TY - JOUR
T1 - Binary and analog variation of synapses between cortical pyramidal neurons
AU - Dorkenwald, Sven
AU - Turner, Nicholas L.
AU - Macrina, Thomas
AU - Lee, Kisuk
AU - Lu, Ran
AU - Wu, Jingpeng
AU - Bodor, Agnes L.
AU - Bleckert, Adam A.
AU - Brittain, Derrick
AU - Kemnitz, Nico
AU - Silversmith, William M.
AU - Ih, Dodam
AU - Zung, Jonathan
AU - Zlateski, Aleksandar
AU - Tartavull, Ignacio
AU - Yu, Szi Chieh
AU - Popovych, Sergiy
AU - Wong, William
AU - Castro, Manuel
AU - Jordan, Chris S.
AU - Wilson, Alyssa M.
AU - Froudarakis, Emmanouil
AU - Buchanan, Joann
AU - Takeno, Marc M.
AU - Torres, Russel
AU - Mahalingam, Gayathri
AU - Collman, Forrest
AU - Schneider-Mizell, Casey M.
AU - Bumbarger, Daniel J.
AU - Li, Yang
AU - Becker, Lynne
AU - Suckow, Shelby
AU - Reimer, Jacob
AU - Tolias, Andreas S.
AU - da Costa, Nuno Macarico
AU - Clay Reid, R.
AU - Sebastian Seung, H.
N1 - Publisher Copyright:
© Dorkenwald, Turner, Macrina et al.
PY - 2022
Y1 - 2022
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 (layer 2/3 [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.
AB - 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 (layer 2/3 [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.
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U2 - 10.7554/eLife.76120
DO - 10.7554/eLife.76120
M3 - Article
C2 - 36382887
AN - SCOPUS:85142941186
SN - 2050-084X
VL - 11
JO - eLife
JF - eLife
M1 - e76120
ER -