TY - JOUR T1 - Binary and analog variation of synapses between cortical pyramidal neurons JF - bioRxiv DO - 10.1101/2019.12.29.890319 SP - 2019.12.29.890319 AU - Sven Dorkenwald AU - Nicholas L. Turner AU - Thomas Macrina AU - Kisuk Lee AU - Ran Lu AU - Jingpeng Wu AU - Agnes L. Bodor AU - Adam A. Bleckert AU - Derrick Brittain AU - Nico Kemnitz AU - William M. Silversmith AU - Dodam Ih AU - Jonathan Zung AU - Aleksandar Zlateski AU - Ignacio Tartavull AU - Szi-Chieh Yu AU - Sergiy Popovych AU - William Wong AU - Manuel Castro AU - Chris S. Jordan AU - Alyssa M. Wilson AU - Emmanouil Froudarakis AU - JoAnn Buchanan AU - Marc Takeno AU - Russel Torres AU - Gayathri Mahalingam AU - Forrest Collman AU - Casey Schneider-Mizell AU - Daniel J. Bumbarger AU - Yang Li AU - Lynne Becker AU - Shelby Suckow AU - Jacob Reimer AU - Andreas S. Tolias AU - Nuno Maçarico da Costa AU - R. Clay Reid AU - H. Sebastian Seung Y1 - 2021/01/01 UR - http://biorxiv.org/content/early/2021/12/03/2019.12.29.890319.abstract N2 - Learning from experience depends at least in part on changes in neuronal connections. We present the largest map of connectivity to date between cortical neurons of a defined type (L2/3 pyramidal cells), which was enabled by automated analysis of serial section electron microscopy images with improved handling of image defects. We used the map to identify constraints on the learning algorithms employed by the cortex. Previous cortical studies modeled a continuum of synapse sizes (Arellano et al. 2007) by a log-normal distribution (Loewenstein, Kuras, and Rumpel 2011; de Vivo et al. 2017; Santuy et al. 2018). A continuum is consistent with most neural network models of learning, in which synaptic strength is a continuously graded analog variable. Here we show that synapse size, when restricted to synapses between L2/3 pyramidal cells, is well-modeled by the sum of a binary variable and an analog variable drawn from a log-normal distribution. Two synapses sharing the same presynaptic and postsynaptic cells are known to be correlated in size (Sorra and Harris 1993; Koester and Johnston 2005; Bartol et al. 2015; Kasthuri et al. 2015; Dvorkin and Ziv 2016; Bloss et al. 2018; Motta et al. 2019). We show that the binary variables of the two synapses are highly correlated, while the analog variables are not. Binary variation could be the outcome of a Hebbian or other synaptic plasticity rule depending on activity signals that are relatively uniform across neuronal arbors, while analog variation may be dominated by other influences. We discuss the implications for the stability-plasticity dilemma.Competing Interest StatementTM and HSS disclose financial interests in Zetta AI LLC. JR and AST disclose financial interests in Vathes LLC. ER -