Memory Instability
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:
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An external stimulus induces a localised activation peak in the input field.
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The input field peak is self-stabilised only during stimulus presentation.
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Activity is transmitted to the output field.
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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.
The highest-fitness solutions again converge to a minimal topology, identical in structure to the detection task but differing in field dynamics:
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Fields: 2 (input → output)
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Hidden fields: 0
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Connections: 1 excitatory feedforward coupling
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Dynamics:
- Input field: Mexican-hat lateral interactions (transient peak)
- Output field: Gaussian kernel enabling self-sustained activation
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.
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 | 2026-01-23 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 |
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
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Field kernels:
- Gaussian: 3079 (77.0%)
- Mexican-hat: 921 (23.0%)
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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.
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.