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Journal articles on the topic 'Neurons - Modeling'

Author: Grafiati

Published: 4 June 2021

Last updated: 6 September 2023

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1

JOHNSTON, DAVID, SIMON PETER MEKHAIL, MARY ANN GO, and VINCENT R. DARIA. "MODELING NEURONAL RESPONSE TO SIMULTANEOUS AND SEQUENTIAL MULTI-SITE SYNAPTIC STIMULATION." International Journal of Modern Physics: Conference Series 17 (January 2012): 1–8. http://dx.doi.org/10.1142/s2010194512007878.

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The flow of information in the brain theorizes that each neuron in a network receives synaptic inputs and sends off its processed signals to neighboring neurons. Here, we model these synaptic inputs to understand how each neuron processes these inputs and transmits neurotransmitters to neighboring neurons. We use the NEURON simulation package to stimulate a neuron at multiple synaptic locations along its dendritic tree. Accumulation of multiple synaptic inputs causes changes in the neuron's membrane potential leading to firing of an action potential. Our simulations show that simultaneous syna

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Wang, Wei, Jiqing Han, Tieran Zheng, Guibin Zheng, and Xingyu Zhou. "Speaker Verification via Modeling Kurtosis Using Sparse Coding." International Journal of Pattern Recognition and Artificial Intelligence 30, no. 03 (2016): 1659008. http://dx.doi.org/10.1142/s0218001416590084.

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This paper proposes a new model for speaker verification by employing kurtosis statistical method based on sparse coding of human auditory system. Since only a small number of neurons in primary auditory cortex are activated in encoding acoustic stimuli and sparse independent events are used to represent the characteristics of the neurons. Each individual dictionary is learned from individual speaker samples where dictionary atoms correspond to the cortex neurons. The neuron responses possess statistical properties of acoustic signals in auditory cortex so that the activation distribution of i

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Nykamp, Duane Q., and Daniel Tranchina. "A Population Density Approach That Facilitates Large-Scale Modeling of Neural Networks: Extension to Slow Inhibitory Synapses." Neural Computation 13, no. 3 (2001): 511–46. http://dx.doi.org/10.1162/089976601300014448.

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A previously developed method for efficiently simulating complex networks of integrate-and-fire neurons was specialized to the case in which the neurons have fast unitary postsynaptic conductances. However, inhibitory synaptic conductances are often slower than excitatory ones for cortical neurons, and this difference can have a profound effect on network dynamics that cannot be captured with neurons that have only fast synapses. We thus extend the model to include slow inhibitory synapses. In this model, neurons are grouped into large populations of similar neurons. For each population, we ca

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KINOUCHI, OSAME, and MARCELO H. R. TRAGTENBERG. "MODELING NEURONS BY SIMPLE MAPS." International Journal of Bifurcation and Chaos 06, no. 12a (1996): 2343–60. http://dx.doi.org/10.1142/s0218127496001508.

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We introduce a simple generalization of graded response formal neurons which presents very complex behavior. Phase diagrams in full parameter space are given, showing regions with fixed points, periodic, quasiperiodic and chaotic behavior. These diagrams also represent the possible time series learnable by the simplest feed-forward network, a two input single-layer perceptron. This simple formal neuron (‘dynamical perceptron’) behaves as an excitable ele ment with characteristics very similar to those appearing in more complicated neuron models like FitzHugh-Nagumo and Hodgkin-Huxley systems:

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Sun, Yu-Juan, and Wei-Min Zhang. "Modeling Neuronal Systems as an Open Quantum System." Symmetry 13, no. 9 (2021): 1603. http://dx.doi.org/10.3390/sym13091603.

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We propose a physical model for neurons to describe how neurons interact with one another through the surrounding materials of neuronal cell bodies. We model the neuronal cell surroundings, include the dendrites, the axons and the synapses, as well as the surrounding glial cells, as a continuous distribution of oscillating modes inspired from the electric circuital picture of neuronal action potential. By analyzing the dynamics of this neuronal model by using the master equation approach of open quantum systems, we investigated the collective behavior of neurons. After applying stimulations to

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Almog, Mara, and Alon Korngreen. "Is realistic neuronal modeling realistic?" Journal of Neurophysiology 116, no. 5 (2016): 2180–209. http://dx.doi.org/10.1152/jn.00360.2016.

