Binary and analog variation of synapses between cortical pyramidal neurons

Abstract

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 in mouse primary visual cortex), which was enabled by automated analysis of serial section electron microscopy images with improved handling of image defects (250×140×90 μm3 volume). We used the map to identify constraints on the learning algorithms employed by the cortex. Previous cortical studies modeled a continuum of synapse sizes by a log-normal distribution. 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 . 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 such as spontaneous dynamical fluctuations. We discuss the implications for the longstanding hypothesis that activity-dependent plasticity switches synapses between bistable states.

Skewed distribution of spines is independent of presynaptic transmitter release and synaptic plasticity and emerges early during adult neurogenesis

“…However, our present study and previously published data (Hazan & Ziv, 2020; Sando et al, 2017; Sigler et al, 2017, Kleinjan et al, 2023) show clearly that synaptic activity is not necessary for the emergence of large spines (Ziv & Brenner, 2018). In line with this, the diversity of spine types – in terms of fractions of mushroom, stubby and thin spines – is not affected in mice with a complete suppression of synaptic transmitter release from glutamatergic neurons upon Cre-inducible expression of tetanus toxin (Dorkenwald et al, 2019; Sando et al, 2017). Consistently, spinogenesis in CA1 PCs has been shown to be independent of the activation of ionotropic glutamate receptors (Lu et al, 2013), although their numbers might be modulated by the lack of activity (Sigler et al, 2017; Hazan and Ziv, 2020).…”

Ubiquitous lognormal distribution of neuron densities in mammalian cerebral cortex

“…Based on the sample histograms (Figure 1) we hypothesize that 𝜌 follows a lognormal distribution, similar to many other neuroanatomical and physiological variables (Buzsáki and Mizuseki, 2014) such as synaptic strengths (Robinson et al, 2021), synapse sizes (Loewenstein et al, 2011;Santuy et al, 2018;Dorkenwald et al, 2022), axonal widths (Wang et al, 2008;Liewald et al, 2014), and cortico-cortical connection densities (Markov et al, 2014;Gămănuţ et al, 2018). We used neuron density data from mouse (Mus musculus), marmoset (Callithrix jacchus), macaque (Macaca mulatta), human (Homo sapiens), galago (Otolemur garnettii), owl monkey (Aotus nancymaae), and baboon (Papio cynocephalus anubis) to test this hypothesis (see Cell density data for a detailed description of the data).…”

Topology recapitulates ontogeny of dendritic arbors

“…Methods for MICrONS dataset analysis Spine data was recovered from an electron microscopy dataset on layer 2/3 of the mouse visual cortex generated by the MICrONS program. 301 publicly available neuron reconstructions without spines (Turner et al, 2022) were cross-referenced with ~3.2 million automatically identified synapses from the same volume (Dorkenwald et al, 2022). As the synapse dataset is known to contain false-positives, synapses that would imply a spine length of greater than 4 μm were excluded from our analysis.…”