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.

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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.

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

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

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

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

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

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This section reports detailed statistics from the evolutionary run that produced the highest-fitness solution for the Memory Instability task.

Final generation: g = 2

• Best fitness: 0.9626
• Target fitness: 0.950
• Average fitness: 0.5870

Overall run statistics

• Max best fitness: 0.9626 (reached at generation 2)
• Mean best fitness over run (AUC): 0.7986
• Mean average fitness over run (AUC): 0.4609
• Longest stagnation period: 0 generations
• Target fitness first reached: generation 2 (best fitness ≈ 0.9626)

Final generation (g = 2)

• Species: 4
• Active species: 4

Across run

• Total distinct species created: 3
• Species extinct by final generation: 0
• Average species per generation: 2.67
• Average active species per generation: 2.67
• Max active species in a generation: 4 (at g = 2)

Species lifetime & size

• Average species lifespan: 1.33 generations
• Longest-lived species: 0 (lifespan 2)
• Average max members per species: 336.00
• Average offspring per species: 333.33

Final generation (g = 2)

• Avg genome size: 2.80
• Avg field genes: 2.00
• Avg connection genes: 0.80

Growth over run

• Genome size change: +0.80 (≈ +0.401 / gen)
• Field genes change: +0.00 (≈ +0.000 / gen)
• Connection genes change: +0.80 (≈ +0.401 / gen)

Ratios

• Avg connections per field (final gen): 0.40

• Field kernels:

  • Gaussian: 3079 (77.0%)
  • Mexican-hat: 921 (23.0%)

• Interaction kernels:

  • Gaussian: 107 (77.5%)
  • Mexican-hat: 31 (22.5%)

Generation: 2 Fitness: 0.9626

Field	Role	Kernel type	Field parameters	Kernel parameters
nf 1	Input	Mexican-hat	h = -13.19, τ = 45.00	A_exc = 22.82, σ_exc = 23.14, A_inh = 10.61, σ_inh = 34.35, A_glob = -0.08
nf 2	Output	Gaussian	h = -12.65, τ = 162.45	A = 24.62, σ = 1.95, A_glob = -0.16

Interaction gene	From → To	Kernel parameters
gk cg 1-2-0	nf 1 → nf 2	A = 28.66, σ = 6.15, A_glob = 0.00

memory-cut-compressed.mp4

Compared to Detection Instability, the best Memory Instability solution:

• Requires non-trivial speciation to explore parameter space
• Introduces Mexican-hat dynamics to control transient vs persistent activity
• Exhibits early but non-instantaneous convergence (generation 2)

Despite identical topology, dynamic specialisation—not structural growth—is what enables working memory.

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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.