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Artykuły w czasopismach na temat „Inhibitory Network Model”

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Data publikacji: 6 września 2023

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1

Skinner, F. K., J. Y. J. Chung, I. Ncube, P. A. Murray, and S. A. Campbell. "Using Heterogeneity to Predict Inhibitory Network Model Characteristics." Journal of Neurophysiology 93, no. 4 (2005): 1898–907. http://dx.doi.org/10.1152/jn.00619.2004.

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From modeling studies it has been known for >10 years that purely inhibitory networks can produce synchronous output given appropriate balances of intrinsic and synaptic parameters. Several experimental studies indicate that synchronous activity produced by inhibitory networks is critical to the production of population rhythms associated with various behavioral states. Heterogeneity of inputs to inhibitory networks strongly affect their ability to synchronize. In this paper, we explore how the amount of input heterogeneity to two-cell inhibitory networks affects their dynamics. Using numer

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2

Vassiliev, P. M., A. A. Spasov, A. N. Kochetkov, M. A. Perfilev, and A. R. Koroleva. "Consensus ensemble neural network multitarget model of RAGE inhibitory activity of chemical compounds." Biomeditsinskaya Khimiya 67, no. 3 (2021): 268–77. http://dx.doi.org/10.18097/pbmc20216703268.

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RAGE signal transduction via the RAGE-NF-κB signaling pathway is one of the mechanisms of inflammatory reactions that cause severe complications in diabetes mellitus. RAGE inhibitors are promising pharmacological compounds that require the development of new predictive models. Based on the methodology of artificial neural networks, consensus ensemble neural network multitarget model has been constructed. This model describes the dependence of the level of the RAGE inhibitory activity on the affinity of compounds for 34 target proteins of the RAGE-NF-κB signal pathway. For this purpose an expan

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3

Bryson, Alexander, Samuel F. Berkovic, Steven Petrou, and David B. Grayden. "State transitions through inhibitory interneurons in a cortical network model." PLOS Computational Biology 17, no. 10 (2021): e1009521. http://dx.doi.org/10.1371/journal.pcbi.1009521.

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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 re

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4

Chou, Kenny F., and Kamal Sen. "AIM: A network model of attention in auditory cortex." PLOS Computational Biology 17, no. 8 (2021): e1009356. http://dx.doi.org/10.1371/journal.pcbi.1009356.

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Attentional modulation of cortical networks is critical for the cognitive flexibility required to process complex scenes. Current theoretical frameworks for attention are based almost exclusively on studies in visual cortex, where attentional effects are typically modest and excitatory. In contrast, attentional effects in auditory cortex can be large and suppressive. A theoretical framework for explaining attentional effects in auditory cortex is lacking, preventing a broader understanding of cortical mechanisms underlying attention. Here, we present a cortical network model of attention in pr

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5

Rich, Scott, Michal Zochowski, and Victoria Booth. "Effects of Neuromodulation on Excitatory–Inhibitory Neural Network Dynamics Depend on Network Connectivity Structure." Journal of Nonlinear Science 30, no. 5 (2018): 2171–94. http://dx.doi.org/10.1007/s00332-017-9438-6.

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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 conne

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6

Cao, Ying, Xiaoyan He, Yuqing Hao, and Qingyun Wang. "Transition Dynamics of Epileptic Seizures in the Coupled Thalamocortical Network Model." International Journal of Bifurcation and Chaos 28, no. 08 (2018): 1850104. http://dx.doi.org/10.1142/s0218127418501043.

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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

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7

Tiesinga, Paul H. E. "Stimulus Competition by Inhibitory Interference." Neural Computation 17, no. 11 (2005): 2421–53. http://dx.doi.org/10.1162/0899766054796905.

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When two stimuli are present in the receptive field of a V4 neuron, the firing rate response is between the weakest and strongest response elicited by each of the stimuli when presented alone (Reynolds, Chelazzi, & Desimone, 1999). When attention is directed toward the stimulus eliciting the strongest response (the preferred stimulus), the response to the pair is increased, whereas the response decreases when attention is directed to the other stimulus (the poor stimulus). When attention is directed to either of the two stimuli presented alone, the firing rate remains the same or increases

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8

YAMAZAKI, TADASHI, and SHIGERU TANAKA. "A NEURAL NETWORK MODEL FOR TRACE CONDITIONING." International Journal of Neural Systems 15, no. 01n02 (2005): 23–30. http://dx.doi.org/10.1142/s0129065705000037.

