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Memory Instability

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anonymous-author-submissions edited this page Feb 3, 2026 · 7 revisions

Memory Instability

Task description

The Memory Instability task evaluates whether an evolved Dynamic Neural Field (DNF) architecture can maintain a working-memory representation after the external stimulus has been removed.

The desired behaviour is:

  1. An external stimulus induces a localised activation peak in the input field.

  2. The input field peak is self-stabilised only during stimulus presentation.

  3. Activity is transmitted to the output field.

  4. After stimulus offset:

    • The input field relaxes to baseline.
    • The output field maintains a self-sustained peak, representing working memory.

This task tests the ability of the system to transition from pure detection to persistent internal activation.

Highest-fitness minimal phenotype

The highest-fitness solutions again converge to a minimal topology, identical in structure to the detection task but differing in field dynamics:

  • Fields: 2 (input → output)

  • Hidden fields: 0

  • Connections: 1 excitatory feedforward coupling

  • Dynamics:

    • Input field: Mexican-hat lateral interactions (transient peak)
    • Output field: Gaussian kernel enabling self-sustained activation
phenotype-memory-wbg

The figure above shows:

  • Blue: activity during stimulus presentation
  • Red: activity after stimulus removal

Only the output field preserves the peak after stimulus offset, satisfying the working-memory criterion.

Experiment-level statistics across runs

Analysed 100 runs; 100 reached the fitness threshold (100.0% success, threshold = 0.950)

Generations to threshold (successful runs)

  • Mean: 2.43
  • Median: 2.00
  • Std: 1.23

Convergence speed

  • Mean convergence rate (fitness gain/gen): 0.2241
  • Mean fitness improvement/gen: 0.2241

Architecture (successful solutions)

  • Hidden fields (mean ± std): 0.00 ± 0.00
  • Enabled connections (mean ± std): 1.00 ± 0.00

Notable runs

label run value
Highest max fitness 2026-01-23 16h12m54s 0.962585
Lowest max fitness 2026-01-23 15h55m09s 0.950034
Fastest to threshold 2026-01-23 15h49m05s 1.0
Slowest to threshold 2026-01-23 16h28m09s 7.0
Most hidden fields 2026-01-23 15h47m59s 0.0
Most enabled connections 2026-01-23 15h47m59s 1.0

Takeaway

Memory Instability remains consistently solvable, but requires more precise parameter tuning than pure detection:

  • Convergence is still fast, but no longer instantaneous.
  • The evolved solution introduces self-sustaining dynamics without increasing topological complexity.
  • This task marks the first transition from reactive to representational behavior.

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