Verified adaptive teaching with native R cells
Year: 2026
Type: article
Abstract: Networks of R hybrid cells can carry out finite adaptive teaching programs in which latertrials depend on the state written by earlier trials. Pulses change the input log-gains of freshlearner cells. A read pulse then tests those gains, and its report can trigger further trials or alesson and credit write. Native AND and NOT gates connect the reports.For any positive integer K, a single compiled network identifies a hidden need m ∈ {1, …, K}by binary search. The router, timing and initial pulse queue are fixed across needs. Only theselected leaf receives its declared lesson and credit. A separate nine-cell experiment deliverslessons of 8, 7 and 6 pulses. Cutting one feedback wire prevents the second improvement.Lean proves these results in upward-crossing semantics for independent, fixed fabricationparameters in the ±0.3% or ±10% box. For a fixed setup, complete executions agree onterminal observations and each cell’s ordered firing times.
Author: Breuillaud, Nicolas
Language: English
Subfield: Electrical and Electronic Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7225632073
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