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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 sig

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9

Andreev, Andrey, and Vladimir Maksimenko. "Synchronization in coupled neural network with inhibitory coupling." Cybernetics and Physics, Volume 8, 2019, Number 4 (December 30, 2019): 199–204. http://dx.doi.org/10.35470/2226-4116-2019-8-4-199-204.

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A theoretical model of a network of neuron-like elements was constructed. The network included several subnetworks. The first subnetwork was used to translate a constant-amplitude signal into a spike sequence (conversion of amplitude to frequency). A similar process occurs in the brain when perceiving visual information. With an increase in the flow of information, the generation frequency of the neural ensemble participating in the processing increases. Further, the first subnetwork transmitted excitation to two large interconnected subnetworks. These subnetworks simulated the dynamics of the

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10

Blazis, Diana E. J., Thomas M. Fischer, and Thomas J. Carew. "A Neural Network Model of Inhibitory Information Processing in Aplysia." Neural Computation 5, no. 2 (1993): 213–27. http://dx.doi.org/10.1162/neco.1993.5.2.213.

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Recent cellular studies have revealed a novel form of inhibitory information processing in the siphon withdrawal reflex of the marine mollusc Aplysia: Motorneuronal output is significantly reduced by activity-dependent potentiation of recurrent inhibition within the siphon withdrawal network (Fischer and Carew 1991, 1993). This inhibitory modulation is mediated by two types of identified interneurons, L29s and L30s. In an effort to describe and analyze this and other forms of inhibitory information processing in Aplysia, and to compare it with similar processing in other nervous systems, we ha

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11

Weissenberger, Felix, Marcelo Matheus Gauy, Xun Zou, and Angelika Steger. "Mutual Inhibition with Few Inhibitory Cells via Nonlinear Inhibitory Synaptic Interaction." Neural Computation 31, no. 11 (2019): 2252–65. http://dx.doi.org/10.1162/neco_a_01230.

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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 interac

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12

Vreeswijk, C. van, and H. Sompolinsky. "Chaotic Balanced State in a Model of Cortical Circuits." Neural Computation 10, no. 6 (1998): 1321–71. http://dx.doi.org/10.1162/089976698300017214.

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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 o

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13

Zonca, Lou, and David Holcman. "Emergence and fragmentation of the alpha-band driven by neuronal network dynamics." PLOS Computational Biology 17, no. 12 (2021): e1009639. http://dx.doi.org/10.1371/journal.pcbi.1009639.

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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 emer

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14

CATSIGERAS, ELEONORA. "CHAOS AND STABILITY IN A MODEL OF INHIBITORY NEURONAL NETWORK." International Journal of Bifurcation and Chaos 20, no. 02 (2010): 349–60. http://dx.doi.org/10.1142/s0218127410025806.

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We analyze the dynamics of a deterministic model of inhibitory neuronal networks proving that the discontinuities of the Poincaré map produce a never empty chaotic set, while its continuity pieces produce stable orbits. We classify the systems in three types: the almost everywhere (a.e.) chaotic, the a.e. stable, and the combined systems. The a.e. stable are periodic and chaos appears as bifurcations. We prove that a.e. stable systems exhibit limit cycles, attracting a.e. the orbits.

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15

Bowman, Howard, Friederike Schlaghecken, and Martin Eimer. "A neural network model of inhibitory processes in subliminal priming." Visual Cognition 13, no. 4 (2006): 401–80. http://dx.doi.org/10.1080/13506280444000823.

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16

Sinha, Ankur, Christoph Metzner, Neil Davey, Roderick Adams, Michael Schmuker, and Volker Steuber. "Growth rules for the repair of Asynchronous Irregular neuronal networks after peripheral lesions." PLOS Computational Biology 17, no. 6 (2021): e1008996. http://dx.doi.org/10.1371/journal.pcbi.1008996.