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Scientific models are abstractions that aim to explain natural phenomena. A successful model shows how a complex phenomenon arises from relatively simple principles while preserving major physical or biological rules and predicting novel experiments. A model should not be a facsimile of reality; it is an aid for understanding it. Contrary to this basic premise, with the 21st century has come a surge in computational efforts to model biological processes in great detail. Here we discuss the oxymoronic, realistic modeling of single neurons. This rapidly advancing field is driven by the discovery

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Fan, Yile, Yuanpeng Li, Naiyang Xue, and Dan Ding. "Analysis of Regularized Poisson GLM Spike-Train Modeling." Journal of Physics: Conference Series 2173, no. 1 (2022): 012019. http://dx.doi.org/10.1088/1742-6596/2173/1/012019.

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Abstract This paper introduces a method for modeling and analyzing neural impulse sequences. In this paper, we define the response value of a scale-independent neuron and construct the correlation graph of the neuron under the response value. The minimum cut algorithm is applied continuously to obtain the maximum group of neurons. According to the characteristics of the firing of neurons, a Poisson-process based model is proposed to mathematically model the neural coding, and the gradient descent method is used to optimize it. Through the modeling analysis method, information such as maximum n

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Chambers, Jordan D., Joel C. Bornstein, Henrik Sjövall, and Evan A. Thomas. "Recurrent networks of submucous neurons controlling intestinal secretion: a modeling study." American Journal of Physiology-Gastrointestinal and Liver Physiology 288, no. 5 (2005): G887—G896. http://dx.doi.org/10.1152/ajpgi.00491.2004.

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Secretomotor neurons, immunoreactive for vasoactive intestinal peptide (VIP), are important in controlling chloride secretion in the small intestine. These neurons form functional synapses with other submucosal VIP neurons and transmit via slow excitatory postsynaptic potentials (EPSPs). Thus they form a recurrent network with positive feedback. Intrinsic sensory neurons within the submucosa are also likely to form recurrent networks with positive feedback, provide substantial output to VIP neurons, and receive input from VIP neurons. If positive feedback within recurrent networks is sufficien

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Todo, Yuki, Zheng Tang, Hiroyoshi Todo, Junkai Ji, and Kazuya Yamashita. "Neurons with Multiplicative Interactions of Nonlinear Synapses." International Journal of Neural Systems 29, no. 08 (2019): 1950012. http://dx.doi.org/10.1142/s0129065719500126.

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Neurons are the fundamental units of the brain and nervous system. Developing a good modeling of human neurons is very important not only to neurobiology but also to computer science and many other fields. The McCulloch and Pitts neuron model is the most widely used neuron model, but has long been criticized as being oversimplified in view of properties of real neuron and the computations they perform. On the other hand, it has become widely accepted that dendrites play a key role in the overall computation performed by a neuron. However, the modeling of the dendritic computations and the assi

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Price, N. S. C., S. Ono, M. J. Mustari, and M. R. Ibbotson. "Comparing Acceleration and Speed Tuning in Macaque MT: Physiology and Modeling." Journal of Neurophysiology 94, no. 5 (2005): 3451–64. http://dx.doi.org/10.1152/jn.00564.2005.

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Studies of individual neurons in area MT have traditionally investigated their sensitivity to constant speeds. We investigated acceleration sensitivity in MT neurons by comparing their responses to constant steps and linear ramps in stimulus speed. Speed ramps constituted constant accelerations and decelerations between 0 and 240°/s. Our results suggest that MT neurons do not have explicit acceleration sensitivity, although speed changes affected their responses in three main ways. First, accelerations typically evoked higher responses than the corresponding deceleration rate at all rates test

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Masuda, Naoki, and Kazuyuki Aihara. "Spatiotemporal Spike Encoding of a Continuous External Signal." Neural Computation 14, no. 7 (2002): 1599–628. http://dx.doi.org/10.1162/08997660260028638.

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Interspike intervals of spikes emitted from an integrator neuron model of sensory neurons can encode input information represented as a continuous signal from a deterministic system. If a real brain uses spike timing as a means of information processing, other neurons receiving spatiotemporal spikes from such sensory neurons must also be capable of treating information included in deterministic interspike intervals. In this article, we examine functions of neurons modeling cortical neurons receiving spatiotemporal spikes from many sensory neurons. We show that such neuron models can encode sti

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KATORI, YUICHI, ERIC J. LANG, MIHO ONIZUKA, MITSUO KAWATO, and KAZUYUKI AIHARA. "QUANTITATIVE MODELING OF SPATIO-TEMPORAL DYNAMICS OF INFERIOR OLIVE NEURONS WITH A SIMPLE CONDUCTANCE-BASED MODEL." International Journal of Bifurcation and Chaos 20, no. 03 (2010): 583–603. http://dx.doi.org/10.1142/s0218127410025909.

