Academic literature on the topic 'Excitatory-inhibitory Network Model'

Abstract Acetylcholine (ACh), one of the brain’s most potent neuromodulators, can affect intrinsic neuron properties through blockade of an M-type potassium current. The effect of ACh on excitatory and inhibitory cells with this potassium channel modulates their membrane excitability, which in turn affects their tendency to synchronize in networks. Here, we study the resulting changes in dynamics in networks with inter-connected excitatory and inhibitory populations (E–I networks), which are ubiquitous in the brain. Utilizing biophysical models of E–I networks, we analyze how the network connectivity structure in terms of synaptic connectivity alters the influence of ACh on the generation of synchronous excitatory bursting. We investigate networks containing all combinations of excitatory and inhibitory cells with high (Type I properties) or low (Type II properties) modulatory tone. To vary network connectivity structure, we focus on the effects of the strengths of inter-connections between excitatory and inhibitory cells (E–I synapses and I–E synapses), and the strengths of intra-connections among excitatory cells (E–E synapses) and among inhibitory cells (I-I synapses). We show that the presence of ACh may or may not affect the generation of network synchrony depending on the network connectivity. Specifically, strong network inter-connectivity induces synchronous excitatory bursting regardless of the cellular propensity for synchronization, which aligns with predictions of the PING model. However, when a network’s intra-connectivity dominates its inter-connectivity, the propensity for synchrony of either inhibitory or excitatory cells can determine the generation of network-wide bursting.

In computational neural network models, neurons are usually allowed to excite some and inhibit other neurons, depending on the weight of their synaptic connections. The traditional way to transform such networks into networks that obey Dale's law (i.e., a neuron can either excite or inhibit) is to accompany each excitatory neuron with an inhibitory one through which inhibitory signals are mediated. However, this requires an equal number of excitatory and inhibitory neurons, whereas a realistic number of inhibitory neurons is much smaller. In this letter, we propose a model of nonlinear interaction of inhibitory synapses on dendritic compartments of excitatory neurons that allows the excitatory neurons to mediate inhibitory signals through a subset of the inhibitory population. With this construction, the number of required inhibitory neurons can be reduced tremendously.

We studied the dynamics of a neural network that has both recurrent excitatory and random inhibitory connections. Neurons started to become active when a relatively weak transient excitatory signal was presented and the activity was sustained due to the recurrent excitatory connections. The sustained activity stopped when a strong transient signal was presented or when neurons were disinhibited. The random inhibitory connections modulated the activity patterns of neurons so that the patterns evolved without recurrence with time. Hence, a time passage between the onsets of the two transient signals was represented by the sequence of activity patterns. We then applied this model to represent the trace eyeblink conditioning, which is mediated by the hippocampus. We assumed this model as CA3 of the hippocampus and considered an output neuron corresponding to a neuron in CA1. The activity pattern of the output neuron was similar to that of CA1 neurons during trace eyeblink conditioning, which was experimentally observed.

Inhibitory interneurons shape the spiking characteristics and computational properties of cortical networks. Interneuron subtypes can precisely regulate cortical function but the roles of interneuron subtypes for promoting different regimes of cortical activity remains unclear. Therefore, we investigated the impact of fast spiking and non-fast spiking interneuron subtypes on cortical activity using a network model with connectivity and synaptic properties constrained by experimental data. We found that network properties were more sensitive to modulation of the fast spiking population, with reductions of fast spiking excitability generating strong spike correlations and network oscillations. Paradoxically, reduced fast spiking excitability produced a reduction of global excitation-inhibition balance and features of an inhibition stabilised network, in which firing rates were driven by the activity of excitatory neurons within the network. Further analysis revealed that the synaptic interactions and biophysical features associated with fast spiking interneurons, in particular their rapid intrinsic response properties and short synaptic latency, enabled this state transition by enhancing gain within the excitatory population. Therefore, fast spiking interneurons may be uniquely positioned to control the strength of recurrent excitatory connectivity and the transition to an inhibition stabilised regime. Overall, our results suggest that interneuron subtypes can exert selective control over excitatory gain allowing for differential modulation of global network state.