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Several homeostatic mechanisms enable the brain to maintain desired levels of neuronal activity. One of these, homeostatic structural plasticity, has been reported to restore activity in networks disrupted by peripheral lesions by altering their neuronal connectivity. While multiple lesion experiments have studied the changes in neurite morphology that underlie modifications of synapses in these networks, the underlying mechanisms that drive these changes are yet to be explained. Evidence suggests that neuronal activity modulates neurite morphology and may stimulate neurites to selective sprou

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17

Yang, Geunbo, Wongyu Lee, Youjung Seo, et al. "Unsupervised Spiking Neural Network with Dynamic Learning of Inhibitory Neurons." Sensors 23, no. 16 (2023): 7232. http://dx.doi.org/10.3390/s23167232.

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A spiking neural network (SNN) is a type of artificial neural network that operates based on discrete spikes to process timing information, similar to the manner in which the human brain processes real-world problems. In this paper, we propose a new spiking neural network (SNN) based on conventional, biologically plausible paradigms, such as the leaky integrate-and-fire model, spike timing-dependent plasticity, and the adaptive spiking threshold, by suggesting new biological models; that is, dynamic inhibition weight change, a synaptic wiring method, and Bayesian inference. The proposed networ

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18

Armstrong, Eve, and Henry D. I. Abarbanel. "Model of the songbird nucleus HVC as a network of central pattern generators." Journal of Neurophysiology 116, no. 5 (2016): 2405–19. http://dx.doi.org/10.1152/jn.00438.2016.

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We propose a functional architecture of the adult songbird nucleus HVC in which the core element is a “functional syllable unit” (FSU). In this model, HVC is organized into FSUs, each of which provides the basis for the production of one syllable in vocalization. Within each FSU, the inhibitory neuron population takes one of two operational states: 1) simultaneous firing wherein all inhibitory neurons fire simultaneously, and 2) competitive firing of the inhibitory neurons. Switching between these basic modes of activity is accomplished via changes in the synaptic strengths among the inhibitor

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19

Dasika, Vasant K., John A. White, Laurel H. Carney, and H. Steven Colburn. "Effects of Inhibitory Feedback in a Network Model of Avian Brain Stem." Journal of Neurophysiology 94, no. 1 (2005): 400–414. http://dx.doi.org/10.1152/jn.01065.2004.

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The avian auditory brain stem consists of a network of specialized nuclei, including nucleus laminaris (NL) and superior olivary nucleus (SON). NL cells show sensitivity to interaural time difference (ITD), a critical cue that underlies spatial hearing. SON cells provide inhibitory feedback to the rest of the network. Empirical data suggest that feedback inhibition from SON could increase the ITD sensitivity of NL across sound level. Using a bilateral network model, we assess the effects of SON feedback inhibition. Individual cells are specified as modified leaky-integrate-and-fire neurons wit

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20

Gelenbe, Erol. "Stability of the Random Neural Network Model." Neural Computation 2, no. 2 (1990): 239–47. http://dx.doi.org/10.1162/neco.1990.2.2.239.

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In a recent paper (Gelenbe 1989) we introduced a new neural network model, called the Random Network, in which “negative” or “positive” signals circulate, modeling inhibitory and excitatory signals. These signals can arrive either from other neurons or from the outside world: they are summed at the input of each neuron and constitute its signal potential. The state of each neuron in this model is its signal potential, while the network state is the vector of signal potentials at each neuron. If its potential is positive, a neuron fires, and sends out signals to the other neurons of the network

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21

Taylor, Adam L., Garrison W. Cottrell, and William B. Kristan. "Analysis of Oscillations in a Reciprocally Inhibitory Network with Synaptic Depression." Neural Computation 14, no. 3 (2002): 561–81. http://dx.doi.org/10.1162/089976602317250906.

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We present and analyze a model of a two-cell reciprocally inhibitory network that oscillates. The principal mechanism of oscillation is short-term synaptic depression. Using a simple model of depression and analyzing the system in certain limits, we can derive analytical expressions for various features of the oscillation, including the parameter regime in which stable oscillations occur, as well as the period and amplitude of these oscillations. These expressions are functions of three parameters: the time constant of depression, the synaptic strengths, and the amount of tonic excitation the

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22

Ouardouz, Mohamed, and Lionel Carmant. "Changes in inhibitory CA1 network in dual pathology model of epilepsy." Channels 6, no. 1 (2012): 18–25. http://dx.doi.org/10.4161/chan.18615.

