Sparse synaptic connectivity is required for decorrelation and pattern separation in feedforward networks
By N. A. Cayco-Gajic, Claudia Clopath, R. Silver. 2017
153 citations recorded by the source corpus.
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. Input decorrelation, expansion recoding and sparse activity have been proposed to separate overlapping activity patterns in feedforward networks. Here the authors use reduced and detailed spiking models to elucidate how synaptic connectivity affects the contribution of these mechanisms to pattern separation in cerebellar cortex.
Subfields: Cognitive Neuroscience, Neurology
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Citations in the corpus
- Re-evaluating circuit mechanisms underlying pattern separation (2019, positive): “Second, substantial theoretical work based on Marr-Albus theory has shown that the circuitry of the input layer of the cerebellar cortex is well suited for pattern separation (Marr, 1969; Albus, 1971; Kanerva, 1988; Tyrrell and Willshaw, 1992; Billings et al., 2014; Cayco-Gajic et al., 2017; Litwin-”
- Computational Principles of Supervised Learning in the Cerebellum (2018, unknown): “…separation; the number of mossy fiber inputs per granule cell is low, which should support decorrelation of spatially overlapping mossy fiber patterns and the generation of high-dimensional representations in granule cells (Billings et al. 2014, Cayco-Gajic et al. 2017, Litwin-Kumar et al. 2017).”
- Shared cortex-cerebellum dynamics in the execution and learning of a motor task (2019, unknown): “This basic, conserved anatomical feature is thought to allow the GrC layer to produce outputs highly distinct from those of cortex (Albus, 1971; Babadi and Sompolinsky, 2014; Billings et al., 2014; Cayco-Gajic et al., 2017; Chabrol et al., 2015; Litwin-Kumar et al., 2017; Marr, 1969).”