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Neural Modularity Helps Organisms Evolve to Learn New Skills without Forgetting Old Skills

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Academic year: 2021

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Fig 1. Two hypotheses for how neural modularity can improve learning. Hypothesis 1: Evolving non-modular networks leads to the forgetting of old skills as new skills are learned
Fig 3. Randomizing food associations between generations. To ensure that agents learn associations within their lifetimes instead of genetically hardcoding associations, whether each food item is nutritious or poisonous is randomized each generation
Fig 4. The addition of a cost for network connections, which is present only in the P&CC treatment, significantly increases performance and modularity
Fig 5. Performance each day for evolved agents from both treatments. Plotted is median performance per day ( ± 95% bootstrapped confidence intervals of the median) measured across 100 organisms (the highest-performing organism from each experiment per trea
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