Optimal degrees of synaptic connectivity

By Ashok Litwin-Kumar, Kameron Decker Harris, Richard Axel, Haim Sompolinsky, L. F. Abbott. 2017

339 citations recorded by the source corpus.

Summary Synaptic connectivity varies widely across neuronal types. Cerebellar granule cells receive five orders of magnitude fewer inputs than the Purkinje cells they innervate, and cerebellum-like circuits including the insect mushroom body also exhibit large divergences in connectivity. In contrast, the number of inputs per neuron in cerebral cortex is more uniform and large. We investigate how the dimension of a representation formed by a population of neurons depends on how many inputs they each receive and what this implies for learning associations. Our theory predicts that the dimensions of the cerebellar granule-cell and Drosophila Kenyon-cell representations are maximized at degrees of synaptic connectivity that match those observed anatomically, showing that sparse connectivity is sometimes superior to dense connectivity. When input synapses are subject to supervised plasticity, however, dense wiring becomes advantageous, suggesting that the type of plasticity exhibited by a set of synapses is a major determinant of connection density.

Subfields: Cellular and Molecular Neuroscience, Cognitive Neuroscience

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Cellular and Molecular Neuroscience

In Cognitive Neuroscience

Citations in the corpus

Re-evaluating circuit mechanisms underlying pattern separation (2019, positive): “Anatomical estimates of the ratio of projection neurons to Kenyon cells in the mushroom body are roughly 1:40 (Laurent, 2002; Litwin-Kumar et al., 2017).”
Morphological Constraints on Cerebellar Granule Cell Combinatorial Diversity (2017, methodology): “2 is the squared mean correlation coefficient of the activities of GrCi and GrCj over all pairwise GrC comparisons, i and j, across all epochs, and <var(GrCi,GrCj)> is the mean variance of all pairwise correlations across all epochs as described in Litwin-Kumar et al., 2017.”
Origins of Cell-Type-Specific Olfactory Processing in the Drosophila Mushroom Body Circuit. (2017, positive): “This functional organization of the MB circuit is advantageous in that it represents odors distinctively in a high-dimensional coding space and thus can enhance odor discriminability (Babadi and Sompolinsky, 2014; Litwin-Kumar et al., 2017).”
Classification and Geometry of General Perceptual Manifolds (2017, background): “It is interesting to compare DM with a well-known measure of an effective dimension of a covariance matrix, the participation ratio [26,27].”
Shared cortex-cerebellum dynamics in the execution and learning of a motor task (2019, positive): “Finally, while the classical theory of granule cell function (Albus, 1971; Marr, 1969) focused on dimensionality expansion, a modern reanalysis of this theory indicates that maximal dimensionality expansion is not always preferable, as such expansion alone can also amplify input noise (Babadi and So”
A Complete Electron Microscopy Volume of the Brain of Adult Drosophila melanogaster (2017, methodology): “Furthermore the logic of PN to KC connectivity ratios has been the subject of detailed experimental and theoretical analysis as a model for the construction of high dimensional sensory representations (e.g., Caron et al., 2013; Litwin-Kumar et al., 2017).”