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Inferior olive (IO) neurons project to the cerebellum and contribute to motor control. They can show intriguing spatio-temporal dynamics with rhythmic and synchronized spiking. IO neurons are connected to their neighbors via gap junctions to form an electrically coupled network, and so it is considered that this coupling contributes to the characteristic dynamics of this nucleus. Here, we demonstrate that a gap junction-coupled network composed of simple conductance-based model neurons (a simplified version of a Hodgkin–Huxley type neuron) reproduce important aspects of IO activity. The simpli

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HAYOT, FERNAND, and DANIEL TRANCHINA. "Modeling corticofugal feedback and the sensitivity of lateral geniculate neurons to orientation discontinuity." Visual Neuroscience 18, no. 6 (2001): 865–77. http://dx.doi.org/10.1017/s0952523801186037.

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We model feedback from primary visual cortex to the dorsal lateral geniculate nucleus (dLGN). This feedback makes dLGN neurons sensitive to orientation discontinuity (Sillito et al., 1993; Cudeiro & Sillito, 1996). In the model, each dLGN neuron receives retinotopic input driven by layer 6 cortical neurons in a full set of orientation columns. Excitation is monosynaptic, while inhibition is through perigeniculate neurons and dLGN interneurons. The stimulus consists of drifting gratings, one within and the other outside a circular region centered over the receptive field of the model dLGN r

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Buccino, Alessio P., Michael Kordovan, Torbjørn V. Ness, et al. "Combining biophysical modeling and deep learning for multielectrode array neuron localization and classification." Journal of Neurophysiology 120, no. 3 (2018): 1212–32. http://dx.doi.org/10.1152/jn.00210.2018.

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Neural circuits typically consist of many different types of neurons, and one faces a challenge in disentangling their individual contributions in measured neural activity. Classification of cells into inhibitory and excitatory neurons and localization of neurons on the basis of extracellular recordings are frequently employed procedures. Current approaches, however, need a lot of human intervention, which makes them slow, biased, and unreliable. In light of recent advances in deep learning techniques and exploiting the availability of neuron models with quasi-realistic three-dimensional morph

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Eggert, J., and J. L. van Hemmen. "Modeling Neuronal Assemblies: Theory and Implementation." Neural Computation 13, no. 9 (2001): 1923–74. http://dx.doi.org/10.1162/089976601750399254.

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Models that describe qualitatively and quantitatively the activity of entire groups of spiking neurons are becoming increasingly important for biologically realistic large-scale network simulations. At the systems and areas modeling level, it is necessary to switch the basic descriptional level from single spiking neurons to neuronal assemblies. In this article, we present and review work that allows a macroscopic description of the assembly activity. We show that such macroscopic models can be used to reproduce in a quantitatively exact manner the joint activity of groups of spike-response or

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GÜLER, MARİFİ. "MODELING THE EFFECTS OF CHANNEL NOISE IN NEURONS: A STUDY BASED ON DISSIPATIVE STOCHASTIC MECHANICS." Fluctuation and Noise Letters 06, no. 02 (2006): L147—L159. http://dx.doi.org/10.1142/s0219477506003239.

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A framework for modeling the effects of channel noise in neurons is proposed through an adoption of stochastic mechanics within an approach based on the coupling between collective and intrinsic states. A model of the effects in a neuron of Rose-Hindmarsh type is introduced consequently. It is found, using the model, that even though the channel noise alters the neuron's quantitative behaviour, the repertoire of basic behaviour patterns remains to be the same; and that the region of bursting activity is extended to significantly higher input current values.

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Rybak, Ilya A., Julian F. R. Paton, and James S. Schwaber. "Modeling Neural Mechanisms for Genesis of Respiratory Rhythm and Pattern. I. Models of Respiratory Neurons." Journal of Neurophysiology 77, no. 4 (1997): 1994–2006. http://dx.doi.org/10.1152/jn.1997.77.4.1994.

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Rybak, Ilya A., Julian F. R. Paton, and James S. Schwaber. Modeling neural mechanisms for genesis of respiratory rhythym and pattern. I. Models of respiratory neurons. J. Neurophysiol. 77: 1994–2006, 1997. The general objectives of our research, presented in this series of papers, were to develop a computational model of the brain stem respiratory neural network and to explore possible neural mechanisms that provide the genesis of respiratory oscillations and the specific firing patterns of respiratory neurons. The present paper describes models of single respiratory neurons that have been use

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Sohal, Vikaas S., Charles L. Cox, and John R. Huguenard. "Localization of CCK Receptors in Thalamic Reticular Neurons: A Modeling Study." Journal of Neurophysiology 79, no. 5 (1998): 2820–24. http://dx.doi.org/10.1152/jn.1998.79.5.2820.

