Networks Of Inhibitory Neurons Research Articles

Functional networks of inhibitory neurons orchestrate synchrony in the hippocampus.

Inhibitory interneurons are pivotal components of cortical circuits. Beyond providing inhibition, they have been proposed to coordinate the firing of excitatory neurons within cell assemblies. While the roles of specific interneuron subtypes have been extensively studied, their influence on pyramidal cell synchrony in vivo remains elusive. Employing an all-optical approach in mice, we simultaneously recorded hippocampal interneurons and pyramidal cells and probed the network influence of individual interneurons using optogenetics. We demonstrate that CA1 interneurons form a functionally interconnected network that promotes synchrony through disinhibition during awake immobility, while preserving endogenous cell assemblies. Our network model underscores the importance of both cell assemblies and dense, unspecific interneuron connectivity in explaining our experimental findings, suggesting that interneurons may operate not only via division of labor but also through concerted activity.

Transcriptomic cell-type specificity of local cortical circuits.

Complex neocortical functions rely on networks of diverse excitatory and inhibitory neurons. While local connectivity rules between major neuronal subclasses have been established, the specificity of connections at the level of transcriptomic subtypes remains unclear. We introduce single transcriptome assisted rabies tracing (START), a method combining monosynaptic rabies tracing and single-nuclei RNA sequencing to identify transcriptomic cell types, providing inputs to defined neuron populations. We employ START to transcriptomically characterize inhibitory neurons providing monosynaptic input to 5 different layer-specific excitatory cortical neuron populations in mouse primary visual cortex (V1). At the subclass level, we observe results consistent with findings from prior studies that resolve neuronal subclasses using antibody staining, transgenic mouse lines, and morphological reconstruction. With improved neuronal subtype granularity achieved with START, we demonstrate transcriptomic subtype specificity of inhibitory inputs to various excitatory neuron subclasses. These results establish local connectivity rules at the resolution of transcriptomic inhibitory cell types.

The recovery of parabolic avalanches in spatially subsampled neuronal networks at criticality

Scaling relationships are key in characterizing complex systems at criticality. In the brain, they are evident in neuronal avalanches—scale-invariant cascades of neuronal activity quantified by power laws. Avalanches manifest at the cellular level as cascades of neuronal groups that fire action potentials simultaneously. Such spatiotemporal synchronization is vital to theories on brain function yet avalanche synchronization is often underestimated when only a fraction of neurons is observed. Here, we investigate biases from fractional sampling within a balanced network of excitatory and inhibitory neurons with all-to-all connectivity and critical branching process dynamics. We focus on how mean avalanche size scales with avalanche duration. For parabolic avalanches, this scaling is quadratic, quantified by the scaling exponent, χ = 2, reflecting rapid spatial expansion of simultaneous neuronal firing over short durations. However, in networks sampled fractionally, χ is significantly lower. We demonstrate that applying temporal coarse-graining and increasing a minimum threshold for coincident firing restores χ = 2, even when as few as 0.1% of neurons are sampled. This correction crucially depends on the network being critical and fails for near sub- and supercritical branching dynamics. Using cellular 2-photon imaging, our approach robustly identifies χ = 2 over a wide parameter regime in ongoing neuronal activity from frontal cortex of awake mice. In contrast, the common ‘crackling noise’ approach fails to determine χ under similar sampling conditions at criticality. Our findings overcome scaling bias from fractional sampling and demonstrate rapid, spatiotemporal synchronization of neuronal assemblies consistent with scale-invariant, parabolic avalanches at criticality.

A mean-field model of gamma-frequency oscillations in networks of excitatory and inhibitory neurons.

Gamma oscillations are widely seen in the cerebral cortex in different states of the wake-sleep cycle and are thought to play a role in sensory processing and cognition. Here, we study the emergence of gamma oscillations at two levels, in networks of spiking neurons, and a mean-field model. At the network level, we consider two different mechanisms to generate gamma oscillations and show that they are best seen if one takes into account the synaptic delay between neurons. At the mean-field level, we show that, by introducing delays, the mean-field can also produce gamma oscillations. The mean-field matches the mean activity of excitatory and inhibitory populations of the spiking network, as well as their oscillation frequencies, for both mechanisms. This mean-field model of gamma oscillations should be a useful tool to investigate large-scale interactions through gamma oscillations in the brain.

