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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

The environment for one individual’s lifetime.A lifetime lasts 3 years. Each year has 2 seasons: winter and summer. Each season consists of 5 days. In each day, each individual sees all food items available in that season (only two are shown) in a random order.
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pcbi.1004128.g002: The environment for one individual’s lifetime.A lifetime lasts 3 years. Each year has 2 seasons: winter and summer. Each season consists of 5 days. In each day, each individual sees all food items available in that season (only two are shown) in a random order.

Mentions: We consider each pair of a summer and winter season a year. Every season lasts for five days, and in each day an individual encounters all four food items for that season in a random order. A lifetime is three years (Fig. 2). To ensure that individuals must learn associations within their lifetimes instead of having genetically hardcoded associations [47, 62], in each lifetime two food items are randomly assigned as nutritious and the other two food items are assigned as poisonous (Fig. 3). To select for general learners rather than individuals that by chance do well in a specific environment, performance is averaged over four random environments (lifetimes) for each individual during evolution, and over 80 random environments (lifetimes) when assessing the performance of final, end-of-experiment individuals (Methods).


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

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

The environment for one individual’s lifetime.A lifetime lasts 3 years. Each year has 2 seasons: winter and summer. Each season consists of 5 days. In each day, each individual sees all food items available in that season (only two are shown) in a random order.
© Copyright Policy
Related In: Results  -  Collection

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getmorefigures.php?uid=PMC4383335&req=5

pcbi.1004128.g002: The environment for one individual’s lifetime.A lifetime lasts 3 years. Each year has 2 seasons: winter and summer. Each season consists of 5 days. In each day, each individual sees all food items available in that season (only two are shown) in a random order.
Mentions: We consider each pair of a summer and winter season a year. Every season lasts for five days, and in each day an individual encounters all four food items for that season in a random order. A lifetime is three years (Fig. 2). To ensure that individuals must learn associations within their lifetimes instead of having genetically hardcoded associations [47, 62], in each lifetime two food items are randomly assigned as nutritious and the other two food items are assigned as poisonous (Fig. 3). To select for general learners rather than individuals that by chance do well in a specific environment, performance is averaged over four random environments (lifetimes) for each individual during evolution, and over 80 random environments (lifetimes) when assessing the performance of final, end-of-experiment individuals (Methods).

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