Inhibitory Coupling Strength Research Articles

A human cortical adaptive mutual inhibition circuit underlying competition for perceptual decision and repetition suppression reversal

A model based on inhibitory coupling has been proposed to explain perceptual oscillations. This 'adapting reciprocal inhibition' model postulates that it is the strength of inhibitory coupling that determines the fate of competition between percepts. Here, we used an fMRI-based adaptation technique to reveal the influence of neighboring neuronal populations, such as reciprocal inhibition, in motion-selective hMT+/V5. If reciprocal inhibition exists in this region, the following predictions should hold: 1. stimulus-driven response would not simply decrease, as predicted by simple repetition-suppression of neuronal populations, but instead, increase due to the activity from adjacent populations; 2. perceptual decision involving competing representations, should reflect decreased reciprocal inhibition by adaptation; 3. neural activity for the competing percept should also later on increase upon adaptation. Our results confirm these three predictions, showing that a model of perceptual decision based on adapting reciprocal inhibition holds true. Finally, they also show that the net effect of the well-known repetition suppression phenomenon can be reversed by this mechanism.

A multiscale brain network model links Alzheimer’s disease-mediated neuronal hyperactivity to large-scale oscillatory slowing

BackgroundNeuronal hyperexcitability and inhibitory interneuron dysfunction are frequently observed in preclinical animal models of Alzheimer’s disease (AD). This study investigates whether these microscale abnormalities explain characteristic large-scale magnetoencephalography (MEG) activity in human early-stage AD patients.MethodsTo simulate spontaneous electrophysiological activity, we used a whole-brain computational network model comprised of 78 neural masses coupled according to human structural brain topology. We modified relevant model parameters to simulate six literature-based cellular scenarios of AD and compare them to one healthy and six contrast (non-AD-like) scenarios. The parameters include excitability, postsynaptic potentials, and coupling strength of excitatory and inhibitory neuronal populations. Whole-brain spike density and spectral power analyses of the simulated data reveal mechanisms of neuronal hyperactivity that lead to oscillatory changes similar to those observed in MEG data of 18 human prodromal AD patients compared to 18 age-matched subjects with subjective cognitive decline.ResultsAll but one of the AD-like scenarios showed higher spike density levels, and all but one of these scenarios had a lower peak frequency, higher spectral power in slower (theta, 4–8Hz) frequencies, and greater total power. Non-AD-like scenarios showed opposite patterns mainly, including reduced spike density and faster oscillatory activity. Human AD patients showed oscillatory slowing (i.e., higher relative power in the theta band mainly), a trend for lower peak frequency and higher total power compared to controls. Combining model and human data, the findings indicate that neuronal hyperactivity can lead to oscillatory slowing, likely due to hyperexcitation (by hyperexcitability of pyramidal neurons or greater long-range excitatory coupling) and/or disinhibition (by reduced excitability of inhibitory interneurons or weaker local inhibitory coupling strength) in early AD.ConclusionsUsing a computational brain network model, we link findings from different scales and models and support the hypothesis of early-stage neuronal hyperactivity underlying E/I imbalance and whole-brain network dysfunction in prodromal AD.

Synchronization of interacted spiking neuronal networks with inhibitory coupling

• The synchronization index (SI) in two large interacted via inhibitory coupling networks oscillates periodically in time; the time intervals of high SI alternates with the time intervals of low SI. • The synchronization indexes in these networks exhibit either inphase or antiphase synchronization depending on the inhibitory coupling strength between them. We suppose that the underlying mechanism behind the antiphase dynamics lies in the cognitive resource redistribution between neuronal ensembles in the brain. • Excitatory coupling between the neurons inside the network affects the synchronization index. To maintain the neural network in the regimes of inphase or antiphase SI oscillations, we should keep a balance between excitatory and inhibitory connections. It suggests that the excitatory and the inhibitory currents should compensate each other. In other words, when one of them increases, the other must be increased too, and vice versa. • The coupling inside the input small network affects antiphase synchronization between two large networks. However, the scenario from positive and negative correlation and back between the large networks are only determined by inhibitory coupling between them. The development of mathematical models to describe neuronal interaction processes in the brain is a challenging task of nonlinear dynamics. Recent advances in biochemistry and neuroscience allow better understanding of biological mechanisms underlying the neuron functioning and synaptic connections between neurons. Moreover, significant progress in brain imaging sheds light on the structure of the brain network and certain aspects of neuronal dynamics. However, dynamical mechanisms leading to synchronization between different brain areas still remain unknown and require further investigation. To shed light on this issue, we consider two small-world networks of Hodgkin-Huxley neurons interacting via inhibitory coupling. We found that synchronization indices (SI) in both networks oscillate periodically in time, so that time intervals of high SI alternate with time intervals of low SI. Depending on the coupling strength, the two coupled networks can be in the regime of either in-phase or anti-phase synchronization. We suppose that the inherent mechanism behind such a behavior lies in the cognitive resource redistribution between neuronal ensembles of the brain.

