Cortical Dynamics in Presence of Assemblies of Densely Connected Weight-Hub Neurons

Abstract

Experimental measurements of pairwise connection probability of pyramidal neurons together with the distribution of synaptic weights have been used to construct randomly connected model networks. However, several experimental studies suggest that both wiring and synaptic weight structure between neurons show statistics that differ from random networks. Here we study a network containing a subset of neurons which we call weight-hub neurons, that are characterized by strong inward synapses. We propose a connectivity structure for excitatory neurons that contain assemblies of densely connected weight-hub neurons, while the pairwise connection probability and synaptic weight distribution remain consistent with experimental data. Simulations of such a network with generalized integrate-and-fire neurons display regular and irregular slow oscillations akin to experimentally observed up/down state transitions in the activity of cortical neurons with a broad distribution of pairwise spike correlations. Moreover, stimulation of a model network in the presence or absence of assembly structure exhibits responses similar to light-evoked responses of cortical layers in optogenetically modified animals. We conclude that a high connection probability into and within assemblies of excitatory weight-hub neurons, as it likely is present in some but not all cortical layers, changes the dynamics of a layer of cortical microcircuitry significantly.

Criteria on Balance, Stability, and Excitability in Cortical Networks for Constraining Computational Models

“…Such precise balance may allow inhibition to suppress fluctuations caused by excitatory inputs and limit spike count correlations without compromising excitability (Vogels et al, 2011 ), and may moreover be key to efficient spike coding (Denève and Machens, 2016 ). Further proposals for combining excitability with excitatory-inhibitory balance and stability include so-called balanced amplification due to near-critical eigenvalues in the effective connectivity matrix (Murphy and Miller, 2009 ), amplification of inputs due to non-normality of the effective connectivity (Hennequin et al, 2012 ), structured connectivity featuring interconnected hub neurons (Setareh et al, 2017 ), differential firing rate response curves of excitatory and inhibitory neurons (Pinto et al, 2003 ), and attractor networks with sufficient coupling between the attractors and an inhibition-dominated background (Latham and Nirenberg, 2004 ). Future work may investigate these and further models incorporating mechanisms proposed for excitability and low-rate balanced activity occurring spontaneously or in response to transient stimulation in cortical networks, and test them systematically on the defined criteria.…”

In vitro Cortical Network Firing is Homeostatically Regulated: A Model for Sleep Regulation

“…Supplementary Table 2 shows the parameters used for building the network. In a previous work 57 , we investigated the role of these different network elements in oscillation properties.…”

Excitable neuronal assemblies with adaptation as a building block of brain circuits for velocity-controlled signal propagation

“…Spike-frequency adaptation is responsible for the transition to the dormant mode by progressively changing the gain function ( Fig 3A -bottom) during the active phase of an assembly, and eventually for its termination. By modification of the adaptation parameters of excitatory neurons, we are able to adjust the duration of the activate phase of each assembly [ 34 ].…”

“…The duration of the active phase can be adjusted by modification of adaptation parameters of excitatory neurons [ 34 ]. Each time a neuron fires a spike, several adaptation processes on several time scales are triggered and generate both a spike-triggered current and an increase in firing threshold [ 37 ].…”