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The weights in an artificial neural network are an approximation of multiple processes combined that take place in biological neurons. Myelination plays a role, but not a major one. Weights in artificial neural networks can be positive or negative numbers. Weight magnitude. The magnitude of a weight is analogous to a combination of increased dendritic ...


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Historically, neuroscientists have not thought of myelination to be the biological analogue of weights in an artificial neural network (but see below!). Instead, the strength of synaptic connections seems to be the most likely analogue. Here, strength refers mostly to the size and of the voltage response in the receiving neuron due to the release of ...


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The weight matrix is typically considered to be a strength of connectivity metric between nodes in the parallel computronium model that neural networks are based on. That fact is fairly evident when you investigate ANN learning algorithms. For instance, the backpropagation algorithm for feedforward networks is designed to strengthen a string of associations ...


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Short answer Mostly Cl- is disregarded in calculations of the resting membrane potential and action potential voltage changes, because it is less important for the neural membrane characteristics than Na+ and K+. Background In some neurons Cl- is not actively transported. In terms of the resting membrane potential, Cl- hence settles its gradient passively ...


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In normal neurons, Chloride's reversal potential is near the resting potential for the neuron and also happens to be near the leak conductance reversal potential for the neuron. While not exactly the same these three are sometimes confused. The difference between these three reversal potentials is subtle. Chloride Reversal Potential: is the potential ...



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