Effects of Inhibitory Signal on Criticality in Excitatory-Inhibitory Networks**Supported by National Natural Science Foundation of China under Grants No. 11675096, Fundamental Research Funds for the Central Universities under Grant No. GK201702001, and FPALAB-SNNU under Grant No. 16QNGG007

We study the criticality in excitatory-inhibitory networks consisting of excitable elements. We investigate the effects of the inhibitory strength using both numerical simulations and theoretical analysis. We show that the inhibitory strength cannot affect the critical point. The dynamic range is decreased as the inhibitory strength increases. To simulate of decreasing the efficacy of excitation and inhibition which was studied in experiments, we remove excitatory or inhibitory nodes, delete excitatory or inhibitory links, and weaken excitatory or inhibitory coupling strength in critical excitatory-inhibitory network. Decreasing the excitation, the change of the dynamic range is most dramatic as the same as previous experimental results. However, decreasing inhibition has no effect on the criticality in excitatory-inhibitory network.

Statistical mechanics of spike events underlying phase space partitioning and sequence codes in large-scale models of neural circuits.

Cortical circuits operate in an inhibition-dominated regime of spiking activity. Recently, it was found that spiking circuit models in this regime can, despite disordered connectivity and asynchronous irregular activity, exhibit a locally stable dynamics that may be used for neural computation. The lack of existing mathematical tools has precluded analytical insight into this phase. Here we present analytical methods tailored to the granularity of spike-based interactions for analyzing attractor geometry in high-dimensional spiking dynamics. We apply them to reveal the properties of the complex geometry of trajectories of population spiking activity in a canonical model of locally stable spiking dynamics. We find that attractor basin boundaries are the preimages of spike-time collision events involving connected neurons. These spike-based instabilities control the divergence rate of neighboring basins and have no equivalent in rate-based models. They are located according to the disordered connectivity at a random subset of edges in a hypercube representation of the phase space. Iterating backward these edges using the stable dynamics induces a partition refinement on this space that converges to the attractor basins. We formulate a statistical theory of the locations of such events relative to attracting trajectories via a tractable representation of local trajectory ensembles. Averaging over the disorder, we derive the basin diameter distribution, whose characteristic scale emerges from the relative strengths of the stabilizing inhibitory coupling and destabilizing spike interactions. Our study provides an approach to analytically dissect how connectivity, coupling strength, and single-neuron dynamics shape the phase space geometry in the locally stable regime of spiking neural circuit dynamics.

Transition Dynamics of Epileptic Seizures in the Coupled Thalamocortical Network Model

In this paper, based on the two-compartment unidirectionally coupled thalamocortical model network, we investigated the transition dynamics of epileptic seizures, by considering the inhibitory coupling strength from cortical inhibitory interneuronal (IN) population to excitatory pyramidal (PY) neuronal population as the key bifurcation parameter. The results show that in the single compartment thalamocortical model, inner-compartment inhibitory functions of IN can make the system transit from the absence seizures to the tonic oscillations. In the case of two-compartment coupled thalamocortical model network, the inter-compartment inhibitory coupling functions from the first compartment can drive the second compartment to more easily initiate the absence and tonic seizures at the lower inhibitory coupling strengths, respectively. Also, the driven functions can make the amplitudes of these seizures vary irregularly. Detailed investigations reveal that along with the various state transitions, the system consecutively undergoes Hopf bifurcations, fold of cycles bifurcations and torus bifurcations, respectively. In particular, the reinforcing inter-compartment inhibitory coupling function can induce the chaotic dynamics. We highlight the unidirectional coupling functions between two compartments which might give new insights into the propagation and evolution dynamics of epileptic seizures.

