A single artificial neuron could be implemented as:
Weighted Sum
Using a summing amplifier:
net = Σ_i (Rf/Ri * xi)
Where resistor ratios set the synaptic weights.
Activation Function
Common op-amp activation circuits:
Saturating function: via op-amp with clipping diodes → approximated sigmoid
Hard limiter: comparator behavior for step activation
Tanh-like response: differential pair circuits
Learning
Early analog systems often lacked on-device learning; weights were manually set with potentiometers or stored using:
Memristive elements (recent)
Floating-gate MOSFETs
Programmable resistor networks
A single artificial neuron could be implemented as:
Weighted Sum
Using a summing amplifier:
net = Σ_i (Rf/Ri * xi)
Where resistor ratios set the synaptic weights.
Activation Function
Common op-amp activation circuits:
Saturating function: via op-amp with clipping diodes → approximated sigmoid
Hard limiter: comparator behavior for step activation
Tanh-like response: differential pair circuits
Learning
Early analog systems often lacked on-device learning; weights were manually set with potentiometers or stored using:
Memristive elements (recent)
Floating-gate MOSFETs
Programmable resistor networks