Neuronal firing is often understood as a fundamentally binary process, because a neuron either fires an action potential or it does not. This is often referred to as the “all-or-none” principle. Once the membrane potential of a neuron reaches a certain threshold, an action potential will fire. If this threshold is not reached, it won’t fire. There’s no such thing as a “partial” action potential; it’s a binary, all-or-none process.
Frequency Modulation: Even though an individual neuron’s action potential can be considered binary, neurons encode the intensity of the stimulation in the frequency of action potentials. A stronger stimulus causes the neuron to fire action potentials more rapidly. Again binary in nature not analog.
Neuronal firing is often understood as a fundamentally binary process, because a neuron either fires an action potential or it does not. This is often referred to as the “all-or-none” principle.
Isn’t this true of standard multi-bit neural networks too? This seems to be what a nonlinear activation function achieves: translating the input values into an all-or-nothing activation.
The characteristic of a 1-bit model is not that its activations are recorded in a single but but that its weights are. There are no gradations of connection weights: they are just on or off. As far as I know, that’s different from both standard neural nets and from how the brain works.
