Inhibitory neurons exhibit high controlling ability in the cortical microconnectome

Abstract

The brain is a network system in which excitatory and inhibitory neurons keep activity balanced in the highly non-random connectivity pattern of the microconnectome. It is well known that the relative percentage of inhibitory neurons is much smaller than excitatory neurons in the cortex. So, in general, how inhibitory neurons can keep the balance with the surrounding excitatory neurons is an important question. There is much accumulated knowledge about this fundamental question. This study quantitatively evaluated the relatively higher functional contribution of inhibitory neurons in terms of not only properties of individual neurons, such as firing rate, but also in terms of topological mechanisms and controlling ability on other excitatory neurons. We combined simultaneous electrical recording (~2.5 hours) of ~1000 neurons in vitro, and quantitative evaluation of neuronal interactions including excitatory-inhibitory categorization. This study accurately defined recording brain anatomical targets, such as brain regions and cortical layers, by inter-referring MRI and immunostaining recordings. The interaction networks enabled us to quantify topological influence of individual neurons, in terms of controlling ability to other neurons. Especially, the result indicated that highly influential inhibitory neurons show higher controlling ability of other neurons than excitatory neurons, and are relatively often distributed in deeper layers of the cortex. Furthermore, the neurons having high controlling ability are more effectively limited in number than central nodes of k-cores, and these neurons also participate in more clustered motifs. In summary, this study suggested that the high controlling ability of inhibitory neurons is a key mechanism to keep balance with a large number of other excitatory neurons beyond simple higher firing rate. Application of the selection method of limited important neurons would be also applicable for the ability to effectively and selectively stimulate E/I imbalanced disease states.

Early lock-in of structured and specialised information flows during neural development

“…Previous work on the information flow component of computational capacity in neural cell cultures [14][15][16][17][18][19][20] has focussed on the static structure of information flow networks at single points in time. This has mostly taken the form of elucidating properties of the functional networks implied by the information flows.…”

“…The principle challenge which is faced when using the discrete-time estimator is that the curse of dimensionality limits the number of previous time bins that can be used to estimate the historydependent spike rates. All applications of this estimator to spiking data from cell cultures of which the authors are aware [14][15][16][17][18][19] made use of only a single previous bin in the estimation of these rates. This makes it impossible to simultaneously achieve high time-precision and capture the dependence of the spike rate on spikes occurring further back in time.…”

“…Previous applications of TE to spiking data from neural cell cultures [14][15][16][17][18][19][20] made use of this discrete-time formulation of TE. This work was primarily focussed on the directed functional networks implied by the estimated TE values between pairs of nodes which has revealed interesting features of the information flow structure.…”

Inferring Excitatory and Inhibitory Connections in Neuronal Networks

“…For this purpose, the aim of this work is to combine a generalized version of TE [ 15 ], suitable for calcium imaging data, with a proper splitting in local excitatory and inhibitory contributions. This step is fulfilled by defining appropriate subsets of states related to excitation and inhibition, in line with a recent study focusing on the identification of inhibitory and excitatory connections from multi-electrode array data [ 22 ]. This distinction of connections is fundamental to understanding how a small number of inhibitory neurons functionally interplay with a large number of excitatory neurons controlling network dynamics, bursts and synchronization [ 40 , 41 , 42 , 43 ].…”

“…Among these techniques, TE, from information theory, has been shown to be a promising and accurate tool to predict structural interactions between neurons, and it was extensively applied to infer both effective and structural connectivity in small- and large-scale networks [ 15 , 18 , 19 , 20 , 21 ]. In the context of structural interactions inference, TE approaches generalized to account both for excitatory and inhibitory couplings have been developed and tested on in silico data, showing good performance in labelling structural connections [ 18 , 22 ]. However, the major drawbacks of those methods are the need for a double acquisition of network activity, successively blocking and non-blocking inhibitory interactions, the introduction of negative terms in the TE computation, and additional post-analysis based on TE features.…”