Emergent properties in a V1-inspired network of Hodgkin–Huxley neurons

This article is devoted to the theoretical and numerical analysis of a network of excitatory and inhibitory neurons of Hodgkin–Huxley (HH) type, for which the topology is inspired by that of a single local layer of visual cortex V1. Our model relies on recent work in this area and thus combines a stochastic drive – which may be interpreted as an ambient drive for each neuron – with recurrent inputs resulting from the network activity. After a review of the dynamics of a single HH equation for both the deterministic and the stochastically driven case, we proceed to an analysis of the network. This analysis reveals emergent properties of the system such as partial synchronization and synchronization (defined here as a state of the network for which all the neurons spike within a short interval of time), correlation between excitatory and inhibitory conductances, and oscillations in the Gamma-band frequency. The collective behavior enumerated herein is observed when the input-amplitude parameter SEE measuring excitatory-to-excitatory coupling (recurrent excitation) increases to within a certain range. Of note, our work indicates a distinct mechanism for obtaining the emergent properties, some of which have been classically observed. As a consequence our article contributes to the understanding of how assemblies of inhibitory and excitatory cells interact together to produce rhythms in the network. It also aims to bring problems from neuroscience to the realm of mathematics, where they can be analyzed rigorously.

Signal transmission and energy consumption in excitatory–inhibitory cortical neuronal network

Signal transmission and energy consumption in excitatory–inhibitory cortical neuronal network

Epilepsy is more than a simple seizure disorder: Causal relationships between epilepsy and its comorbidities

This review draws connections between the pathogenesis of canine epilepsy and its most commonly recognised comorbidities: cognitive impairment (CI), attention deficit hyperactivity disorder (ADHD)-like behaviour, fear and anxiety. Uni/bidirectional causalities and the possibility of a common aetiology triggering both epilepsy and the associated diseases are considered. Research on this topic is sparse in dogs, so information has been gathered and assessed from human and laboratory animal studies. Anatomical structures, functional connections, disrupted neurotransmission and neuroinflammatory processes collectively serve as a common foundation for epilepsy and its comorbidities. Specific anatomical structures, especially parts of the limbic system, such as the amygdala and the hippocampus, are involved in generating seizures, as well as cognitive- and behavioural disorders. Furthermore, disturbances in inhibitory and excitatory neurotransmission influence neuronal excitability and networks, leading to underlying brain dysfunction. Functional magnetic resonance imaging (fMRI), interictal epileptiform discharges (IEDs), and electroencephalography (EEG) have demonstrated functional brain connections that are related to the emergence of both epilepsy and its various comorbidities. Neuroinflammatory processes can either cause or be a consequence of seizures, and inflammatory mediators, oxidative stress and mitochondrial dysfunction, can equally evoke mood disorders. The extensive relationships contributing to the development and progression of seizures and comorbid cognitive and behavioural conditions illustrate the complexity of the disease that is epilepsy.

Chaotic dynamics in spatially distributed neuronal networks generate population-wide shared variability.

Neural activity in the cortex is highly variable in response to repeated stimuli. Population recordings across the cortex demonstrate that the variability of neuronal responses is shared among large groups of neurons and concentrates in a low dimensional space. However, the source of the population-wide shared variability is unknown. In this work, we analyzed the dynamical regimes of spatially distributed networks of excitatory and inhibitory neurons. We found chaotic spatiotemporal dynamics in networks with similar excitatory and inhibitory projection widths, an anatomical feature of the cortex. The chaotic solutions contain broadband frequency power in rate variability and have distance-dependent and low-dimensional correlations, in agreement with experimental findings. In addition, rate chaos can be induced by globally correlated noisy inputs. These results suggest that spatiotemporal chaos in cortical networks can explain the shared variability observed in neuronal population responses.

