A neuron's predicted activity is given by the weighted sum of activities of upstream neurons, determined by synaptic weights which are typically followed by a nonlinear activation function (like sigmoid or ReLU), though the speaker simplifies this for pedagogical clarity

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We can visualize it as rods connecting neuron nodes on the layer above to the platforms at a current level positioned at variable angles corresponded to synaptic weights which determine how other neurons activities influence the prediction. The sum of activities from all neurons in the layer above multiplied by synaptic weights connecting them. Note that typically activities pass through a nonlinear activation function like sigmoid or relu, but I'm omitting it here for simplicity.

Source

The Brain’s Learning Algorithm Isn’t BackpropagationArtem Kirsanov
Created: 8/12/2026, 6:30:20 PM

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