Highly Nonrandom Features of Synaptic Connectivity in Local Cortical Circuits

By Sen Song, Per Jesper Sjöström, Markus Reigl, Sacha B. Nelson, Dmitri "Mitya" Chklovskii. 2005

1,602 citations recorded by the source corpus.

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.

Subfields: Cellular and Molecular Neuroscience, Cognitive Neuroscience, Electrical and Electronic Engineering

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Citations in the corpus

Limits to the Development of Feed-Forward Structures in Large Recurrent Neuronal Networks (2011, result): “…behavior and a unimodal distribution of synaptic strengths (van Rossum et al., 2000; Rubin et al., 2001; Gütig et al., 2003; see also Morrison et al., 2008) which is more similar to the distributions observed experimentally (Turrigiano et al., 1998; Sjöström et al., 2001; Song et al., 2005).”
Connectivity reflects coding: a model of voltage-based STDP with homeostasis (2010, methodology): “Using the model (equation (3)) with this setting yielded potentiation.”
Structural Properties of the Caenorhabditis elegans Neuronal Network (2009, methodology): “In addition, we found that motif frequencies in the chemical synapse network are similar to those in the mammalian cortex [68].”
Structured Connectivity in Cerebellar Inhibitory Networks (2014, background): “For instance, a network with a high clustering coefficient may deliver an excess of bidirectional connections, as for the network of layer 5 pyramidal cells in neocortex (Markram et al., 1997; Song et al., 2005).”
A Predictive Network Model of Cerebral Cortical Connectivity Based on a Distance Rule (2013, neutral): “, 2011) and reflects a decrease in the likelihood of synaptic contact with distance (Braitenberg and Schüz, 1998); (2) the lognormal distribution that is observed for interareal weights has also been found for the distribution of synaptic strengths of single neurons (Markov et al., 2012; Song et al.”
Neuronal circuits underlying persistent representations despite time varying activity. (2012, background): “As in cortical networks, the frequency with which a number of motifs occur differs greatly from that expected in a degreematched random network [41, 42].”
Neural Elements for Predictive Coding (2016, background): “There are indications that CC and CS neurons form intermingled local networks and subnets (Song et al., 2005; Perin et al., 2011; Zarrinpar and Callaway, 2016), that may receive selective contacts from separate networks in layer 2/3 (Kampa et al., 2006).”
Sparse Representation of Sounds in the Unanesthetized Auditory Cortex (2008, background): “The distribution of synaptic weights also follows lognormal distribution [38].”
Cortical High-Density Counterstream Architectures (2013, background): “Also, log-normal distributions like that of interareal weights have been observed for the distribution of synaptic strengths of single neurons (109).”
Signal Propagation and Logic Gating in Networks of Integrate-and-Fire Neurons (2005, background): “This corresponds to evoked EPSPs, within the active network, of 8 mV, which is relatively large but not unheard of (Song et al., 2005).”
Spatiotemporal Dynamics of Functional Clusters of Neurons in the Mouse Motor Cortex during a Voluntary Movement (2013, background): “…probabilities of reciprocal connections and/or stronger common synaptic inputs might cause clustered cells to be both more active (i.e., higher ATR) and more frequently covary in their activity (i.e., higher tCC) than nonclustered cells (Song et al., 2005; Yoshimura et al., 2005; Ko et al., 2011).”