Unitary synaptic responses of parvalbumin interneurons evoked by excitatory neurons in the mouse barrel cortex.

The mammalian cortex integrates and processes information to transform sensory inputs into perceptions and motor outputs. These operations are performed by networks of excitatory and inhibitory neurons distributed through the cortical layers. Parvalbumin interneurons (PVIs) are the most abundant type of inhibitory cortical neuron. With axons projecting within and between layers, PVIs supply feedforward and feedback inhibition to control and modulate circuit function. Distinct populations of excitatory neurons recruit different PVI populations, but the specializations of these synapses are poorly understood. Here, we targeted a genetically encoded hybrid voltage sensor to PVIs and used fluorescence imaging in mouse somatosensory cortex slices to record their voltage changes. Stimulating a single visually identified excitatory neuron with small-tipped theta-glass electrodes depolarized multiple PVIs, and a common threshold suggested that stimulation elicited unitary synaptic potentials in response to a single excitatory neuron. Excitatory neurons depolarized PVIs in multiple layers, with the most residing in the layer of the stimulated neuron. Spiny stellate cells depolarized PVIs more strongly than pyramidal cells by up to 77%, suggesting a greater role for stellate cells in recruiting PVI inhibition and controlling cortical computations. Response half-width also varied between different excitatory inputs. These results demonstrate functional differences between excitatory synapses on PVIs.

Patterns of synchronization in 2D networks of inhibitory neurons.

Neural firing in many inhibitory networks displays synchronous assembly or clustered firing, in which subsets of neurons fire synchronously, and these subsets may vary with different inputs to, or states of, the network. Most prior analytical and computational modeling of such networks has focused on 1D networks or 2D networks with symmetry (often circular symmetry). Here, we consider a 2D discrete network model on a general torus, where neurons are coupled to two or more nearest neighbors in three directions (horizontal, vertical, and diagonal), and allow different coupling strengths in all directions. Using phase model analysis, we establish conditions for the stability of different patterns of clustered firing behavior in the network. We then apply our results to study how variation of network connectivity and the presence of heterogeneous coupling strengths influence which patterns are stable. We confirm and supplement our results with numerical simulations of biophysical inhibitory neural network models. Our work shows that 2D networks may exhibit clustered firing behavior that cannot be predicted as a simple generalization of a 1D network, and that heterogeneity of coupling can be an important factor in determining which patterns are stable.

Regimes and mechanisms of transient amplification in abstract and biological neural networks.

Neuronal networks encode information through patterns of activity that define the networks’ function. The neurons’ activity relies on specific connectivity structures, yet the link between structure and function is not fully understood. Here, we tackle this structure-function problem with a new conceptual approach. Instead of manipulating the connectivity directly, we focus on upper triangular matrices, which represent the network dynamics in a given orthonormal basis obtained by the Schur decomposition. This abstraction allows us to independently manipulate the eigenspectrum and feedforward structures of a connectivity matrix. Using this method, we describe a diverse repertoire of non-normal transient amplification, and to complement the analysis of the dynamical regimes, we quantify the geometry of output trajectories through the effective rank of both the eigenvector and the dynamics matrices. Counter-intuitively, we find that shrinking the eigenspectrum’s imaginary distribution leads to highly amplifying regimes in linear and long-lasting dynamics in nonlinear networks. We also find a trade-off between amplification and dimensionality of neuronal dynamics, i.e., trajectories in neuronal state-space. Networks that can amplify a large number of orthogonal initial conditions produce neuronal trajectories that lie in the same subspace of the neuronal state-space. Finally, we examine networks of excitatory and inhibitory neurons. We find that the strength of global inhibition is directly linked with the amplitude of amplification, such that weakening inhibitory weights also decreases amplification, and that the eigenspectrum’s imaginary distribution grows with an increase in the ratio between excitatory-to-inhibitory and excitatory-to-excitatory connectivity strengths. Consequently, the strength of global inhibition reveals itself as a strong signature for amplification and a potential control mechanism to switch dynamical regimes. Our results shed a light on how biological networks, i.e., networks constrained by Dale’s law, may be optimised for specific dynamical regimes.

