Relevant bibliographies by topics / Neurons Models

Academic literature on the topic 'Neurons Models'

Author: Grafiati
Published: 10 December 2022
Last updated: 28 January 2023

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles

Select a source type:

Contents

Consult the lists of relevant articles, books, theses, conference reports, and other scholarly sources on the topic 'Neurons Models.'

Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.

You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.

Journal articles on the topic "Neurons Models"

1

Dasika, Vasant, John A. White, and H. Steven Colburn. "Simple neuron models of ITD sensitive neurons." Journal of the Acoustical Society of America 111, no. 5 (2002): 2355. http://dx.doi.org/10.1121/1.4777912.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

Holmstrom, Lars, Patrick D. Roberts, and Christine V. Portfors. "Responses to Social Vocalizations in the Inferior Colliculus of the Mustached Bat Are Influenced by Secondary Tuning Curves." Journal of Neurophysiology 98, no. 6 (December 2007): 3461–72. http://dx.doi.org/10.1152/jn.00638.2007.

Full text
Abstract:
Neurons in the inferior colliculus (IC) of the mustached bat integrate input from multiple frequency bands in a complex fashion. These neurons are important for encoding the bat's echolocation and social vocalizations. The purpose of this study was to quantify the contribution of complex frequency interactions on the responses of IC neurons to social vocalizations. Neural responses to single tones, two-tone pairs, and social vocalizations were recorded in the IC of the mustached bat. Three types of data driven stimulus-response models were designed for each neuron from single tone and tone pair stimuli to predict the responses of individual neurons to social vocalizations. The first model was generated only using the neuron's primary frequency tuning curve, whereas the second model incorporated the entire hearing range of the animal. The extended model often predicted responses to many social vocalizations more accurately for multiply tuned neurons. One class of multiply tuned neuron that likely encodes echolocation information also responded to many of the social vocalizations, suggesting that some neurons in the mustached bat IC have dual functions. The third model included two-tone frequency tunings of the neurons. The responses to vocalizations were better predicted by the two-tone models when the neuron had inhibitory frequency tuning curves that were not near the neuron's primary tuning curve. Our results suggest that complex frequency interactions in the IC determine neural responses to social vocalizations and some neurons in IC have dual functions that encode both echolocation and social vocalization signals.
APA, Harvard, Vancouver, ISO, and other styles
3

Duggins, Peter, and Chris Eliasmith. "Constructing functional models from biophysically-detailed neurons." PLOS Computational Biology 18, no. 9 (September 8, 2022): e1010461. http://dx.doi.org/10.1371/journal.pcbi.1010461.

Full text
Abstract:
Improving biological plausibility and functional capacity are two important goals for brain models that connect low-level neural details to high-level behavioral phenomena. We develop a method called “oracle-supervised Neural Engineering Framework” (osNEF) to train biologically-detailed spiking neural networks that realize a variety of cognitively-relevant dynamical systems. Specifically, we train networks to perform computations that are commonly found in cognitive systems (communication, multiplication, harmonic oscillation, and gated working memory) using four distinct neuron models (leaky-integrate-and-fire neurons, Izhikevich neurons, 4-dimensional nonlinear point neurons, and 4-compartment, 6-ion-channel layer-V pyramidal cell reconstructions) connected with various synaptic models (current-based synapses, conductance-based synapses, and voltage-gated synapses). We show that osNEF networks exhibit the target dynamics by accounting for nonlinearities present within the neuron models: performance is comparable across all four systems and all four neuron models, with variance proportional to task and neuron model complexity. We also apply osNEF to build a model of working memory that performs a delayed response task using a combination of pyramidal cells and inhibitory interneurons connected with NMDA and GABA synapses. The baseline performance and forgetting rate of the model are consistent with animal data from delayed match-to-sample tasks (DMTST): we observe a baseline performance of 95% and exponential forgetting with time constant τ = 8.5s, while a recent meta-analysis of DMTST performance across species observed baseline performances of 58 − 99% and exponential forgetting with time constants of τ = 2.4 − 71s. These results demonstrate that osNEF can train functional brain models using biologically-detailed components and open new avenues for investigating the relationship between biophysical mechanisms and functional capabilities.
APA, Harvard, Vancouver, ISO, and other styles
4

Prinz, Astrid A., Cyrus P. Billimoria, and Eve Marder. "Alternative to Hand-Tuning Conductance-Based Models: Construction and Analysis of Databases of Model Neurons." Journal of Neurophysiology 90, no. 6 (December 2003): 3998–4015. http://dx.doi.org/10.1152/jn.00641.2003.