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Sohal, Vikaas S., Charles L. Cox, and John R. Huguenard. Localization of CCK receptors in thalamic reticular neurons: a modeling study. J. Neurophysiol. 79: 2827–2831, 1998. In an earlier experimental study, intracellular recording suggested that cholecystokinin (CCK) suppresses a K+ conductance in thalamic reticular (RE) neurons, yet the reversal potential of the CCK response, revealed using voltage clamp, was hyperpolarized significantly relative to the K+ equilibrium potential. Here, biophysical models of RE neurons were developed and used to test whether suppression of the K+ conductance,

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Akter, Masuma, and Baojin Ding. "Modeling Movement Disorders via Generation of hiPSC-Derived Motor Neurons." Cells 11, no. 23 (2022): 3796. http://dx.doi.org/10.3390/cells11233796.

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Generation of motor neurons (MNs) from human-induced pluripotent stem cells (hiPSCs) overcomes the limited access to human brain tissues and provides an unprecedent approach for modeling MN-related diseases. In this review, we discuss the recent progression in understanding the regulatory mechanisms of MN differentiation and their applications in the generation of MNs from hiPSCs, with a particular focus on two approaches: induction by small molecules and induction by lentiviral delivery of transcription factors. At each induction stage, different culture media and supplements, typical growth

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Vazifehkhah Ghaffari, Babak, Mojgan Kouhnavard, Takeshi Aihara, and Tatsuo Kitajima. "Mathematical Modeling of Subthreshold Resonant Properties in Pyloric Dilator Neurons." BioMed Research International 2015 (2015): 1–21. http://dx.doi.org/10.1155/2015/135787.

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Various types of neurons exhibit subthreshold resonance oscillation (preferred frequency response) to fluctuating sinusoidal input currents. This phenomenon is well known to influence the synaptic plasticity and frequency of neural network oscillation. This study evaluates the resonant properties of pacemaker pyloric dilator (PD) neurons in the central pattern generator network through mathematical modeling. From the pharmacological point of view, calcium currents cannot be blocked in PD neurons without removing the calcium-dependent potassium current. Thus, the effects of calciumICaand calciu

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Zhang, Shu-Zhen, Li-Xiang Ma, Wen-Jing Qian, et al. "Modeling Neurological Disease by Rapid Conversion of Human Urine Cells into Functional Neurons." Stem Cells International 2016 (2016): 1–8. http://dx.doi.org/10.1155/2016/2452985.

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Somatic cells can be directly converted into functional neurons by ectopic expression of defined factors and/or microRNAs. Since the first report of conversion mouse embryonic fibroblasts into functional neurons, the postnatal mouse, and human fibroblasts, astroglia, hepatocytes, and pericyte-derived cells have been converted into functional dopaminergic and motor neurons bothin vitroandin vivo. However, it is invasive to get all these materials. In the current study, we provide a noninvasive approach to obtain directly reprogrammed functional neurons by overexpression of the transcription fac

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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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Abbott, L. F., E. Marder, and S. L. Hooper. "Oscillating Networks: Control of Burst Duration by Electrically Coupled Neurons." Neural Computation 3, no. 4 (1991): 487–97. http://dx.doi.org/10.1162/neco.1991.3.4.487.

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The pyloric network of the stomatogastric ganglion in crustacea is a central pattern generator that can produce the same basic rhythm over a wide frequency range. Three electrically coupled neurons, the anterior burster (AB) neuron and two pyloric dilator (PD) neurons, act as a pacemaker unit for the pyloric network. The functional characteristics of the pacemaker network are the result of electrical coupling between neurons with quite different intrinsic properties, each contributing a basic feature to the complete circuit. The AB neuron, a conditional oscillator, plays a dominant role in rhy

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Rybak, Ilya A., Julian F. R. Paton, and James S. Schwaber. "Modeling Neural Mechanisms for Genesis of Respiratory Rhythm and Pattern. II. Network Models of the Central Respiratory Pattern Generator." Journal of Neurophysiology 77, no. 4 (1997): 2007–26. http://dx.doi.org/10.1152/jn.1997.77.4.2007.

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Rybak, Ilya A., Julian F. R. Paton, and James S. Schwaber. Modeling neural mechanisms for genesis of respiratory rhythm and pattern. II. Network models of the central respiratory pattern generator. J. Neurophysiol. 77: 2007–2026, 1997. The present paper describes several models of the central respiratory pattern generator (CRPG) developed employing experimental data and current hypotheses for respiratory rhythmogenesis. Each CRPG model includes a network of respiratory neuron types (e.g., early inspiratory; ramp inspiratory; late inspiratory; decrementing expiratory; postinspiratory; stage II

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GHAHARI, ALIREZA, and JOHN D. ENDERLE. "A NEURON-BASED TIME-OPTIMAL CONTROLLER OF HORIZONTAL SACCADIC EYE MOVEMENTS." International Journal of Neural Systems 24, no. 06 (2014): 1450017. http://dx.doi.org/10.1142/s0129065714500178.

