Biological Neuron: Processes information through complex, nonlinear integration of thousands of excitatory and inhibitory inputs across dendritic trees, producing spiking outputs with rich temporal patterns. It adapts dynamically via synaptic plasticity, neuromodulation, and structural changes, operating in a probabilistic, energy-efficient manner within oscillatory networks.
Artificial Neuron: Performs simple, linear summation of weighted inputs, applies a static activation function, and produces a single scalar output. It lacks temporal dynamics, local plasticity, or neuromodulation, operating deterministically with high computational cost and fixed connectivity.
"Dendrites can implement non‑linear sub‑units and even logic‑gate‑like behavior before the soma integrates them, whereas the standard artificial neuron uses a plain weighted sum."
"Neurotransmitter diversity (e.g., glutamate, GABA, dopamine) allows different semantics on each connection. An artificial edge conveys only a signed scalar."
https://grok.com/share/bGVnYWN5_ab498084-58c4-4345-9140-07b5...
Biological Neuron: Processes information through complex, nonlinear integration of thousands of excitatory and inhibitory inputs across dendritic trees, producing spiking outputs with rich temporal patterns. It adapts dynamically via synaptic plasticity, neuromodulation, and structural changes, operating in a probabilistic, energy-efficient manner within oscillatory networks.
Artificial Neuron: Performs simple, linear summation of weighted inputs, applies a static activation function, and produces a single scalar output. It lacks temporal dynamics, local plasticity, or neuromodulation, operating deterministically with high computational cost and fixed connectivity.