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Sparse synaptic connectivity is required for decorrelation and pattern separation in feedforward networks
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DOI：10.1038/s41467-017-01109-y.png)
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Abstract
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Pattern separation is a fundamental function of the brain. The divergent feedforward networks thought to underlie this computation are widespread, yet exhibit remarkably similar sparse synaptic connectivity. Marr-Albus theory postulates that such networks separate overlapping activity patterns by mapping them onto larger numbers of sparsely active neurons. But spatial correlations in synaptic input and those introduced by network connectivity are likely to compromise performance. To investigate the structural and functional determinants of pattern separation we built models of the cerebellar input layer with spatially correlated input patterns, and systematically varied their synaptic connectivity. Performance was quantified by the learning speed of a classifier trained on either the input or output patterns. Our results show that sparse synaptic connectivity is essential for separating spatially correlated input patterns over a wide range of network activity, and that expansion and correlations, rather than sparse activity, are the major determinants of pattern separation.
Keywords:
CEREBELLAR GRANULE CELLS
OLFACTORY-BULB
ODOR DISCRIMINATION
SENSORY INFORMATION
LATERAL INHIBITION
MUSHROOM BODY
SPIKE TRAINS
GOLGI CELLS
INPUT LAYER
IN-VIVO
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