During the system's evolution, it effectively rolls downhill on the energy surface defined in high-dimensional space, and mathematically this corresponds to moving in the direction of steepest descent opposite to the gradient, where the gradient vector points in the direction of steepest ascent and is composed of derivatives with respect to each parameter
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“During the systems evolution, it effectively rolls downhill on the energy surface defined in a highdimensional space where each coordinate represents a parameter such as neural activity or synaptic weight. Mathematically, this downhill roll corresponds to moving in the direction of steepest descent opposite to what's called the gradient of the function where the gradient vector points in the direction of steepest asend and is composed of derivatives with respect to each parameter.”
Created: 8/12/2026, 6:30:20 PM
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