Müller, Thomas H., D. Swandulla, and H. U. Zeilhofer. Synaptic connectivity in cultured hypothalamic neuronal networks. J. Neurophysiol. 77: 3218–3225, 1997. We have developed a novel approach to analyze the synaptic connectivity of spontaneously active networks of hypothalamic neurons in culture. Synaptic connections were identified by recording simultaneously from pairs of neurons using the whole cell configuration of the patch-clamp technique and testing for evoked postsynaptic current responses to electrical stimulation of one of the neurons. Excitatory and inhibitory responses were distinguished on the basis of their voltage and time dependence. The distribution of latencies between presynaptic stimulation and postsynaptic response showed multiple peaks at regular intervals, suggesting that responses via both monosynaptic and polysynaptic paths were recorded. The probability that an excitatory event is transmitted to another excitatory neuron and results in an above-threshold stimulation was found to be only one in three to four. This low value indicates that in addition to evoked synaptic responses other sources of excitatory drive must contribute to the spontaneous activity observed in these networks. The various types of synaptic connections (excitatory and inhibitory, monosynaptic, and polysynaptic) were counted, and the observations analyzed using a probabilistic model of the network structure. This analysis provides estimates for the ratio of inhibitory to excitatory neurons in the network (1:1.5) and for the ratio of postsynaptic cells receiving input from a single GABAergic or glutamatergic neuron (3:1). The total number of inhibitory synaptic connections was twice that of excitatory connections. Cell pairs mutually connected by an excitatory and an inhibitory synapse occurred significantly more often than predicted by a random process. These results suggests that the formation of neuronal networks in vitro is controlled by cellular mechanisms that favor inhibitory connections in general and specifically enhance the formation of reciprocal connections between pairs of excitatory and inhibitory neurons. These mechanisms may contribute to network formation and function in vivo.

Rhythmic neuronal network activity underlies brain oscillations. To investigate how connected neuronal networks contribute to the emergence of the α-band and to the regulation of Up and Down states, we study a model based on synaptic short-term depression-facilitation with afterhyperpolarization (AHP). We found that the α-band is generated by the network behavior near the attractor of the Up-state. Coupling inhibitory and excitatory networks by reciprocal connections leads to the emergence of a stable α-band during the Up states, as reflected in the spectrogram. To better characterize the emergence and stability of thalamocortical oscillations containing α and δ rhythms during anesthesia, we model the interaction of two excitatory networks with one inhibitory network, showing that this minimal topology underlies the generation of a persistent α-band in the neuronal voltage characterized by dominant Up over Down states. Finally, we show that the emergence of the α-band appears when external inputs are suppressed, while fragmentation occurs at small synaptic noise or with increasing inhibitory inputs. To conclude, α-oscillations could result from the synaptic dynamics of interacting excitatory neuronal networks with and without AHP, a principle that could apply to other rhythms.

Previous work has shown that networks of neurons with two coupled layers of excitatory and inhibitory neurons can reveal oscillatory activity. For example, Börgers and Kopell (2003) have shown that oscillations occur when the excitatory neurons receive a sufficiently large input. A constant drive to the excitatory neurons is sufficient for oscillatory activity. Other studies (Doiron, Chacron, Maler, Longtin, & Bastian, 2003; Doiron, Lindner, Longtin, Maler, & Bastian, 2004) have shown that networks of neurons with two coupled layers of excitatory and inhibitory neurons reveal oscillatory activity only if the excitatory neurons receive correlated input, regardless of the amount of excitatory input. In this study, we show that these apparently contradictory results can be explained by the behavior of a single model operating in different regimes of parameter space. Moreover, we show that adding dynamic synapses in the inhibitory feedback loop provides a robust network behavior over a broad range of stimulus intensities, contrary to that of previous models. A remarkable property of the introduction of dynamic synapses is that the activity of the network reveals synchronized oscillatory components in the case of correlated input, but also reflects the temporal behavior of the input signal to the excitatory neurons. This allows the network to encode both the temporal characteristics of the input and the presence of spatial correlations in the input simultaneously.

