Inhibitory Units: An Organizing Nidus for Feature-Selective SubNetworks in Area V1

Abstract

Neuronal circuits often display small-world network architecture characterized by neuronal cliques of dense local connectivity communicating with each other through a limited number of cells that participate in multiple cliques. The principles by which such cliques organize to encode information remain poorly understood. Similarly tuned pyramidal cells that preferentially target each other may form multicellular encoding units performing distinct computational tasks. The existence of such units can reflect upon both spontaneous and stimulus-driven population events. We applied two-photon calcium imaging to study spontaneous population bursts in layer 2/3 of area V1 in male C57BL/6 mice. To identify potential small-world cliques, we searched for pyramidal cells whose calcium events had a consistent temporal relationship with the events of local inhibitory interneurons. This was guided by the intuition that groups of neurons whose synchronous firing represents a temporally coherent computational unit should be inhibited together. Pyramidal members of these interneuron-centered clusters on average displayed stronger functional connectivity between each other than with nonmember pyramidal neurons. The structure of the clusters evolved during postnatal development: cluster size and overlap between clusters decreased with developmental maturation. Pyramidal neurons in a cluster showed higher than chance tuning function similarity between each other and with the linked interneuron. Thus, spontaneous population events in V1 are shaped by small-world subnetworks of pyramidal neurons that share functional properties and work as a coherent unit with a local interneuron. These interneuron-pyramidal cell partnerships may represent a fundamental neocortical unit of computation at the population level.

Correlated somatosensory input in parvalbumin/pyramidal cells in mouse motor cortex

“…Excitatory subnetworks are a set of connected Pyr cells receiving shared interlaminar (excitatory), intralaminar (inhibitory and excitatory) or long-range inputs Morgenstern et al, 2016b), or sharing a single inhibitory cell as a hub (Palagina et al, 2019;. Local connectivity supports the existence of such excitatory subnetworks within small anatomical loci (Faber et al, 2019;Palagina et al, 2019;Vegué et al, 2017;Lee, W. A. et al, 2016;. Further, subnetworks might share common functional properties, such as receptive fields in visual areas (Ko et al, 2011;Ohki et al, 2006;.…”

“…Excitatory subnetworks are a set of connected Pyr cells receiving shared interlaminar (excitatory), intralaminar (inhibitory and excitatory) or long-range inputs (Yoshimura et al, 2005; Morgenstern et al, 2016b), or sharing a single inhibitory cell as a hub (Palagina et al, 2019; Yoshimura and Callaway, 2005). Local connectivity supports the existence of such excitatory subnetworks within small anatomical loci (Faber et al, 2019; Palagina et al, 2019; Vegué et al, 2017; Lee, W. A. et al, 2016; Yoshimura et al, 2005). Further, subnetworks might share common functional properties, such as receptive fields in visual areas (Ko et al, 2011; Ohki et al, 2006; 2005).…”

“…Both vS1 and PO target Pyr excitatory and inhibitory neurons, with layer-specific complementary activation of PV+ and somatostatin (SOM+) inhibitory neurons in vM1 (Okoro et al, 2022). The organizational principles of cortical neurons studied to date in visual (Palagina et al, 2019; Lee et al, 2016; Morgenstern et al, 2016b; Cossell et al, 2015; Wertz et al, 2015; Glickfeld et al, 2013; Bock et al, 2011; Kätzel et al, 2011; Ko et al, 2014; 2013; 2011; Brown and Hestrin, 2009; Song et al, 2005; Yoshimura and Callaway, 2005; Yoshimura et al, 2005; Gonchar and Burkhalter, 2003; Alonso et al, 2001; Dantzker and Callaway, 2000; Alonso and Martinez, 1998), somatosensory (Naka et al, 2019; Hayashi et al, 2018; Kim et al, 2016; Kätzel et al, 2011; Perin et al, 2011; Brown and Hestrin, 2009; Kampa et al, 2006; Shepherd and Svoboda, 2005; Shepherd et al, 2005; Gibson, J. R. et al, 1999), auditory (Ji et al, 2016; Li et al, 2014; Levy and Reyes, 2012), frontal (Kells et al, 2019; Morishima et al, 2017; 2011; 2006; Kiritani et al, 2012; Kätzel et al, 2011; Hira et al, 2013; Komiyama et al, 2010; Otsuka and Kawaguchi, 2009; 2008; Brown and Hestrin, 2009) and prefrontal (Lee et al, 2014; Wang et al, 2006) cortices suggest existence of subnetworks, a small number of neurons that have higher than random probability of connecting to each other compared to the surrounding cells (Vegué et al, 2017; Buxhoeveden and Casanova, 2002; Mountcastle, 1997). Subnetworks may also share common excitatory inputs or long-range targets (Perin et al, 2013; Yoshimura and Callaway, 2005; Yoshimura et al, 2005; Brown and Hestrin, 2009; Wang et al, 2006).…”

Scale-Free Dynamics in Animal Groups and Brain Networks

“…Yet, it is currently not known how the heterogeneity of cell types, layers and areas contribute to scale-free correlation lengths measured in the awake brain at macro-, meso-, and microscale. In a first attempt to address this issue, we studied functional subnetworks in cortical circuits, such as the one formed by orientation selective, i.e., “tuned” cells with similar tuning preference in V1 (Palagina et al, 2019 ). When separately analyzing tuned and non-tuned cells, despite significant changes in the absolute value of correlation changes (evidencing the different structure present in these subgroups), we were able to show that scale-free correlations are present along the tuning dimension (Ribeiro et al, 2020 ).…”

Scale-free dynamics in animal groups and brain networks

“…In a first attempt to address this issue, we studied functional subnetworks in cortical circuits, such as the one formed by orientation selective, i.e. "tuned" cells with similar tuning preference in V1 (Palagina et al, 2019). When separately analyzing tuned and non-tuned cells, despite (Cavagna et al, 2010).…”