Spontaneous formation of ordered waves in chaotic neuronal network with excitory-inhibitory connections

Spiral waves are a particular form of propagating waves, which rotate around a center point known as a rotor. Spiral waves have been found to play an important role in cardiac arrhythmia. Using voltage-sensitive dye imaging, one can find that spiral waves and plannar waves can occur in the mammalian cortex in vivo. The electrode array conduces to discovering that the seizures may manifest as recurrent spiral waves which propagate in the neocortex. However, the formation mechanism of the ordered waves and its potential function in the nervous system remain uncertain. In order to understand the formation mechanism of the ordered waves, we construct a double-layer two-dimensional -network of neuron, which is composed of nearest-neighbor excitatory coupling and long-range inhibitory coupling layers. The inhibitory grid points account for 25% of total number of grid points in the network. We propose a modified Hindmarsh-Rose neuron model to study whether differently ordered waves can occur spontaneously in the chaotic neuronal network evolving from the initial state with a random phase distribution. The numerical simulation results show that when the inhibitory coupling strength is small the spontaneous formation of ordered wave does not generally appear in the network. The larger inhibitory coupling strength, the more easily the system generates an ordered wave for sufficiently large strength of excitatory coupling. The appearance of differently ordered waves is closely related to the initial state of the system and coupling strength. As the excitatory and inhibitory coupling strengths are appropriately selected, the system can spontaneously generate the maze pattern, planar wave, single spiral wave, multiple spiral wave, paired spiral waves rotating in the opposite directions, two-arm spiral wave, target wave and inward square wave and so on. The probability for spontaneously forming a single spiral wave is far less than that for forming a small spiral wave. The occurrence probabilities of spiral wave, maze pattern and inward square wave reach 27.5%, 21.5% and 10%, respectively. The maze pattern is composed of many plane waves with different propagation directions. The occurrence probabilities of other ordered waves are quite small. These results conduce to understanding the self-organization phenomena occurring in the cerebral cortex.

Exploring the role of inhibitory coupling in duplex networks**Project supported by the National Natural Science Foundation of China (Grant Nos. 11565005 and 11365003).

The electrical coupling of myocytes and fibroblasts can play a role in inhibiting electrical impluse propagation in cardiac muscle. To understand the function of fibroblast–myocyte coupling in the aging heart, the spiral-wave dynamics in the duplex networks with inhibitory coupling is numerically investigated by the Bär–Eiswirth model. The numerical results show that the inhibitory coupling can change the wave amplitude, excited phase duration and excitability of the system. When the related parameters are properly chosen, the inhibitory coupling can induce local abnormal oscillation in the system and the Eckhaus instability of the spiral wave. For the dense inhibitory network, the maximal decrement (maximal increment) in the excited phase duration can reach 24.3%(13.4%), whereas the maximal decrement in wave amplitude approaches 28.1%. Upon increasing the inhibitory coupling strength, the system excitability is reduced and even completely suppressed when the interval between grid points in the inhibitory network is small enough. Moreover, the inhibitory coupling can lead to richer phase transition scenarios of the system, such as the transition from a stable spiral wave to turbulence and the transition from a meandering spiral wave to a planar wave. In addition, the self-sustaining planar wave, the unique meandering of spiral wave and inward spiral wave are observed. The physical mechanisms behind the phenomena are analyzed.

Anticipated synchronization in neuronal circuits unveiled by a phase-response-curve analysis.

Anticipated synchronization (AS) is a counterintuitive behavior that has been observed in several systems. When AS occurs in a sender-receiver configuration, the latter can predict the future dynamics of the former for certain parameter values. In particular, in neuroscience AS was proposed to explain the apparent discrepancy between information flow and time lag in the cortical activity recorded in monkeys. Despite its success, a clear understanding of the mechanisms yielding AS in neuronal circuits is still missing. Here we use the well-known phase-response-curve (PRC) approach to study the prototypical sender-receiver-interneuron neuronal motif. Our aim is to better understand how the transitions between delayed to anticipated synchronization and anticipated synchronization to phase-drift regimes occur. We construct a map based on the PRC method to predict the phase-locking regimes and their stability. We find that a PRC function of two variables, accounting simultaneously for the inputs from sender and interneuron into the receiver, is essential to reproduce the numerical results obtained using a Hodgkin-Huxley model for the neurons. On the contrary, the typical approximation that considers a sum of two independent single-variable PRCs fails for intermediate to high values of the inhibitory coupling strength of the interneuron. In particular, it loses the delayed-synchronization to anticipated-synchronization transition.