We investigate feedback networks containing excitatory and inhibitory neurons. The couplings between the neurons follow a Hebbian rule in which the memory patterns are encoded as cell assemblies of the excitatory neurons. Using disjoint patterns, we study the attractors of this model and point out the importance of mixed states. The latter become dominant at temperatures above 0.25. We use both numerical simulations and an analytic approach for our investigation. The latter is based on differential equations for the activity of the different memory patterns in the network configuration. Allowing the excitatory thresholds to develop dynamic features which correspond to fatigue of individual neurons, we obtain motion in pattern space, the space of all memories. The attractors turn into transients leading to chaotic motion for appropriate values of the dynamical parameters. The motion can be guided by overlaps between patterns, resembling a process of free associative thinking in the absence of any input.

This paper investigates the neural dynamics and gamma oscillation on a complex network with excitatory and inhibitory neurons (E-I network), as such network is ubiquitous in the brain. The system consists of a small-world network of neurons, which are emulated by Izhikevich model. Moreover, mixed Regular Spiking (RS) and Chattering (CH) neurons are considered to imitate excitatory neurons, and Fast Spiking (FS) neurons are used to mimic inhibitory neurons. Besides, the relationship between synchronization and gamma rhythm is explored by adjusting the critical parameters of our model. Experiments visually demonstrate that the gamma oscillations are generated by synchronous behaviors of our neural network. We also discover that the Chattering(CH) excitatory neurons can make the system easier to synchronize.

The nature and origin of the temporal irregularity in the electrical activity of cortical neurons in vivo are not well understood. We consider the hypothesis that this irregularity is due to a balance of excitatory and inhibitory currents into the cortical cells. We study a network model with excitatory and inhibitory populations of simple binary units. The internal feedback is mediated by relatively large synaptic strengths, so that the magnitude of the total excitatory and inhibitory feedback is much larger than the neuronal threshold. The connectivity is random and sparse. The mean number of connections per unit is large, though small compared to the total number of cells in the network. The network also receives a large, temporally regular input from external sources. We present an analytical solution of the mean-field theory of this model, which is exact in the limit of large network size. This theory reveals a new cooperative stationary state of large networks, which we term a balanced state. In this state, a balance between the excitatory and inhibitory inputs emerges dynamically for a wide range of parameters, resulting in a net input whose temporal fluctuations are of the same order as its mean. The internal synaptic inputs act as a strong negative feedback, which linearizes the population responses to the external drive despite the strong nonlinearity of the individual cells. This feedback also greatly stabilizes the system's state and enables it to track a time-dependent input on time scales much shorter than the time constant of a single cell. The spatiotemporal statistics of the balanced state are calculated. It is shown that the autocorrelations decay on a short time scale, yielding an approximate Poissonian temporal statistics. The activity levels of single cells are broadly distributed, and their distribution exhibits a skewed shape with a long power-law tail. The chaotic nature of the balanced state is revealed by showing that the evolution of the microscopic state of the network is extremely sensitive to small deviations in its initial conditions. The balanced state generated by the sparse, strong connections is an asynchronous chaotic state. It is accompanied by weak spatial cross-correlations, the strength of which vanishes in the limit of large network size. This is in contrast to the synchronized chaotic states exhibited by more conventional network models with high connectivity of weak synapses.

