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Neural modularity helps organisms evolve to learn new skills without forgetting old skills.

Ellefsen KO, Mouret JB, Clune J - PLoS Comput. Biol. (2015)

Bottom Line: Modularity intuitively should reduce learning interference between tasks by separating functionality into physically distinct modules in which learning can be selectively turned on or off.To produce modularity, we evolve neural networks with a cost for neural connections.We show that this connection cost technique causes modularity, confirming a previous result, and that such sparsely connected, modular networks have higher overall performance because they learn new skills faster while retaining old skills more and because they have a separate reinforcement learning module.

View Article: PubMed Central - PubMed

Affiliation: Department of Computer and Information Science, Norwegian University of Science and Technology, Trondheim, Norway.

ABSTRACT
A long-standing goal in artificial intelligence is creating agents that can learn a variety of different skills for different problems. In the artificial intelligence subfield of neural networks, a barrier to that goal is that when agents learn a new skill they typically do so by losing previously acquired skills, a problem called catastrophic forgetting. That occurs because, to learn the new task, neural learning algorithms change connections that encode previously acquired skills. How networks are organized critically affects their learning dynamics. In this paper, we test whether catastrophic forgetting can be reduced by evolving modular neural networks. Modularity intuitively should reduce learning interference between tasks by separating functionality into physically distinct modules in which learning can be selectively turned on or off. Modularity can further improve learning by having a reinforcement learning module separate from sensory processing modules, allowing learning to happen only in response to a positive or negative reward. In this paper, learning takes place via neuromodulation, which allows agents to selectively change the rate of learning for each neural connection based on environmental stimuli (e.g. to alter learning in specific locations based on the task at hand). To produce modularity, we evolve neural networks with a cost for neural connections. We show that this connection cost technique causes modularity, confirming a previous result, and that such sparsely connected, modular networks have higher overall performance because they learn new skills faster while retaining old skills more and because they have a separate reinforcement learning module. Our results suggest (1) that encouraging modularity in neural networks may help us overcome the long-standing barrier of networks that cannot learn new skills without forgetting old ones, and (2) that one benefit of the modularity ubiquitous in the brains of natural animals might be to alleviate the problem of catastrophic forgetting.

No MeSH data available.


Related in: MedlinePlus

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 treatment) tested in 80 new environments (lifetimes) with random associations (Methods). P&CC networks significantly outperform PA networks on every day (asterisks). Eating no items or all items produces a score of 0.5; eating all and only nutritious food items achieves the maximum score of 1.0.
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pcbi.1004128.g005: 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 treatment) tested in 80 new environments (lifetimes) with random associations (Methods). P&CC networks significantly outperform PA networks on every day (asterisks). Eating no items or all items produces a score of 0.5; eating all and only nutritious food items achieves the maximum score of 1.0.

Mentions: The addition of a cost for connections (the P&CC treatment) leads to a rapid, sustained, and statistically significant fitness advantage versus not having a connection cost (the PA treatment) (Fig. 4). In addition to overall performance across generations, we looked at the day-to-day performance of final, evolved individuals (Fig. 5). P&CC networks learn associations faster in their first summer and winter, and maintain higher performance over multiple years (pairs of seasons).


Neural modularity helps organisms evolve to learn new skills without forgetting old skills.

Ellefsen KO, Mouret JB, Clune J - PLoS Comput. Biol. (2015)

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 treatment) tested in 80 new environments (lifetimes) with random associations (Methods). P&CC networks significantly outperform PA networks on every day (asterisks). Eating no items or all items produces a score of 0.5; eating all and only nutritious food items achieves the maximum score of 1.0.
© Copyright Policy
Related In: Results  -  Collection

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Show All Figures
getmorefigures.php?uid=PMC4383335&req=5

pcbi.1004128.g005: 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 treatment) tested in 80 new environments (lifetimes) with random associations (Methods). P&CC networks significantly outperform PA networks on every day (asterisks). Eating no items or all items produces a score of 0.5; eating all and only nutritious food items achieves the maximum score of 1.0.
Mentions: The addition of a cost for connections (the P&CC treatment) leads to a rapid, sustained, and statistically significant fitness advantage versus not having a connection cost (the PA treatment) (Fig. 4). In addition to overall performance across generations, we looked at the day-to-day performance of final, evolved individuals (Fig. 5). P&CC networks learn associations faster in their first summer and winter, and maintain higher performance over multiple years (pairs of seasons).

Bottom Line: Modularity intuitively should reduce learning interference between tasks by separating functionality into physically distinct modules in which learning can be selectively turned on or off.To produce modularity, we evolve neural networks with a cost for neural connections.We show that this connection cost technique causes modularity, confirming a previous result, and that such sparsely connected, modular networks have higher overall performance because they learn new skills faster while retaining old skills more and because they have a separate reinforcement learning module.

View Article: PubMed Central - PubMed

Affiliation: Department of Computer and Information Science, Norwegian University of Science and Technology, Trondheim, Norway.

ABSTRACT
A long-standing goal in artificial intelligence is creating agents that can learn a variety of different skills for different problems. In the artificial intelligence subfield of neural networks, a barrier to that goal is that when agents learn a new skill they typically do so by losing previously acquired skills, a problem called catastrophic forgetting. That occurs because, to learn the new task, neural learning algorithms change connections that encode previously acquired skills. How networks are organized critically affects their learning dynamics. In this paper, we test whether catastrophic forgetting can be reduced by evolving modular neural networks. Modularity intuitively should reduce learning interference between tasks by separating functionality into physically distinct modules in which learning can be selectively turned on or off. Modularity can further improve learning by having a reinforcement learning module separate from sensory processing modules, allowing learning to happen only in response to a positive or negative reward. In this paper, learning takes place via neuromodulation, which allows agents to selectively change the rate of learning for each neural connection based on environmental stimuli (e.g. to alter learning in specific locations based on the task at hand). To produce modularity, we evolve neural networks with a cost for neural connections. We show that this connection cost technique causes modularity, confirming a previous result, and that such sparsely connected, modular networks have higher overall performance because they learn new skills faster while retaining old skills more and because they have a separate reinforcement learning module. Our results suggest (1) that encouraging modularity in neural networks may help us overcome the long-standing barrier of networks that cannot learn new skills without forgetting old ones, and (2) that one benefit of the modularity ubiquitous in the brains of natural animals might be to alleviate the problem of catastrophic forgetting.

No MeSH data available.


Related in: MedlinePlus