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A neural network model of biophysical neurons in the midbrain for controlling oculomotor muscles during horizontal human saccades is presented. Neural circuitry that includes omnipause neuron, premotor excitatory and inhibitory burst neurons, long lead burst neuron, tonic neuron, interneuron, abducens nucleus and oculomotor nucleus is developed to investigate saccade dynamics. The final motoneuronal signals drive a time-optimal controller that stimulates a linear homeomorphic model of the oculomotor plant. To our knowledge, this is the first report on modeling the neural circuits at both premo

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Zhou, Ying, Henry C. Tuckwell, and Nicholas J. Penington. "Journal of Multiscale Neuroscience." Journal of Multiscale Neuroscience 1, no. 2 (2022): 83–95. http://dx.doi.org/10.56280/1532965848.

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Many central neurons, particularly certain brainstem aminergic neurons, exhibit spontaneous and fairly regular spiking with frequencies of order a few Hz. Many ion channel types contribute to such spiking, so accurate modeling of spike generation requires solving very large systems of differential equations, ordinary in the first instance. Since the analysis of spiking behavior when many synaptic inputs are active adds further to the number of components, it is useful to have simplified mathematical models of spiking in such neurons so that, for example, inputs and output spike features trains

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Medlock, Laura, Lauren Shute, Mark Fry, Dominic Standage, and Alastair V. Ferguson. "Ionic mechanisms underlying tonic and burst firing behavior in subfornical organ neurons: a combined experimental and modeling study." Journal of Neurophysiology 120, no. 5 (2018): 2269–81. http://dx.doi.org/10.1152/jn.00340.2018.

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Subfornical organ (SFO) neurons exhibit heterogeneity in current expression and spiking behavior, where the two major spiking phenotypes appear as tonic and burst firing. Insight into the mechanisms behind this heterogeneity is critical for understanding how the SFO, a sensory circumventricular organ, integrates and selectively influences physiological function. To integrate efficient methods for studying this heterogeneity, we built a single-compartment, Hodgkin-Huxley-type model of an SFO neuron that is parameterized by SFO-specific in vitro patch-clamp data. The model accounts for the membr

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Enochson, Avery, Daniel Ewert, Dipankar Mitar, et al. "MODELING THE HODGKIN-HUXLEY NEURON TO DETERMINE NEURONAL ENERGY CONSUMPTION EFFICIENCY AND OXYGEN CONSUMPTION VALUES." Biomedical Sciences Instrumentation 58, no. 2 (2022): 50–58. http://dx.doi.org/10.34107/nsjx733550.

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Understanding neuronal structure and function is essential to studying the human brain. The goal of this project was to create a model of human brain neurons that accurately reflects neuronal function, energy consumption, and oxygen consumption. Extensive work has been performed on the Hodgkin-Huxley model of neurons to accurately model neuronal firing. This study focuses on the creation of a model of the Hodgkin-Huxley neuron in MATLAB with the assistance of the DynaSim toolbox. This model was used to compute values and using another model the energy efficiency of the neuron was calculated an

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Lovelace, Jeffrey J., and Krzysztof J. Cios. "A Very Simple Spiking Neuron Model That Allows for Modeling of Large, Complex Systems." Neural Computation 20, no. 1 (2008): 65–90. http://dx.doi.org/10.1162/neco.2008.20.1.65.

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This letter introduces a biologically inspired very simple spiking neuron model. The model retains only crucial aspects of biological neurons: a network of time-delayed weighted connections to other neurons, a threshold-based generation of action potentials, action potential frequency proportional to stimulus intensity, and interneuron communication that occurs with time-varying potentials that last longer than the associated action potentials. The key difference between this model and existing spiking neuron models is its great simplicity: it is basically a collection of linear and discontinu

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Garashchuk, I. R., and D. I. Sinelshchikov. "Excitation of a Group of Two Hindmarsh – Rose Neurons with a Neuron-Generated Signal." Nelineinaya Dinamika 18, no. 4 (2022): 0. http://dx.doi.org/10.20537/nd220901.

