Editing Openai/6939b23e-87cc-8001-bdd9-9546d1143ce9 (section)

=== Assistant: Exactly — now you’re getting very close to the core. ===
Exactly — now you’re getting very close to the core. Let me explain carefully and intuitively.

==== - Each neuron is essentially a tiny calculator. ====
* It receives multiple inputs (numbers from previous layer’s neurons) and produces an output (another number) for the next layer.
* Neurons don’t have “physical” form — they are just mathematical functions.

==== - Each input to a neuron comes via a connection. ====
* Every connection has a weight, which determines how strongly the input affects the neuron. - Think of it as the thickness of a dendrite: thicker = stronger influence, thinner = weaker influence.
* Formally, the neuron does something like:

<syntaxhighlight>output = activation(sum(input_i * weight_i) + bias)

</syntaxhighlight>
* Here: - input_i = signal from previous neuron - weight_i = strength of that connection (like thickness) - bias = baseline adjustment - activation = non-linear function (e.g., ReLU, GELU) to decide output

==== 1. Layer L → group of neurons ====
# Layer L+1 → another group of neurons
# Each neuron in L+1 is connected to all neurons in L (in most transformer architectures).
# Weights determine how much each neuron in L influences neurons in L+1.

: 

==== - Normally, connections are static (weights fixed during inference). ====
* Attention heads temporarily adjust the “strength” of connections depending on which tokens matter most.
* So you can think of attention as moving spotlights, highlighting which input neurons should have more influence in this particular token processing step.

===== - Neurons = tiny calculators =====
* Connections = links between neurons
* Weights = how strongly the input travels across the connection (thicker = stronger)
* Activation = the decision function the neuron uses
* Attention = dynamic adjustment of which connections are emphasized during processing

: 

If you want, I can make a visual diagram showing neurons, connections, weights as thickness, and attention as moving spotlights, which makes the whole picture very concrete.

Do you want me to do that?