8,081 research outputs found
Evolving Plasticity for Autonomous Learning under Changing Environmental Conditions
A fundamental aspect of learning in biological neural networks is the
plasticity property which allows them to modify their configurations during
their lifetime. Hebbian learning is a biologically plausible mechanism for
modeling the plasticity property in artificial neural networks (ANNs), based on
the local interactions of neurons. However, the emergence of a coherent global
learning behavior from local Hebbian plasticity rules is not very well
understood. The goal of this work is to discover interpretable local Hebbian
learning rules that can provide autonomous global learning. To achieve this, we
use a discrete representation to encode the learning rules in a finite search
space. These rules are then used to perform synaptic changes, based on the
local interactions of the neurons. We employ genetic algorithms to optimize
these rules to allow learning on two separate tasks (a foraging and a
prey-predator scenario) in online lifetime learning settings. The resulting
evolved rules converged into a set of well-defined interpretable types, that
are thoroughly discussed. Notably, the performance of these rules, while
adapting the ANNs during the learning tasks, is comparable to that of offline
learning methods such as hill climbing.Comment: Evolutionary Computation Journa
Evolving Plasticity for Autonomous Learning under Changing Environmental Conditions
A fundamental aspect of learning in biological neural networks is the
plasticity property which allows them to modify their configurations during
their lifetime. Hebbian learning is a biologically plausible mechanism for
modeling the plasticity property in artificial neural networks (ANNs), based on
the local interactions of neurons. However, the emergence of a coherent global
learning behavior from local Hebbian plasticity rules is not very well
understood. The goal of this work is to discover interpretable local Hebbian
learning rules that can provide autonomous global learning. To achieve this, we
use a discrete representation to encode the learning rules in a finite search
space. These rules are then used to perform synaptic changes, based on the
local interactions of the neurons. We employ genetic algorithms to optimize
these rules to allow learning on two separate tasks (a foraging and a
prey-predator scenario) in online lifetime learning settings. The resulting
evolved rules converged into a set of well-defined interpretable types, that
are thoroughly discussed. Notably, the performance of these rules, while
adapting the ANNs during the learning tasks, is comparable to that of offline
learning methods such as hill climbing.Comment: Evolutionary Computation Journa
Born to learn: The inspiration, progress, and future of evolved plastic artificial neural networks
Biological plastic neural networks are systems of extraordinary computational
capabilities shaped by evolution, development, and lifetime learning. The
interplay of these elements leads to the emergence of adaptive behavior and
intelligence. Inspired by such intricate natural phenomena, Evolved Plastic
Artificial Neural Networks (EPANNs) use simulated evolution in-silico to breed
plastic neural networks with a large variety of dynamics, architectures, and
plasticity rules: these artificial systems are composed of inputs, outputs, and
plastic components that change in response to experiences in an environment.
These systems may autonomously discover novel adaptive algorithms, and lead to
hypotheses on the emergence of biological adaptation. EPANNs have seen
considerable progress over the last two decades. Current scientific and
technological advances in artificial neural networks are now setting the
conditions for radically new approaches and results. In particular, the
limitations of hand-designed networks could be overcome by more flexible and
innovative solutions. This paper brings together a variety of inspiring ideas
that define the field of EPANNs. The main methods and results are reviewed.
Finally, new opportunities and developments are presented
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