods.
To assess the performance of our method and compare ENIGMA to other methods, we performed tests on artificial gene expression data.
We therefore designed artificial neuron-glia networks and investigated the consequences of the presence of artificial astrocytes on the performance of artificial neural networks.
It is based on artificial data and focused on comparing the performance of d-Confidence to that of confidence w.r.t. the coverage of the instance space.
