Functional organization of excitatory synaptic strength in primary visual cortex

Abstract

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 [1][2][3][4][5][6][7][8][9] . Although this highly skewed distribution of connection strengths is observed in diverse cortical areas 1-9 , 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 Reprints and permissions information is available at www.nature.com/reprints.

High noise correlation between the functionally connected neurons in emergent V1 microcircuits

“…To 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 our knowledge, this investigation is the first to systematically investigate the connected and unconnected pairs based on correlations (Rsc and CCG) in a microcircuit. This report further corroborates the earlier finding (Cossell et al 2015) that, high Rsc between the strongly connected neurons carries well defined and structured information in microcircuits. However, importantly, Rsc has to be calculated in optimal resolution-windows to extract meaningful information from these microcircuits.…”

“…Recently, in mouse visual cortex, it has been shown that highly correlated (Rsc) neurons are strongly connected to each other (Cossell et al 2015). After computing CCGs that revealed the functional connections, we calculated Rsc for the connected and unconnected pairs in an ensemble (at a specific microcircuit) at different resolution-windows.…”

“…Many investigators (Zohary et al 1994;Graf et al 2011;Hansen et al 2012;Cossell et al 2015) have suggested that high Rsc is directly related to the strength of the connection between neurons. We thus investigated how the probability of the peak (P) in the CCG might be related to Rsc between the same neurons.…”

“…On one hand, many previous studies (in the visual cortex of different species) have reported high Rsc between neurons (Gawne et al 1996;Kohn and Smith 2005;Gutnisky and Dragoi 2008;Cohen and Kohn 2011;Cotton et al 2013;Cossell et al 2015), suggesting that highly correlated neurons may share a great deal of sensory input (Zohary et al 1994;Shadlen and Newsome 1998;Bair et al 2001;Kohn and Smith 2005). On the other hand, decorrelated firing (low Rsc) has also been observed in V1 microcircuits (Ecker et al 2010;Renart etl.…”

“…There is mounting evidence that understanding of information encoding in brain requires studying the correlation (noise correlation or Rsc; cross-correlations or CCG) between neurons (Perkel et al 1967;Alloway and Roy 2002;Bach and Kruger 1986;Zohary et al 1994;Averbeck and Lee 2003;Barthó et al 2004;Uhlhaas et al 2009;Cohen and Kohn 2011;Graf et al 2011;Cotton et al 2013;Cossell et al 2015).…”