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We study a model of three Hindmarsh – Rose neurons with directional electrical connections. We consider two fully-connected neurons that form a slave group which receives the signal from the master neuron via a directional coupling. We control the excitability of the neurons by setting the constant external currents. We study the possibility of excitation of the slave system in the stable resting state by the signal coming from the master neuron, to make it fire spikes/bursts tonically. We vary the coupling strength between the master and the slave systems as another control parameter. We calc

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Nadkarni, Suhita, and Peter Jung. "Dressed neurons: modeling neural–glial interactions." Physical Biology 1, no. 1 (2004): 35–41. http://dx.doi.org/10.1088/1478-3967/1/1/004.

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Melov, Simon. "Modeling mitochondrial function in aging neurons." Trends in Neurosciences 27, no. 10 (2004): 601–6. http://dx.doi.org/10.1016/j.tins.2004.08.004.

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Chailangkarn, Thanathom, Allan Acab, and Alysson Renato Muotri. "Modeling neurodevelopmental disorders using human neurons." Current Opinion in Neurobiology 22, no. 5 (2012): 785–90. http://dx.doi.org/10.1016/j.conb.2012.04.004.

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Shahbaba, Babak, Bo Zhou, Shiwei Lan, Hernando Ombao, David Moorman, and Sam Behseta. "A Semiparametric Bayesian Model for Detecting Synchrony Among Multiple Neurons." Neural Computation 26, no. 9 (2014): 2025–51. http://dx.doi.org/10.1162/neco_a_00631.

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We propose a scalable semiparametric Bayesian model to capture dependencies among multiple neurons by detecting their cofiring (possibly with some lag time) patterns over time. After discretizing time so there is at most one spike at each interval, the resulting sequence of 1s (spike) and 0s (silence) for each neuron is modeled using the logistic function of a continuous latent variable with a gaussian process prior. For multiple neurons, the corresponding marginal distributions are coupled to their joint probability distribution using a parametric copula model. The advantages of our approach

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Zhou, Lei, Jian Hui Wu, Guo Li Wang, Yu Su, and Guo Bin Zhang. "Modeling Research on Hospitalization Cost in Patients with Cerebral Infarction Based on BP Neural Network." Applied Mechanics and Materials 50-51 (February 2011): 944–48. http://dx.doi.org/10.4028/www.scientific.net/amm.50-51.944.

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To establish the model of hospitalization cost in patients with cerebral infarction based on neural network using artificial network mathematical model, and setting the appropriate parameters. LM, BR, OSS, SCG four algorithms were used respectively to model 3, 5,10,15, 20 neuron number in different hidden layers and comparisons between model fitting ability and its generalization ability were made. The model which involves a hidden layer, 8 input neurons, 15 neurons in output layer network is the optimal model by using OSS system.

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Skryabina, Olga V., Andrey E. Schegolev, Nikolay V. Klenov, et al. "Superconducting Bio-Inspired Au-Nanowire-Based Neurons." Nanomaterials 12, no. 10 (2022): 1671. http://dx.doi.org/10.3390/nano12101671.

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High-performance modeling of neurophysiological processes is an urgent task that requires new approaches to information processing. In this context, two- and three-junction superconducting quantum interferometers with Josephson weak links based on gold nanowires are fabricated and investigated experimentally. The studied cells are proposed for the implementation of bio-inspired neurons—high-performance, energy-efficient, and compact elements of neuromorphic processor. The operation modes of an advanced artificial neuron capable of generating the burst firing activation patterns are explored th

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Guo, Liang, Shuai Zhang, Jiankang Wu, Xinyu Gao, Mingkang Zhao, and Guizhi Xu. "Theoretical Analysis of Coupled Modified Hindmarsh-rose Model Under Transcranial Magnetic-acoustic Electrical Stimulation." International Journal of Circuits, Systems and Signal Processing 16 (January 15, 2022): 610–17. http://dx.doi.org/10.46300/9106.2022.16.76.

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Transcranial magnetic-acoustic electrical stimulation (TMAES) is a new technology with ultrasonic waves and a static magnetic field to generate an electric current in nerve tissues to modulate neuronal firing activities. The existing neuron models only simulate a single neuron, and there are few studies on coupled neurons models about TMAES. Most of the neurons in the cerebral cortex are not isolated but are coupled to each other. It is necessary to study the information transmission of coupled neurons. The types of neuron coupled synapses include electrical synapse and chemical synapse. A neu

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Weber, Cornelius, and Jochen Triesch. "A Sparse Generative Model of V1 Simple Cells with Intrinsic Plasticity." Neural Computation 20, no. 5 (2008): 1261–84. http://dx.doi.org/10.1162/neco.2007.02-07-472.

