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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

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. Evolving networks with a pressure to minimize connection costs leads to modular solutions that can retain old skills as new skills are learned. Hypothesis 2: Evolving modular networks makes reward-based learning easier, because it allows a clear separation of reward signals and learned skills. We present evidence for both hypotheses in this paper.
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pcbi.1004128.g001: 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. Evolving networks with a pressure to minimize connection costs leads to modular solutions that can retain old skills as new skills are learned. Hypothesis 2: Evolving modular networks makes reward-based learning easier, because it allows a clear separation of reward signals and learned skills. We present evidence for both hypotheses in this paper.

Mentions: In this paper, we investigate the hypothesis that modularity, which is widespread in biological neural networks [17–21], helps reduce catastrophic forgetting in artificial neural networks. Modular networks are those that have many clusters (modules) of highly connected neurons that are only sparsely connected to neurons in other modules [19, 22, 23]. The intuition behind this hypothesis is that modularity could allow learning new skills without forgetting old skills because learning can be selectively turned on only in modules learning a new task (Fig. 1, top). Selective regulation of learning occurs in natural brains via neuromodulation [24], and we incorporate an abstraction of it in our model [25]. We also investigate a second hypothesis: that modularity can improve skill learning by separating networks into a skill module and a reward module, resulting in more precise control of learning (Fig. 1, bottom).


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

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

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. Evolving networks with a pressure to minimize connection costs leads to modular solutions that can retain old skills as new skills are learned. Hypothesis 2: Evolving modular networks makes reward-based learning easier, because it allows a clear separation of reward signals and learned skills. We present evidence for both hypotheses in this paper.
© Copyright Policy
Related In: Results  -  Collection

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

pcbi.1004128.g001: 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. Evolving networks with a pressure to minimize connection costs leads to modular solutions that can retain old skills as new skills are learned. Hypothesis 2: Evolving modular networks makes reward-based learning easier, because it allows a clear separation of reward signals and learned skills. We present evidence for both hypotheses in this paper.
Mentions: In this paper, we investigate the hypothesis that modularity, which is widespread in biological neural networks [17–21], helps reduce catastrophic forgetting in artificial neural networks. Modular networks are those that have many clusters (modules) of highly connected neurons that are only sparsely connected to neurons in other modules [19, 22, 23]. The intuition behind this hypothesis is that modularity could allow learning new skills without forgetting old skills because learning can be selectively turned on only in modules learning a new task (Fig. 1, top). Selective regulation of learning occurs in natural brains via neuromodulation [24], and we incorporate an abstraction of it in our model [25]. We also investigate a second hypothesis: that modularity can improve skill learning by separating networks into a skill module and a reward module, resulting in more precise control of learning (Fig. 1, bottom).

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