4 ms·

Neurons are surprisingly not simple. Vastly more complex than the ultra simplified model in artificial neural networks.

jiggawatts 1y ago
Most of the complexity is incidental to intelligence. It's mostly just the machinery of keeping the cell alive. Most everything in biology is a clumsy hack accidentally discovered via evolution, and then optimised to death over aeons. We can sidestep all that mess and extract just the core algorithm that is actually required for intelligence.
- voidspark 1y ago
  That is your assumption and it is wrong. https://grok.com/share/bGVnYWN5_ab498084-58c4-4345-9140-07b5af1ad37a 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.
  - voidspark 1y ago
    This is interesting https://chatgpt.com/share/68219da9-1e78-8007-b083-8a81bfbea2a2 https://chatgpt.com/share/68219da9-1e78-8007-b083-8a81bfbea2... "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."
laichzeit0 1y ago
Neither are most functions, but locally, at a point, a linear approximation works just fine in practice.
- voidspark 1y ago
  https://news.ycombinator.com/item?id=43959553 https://news.ycombinator.com/item?id=43959553

Most of the complexity is incidental to intelligence. It's mostly just the machinery of keeping the cell alive. Most everything in biology is a clumsy hack accidentally discovered via evolution, and then optimised to death over aeons. We can sidestep all that mess and extract just the core algorithm that is actually required for intelligence.

voidspark 1y ago
That is your assumption and it is wrong. https://grok.com/share/bGVnYWN5_ab498084-58c4-4345-9140-07b5af1ad37a 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.
- voidspark 1y ago
  This is interesting https://chatgpt.com/share/68219da9-1e78-8007-b083-8a81bfbea2a2 https://chatgpt.com/share/68219da9-1e78-8007-b083-8a81bfbea2... "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."

laichzeit0 1y ago

Neither are most functions, but locally, at a point, a linear approximation works just fine in practice.

voidspark 1y ago
https://news.ycombinator.com/item?id=43959553 https://news.ycombinator.com/item?id=43959553