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23

Ponzi, Adam P. D., and Jeff Wickens. "Input dependent cell assembly dynamics in an inhibitory spiking network model." Neuroscience Research 68 (January 2010): e213. http://dx.doi.org/10.1016/j.neures.2010.07.2511.

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24

Miyawaki, Yoichi, and Masato Okada. "A network model of inhibitory effects induced by transcranial magnetic stimulation." Neurocomputing 52-54 (June 2003): 837–42. http://dx.doi.org/10.1016/s0925-2312(02)00810-x.

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25

Omori, Toshiaki, and Tsuyoshi Horiguchi. "Dynamical Neural Network Model of Hippocampus with Excitatory and Inhibitory Neurons." Journal of the Physical Society of Japan 73, no. 3 (2004): 749–55. http://dx.doi.org/10.1143/jpsj.73.749.

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26

Lee, Euiwoo, and David Terman. "Oscillatory Rhythms in a Model Network of Excitatory and Inhibitory Neurons." SIAM Journal on Applied Dynamical Systems 18, no. 1 (2019): 354–92. http://dx.doi.org/10.1137/18m1200877.

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27

Vreeswijk, C. van, and D. Hansel. "Patterns of Synchrony in Neural Networks with Spike Adaptation." Neural Computation 13, no. 5 (2001): 959–92. http://dx.doi.org/10.1162/08997660151134280.

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We study the emergence of synchronized burst activity in networks of neurons with spike adaptation. We show that networks of tonically firing adapting excitatory neurons can evolve to a state where the neurons burst in a synchronized manner. The mechanism leading to this burst activity is analyzed in a network of integrate-and-fire neurons with spike adaptation. The dependence of this state on the different network parameters is investigated, and it is shown that this mechanism is robust against inhomogeneities, sparseness of the connectivity, and noise. In networks of two populations, one exc

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28

Linster, Christiane, Silke Sachse, and C. Giovanni Galizia. "Computational Modeling Suggests That Response Properties Rather Than Spatial Position Determine Connectivity Between Olfactory Glomeruli." Journal of Neurophysiology 93, no. 6 (2005): 3410–17. http://dx.doi.org/10.1152/jn.01285.2004.

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Olfactory responses require the representation of high-dimensional olfactory stimuli within the constraints of two-dimensional neural networks. We used a computational model of the honeybee antennal lobe to test how inhibitory interactions in the antennal lobe should be organized to best reproduce the experimentally measured input-output function in this structure. Our simulations show that a functionally organized inhibitory network, as opposed to an anatomically or all-to-all organized inhibitory network, best reproduces the input-output function of the antennal lobe observed with calcium im

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29

Arunachalam, Viswanathan, Raha Akhavan-Tabatabaei, and Cristina Lopez. "Results on a Binding Neuron Model and Their Implications for Modified Hourglass Model for Neuronal Network." Computational and Mathematical Methods in Medicine 2013 (2013): 1–8. http://dx.doi.org/10.1155/2013/374878.

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The classical models of single neuron like Hodgkin-Huxley point neuron or leaky integrate and fire neuron assume the influence of postsynaptic potentials to last till the neuron fires. Vidybida (2008) in a refreshing departure has proposed models for binding neurons in which the trace of an input is remembered only for a finite fixed period of time after which it is forgotten. The binding neurons conform to the behaviour of real neurons and are applicable in constructing fast recurrent networks for computer modeling. This paper develops explicitly several useful results for a binding neuron li

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30

Nawrot, Mark, and Randolph Blake. "A neural network model of kinetic depth." Visual Neuroscience 6, no. 3 (1991): 219–27. http://dx.doi.org/10.1017/s0952523800006234.

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AbstractWe propose a network model that accounts for the kinetic depth in structure from motion phenomena. Using plausible neural mechanisms, the model accounts for (1) fluctuations in perception when viewing a simple kinetic depth stimulus, (2) disambiguation of this stimulus with stereoscopic information, and (3) subsequent bias of the percept of this stimulus following stereoscopic adaptation. The model comprises two levels: a layer of monocular directionally selective motion detectors that provide input to a second layer of disparity- selective and direction-selective binocular mechanisms.