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Current models for learning feature detectors work on two timescales: on a fast timescale, the internal neurons' activations adapt to the current stimulus; on a slow timescale, the weights adapt to the statistics of the set of stimuli. Here we explore the adaptation of a neuron's intrinsic excitability, termed intrinsic plasticity, which occurs on a separate timescale. Here, a neuron maintains homeostasis of an exponentially distributed firing rate in a dynamic environment. We exploit this in the context of a generative model to impose sparse coding. With natural image input, localized edge de

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Real, Raquel, Manuel Peter, Antonio Trabalza, et al. "In vivo modeling of human neuron dynamics and Down syndrome." Science 362, no. 6416 (2018): eaau1810. http://dx.doi.org/10.1126/science.aau1810.

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Harnessing the potential of human stem cells for modeling the physiology and diseases of cortical circuitry requires monitoring cellular dynamics in vivo. We show that human induced pluripotent stem cell (iPSC)–derived cortical neurons transplanted into the adult mouse cortex consistently organized into large (up to ~100 mm3) vascularized neuron-glia territories with complex cytoarchitecture. Longitudinal imaging of >4000 grafted developing human neurons revealed that neuronal arbors refined via branch-specific retraction; human synaptic networks substantially restructured over 4 months, wi

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Plesser, Hans E., and Markus Diesmann. "Simplicity and Efficiency of Integrate-and-Fire Neuron Models." Neural Computation 21, no. 2 (2009): 353–59. http://dx.doi.org/10.1162/neco.2008.03-08-731.

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Lovelace and Cios ( 2008 ) recently proposed a very simple spiking neuron (VSSN) model for simulations of large neuronal networks as an efficient replacement for the integrate-and-fire neuron model. We argue that the VSSN model falls behind key advances in neuronal network modeling over the past 20 years, in particular, techniques that permit simulators to compute the state of the neuron without repeated summation over the history of input spikes and to integrate the subthreshold dynamics exactly. State-of-the-art solvers for networks of integrate-and-fire model neurons are substantially more

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Nurrohman, Afidz, and Suyadi -. "Mirror Neurons dan Konsep Uswatun Hasanah dalam Pendidikan Islam." TADRIS: Jurnal Pendidikan Islam 15, no. 2 (2020): 210–24. http://dx.doi.org/10.19105/tjpi.v15i2.3924.

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Dalam kajian neurosains terdapat bagian otak manusia yang disebut mirror neurons. Neuron ini dapat memantulkan kembali tindakan yang dilihat oleh seseorang dan membuat orang tersebut terdorong untuk menirukan dan melakukan hal yang sama. Berawal dari hal tersebut maka penelitian ini bertujuan untuk mengembangkan teori mirror neurons dalam pendidikan Islam terutama kaitannya dengan metode uswatun hasanah. Penelitian ini merupakan penelitian kepustakaan yang menggunakan pendekatan kualitatif dan analisa data dilakukan dengan menggunakan metode deskriptif analitik. Hasil penelitian menunjukkan ba

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Lightheart, Toby, Steven Grainger, and Tien-Fu Lu. "Spike-Timing-Dependent Construction." Neural Computation 25, no. 10 (2013): 2611–45. http://dx.doi.org/10.1162/neco_a_00501.

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Spike-timing-dependent construction (STDC) is the production of new spiking neurons and connections in a simulated neural network in response to neuron activity. Following the discovery of spike-timing-dependent plasticity (STDP), significant effort has gone into the modeling and simulation of adaptation in spiking neural networks (SNNs). Limitations in computational power imposed by network topology, however, constrain learning capabilities through connection weight modification alone. Constructive algorithms produce new neurons and connections, allowing automatic structural responses for app

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Hines, M. L., and N. T. Carnevale. "The NEURON Simulation Environment." Neural Computation 9, no. 6 (1997): 1179–209. http://dx.doi.org/10.1162/neco.1997.9.6.1179.

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The moment-to-moment processing of information by the nervous system involves the propagation and interaction of electrical and chemical signals that are distributed in space and time. Biologically realistic modeling is needed to test hypotheses about the mechanisms that govern these signals and how nervous system function emerges from the operation of these mechanisms. The NEURON simulation program provides a powerful and flexible environment for implementing such models of individual neurons and small networks of neurons. It is particularly useful when membrane potential is nonuniform and me

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Zhang, Tong, and Zhendong Wang. "Self-Organized Fuzzy Neural Network Nonlinear System Modeling Method Based on Clustering Algorithm." Applied Sciences 12, no. 22 (2022): 11435. http://dx.doi.org/10.3390/app122211435.

