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Artykuły w czasopismach na temat "Inhibitory Network Model"
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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 numerical simulations and bifurcation analyses, we find that the ability of inhibitory networks to synchronize in the face of heterogeneity depends nonmonotonically on each of the synaptic time constant, synaptic conductance and external drive parameters. Because of this, an optimal set of parameters for a given cellular model with various biophysical characteristics can be determined. We suggest that this could be a helpful approach to use in determining the importance of different, underlying biophysical details. We further find that two-cell coherence properties are maintained in larger 10-cell networks. As such, we think that a strategy of “embedding” small network dynamics in larger networks is a useful way to understand the contribution of biophysically derived parameters to population dynamics in large networks.
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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 expanded database of valid three-dimensional models of target proteins of the RAGE-NF-κB signal chain was created on the basis of a previously created database of three-dimensional models of relevant biotargets. Ensemble molecular docking of known RAGE inhibitors from a verified database into the sites of added models of target proteins was performed, and the minimum docking energies for each compound in relation to each target were determined. An extended training set for neural network modeling was formed. Using seven variants of sampling by the method of artificial multilayer perceptron neural networks, three ensembles of classification decision rules were constructed to predict three level of the RAGE-inhibitory activity based on the calculated affinity of compounds for significant target proteins of the RAGE-NF-κB signaling pathway. Using a simple consensus of the second level, the predictive ability of the created model was assessed and its high accuracy and statistical significance were shown. The resultant consensus ensemble neural network multitarget model has been used for virtual screening of new derivatives of different chemical classes. The most promising substances have been synthesized and sent for experimental studies.
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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 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.
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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 primary auditory cortex (A1). A key mechanism in our network is attentional inhibitory modulation (AIM) of cortical inhibitory neurons. In this mechanism, top-down inhibitory neurons disinhibit bottom-up cortical circuits, a prominent circuit motif observed in sensory cortex. Our results reveal that the same underlying mechanisms in the AIM network can explain diverse attentional effects on both spatial and frequency tuning in A1. We find that a dominant effect of disinhibition on cortical tuning is suppressive, consistent with experimental observations. Functionally, the AIM network may play a key role in solving the cocktail party problem. We demonstrate how attention can guide the AIM network to monitor an acoustic scene, select a specific target, or switch to a different target, providing flexible outputs for solving the cocktail party problem.
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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 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.
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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 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.
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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 slightly, but the coherence between the neuron's spike train and the local field potential can increase (Fries, Reynolds, Rorie, & Desimone, 2001). These experimental results were reproduced in a model of a V4 neuron under the assumption that attention modulates the activity of local interneuron networks. The V4 model neuron received stimulus-specific excitation from V2 and synchronous inhibitory inputs from two local interneuron networks in V4. Each interneuron network was driven by stimulus-specific excitatory inputs from V2 and was modulated by the activity of the frontal eye fields. Stimulus competition was present because of a delay in arrival time of synchronous volleys from each interneuron network. For small delays, the firing rate was close to the rate elicited by the preferred stimulus alone, whereas for larger delays, it approached the firing rate of the poor stimulus. When either stimulus was presented alone, the neuron's response was not altered by the change in delay, but could change due to modulation of the degree of synchrony of the corresponding interneuron network. The model suggests that top-down attention biases the competition between V2 columns for control of V4 neurons primarily by changing the relative timing of inhibition, whereas changes in the degree of synchrony of interneuron networks modulate the response to a single stimulus. The new mechanism proposed here for attentional modulation of firing rate, gain modulation by inhibitory interference, is likely to have more general applicability to cortical information processing.
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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 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.
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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 cortical neuronal populations. It was shown that in the presence of inhibitory coupling, the neuronal ensembles demonstrate antiphase dynamics. Various connectivity topologies and various types of neuron-like oscillators were investigated. We compare the results obtained in a discrete neuron model (Rulkov model) and a continuous-time model (Hodgkin-Huxley). It is shown that in the case of a discrete neuron model, the periodic dynamics is manifested in the alternate excitation of various neural ensembles. In the case of the continuous-time model, periodic modulation of the synchronization index of neural ensembles is observed.
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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 have constructed a neural network model that incorporates many empirically observed features of these interneurons. The model generates important aspects of the interactions of cells L29 and L30, and with no further modification, exhibits many network level phenomena that were not explicitly incorporated into the model.