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31

Horn, D., D. Sagi, and M. Usher. "Segmentation, Binding, and Illusory Conjunctions." Neural Computation 3, no. 4 (1991): 510–25. http://dx.doi.org/10.1162/neco.1991.3.4.510.

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We investigate binding within the framework of a model of excitatory and inhibitory cell assemblies that form an oscillating neural network. Our model is composed of two such networks that are connected through their inhibitory neurons. The excitatory cell assemblies represent memory patterns. The latter have different meanings in the two networks, representing two different attributes of an object, such as shape and color. The networks segment an input that contains mixtures of such pairs into staggered oscillations of the relevant activities. Moreover, the phases of the oscillating activitie

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32

Manzke, Till, Mathias Dutschmann, Gerald Schlaf, et al. "Serotonin targets inhibitory synapses to induce modulation of network functions." Philosophical Transactions of the Royal Society B: Biological Sciences 364, no. 1529 (2009): 2589–602. http://dx.doi.org/10.1098/rstb.2009.0068.

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The cellular effects of serotonin (5-HT), a neuromodulator with widespread influences in the central nervous system, have been investigated. Despite detailed knowledge about the molecular biology of cellular signalling, it is not possible to anticipate the responses of neuronal networks to a global action of 5-HT. Heterogeneous expression of various subtypes of serotonin receptors (5-HTR) in a variety of neurons differently equipped with cell-specific transmitter receptors and ion channel assemblies can provoke diverse cellular reactions resulting in various forms of network adjustment and, he

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33

Shevtsova, Natalia, and James A. Reggia. "A Neural Network Model of Lateralization during Letter Identification." Journal of Cognitive Neuroscience 11, no. 2 (1999): 167–81. http://dx.doi.org/10.1162/089892999563300.

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The causes of cerebral lateralization of cognitive and other functions are currently not well understood. To investigate one aspect of function lateralization, a bihemispheric neural network model for a simple visual identification task was developed that has two parallel interacting paths of information processing. The model is based on commonly accepted concepts concerning neural connectivity, activity dynamics, and synaptic plasticity. A combination of both unsupervised (Hebbian) and supervised (Widrow-Hoff) learning rules is used to train the model to identify a small set of letters presen

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34

Viriyopase, Atthaphon, Raoul-Martin Memmesheimer, and Stan Gielen. "Cooperation and competition of gamma oscillation mechanisms." Journal of Neurophysiology 116, no. 2 (2016): 232–51. http://dx.doi.org/10.1152/jn.00493.2015.

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Oscillations of neuronal activity in different frequency ranges are thought to reflect important aspects of cortical network dynamics. Here we investigate how various mechanisms that contribute to oscillations in neuronal networks may interact. We focus on networks with inhibitory, excitatory, and electrical synapses, where the subnetwork of inhibitory interneurons alone can generate interneuron gamma (ING) oscillations and the interactions between interneurons and pyramidal cells allow for pyramidal-interneuron gamma (PING) oscillations. What type of oscillation will such a network generate?

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35

Matadi, Maba Boniface. "Application of Lie Symmetry to a Mathematical Model that Describes a Cancer Sub-Network." Symmetry 14, no. 2 (2022): 400. http://dx.doi.org/10.3390/sym14020400.

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In this paper, a mathematical model of a cancer sub-network is analysed from the view point of Lie symmetry methods. This model discusses a human cancer cell which is developed due to the dysfunction of some genes at the R-checkpoint during the cell cycle. The primary purpose of this paper is to apply the techniques of Lie symmetry to the model and present some approximated solutions for the three-dimensional system of first-order ordinary differential equations describing a cancer sub-network. The result shows that the phosphatase gene (Cdc25A) regulates the cyclin-dependent kinases inhibitor

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36

De Schutter, E., J. D. Angstadt, and R. L. Calabrese. "A model of graded synaptic transmission for use in dynamic network simulations." Journal of Neurophysiology 69, no. 4 (1993): 1225–35. http://dx.doi.org/10.1152/jn.1993.69.4.1225.