Modeling the formation and dynamics of cortical waves induced by cholinergic modulation

States of arousal, or consciousness with the brain are regulated largely by the neurotransmitter acetylcholine (ACh). Specifically, ACh is likely responsible for the transition between slow wave sleep (SWS; where ACh is absent) and rapid eye movement sleep or waking states (where ACh is high). Patterns of neural activity within the cerebral cortex corresponding to these states are markedly different. During SWS there are traveling waves of intense activity in the cortex while in other states locally organized stationary patterns occur [1]. From a functional perspective, stationary patterns are likely to be important for working memory and attention dynamics while traveling waves could lead to synaptic renormalization [2]. The mechanism for how changes on the cellular level are translated to patterns on the network level is not understood. In this work we give a model for the action of ACh on a network of neurons of the Hodgkin-Huxley type with a current that is regulated by ACh that induces spike-frequency adaptation (SFA) [3]. The cells are coupled in a center-surround scheme. When SFA is minimal (such as in waking or REM sleep state, high ACh) patterns of activity are localized and easily pinned to regions defined by enhanced recurrent excitation. Increasing the level SFA is present (by increasing ACh), traveling waves of activity naturally arise. Depending on the strength of inhibitory coupling within the network, SFA is able to induce a wide variety of dynamical regimes (Figure ​(Figure1).1). We present a detailed mechanism that shows that the level of inhibition sets the spatial extent of network activity and that SFA defines the temporal scope, which is directly modulated by ACh in the model. These model calculations give unique insights into the role and significance of ACh in determining patterns of cortical activity and functional differences arising from these patterns. Figure 1 An illustration of the dynamics sampled by scannig inhibitory strength,(wi e), and gKs . In this model gKs is increased to simulate decreasing ACh levels. In a general sense, the spatial scope of activity is determined by the excitatory/ inhibitory balance, ...

Effects of reciprocal inhibitory coupling in model neurons

Central pattern generators are neuron networks that produce vital rhythmic motor outputs such as those observed in mastication, walking and breathing. Their activity patterns depend on the tuning of their intrinsic ionic conductances, their synaptic interconnectivity and entrainment by extrinsic neurons. The influence of two commonly found synaptic connectivities – reciprocal inhibition and electrical coupling – are investigated here using a neuron model with subthreshold oscillation capability, in different firing and entrainment regimes. We study the dynamics displayed by a network of a pair of neurons with various firing regimes, coupled by either (i) only reciprocal inhibition or by (ii) electrical coupling first and then reciprocal inhibition. In both scenarios a range of coupling strengths for the reciprocal inhibition is tested, and in general the neuron with the lower firing rate stops spiking for strong enough inhibitory coupling, while the faster neuron remains active. However, in scenario (ii) the originally slower neuron stops spiking at weaker inhibitory coupling strength, suggesting that the electrical coupling introduces an element of instability to the two-neuron network.

The Effect of Inhibitory Neuron on the Evolution Model of Higher-Order Coupling Neural Oscillator Population

We proposed a higher-order coupling neural network model including the inhibitory neurons and examined the dynamical evolution of average number density and phase-neural coding under the spontaneous activity and external stimulating condition. The results indicated that increase of inhibitory coupling strength will cause decrease of average number density, whereas increase of excitatory coupling strength will cause increase of stable amplitude of average number density. Whether the neural oscillator population is able to enter the new synchronous oscillation or not is determined by excitatory and inhibitory coupling strength. In the presence of external stimulation, the evolution of the average number density is dependent upon the external stimulation and the coupling term in which the dominator will determine the final evolution.