Modeling cortical synaptic effects of anesthesia and their cholinergic reversal.

General anesthetics work through a variety of molecular mechanisms while resulting in the common end point of sedation and loss of consciousness. Generally, the administration of common anesthetics induces reduction in synaptic excitation while promoting synaptic inhibition. Exogenous modulation of the anesthetics’ synaptic effects can help determine the neuronal pathways involved in anesthesia. For example, both animal and human studies have shown that exogenously induced increases in acetylcholine in the brain can elicit wakeful-like behavior despite the continued presence of the anesthetic. However, the underlying mechanisms of anesthesia reversal at the cellular level have not been investigated. Here we apply a computational model of a network of excitatory and inhibitory neurons to simulate the network-wide effects of anesthesia, due to changes in synaptic inhibition and excitation, and their reversal by cholinergic activation through muscarinic receptors. We use a differential evolution algorithm to fit model parameters to match measures of spiking activity, neuronal connectivity, and network dynamics recorded in the visual cortex of rodents during anesthesia with desflurane in vivo. We find that facilitating muscarinic receptor effects of acetylcholine on top of anesthetic-induced synaptic changes predicts the reversal of anesthetic suppression of neurons’ spiking activity, functional connectivity, as well as pairwise and population interactions. Thus, our model predicts a specific neuronal mechanism for the cholinergic reversal of anesthesia consistent with experimental behavioral observations.

Mechanism of carbachol-induced 40 Hz gamma oscillations and the effects of NMDA activation on oscillatory dynamics in a model of the CA3 subfield of the hippocampus

Gamma oscillations are a prominent feature of various neural systems, including the CA3 subfield of the hippocampus. In CA3, in vitro carbachol application induces ∼40 Hz gamma oscillations in the network of glutamatergic excitatory pyramidal neurons (PNs) and local GABAergic inhibitory neurons (INs). Activation of NMDA receptors within CA3 leads to an increase in the frequency of carbachol-induced oscillations to ∼60 Hz, a broadening of the distribution of individual oscillation cycle frequencies, and a decrease in the time lag between PN and IN spike bursts. In this work, we develop a biophysical integrate-and-fire model of the CA3 subfield, we show that the dynamics of our model are in concordance with physiological observations, and we provide computational support for the hypothesis that the ‘E-I’ mechanism is responsible for the emergence of ∼40 Hz gamma oscillations in the absence of NMDA activation. We then incorporate NMDA receptors into our CA3 model, and we show that our model exhibits the increase in gamma oscillation frequency, broadening of the cycle frequency distribution, and decrease in the time lag between PN and IN spike bursts observed experimentally. Remarkably, we find an inverse relationship in our model between the net NMDA current delivered to PNs and INs in an oscillation cycle and cycle frequency. Furthermore, we find a disparate effect of NMDA receptors on PNs versus INs – we show that NMDA receptors on INs tend to increase oscillation frequency, while NMDA receptors on PNs tend to slightly decrease or not affect oscillation frequency. We find that these observations can be explained if NMDA activity above a threshold level causes a shift in the mechanism underlying gamma oscillations; in the absence of NMDA receptors, the ‘E-I’ mechanism is primarily responsible for the generation of gamma oscillations (at 40 Hz), while when NMDA receptors are active, the mechanism of gamma oscillations shifts to the ‘I-I’ mechanism, and we argue that within the ‘I-I’ regime (which displays a higher baseline oscillation frequency of ∼60 Hz), slight changes in the level of NMDA activity are inversely related to cycle frequency.

Interlayer Connectivity Affects the Coherence Resonance and Population Activity Patterns in Two-Layered Networks of Excitatory and Inhibitory Neurons.

