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The learning\u2010from\u2010mistakes algorithm, inspired by the brain's simple learning rules of inhibition and pruning, is one of the few brain\u2010like training methods. This algorithm is implemented in neuromorphic memristive hardware through a codesign process that evaluates essential hardware trade\u2010offs. While the algorithm effectively trains small networks as binary classifiers and perceptrons, performance declines significantly with increasing network size unless the hardware is tailored to the algorithm. This work investigates the trade\u2010offs between depth, controllability, and capacity\u2014the number of learnable patterns\u2014in neuromorphic hardware. This highlights the importance of topology and governing equations, providing theoretical tools to evaluate a device's computational capacity based on its measurements and circuit structure. The findings show that breaking neural network symmetry enhances both controllability and capacity. Additionally, by pruning the circuit, neuromorphic algorithms in all\u2010memristive circuits can utilize stochastic resources to create local contrasts in network weights. Through combined experimental and simulation efforts, the parameters are identified that enable networks to exhibit emergent intelligence from simple rules, advancing the potential of neuromorphic computing.<\/jats:p>","DOI":"10.1002\/aisy.202400739","type":"journal-article","created":{"date-parts":[[2025,3,14]],"date-time":"2025-03-14T18:25:12Z","timestamp":1741976712000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Uncontrolled Learning: Codesign of Neuromorphic Hardware Topology for Neuromorphic Algorithms"],"prefix":"10.1002","volume":"7","author":[{"given":"Frank","family":"Barrows","sequence":"first","affiliation":[{"name":"Theoretical Division (T\u20104, Cond. Matt. &amp; Comp. Sys.) 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