Read the equation
Includes each neuron whose conditional loss does not exceed threshold τ. This can activate multiple neurons and requires losses that are comparable across them.
Terms & definitions
The recovered passage does not supply a separate symbol table. Read the source context for the notation used in this version; familiar letters may carry different meanings in other builds.
How it works
Read \mathcal{A}(x) as the quantity being defined or updated by this relation.
Substitute the source-defined variables or recorded values and follow the displayed arithmetic and indexing exactly.
Use this version’s variable definitions and update order. The same symbol may be assigned a different meaning in another build.
Assumptions & limits
Source recovery establishes that the expression appears in this record. Its mathematical validity, implementation behavior, and interpretation must be assessed separately. See the linked source and research notes for the checks available for this version.
Original context
Read the surrounding source passage
\rho(x) = \arg\min_{i} L_i^\*(S',D' \mid x)
Or, if you allow multiple:
\mathcal{A}(x) = \{ i \;|\; L_i^\*(S',D' \mid x) \le \tau \}
“Who works on task x?” → those neurons whose derived loss is smallest (or below threshold τ).
3. Consistency across all 80 neurons
Because each Lᵢ^* is derived from L^* by projection, you get:
• Different domains (system vs network vs website etc.)Exact original notation
\mathcal{A}(x) = \{ i \;|\; L_i^\*(S',D' \mid x) \le \tau \}Related builds
Sources & provenance
\mathcal{A}(x) = \{ i \;|\; L_i^\*(S',D' \mid x) \le \tau \}
\mathcal{A}(x) = \{ i \;|\; L_i^\*(S',D' \mid x) \le \tau \}