The firing patterns of neuronal populations often exhibit emergent collective oscillations, which can display substantial regularity even though the dynamics of individual elements is very stochastic. One of the many phenomena that is often studied in this context is coherence resonance, where additional noise leads to improved regularity of spiking activity in neurons. In this work, we investigate how the coherence resonance phenomenon manifests itself in populations of excitatory and inhibitory neurons. In our simulations, we use the coupled FitzHugh-Nagumo oscillators in the excitable regime and in the presence of neuronal noise. Formally, our model is based on the concept of a two-layered network, where one layer contains inhibitory neurons, the other excitatory neurons, and the interlayer connections represent heterotypic interactions. The neuronal activity is simulated in realistic coupling schemes in which neurons within each layer are connected with undirected connections, whereas neurons of different types are connected with directed interlayer connections. In this setting, we investigate how different neurophysiological determinants affect the coherence resonance. Specifically, we focus on the proportion of inhibitory neurons, the proportion of excitatory interlayer axons, and the architecture of interlayer connections between inhibitory and excitatory neurons. Our results reveal that the regularity of simulated neural activity can be increased by a stronger damping of the excitatory layer. This can be accomplished with a higher proportion of inhibitory neurons, a higher fraction of inhibitory interlayer axons, a stronger coupling between inhibitory axons, or by a heterogeneous configuration of interlayer connections. Our approach of modeling multilayered neuronal networks in combination with stochastic dynamics offers a novel perspective on how the neural architecture can affect neural information processing and provide possible applications in designing networks of artificial neural circuits to optimize their function via noise-induced phenomena.

Power-law statistics of synchronous transition in inhibitory neuronal networks

We investigate the relationship between the synchronous transition and the power law behavior in spiking networks which are composed of inhibitory neurons and balanced by dc current. In the region of the synchronous transition, the avalanche size and duration distribution obey a power law distribution. We demonstrate the robustness of the power law for event sizes at different parameters and multiple time scales. Importantly, the exponent of the event size and duration distribution can satisfy the critical scaling relation. By changing the network structure parameters in the parameter region of transition, quasicriticality is observed, that is, critical exponents depart away from the criticality while still hold approximately to a dynamical scaling relation. The results suggest that power law statistics can emerge in networks composed of inhibitory neurons when the networks are balanced by external driving signal.

Synchronization Through Uncorrelated Noise in Excitatory-Inhibitory Networks.

Gamma rhythms play a major role in many different processes in the brain, such as attention, working memory, and sensory processing. While typically considered detrimental, counterintuitively noise can sometimes have beneficial effects on communication and information transfer. Recently, Meng and Riecke showed that synchronization of interacting networks of inhibitory neurons in the gamma band (i.e., gamma generated through an ING mechanism) increases while synchronization within these networks decreases when neurons are subject to uncorrelated noise. However, experimental and modeling studies point towardz an important role of the pyramidal-interneuronal network gamma (PING) mechanism in the cortex. Therefore, we investigated the effect of uncorrelated noise on the communication between excitatory-inhibitory networks producing gamma oscillations via a PING mechanism. Our results suggest that, at least in a certain range of noise strengths and natural frequency differences between the regions, synaptic noise can have a supporting role in facilitating inter-regional communication, similar to the ING case for a slightly larger parameter range. Furthermore, the noise-induced synchronization between networks is generated via a different mechanism than when synchronization is mediated by strong synaptic coupling. Noise-induced synchronization is achieved by lowering synchronization within networks which allows the respective other network to impose its own gamma rhythm resulting in synchronization between networks.

Poisson-distributed noise induces cortical γ-activity: explanation of γ-enhancement by anaesthetics ketamine and propofol

Additive noise is known to affect the stability of nonlinear systems. To understand better the role of additive noise in neural systems, we investigate the impact of additive noise on a random neural network of excitatory and inhibitory neurons. Here we hypothesize that the noise originates from the ascending reticular activating system. Coherence resonance in the γ-frequency range emerges for intermediate noise levels while the network exhibits non-coherent activity at low and high noise levels. The analytical study of a corresponding mean-field model system explains the resonance effect by a noise-induced phase transition via a saddle-node bifurcation. An analytical study of the linear mean-field systems response to additive noise reveals that the coherent state exhibits a quasi-cycle in the γ-frequency range whose spectral properties are tuned by the additive noise. To illustrate the importance of the work, we show that the quasi-cycle explains γ-enhancement under impact of the anaesthetics ketamine and propofol as a destabilizing effect of the coherent state.

