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So, the error is propagated backwards with each neurons associated with some error value.
Now, the assumption is this error is due to the partial contribution from each neurons in previous layers.
Each neurons has n inputs and calculates its output 'a' using equation a = fleft( {mathop sum limits_{i = 1}^{n} w_{i} p_{i} + b} right)where p i are the ith input, w i are the ith weight, b is the bias and f is the activation or transfer function for the neuron.
If we assume that each neurons' preferred ITD and ILD spectra match the ITD and ILD spectra derived from the HRTFs at the best direction [49], [53], [54], then the inputs to the neuron are very similar across frequency.
Here, the Roex(p) filter shape was chosen only for simplicity and g was used as a factor of difference between each neurons CF and the 1-kHz probe sound.
Similar(55)
Each neuron has a variable connection strength and each connection has a threshold.
TrueNorth creates its own sort of fuzziness by including a random-number generator with each neuron.
Here, the medium is the message; each neuron is both program and processing unit.
A little background on neuroplasticity: all memory is held in the brain's neurons and each neuron only holds a tiny bit of a memory.
Messages are passed down each neuron as electrical currents; chemical neurotransmitters are used to pass the signal across the junction boxes from one neuron to the next.
Neurons, of course, use a different method of communication: passing chemical messengers between the hundreds of filaments radiating from each neuron.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com