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Cell. 2019 Nov 27;179(6):1382-1392.e10. doi: 10.1016/j.cell.2019.10.020. Epub 2019 Nov 14.

Continual Learning in a Multi-Layer Network of an Electric Fish.

Author information

1
Zuckerman Mind Brain Behavior Institute, Department of Neuroscience, Columbia University, New York, NY 10027, USA; Department of Biological Sciences, Columbia University, New York, NY 10027, USA.
2
Zuckerman Mind Brain Behavior Institute, Department of Neuroscience, Columbia University, New York, NY 10027, USA.
3
Zuckerman Mind Brain Behavior Institute, Department of Neuroscience, Columbia University, New York, NY 10027, USA; Department of Physiology and Cellular Biophysics, Columbia University, New York, NY 10027, USA.
4
Zuckerman Mind Brain Behavior Institute, Department of Neuroscience, Columbia University, New York, NY 10027, USA. Electronic address: ns2635@columbia.edu.

Abstract

Distributing learning across multiple layers has proven extremely powerful in artificial neural networks. However, little is known about how multi-layer learning is implemented in the brain. Here, we provide an account of learning across multiple processing layers in the electrosensory lobe (ELL) of mormyrid fish and report how it solves problems well known from machine learning. Because the ELL operates and learns continuously, it must reconcile learning and signaling functions without switching its mode of operation. We show that this is accomplished through a functional compartmentalization within intermediate layer neurons in which inputs driving learning differentially affect dendritic and axonal spikes. We also find that connectivity based on learning rather than sensory response selectivity assures that plasticity at synapses onto intermediate-layer neurons is matched to the requirements of output neurons. The mechanisms we uncover have relevance to learning in the cerebellum, hippocampus, and cerebral cortex, as well as in artificial systems.

KEYWORDS:

corollary discharge; dendritic spikes; electric fish; learning; neural networks; synaptic plasticity

PMID:
31735497
DOI:
10.1016/j.cell.2019.10.020

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