Putting a bug in ML: The moth olfactory network learns to read MNIST
We seek to (i) characterize the learning architectures exploited in biological neural networks for training on very few samples, and (ii) port these algorithmic structures to a machine learning context. The Moth Olfactory Network is among the simplest biological neural systems that can learn, and it...
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Main Authors | , |
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Format | Journal Article |
Language | English |
Published |
14.02.2018
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Subjects | |
Online Access | Get full text |
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Summary: | We seek to (i) characterize the learning architectures exploited in
biological neural networks for training on very few samples, and (ii) port
these algorithmic structures to a machine learning context. The Moth Olfactory
Network is among the simplest biological neural systems that can learn, and its
architecture includes key structural elements and mechanisms widespread in
biological neural nets, such as cascaded networks, competitive inhibition, high
intrinsic noise, sparsity, reward mechanisms, and Hebbian plasticity. These
structural biological elements, in combination, enable rapid learning.
MothNet is a computational model of the Moth Olfactory Network, closely
aligned with the moth's known biophysics and with in vivo electrode data
collected from moths learning new odors. We assign this model the task of
learning to read the MNIST digits. We show that MothNet successfully learns to
read given very few training samples (1 to 10 samples per class). In this
few-samples regime, it outperforms standard machine learning methods such as
nearest-neighbors, support-vector machines, and neural networks (NNs), and
matches specialized one-shot transfer-learning methods but without the need for
pre-training. The MothNet architecture illustrates how algorithmic structures
derived from biological brains can be used to build alternative NNs that may
avoid some of the learning rate limitations of current engineered NNs. |
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DOI: | 10.48550/arxiv.1802.05405 |