Sparse synaptic connectivity is required for decorrelation and pattern separation in feedforward networks

N Alex Cayco-Gajic,R Angus Silver,Claudia Clopath

doi:10.1038/s41467-017-01109-y

N Alex Cayco-Gajic, R Angus Silver + Show 1 more

Open Access

https://doi.org/10.1038/s41467-017-01109-y

Copy DOI

Abstract

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.

Highlights

Pattern separation is a fundamental function of the brain
The cerebellar input layer consists of mossy fibers (MFs), which form large en passant mossy-type presynaptic stuctures called rosettes, granule cells (GCs) which have ~4 short dendrites, and inhibitory Golgi cells which form an extensive dense axonal arbor spanning the local region
To explore how synaptic connectivity and input correlations affect pattern separation we varied the number of synaptic connections per GC (Nsyn) in the model and presented the networks with different activity patterns while varying the fraction of active MFs and σ

Summary

Introduction

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. Other divergent feedforward networks have relatively few synapses: granule cells in the dorsal cochlear nucleus have 2–3 dendrites[16] while Kenyon cells in the fly olfactory system have around 7 synaptic inputs[17] This raises the question of why the synaptic connectivity of these networks is so similar. Marr-Albus theory posits that sparse coding and expansion recoding together reduce pattern overlap[1, 2, 7], while more recent work highlights the importance of input decorrelation[8, 10, 11, 20,21,22,23,24] It is not known how much each factor separately contributes to pattern separation and learning, or how they depend on network structure. Theoretical studies have generally focused on idealized, Downstream decoder a

Methods

Results

Conclusion