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In this paper, an improved self-organizing fuzzy neural network (SOFNN-CA) based on a clustering algorithm is proposed for nonlinear systems modeling in industrial processes. In order to reduce training time and increase training speed, we combine offline learning and online identification. The unsupervised clustering algorithm is used to generate the initial centers of the network in the offline learning phase, and, in the self-organizing phase of the system, the Mahalanobis distance (MD) index and error criterion are adopted to add neurons to learn new features. A new density potential index

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Ostrovskiy, Valeriy, Denis Butusov, Artur Karimov, and Valeriy Andreev. "DISCRETIZATION EFFECTS DURING NUMERICAL INVESTIGATION OF HODGKIN-HUXLEY NEURON MODEL." Bulletin of Bryansk state technical university 2019, no. 12 (2019): 94–101. http://dx.doi.org/10.30987/1999-8775-2019-2019-12-94-101.

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Computer design is a valuable tool in the course of designing neuro-morphic systems. In particular it allows investigating basic mechanisms of neuron pulse activities in networks. For computer modeling it is necessary to digitize a continuous model of the system by means of the application of discrete operators able to keep basic properties of a prototype. But the accuracy of discrete models may decrease because of negative effects caused by the type of the method used, by a discretization pitch and errors in rounding off.  This fact is significant for the analysis of non-linear systems

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Drouhin, Marie, Shuai Li, Matthieu Grelier, et al. "Characterization and modeling of spiking and bursting in experimental NbO_x neuron." Neuromorphic Computing and Engineering 2, no. 4 (2022): 044008. http://dx.doi.org/10.1088/2634-4386/ac969a.

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Abstract Hardware spiking neural networks hold the promise of realizing artificial intelligence with high energy efficiency. In this context, solid-state and scalable memristors can be used to mimic biological neuron characteristics. However, these devices show limited neuronal behaviors and have to be integrated in more complex circuits to implement the rich dynamics of biological neurons. Here we studied a NbO x memristor neuron that is capable of emulating numerous neuronal dynamics, including tonic spiking, stochastic spiking, leaky-integrate-and-fire features, spike latency, temporal inte

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Krumin, Michael, and Shy Shoham. "Multivariate Autoregressive Modeling and Granger Causality Analysis of Multiple Spike Trains." Computational Intelligence and Neuroscience 2010 (2010): 1–9. http://dx.doi.org/10.1155/2010/752428.

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Recent years have seen the emergence of microelectrode arrays and optical methods allowing simultaneous recording of spiking activity from populations of neurons in various parts of the nervous system. The analysis of multiple neural spike train data could benefit significantly from existing methods for multivariate time-series analysis which have proven to be very powerful in the modeling and analysis of continuous neural signals like EEG signals. However, those methods have not generally been well adapted to point processes. Here, we use our recent results on correlation distortions in multi

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Gorman, Julia C., Oliver L. Tufte, Anna V. R. Miller, William M. DeBello, José L. Peña, and Brian J. Fischer. "Diverse processing underlying frequency integration in midbrain neurons of barn owls." PLOS Computational Biology 17, no. 11 (2021): e1009569. http://dx.doi.org/10.1371/journal.pcbi.1009569.

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Emergent response properties of sensory neurons depend on circuit connectivity and somatodendritic processing. Neurons of the barn owl’s external nucleus of the inferior colliculus (ICx) display emergence of spatial selectivity. These neurons use interaural time difference (ITD) as a cue for the horizontal direction of sound sources. ITD is detected by upstream brainstem neurons with narrow frequency tuning, resulting in spatially ambiguous responses. This spatial ambiguity is resolved by ICx neurons integrating inputs over frequency, a relevant processing in sound localization across species.

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Amarasingham, Asohan, Stuart Geman, and Matthew T. Harrison. "Ambiguity and nonidentifiability in the statistical analysis of neural codes." Proceedings of the National Academy of Sciences 112, no. 20 (2015): 6455–60. http://dx.doi.org/10.1073/pnas.1506400112.

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Many experimental studies of neural coding rely on a statistical interpretation of the theoretical notion of the rate at which a neuron fires spikes. For example, neuroscientists often ask, “Does a population of neurons exhibit more synchronous spiking than one would expect from the covariability of their instantaneous firing rates?” For another example, “How much of a neuron’s observed spiking variability is caused by the variability of its instantaneous firing rate, and how much is caused by spike timing variability?” However, a neuron’s theoretical firing rate is not necessarily well-define

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Depannemaecker, Damien, Alain Destexhe, Viktor Jirsa, and Christophe Bernard. "Modeling seizures: From single neurons to networks." Seizure 90 (August 2021): 4–8. http://dx.doi.org/10.1016/j.seizure.2021.06.015.

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