Submillisecond Firing Synchrony between Different Subtypes of Cortical Interneurons Connected Chemically But Not Electrically

Synchronous firing is commonly observed in the brain, but its underlying mechanisms and neurobiological meaning remain debated. Most commonly, synchrony is attributed either to electrical coupling by gap junctions or to shared excitatory inputs. In the cerebral cortex and hippocampus, fast-spiking (FS) or somatostatin-containing (SOM) inhibitory interneurons are electrically coupled to same-type neighbors, and each subtype-specific network tends to fire in synchrony. Electrical coupling across subtypes is weak or absent, but SOM-FS and FS-FS pairs are often connected by inhibitory synapses. Theoretical studies suggest that purely inhibitory coupling can also promote synchrony; however, this has not been confirmed experimentally. We recorded from 74 pairs of electrically noncoupled layer 4 interneurons in mouse somatosensory cortex in vitro, and found that tonically depolarized FS-FS and SOM-FS pairs connected by unidirectional or bidirectional inhibitory synapses often fired within 1 ms of each other. Using a novel, jitter-based measure of synchrony, we found that synchrony correlated with inhibitory coupling strength. Importantly, synchrony was resistant to ionotropic glutamate receptors antagonists but was strongly reduced when GABA(A) receptors were blocked, confirming that in our experimental system IPSPs were both necessary and sufficient for synchrony. Submillisecond firing lags emerged in a computer simulation of pairs of spiking neurons, in which the only assumed interaction between neurons was by inhibitory synapses. We conclude that cortical interneurons are capable of synchronizing both within and across subtypes, and that submillisecond coordination of firing can arise by mutual synaptic inhibition alone, with neither shared inputs nor electrical coupling.

Postinhibitory rebound delay and weak synchronization in Hodgkin-Huxley neuronal networks.

Noise-induced weak synchronized oscillatory activities in a globally inhibitory coupled Hodgkin-Huxley neuronal network are studied numerically. A kind of intrinsic delay induced by the postinhibitory rebound is observed and is found to be important in determining the overall frequency of the network. Synchronization occurs in an optimal range of noise intensity with a bell-shaped curve when the inhibitory coupling strength is sufficiently strong. Comparisons with the results for the excitatory coupling are also addressed.

Sound localisation in crickets

Intracellular recordings were made from the auditory interneurons ON1, AN1 and AN2 in the prothoracic ganglion of the cricket Gryllus bimaculatus. Their responses to synthesized calling song (carrier frequency 5 kHz, intensity 40–90 dB SPL), presented monaurally and binaurally via legphones (acoustic trachea cut), were recorded (Fig. 2). These data were then analysed to determine the strength of inhibitory coupling of ON1-ON1, ON1-AN1 and ON1-AN2 (Fig. 3). Inhibitory coupling of ON1-ON1 and ON1-AN1 are relatively independent of stimulus intensity, ON1-ON1 = -0.25 to -0,4 ap ON1a/ap ON1b; ON1-AN1 = -0.4 to -0.55 ap AN1/ap ON1contra. The inhibition of AN2 by ON1 is rather ineffective, maximally -0.13ap AN2/ap ON1contra. The left-right contrast enhancement or gain, due to lateral inhibition via omega neurons, is 1.6–1.9 for the ON1 pair and 2–3.4 for the AN1 pair, over most of the relevant sound intensity range. For the AN2 pair, there is little contrast enhancement except at sound intensities > 80 dB SPL (Table 1).

Organization of Binocular Pathways: Modeling and Data Related to Rivalry.

It is proposed that inputs to binocular cells are gated by reciprocal inhibition between neurons located either in the lateral geniculate nucleus or in layer 4 of striate cortex. The strength of inhibitory coupling in the gating circuitry is modulated by layer 6 neurons, which are the outputs of binocular matching circuitry. If binocular inputs are matched, the inhibition is modulated to be weak, leading to fused vision, whereas if the binocular inputs are unmatched, inhibition is modulated to be strong, leading to rivalrous oscillations. These proposals are buttressed by psychophysical experiments measuring the strength of adaptational aftereffects following exposure to an adapting stimulus visible only intermittently during binocular rivalry.

An astable multivibrator model of binocular rivalry.

The behavior of a neural network model for binocular rivalry is explored through the development of an analogy between it and an electronic astable multivibrator circuit. The model incorporates reciprocal feedback inhibition between signals from the left and the right eyes prior to binocular convergence. The strength of inhibitory coupling determines whether the system undergoes rivalrous oscillations or remains in stable fusion: strong coupling leads to oscillations, weak coupling to fusion. This implies that correlation between spatial patterns presented to the two eyes can affect the strength of binocular inhibition. Finally, computer simulations are presented which show that a reciprocal inhibition model can reproduce the stochastic behavior of rivalry. The model described is a counterexample to claims that reciprocal inhibition models as a class cannot exhibit many of the experimentally observed properties of rivalry.