Inhibitory GABAergic neurons, although forming a minor proportion of the neuronal population in the central nervous system, have been reported to be crucial for different physiological states of the brain. Among the vast diversity of this neuronal subpopulation, the fast spiking interneurons (FSINs) have been studied in great detail owing to their morphological and physiological attributes and functional correlates. Due to their perisomatic targeting and rapid spiking nature, they have been strongly associated with spike time and gain control of their target neurons in neuronal microcircuits across different regions of the brain. Plastic alterations of neuronal synaptic and intrinsic properties have been associated with learning and memory. However, a vast majority of the studies performed so far pertains to excitatory neurons. Although some recent studies have looked into plasticity of inhibition, little is known about plastic changes in the inhibitory neurons. Owing to the morpho-physiological properties of the FSINs and their massive connectivity, plastic alterations in them can cascade to their connected neuronal microcircuit. The dentate gyrus (DG) forms an important gateway of information for the hippocampus and has been associated with pattern separation. The granule cells which are predominantly known to target interneurons discharge in the gamma frequency range. Hilar interneurons including the FSINs are known to show membrane potential oscillations phase-locked with the extracellularly recorded oscillations. However, the consequent response of a FSIN to repetitive excitatory gamma synaptic bursts presented either in isolation or in association with membrane potential modulations has not received attention. We show that the FSINs of the DG sub field express a robust long lasting decrease in intrinsic excitability after experiencing bursts of synaptic stimulation of the mossy fiber pathway at gamma frequency (30 Hz), repeated at delta (2 Hz) or theta frequency (4 Hz). Interestingly, the GCs did not express any plasticity of intrinsic excitability upon experiencing similar gamma bursts repeated at delta frequency. The change in intrinsic excitability in the FSINs was observed to be strongly dependent on the somatic current supplement that altered the membrane potential in phase with the synaptic gamma bursts. The plasticity was found to be dependent on the post synaptic calcium flux through the calcium-permeable AMPA receptors (CP-AMPARs) and also on post synaptic HCN channel conductance. Further, decreased excitability in the FSINs exhibited decreased inhibition in the post-synaptic putative granule cells. Additionally, we have used network simulations to predict that the spiking rate of an excitatory neuron is strongly dependent on the intrinsic excitability of a perisomatic targeting interneuron; both integrated in a feedback microcircuit. Given the importance of FSINs in network synchronization, understanding how intrinsic excitability and its plasticity in the FSINs can affect the network attributes is of seminal interest in the field of neuronal circuit dynamics and plasticity. We used computational simulation of physiologically scaled down neuronal networks consisting of experimentally constrained models of neurons to address this question. Intrinsic excitability in FSINs has been experimentally observed to be altered due to changes in their input resistance and changes in their action potential threshold. To alter the input resistance of the FSINs, we changed the specific membrane resistance (Rm), while to change the action potential threshold we altered the peak delayed potassium conductance (gKDbar) In Wang-Buzsaki type FSIN-FSIN interconnected network models (II network) we observed an increase in the network frequency with increase in FSIN Rm while the network coherence did not change due to the altered FSIN Rm. However, in the same network there was a drastic decrease in both network coherence and network frequency with increase in gKDbar. Next, we built an EI network using 250 model excitatory neurons (ENs) and 50 model FSINs. The ENs were reciprocally connected to the FSINs. Moreover, the FSINs were also interconnected among themselves while the ENs were not. In these EI networks we observed that decreased FSIN Rm, which decreased their excitability, caused a monotonic increase in the excitatory network coherence. However, increased FSIN gKDbar which also decreased their excitability caused a decrease in the excitatory network coherence. The excitatory network frequency was decreased with decreased FSIN Rm or with increased FSIN gKDbar. However, EI networks having decreased FSIN input resistance (~ 50 MΩ) could partially rescue the excitatory network coherence from the desynchronizing effect of increased FSIN gKDbar. In EI networks having higher FSIN input resistance (~ 110 MΩ); even a small increase in FSIN gKDbar caused a drastic decrease in the excitatory network coherence. The phenomenon of altered EI network activity due to altered FSIN Rm or FSIN gKDbar was observed to be significantly independent of the proportion of the FSIN population undergoing the alterations. The observation that even a small proportion of the entire FSIN population (10% and 40%; for FSIN Rm dependence and FSIN gKDbar dependence respectively) can cause a massive shift in the EI network activity indicated the strong influence of FSIN intrinsic excitability on network dynamics. We also observed that the dependence of FSIN Rm on EI network activity was quite robust in the physiological range of the network synaptic parameters. Overall from these studies we observed that DG FSINs express activity dependent plasticity of intrinsic excitability after experiencing near physiological synaptic excitation. Further, altered intrinsic excitability of FSINs can cause robust changes in the connected network. The study suggests possible intrinsic strategies in FSINs which might be functional in neuronal microcircuits during different physiological and pathological conditions.