Microbiota-dependent increase in δ-valerobetaine alters neuronal function and is responsible for age-related cognitive decline.

Understanding the physiological origins of age-related cognitive decline is of critical importance given the rising age of the world's population1. Previous work in animal models has established a strong link between cognitive performance and the microbiota2-5, and it is known that the microbiome undergoes profound remodeling in older adults6. Despite growing evidence for the association between age-related cognitive decline and changes in the gut microbiome, the mechanisms underlying such interactions between the brain and the gut are poorly understood. Here, using fecal microbiota transplantation (FMT), we demonstrate that age-related remodeling of the gut microbiota leads to decline in cognitive function in mice and that this impairment can be rescued by transplantation of microbiota from young animals. Moreover, using a metabolomic approach, we found elevated concentrations of δ-valerobetaine, a gut microbiota-derived metabolite, in the blood and brain of aged mice and older adults. We then demonstrated that δ-valerobetaine is deleterious to learning and memory processes in mice. At the neuronal level, we showed that δ-valerobetaine modulates inhibitory synaptic transmission and neuronal network activity. Finally, we identified specific bacterial taxa that significantly correlate with δ-valerobetaine levels in the brain. Based on our findings, we propose that δ-valerobetaine contributes to microbiota-driven brain aging and that the associated mechanisms represent a promising target for countering age-related cognitive decline.

Working Memory: From Neural Activity to the Sentient Mind.

Working memory (WM) is the ability to maintain and manipulate information in the conscious mind over a timescale of seconds. This ability is thought to be maintained through the persistent discharges of neurons in a network of brain areas centered on the prefrontal cortex, as evidenced by neurophysiological recordings in nonhuman primates, though both the localization and the neural basis of WM has been a matter of debate in recent years. Neural correlates of WM are evident in species other than primates, including rodents and corvids. A specialized network of excitatory and inhibitory neurons, aided by neuromodulatory influences of dopamine, is critical for the maintenance of neuronal activity. Limitations in WM capacity and duration, as well as its enhancement during development, can be attributed to properties of neural activity and circuits. Changes in these factors can be observed through training-induced improvements and in pathological impairments. WM thus provides a prototypical cognitive function whose properties can be tied to the spiking activity of brain neurons. © 2021 American Physiological Society. Compr Physiol 11:1-41, 2021.

Neuron-specific spinal cord translatomes reveal a neuropeptide code for mouse dorsal horn excitatory neurons

The spinal dorsal horn harbors a sophisticated and heterogeneous network of excitatory and inhibitory neurons that process peripheral signals encoding different sensory modalities. Although it has long been recognized that this network is crucial both for the separation and the integration of sensory signals of different modalities, a systematic unbiased approach to the use of specific neuromodulatory systems is still missing. Here, we have used the translating ribosome affinity purification (TRAP) technique to map the translatomes of excitatory glutamatergic (vGluT2+) and inhibitory GABA and/or glycinergic (vGAT+ or Gad67+) neurons of the mouse spinal cord. Our analyses demonstrate that inhibitory and excitatory neurons are not only set apart, as expected, by the expression of genes related to the production, release or re-uptake of their principal neurotransmitters and by genes encoding for transcription factors, but also by a differential engagement of neuromodulator, especially neuropeptide, signaling pathways. Subsequent multiplex in situ hybridization revealed eleven neuropeptide genes that are strongly enriched in excitatory dorsal horn neurons and display largely non-overlapping expression patterns closely adhering to the laminar and presumably also functional organization of the spinal cord grey matter.