TY  - JOUR
N2  - How different is local cortical circuitry from a random network? To answer this question, we probed synaptic connections with several hundred simultaneous quadruple whole-cell recordings from layer 5 pyramidal neurons in the rat visual cortex. Analysis of this dataset revealed several nonrandom features in synaptic connectivity. We confirmed previous reports that bidirectional connections are more common than expected in a random network. We found that several highly clustered three-neuron connectivity patterns are overrepresented, suggesting that connections tend to cluster together. We also analyzed synaptic connection strength as defined by the peak excitatory postsynaptic potential amplitude. We found that the distribution of synaptic connection strength differs significantly from the Poisson distribution and can be fitted by a lognormal distribution. Such a distribution has a heavier tail and implies that synaptic weight is concentrated among few synaptic connections. In addition, the strengths of synaptic connections sharing pre- or postsynaptic neurons are correlated, implying that strong connections are even more clustered than the weak ones. Therefore, the local cortical network structure can be viewed as a skeleton of stronger connections in a sea of weaker ones. Such a skeleton is likely to play an important role in network dynamics and should be investigated further.
ID  - discovery167506
UR  - http://dx.doi.org/10.1371/journal.pbio.0030068
PB  - PUBLIC LIBRARY SCIENCE
SN  - 1544-9173
JF  - PLOS BIOL
A1  - Song, S
A1  - Sjostrom, PJ
A1  - Reigl, M
A1  - Nelson, S
A1  - Chklovskii, DB
KW  - DEVELOPING RAT NEOCORTEX
KW  -  LONG-TERM POTENTIATION
KW  -  5 PYRAMIDAL NEURONS
KW  -  VISUAL-CORTEX
KW  -  ESCHERICHIA-COLI
KW  -  COMPLEX NETWORKS
KW  -  FIRING PATTERNS
KW  -  BARREL CORTEX
KW  -  ADULT-RAT
KW  -  CELL
TI  - Highly nonrandom features of synaptic connectivity in local cortical circuits
Y1  - 2005/03/01/
AV  - public
VL  - 3
IS  - 3
N1  - © 2005 Song et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
ER  -