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Research map / Probabilistic and scientific modeling / Dynamical systems

Planning with Biological Neurons and Synapses

AAAI 2022 · Probabilistic and scientific modeling › Dynamical systems · approach: Synaptic plasticity learning rules · 4 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Implement the two-approximation blocks-world planning strategy in Assembly CalculusAssembly Calculus · Blocks World, 10 blockscorrect configurations: 100Helped
Increase neurons per brain area from n=10^5 to n=5·10^5 (ablation)Assembly Calculus chaining · Blocks World chain length, 10-block input, k=50mean blocks correctly parsed: 6,36 -> 9,16Helped
Increase neurons per brain area from n=5·10^5 to n=10^6 (ablation)Assembly Calculus chaining · Blocks World chain length, 10-block input, k=50mean blocks correctly parsed: 9,16 -> 9,6Helped
Set assembly size k=30 rather than k=50 (ablation)Assembly Calculus chaining with n=10^6 · Blocks World maximum chain lengthmean maximum chain length correctly parsed: 15 -> 19,4Helped
Set assembly size k=40 rather than k=50 (ablation)Assembly Calculus chaining with n=5·10^5 · Blocks World maximum chain lengthmean maximum chain length correctly parsed: 14,68 -> 17,24Helped
Increase assembly size from k=30 to k=40 (ablation)Assembly Calculus chaining with n=10^6 · Blocks World maximum chain lengthmean maximum chain length correctly parsed: 19,4 -> 17,76Mixed
Use a larger n:k ratio (ablation)Assembly Calculus chaining · Blocks World chainsmaximum chain length correctly parsedHelped
Increase k beyond the narrow range that gives the longest chains (ablation)Assembly Calculus chaining · Blocks World chainsmaximum chain length correctly parsedHurt
Increase chain length (ablation)Assembly Calculus chaining · Blocks World chainsblocks correctly parsedHurt

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