Functional organization of excitatory synaptic strength in primary visual cortex

By L. Cossell, M. Iacaruso, Dylan Richard Muir, R. Houlton, Elie N. Sader, H. Ko, Sonja B. Hofer, Thomas D. Mrsic-Flogel. 2015

514 citations recorded by the source corpus.

The strength of synaptic connections fundamentally determines how neurons influence each other’s firing. Excitatory connection amplitudes between pairs of cortical neurons vary over two orders of magnitude, comprising only very few strong connections among many weaker ones. Although this highly skewed distribution of connection strengths is observed in diverse cortical areas, its functional significance remains unknown: it is not clear how connection strength relates to neuronal response properties, nor how strong and weak inputs contribute to information processing in local microcircuits. Here we reveal that the strength of connections between layer 2/3 (L2/3) pyramidal neurons in mouse primary visual cortex (V1) obeys a simple rule—the few strong connections occur between neurons with most correlated responses, while only weak connections link neurons with uncorrelated responses. Moreover, we show that strong and reciprocal connections occur between cells with similar spatial receptive field structure. Although weak connections far outnumber strong connections, each neuron receives the majority of its local excitation from a small number of strong inputs provided by the few neurons with similar responses to visual features. By dominating recurrent excitation, these infrequent yet powerful inputs disproportionately contribute to feature preference and selectivity. Therefore, our results show that the apparently complex organization of excitatory connection strength reflects the similarity of neuronal responses, and suggest that rare, strong connections mediate stimulus-specific response amplification in cortical microcircuits.

Subfields: Cellular and Molecular Neuroscience, Cognitive Neuroscience

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Citations in the corpus

Model-based Bayesian inference of neural activity and connectivity from all-optical interrogation of a neural circuit (2017, background): “Our inferred weights are consistent with studies using electrophysiological means to measure connectivity in mouse V1 [1, 2].”
Predictive Processing: A Canonical Cortical Computation (2018, unknown): “A number of plausible mechanisms have been proposed for the persistence of neural activity, including strong and selective recurrent excitation between coactive neuronal assemblies within the cortex (Cossell et al., 2015; Li et al., 2016; Perin et al., 2011; Song et al., 2005), or via thalamocortica”
Correlations and Neuronal Population Information (2016, background): “…nl y. connectivity is not random but clustered: Neurons receive preferential inputs from other neurons with similar functional properties (Bosking et al. 1997, Cossell et al. 2015), and connections are much stronger between some neurons than between others (Song et al. 2005, Yoshimura et al. 2005).”
Layer-Specific Organization of Local Excitatory and Inhibitory Synaptic Connectivity in the Rat Presubiculum (2017, background): “Previous studies of synaptic amplitudes between pyramidal cells have shown that their distribution is not normal but rather log-normal, exhibiting a heavy tail of large amplitudes (Song et al. 2005; Lefort et al. 2009; Ikegaya et al. 2013; Cossell et al. 2015).”
Recurrent Network Models of Sequence Generation and Memory. (2016, background): “Synaptic distributions measured in slices have been shown to have long tails (Song et al., 2005), and the experimental result in Cossell et al. (2015) has demonstrated that rather than occurring at random, these strong synapses signiﬁcantly contribute to network tuning by preferentially…”
Neural Elements for Predictive Coding (2016, background): “…with other pyramidal cells are known (West et al., 2006); however, it is important to note that the studies considered above (Ko et al., 2011; Cossell et al., 2015) do not definitively establish a pyramidal cell receiving unidirectional input as belonging to a reified ‘unidirectional’ class,…”
Functional selectivity and specific connectivity of inhibitory neurons in primary visual cortex (2018, methodology): “To compute population selectivity, we first calculated the mean population response across all trials: pi = Z ∑ c=1 rcwc (15)”
In Vivo Monosynaptic Excitatory Transmission between Layer 2 Cortical Pyramidal Neurons (2015, background): “Is this input the result of the summation of synchronous small-amplitude uEPSPs or sparse but large-amplitude inputs? Interestingly, in visual cortex, the amplitude of a connection is correlated with the similarity of sensory tuning between two pyramidal neurons (Cossell et al., 2015).”