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1. The heartbeat central pattern-generating network of the medicinal leech contains elemental neural oscillators, comprising reciprocally inhibitory pairs of segmental heart interneurons, that use graded as well as spike-mediated synaptic transmission. We are in the process of developing a general computer model of this pattern generator. Our modeling goal is to explore the interaction of membrane currents and synaptic transmission that promote oscillation in heart interneurons. As a first step toward this goal, we have developed a computer model of graded synaptic transmission between recipro

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37

Schall, Jeffrey D., Thomas J. Palmeri, and Gordon D. Logan. "Models of inhibitory control." Philosophical Transactions of the Royal Society B: Biological Sciences 372, no. 1718 (2017): 20160193. http://dx.doi.org/10.1098/rstb.2016.0193.

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We survey models of response inhibition having different degrees of mathematical, computational and neurobiological specificity and generality. The independent race model accounts for performance of the stop-signal or countermanding task in terms of a race between GO and STOP processes with stochastic finishing times. This model affords insights into neurophysiological mechanisms that are reviewed by other authors in this volume. The formal link between the abstract GO and STOP processes and instantiating neural processes is articulated through interactive race models consisting of stochastic

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38

Wang, Aiqun, and Nanning Zheng. "Multiplicative inhibitory velocity detector and multi-velocity motion detection neural network model." Journal of Computer Science and Technology 13, no. 1 (1998): 41–54. http://dx.doi.org/10.1007/bf02946613.

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TATENO, KATSUMI, HIDEYUKI TOMONARI, HATSUO HAYASHI, and SATORU ISHIZUKA. "PHASE DEPENDENT TRANSITION BETWEEN MULTISTABLE STATES IN A NEURAL NETWORK WITH RECIPROCAL INHIBITION." International Journal of Bifurcation and Chaos 14, no. 05 (2004): 1559–75. http://dx.doi.org/10.1142/s0218127404010138.

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We studied multistable oscillatory states of a small neural network model and switching of an oscillatory mode. In the present neural network model, two pacemaker neurons are reciprocally inhibited with conduction delay; one pacemaker neuron inhibits the other via an inhibitory nonpacemaker interneuron, and vice versa. The small network model shows bifurcations from quasi-periodic oscillation to chaos via period 3 with increase in the synaptic weight of the reciprocal inhibition. The route to chaos in the network model is different from that in the single pacemaker neuron. The network model ex

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Müller, Thomas H., D. Swandulla, and H. U. Zeilhofer. "Synaptic Connectivity in Cultured Hypothalamic Neuronal Networks." Journal of Neurophysiology 77, no. 6 (1997): 3218–25. http://dx.doi.org/10.1152/jn.1997.77.6.3218.

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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 distin

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Traub, R. D., R. Miles, and R. K. Wong. "Models of synchronized hippocampal bursts in the presence of inhibition. I. Single population events." Journal of Neurophysiology 58, no. 4 (1987): 739–51. http://dx.doi.org/10.1152/jn.1987.58.4.739.

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1. We constructed model networks with 520 or 1,020 cells intended to represent the CA3 region of the hippocampus. Model neurons were simulated in enough detail to reproduce intrinsic bursting and the electrotonic flow of currents along dendritic cables. Neurons exerted either excitatory or inhibitory postsynaptic actions on other cells. The network models were simulated with different levels of excitatory and inhibitory synaptic strengths in order to study epileptic and other interesting collective behaviors in the system. 2. Excitatory synapses between neurons in the network were powerful eno

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Wang, Yuan, Xia Shi, Bo Cheng, and Junliang Chen. "Neural Dynamics and Gamma Oscillation on a Hybrid Excitatory-Inhibitory Complex Network (Student Abstract)." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 10 (2020): 13957–58. http://dx.doi.org/10.1609/aaai.v34i10.7251.

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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. Experimen

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Cáceres, María J., and Ricarda Schneider. "Analysis and numerical solver for excitatory-inhibitory networks with delay and refractory periods." ESAIM: Mathematical Modelling and Numerical Analysis 52, no. 5 (2018): 1733–61. http://dx.doi.org/10.1051/m2an/2018014.