Current theories and models of brain rhythm generation are based on (1) the excitability of individual neurons and whole networks, (2) the structural and functional connectivity of neuronal ensembles, (3) the dynamic interaction of excitatory and inhibitory network components, and (4) the importance of transient local and global states. From the interplay of the above, systemic network properties arise which account for activity overdrive or suppression, and critical-level synchronization. Under certain conditions or states, small-to-large scale neuronal networks can be entrained into excessive and/or hypersynchronous electrical brain activity (epileptogenesis). In this chapter we demonstrate with artificial neuronal network simulations how physiological brain oscillations (delta, theta, alpha, beta and gamma range, and transients thereof, including sleep spindles and larger sleep waves) are generated and how epileptiform phenomena can potentially emerge, as observed at a macroscopic scale on scalp and intracranial EEG recordings or manifested with focal and generalized, aware and unaware, motor and nonmotor or absence seizures in man. Fast oscillations, ripples and sharp waves, spike and slow wave discharges, sharp and rhythmical slow waves, paroxysmal depolarization and DC shifts or attenuation and electrodecremental responses seem to underlie key mechanisms of epileptogenesis across different scales of neural organization and bear clinical implications for the pharmacological and surgical treatment of the various types of epilepsy.

Recent research demonstrating that the perceived contrast of a small central grating patch can be strongly influenced by the presence of another grating in an annular surround implied the presence of two types of lateral interaction networks, one excitatory and one inhibitory. The present paper describes the development of a model for these networks, under the conditions where both center and surround contain gratings of the same spatial frequency and orientation. Two different interconnection networks were studied. In the feed-forward system, the gain of each member of a 2-D array of contrast sensitive mechanisms is adjusted by the weighted sum of the stimulus inputs to adjacent mechanisms. In the feedback system, the gain of each member of the array of contrast sensitive mechanisms is adjusted by a weighted sum of the outputs of adjacent mechanisms. Simulations indicate that psychophysical data can be accounted for only by the feed-back type network and that individual differences in suppression and enhancement effects can be accounted for by minor differences in the shapes of the spatial weighting functions.

Character translation can be accomplished by a heteroassociative memory in an artificial neural network. Because of the similarity among the characters, the special features of the patterns are important in pattern recognition. In this paper, a neural-network model based on the interpattem association (IPA) concept is presented.1 Generalized logical rules are developed to construct the excitatory and inhibitory interconnections in the heteroassociative memory. An adaptive optical neural network using high-resolution liquid-crystal televisions2 is used to translate between English letters and Chinese characters. Experimental and computer-simulated results have revealed that the IPA model is more effective in recognizing input among similar characters and has a larger storage capacity than the Hopfield model. Furthermore, the IPA model has shown two major advantages: fewer interconnections and fewer gray levels.

Due to the nature of Spiking Neural Networks (SNNs), it is challenging to be trained by biologically plausible learning principles. The multi-layered SNNs are with non-differential neurons, temporary-centric synapses, which make them nearly impossible to be directly tuned by back propagation. Here we propose an alternative biological inspired balanced tuning approach to train SNNs. The approach contains three main inspirations from the brain: Firstly, the biological network will usually be trained towards the state where the temporal update of variables are equilibrium (e.g. membrane potential); Secondly, specific proportions of excitatory and inhibitory neurons usually contribute to stable representations; Thirdly, the short-term plasticity (STP) is a general principle to keep the input and output of synapses balanced towards a better learning convergence. With these inspirations, we train SNNs with three steps: Firstly, the SNN model is trained with three brain-inspired principles; then weakly supervised learning is used to tune the membrane potential in the final layer for network classification; finally the learned information is consolidated from membrane potential into the weights of synapses by Spike-Timing Dependent Plasticity (STDP). The proposed approach is verified on the MNIST hand-written digit recognition dataset and the performance (the accuracy of 98.64%) indicates that the ideas of balancing state could indeed improve the learning ability of SNNs, which shows the power of proposed brain-inspired approach on the tuning of biological plausible SNNs.

The incoherent optical neuron (ION) model uses two separate incoherent optical device responses to subtract inhibitory inputs from excitatory inputs for general neural networks. The ION model comprises two elements: an inhibitory element and a nonlinear element. A Hughes liquid crystal light valve (LCLV) can implement both elements. Its nonmonotonic response can be used to approximate a linear, negative slope characteristic for inhibitory inputs as well as a nonlinear positive slope characteristic for the neuron hard-clipping or soft-threshold response. An optical beam is used to provide a suitable neuron threshold. Self-feedback is incorporated to increase the gain and nonlinearity.