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The network of noisy leaky integrate and fire (NNLIF) model is one of the simplest self-contained mean-field models considered to describe the behavior of neural networks. Even so, in studying its mathematical properties some simplifications are required [Cáceres and Perthame, J. Theor. Biol. 350 (2014) 81–89; Cáceres and Schneider, Kinet. Relat. Model. 10 (2017) 587–612; Cáceres, Carrillo and Perthame, J. Math. Neurosci. 1 (2011) 7] which disregard crucial phenomena. In this work we deal with the general NNLIF model without simplifications. It involves a network with two populations (excitato

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Akhmet, Marat, Madina Tleubergenova, Roza Seilova, and Zakhira Nugayeva. "Poisson Stability in Symmetrical Impulsive Shunting Inhibitory Cellular Neural Networks with Generalized Piecewise Constant Argument." Symmetry 14, no. 9 (2022): 1754. http://dx.doi.org/10.3390/sym14091754.

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In the paper, shunting inhibitory cellular neural networks with impulses and the generalized piecewise constant argument are under discussion. The main modeling novelty is that the impulsive part of the systems is symmetrical to the differential part. Moreover, the model depends not only on the continuous time, but also the generalized piecewise constant argument. The process is subdued to Poisson stable inputs, which cause the new type of recurrent signals. The method of included intervals, recently introduced approach of recurrent motions checking, is effectively utilized. The existence and

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Sanchez-Vives, Maria V., Maurizio Mattia, Albert Compte, et al. "Inhibitory Modulation of Cortical Up States." Journal of Neurophysiology 104, no. 3 (2010): 1314–24. http://dx.doi.org/10.1152/jn.00178.2010.

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The balance between excitation and inhibition is critical in the physiology of the cerebral cortex. To understand the influence of inhibitory control on the emergent activity of the cortical network, inhibition was progressively blocked in a slice preparation that generates spontaneous rhythmic up states at a similar frequency to those occurring in vivo during slow-wave sleep or anesthesia. Progressive removal of inhibition induced a parametric shortening of up state duration and elongation of the down states, the frequency of oscillations decaying. Concurrently, a gradual increase in the netw

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Börgers, Christoph, Steven Epstein, and Nancy J. Kopell. "Gamma oscillations mediate stimulus competition and attentional selection in a cortical network model." Proceedings of the National Academy of Sciences 105, no. 46 (2008): 18023–28. http://dx.doi.org/10.1073/pnas.0809511105.

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Simultaneous presentation of multiple stimuli can reduce the firing rates of neurons in extrastriate visual cortex below the rate elicited by a single preferred stimulus. We describe computational results suggesting how this remarkable effect may arise from strong excitatory drive to a substantial local population of fast-spiking inhibitory interneurons, which can lead to a loss of coherence in that population and thereby raise the effectiveness of inhibition. We propose that in attentional states fast-spiking interneurons may be subject to a bath of inhibition resulting from cholinergic activ

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Farah, Firas H., Vasily Grigorovsky, and Berj L. Bardakjian. "Coupled Oscillators Model of Hyperexcitable Neuroglial Networks." International Journal of Neural Systems 29, no. 03 (2019): 1850041. http://dx.doi.org/10.1142/s0129065718500417.

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Glial populations within neuronal networks of the brain have recently gained much interest in the context of hyperexcitability and epilepsy. In this paper, we present an oscillator-based neuroglial model capable of generating Spontaneous Electrical Discharges (SEDs) in hyperexcitable conditions. The network is composed of 16 coupled Cognitive Rhythm Generators (CRGs), which are oscillator-based mathematical constructs previously described by our research team. CRGs are well-suited for modeling assemblies of excitable cells, and in this network, each represents one of the following populations:

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Kiss, Tamás, Gergő Orbán, Máté Lengyel, and Péter Érdi. "Intrahippocampal gamma and theta rhythm generation in a network model of inhibitory interneurons." Neurocomputing 38-40 (June 2001): 713–19. http://dx.doi.org/10.1016/s0925-2312(01)00358-7.

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Verret, Laure, Edward O. Mann, Giao B. Hang, et al. "Inhibitory Interneuron Deficit Links Altered Network Activity and Cognitive Dysfunction in Alzheimer Model." Cell 149, no. 3 (2012): 708–21. http://dx.doi.org/10.1016/j.cell.2012.02.046.

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Horn, D., and M. Usher. "EXCITATORY–INHIBITORY NETWORKS WITH DYNAMICAL THRESHOLDS." International Journal of Neural Systems 01, no. 03 (1990): 249–57. http://dx.doi.org/10.1142/s0129065790000151.

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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. Allowi

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