Full text
Abstract:
Conventionally, the parameters of neuronal models are hand-tuned using trial-and-error searches to produce a desired behavior. Here, we present an alternative approach. We have generated a database of about 1.7 million single-compartment model neurons by independently varying 8 maximal membrane conductances based on measurements from lobster stomatogastric neurons. We classified the spontaneous electrical activity of each model neuron and its responsiveness to inputs during runtime with an adaptive algorithm and saved a reduced version of each neuron's activity pattern. Our analysis of the distribution of different activity types (silent, spiking, bursting, irregular) in the 8-dimensional conductance space indicates that the coarse grid of conductance values we chose is sufficient to capture the salient features of the distribution. The database can be searched for different combinations of neuron properties such as activity type, spike or burst frequency, resting potential, frequency–current relation, and phase-response curve. We demonstrate how the database can be screened for models that reproduce the behavior of a specific biological neuron and show that the contents of the database can give insight into the way a neuron's membrane conductances determine its activity pattern and response properties. Similar databases can be constructed to explore parameter spaces in multicompartmental models or small networks, or to examine the effects of changes in the voltage dependence of currents. In all cases, database searches can provide insight into how neuronal and network properties depend on the values of the parameters in the models.
APA, Harvard, Vancouver, ISO, and other styles
5

Hong, En, Fatma Gurel Kazanci, and Astrid A. Prinz. "Different Roles of Related Currents in Fast and Slow Spiking of Model Neurons From Two Phyla." Journal of Neurophysiology 100, no. 4 (October 2008): 2048–61. http://dx.doi.org/10.1152/jn.90567.2008.

Full text
Abstract:
Neuronal activity arises from the interplay of membrane and synaptic currents. Although many channel proteins conducting these currents are phylogenetically conserved, channels of the same type in different animals can have different voltage dependencies and dynamics. What does this mean for our ability to derive rules about the role of different types of ion channels in neuronal activity? Can results about the role of a particular channel type in a particular type of neuron be generalized to other neuron types? We compare spiking model neurons in two databases constructed by exploring the maximal conductance spaces of two models. The first is a model of crustacean stomatogastric neurons, and the second is a model of rodent thalamocortical neurons, but both models contain similar types of membrane currents. Spiking neurons in both databases show distinct fast and slow subpopulations, but our analysis reveals that related currents play different roles in fast and slow spiking in the stomatogastric versus thalamocortical neurons. This analysis involved conductance-space visualization and comparison of voltage traces, current traces, and frequency-current relationships from all spiker subpopulations. Our results are consistent with previous work indicating that the role a membrane current plays in shaping a neuron's behavior depends on the voltage dependence and dynamics of that current and may be different in different neuron types depending on the properties of other currents it is interacting with. Conclusions about the function of a type of membrane current based on experiments or simulations in one type of neuron may therefore not generalize to other neuron types.
APA, Harvard, Vancouver, ISO, and other styles
6

Karpe, Yashashree, Zhenyu Chen, and Xue-Jun Li. "Stem Cell Models and Gene Targeting for Human Motor Neuron Diseases." Pharmaceuticals 14, no. 6 (June 12, 2021): 565. http://dx.doi.org/10.3390/ph14060565.

Full text
Abstract:
Motor neurons are large projection neurons classified into upper and lower motor neurons responsible for controlling the movement of muscles. Degeneration of motor neurons results in progressive muscle weakness, which underlies several debilitating neurological disorders including amyotrophic lateral sclerosis (ALS), hereditary spastic paraplegias (HSP), and spinal muscular atrophy (SMA). With the development of induced pluripotent stem cell (iPSC) technology, human iPSCs can be derived from patients and further differentiated into motor neurons. Motor neuron disease models can also be generated by genetically modifying human pluripotent stem cells. The efficiency of gene targeting in human cells had been very low, but is greatly improved with recent gene editing technologies such as zinc-finger nucleases (ZFN), transcription activator-like effector nucleases (TALEN), and CRISPR-Cas9. The combination of human stem cell-based models and gene editing tools provides unique paradigms to dissect pathogenic mechanisms and to explore therapeutics for these devastating diseases. Owing to the critical role of several genes in the etiology of motor neuron diseases, targeted gene therapies have been developed, including antisense oligonucleotides, viral-based gene delivery, and in situ gene editing. This review summarizes recent advancements in these areas and discusses future challenges toward the development of transformative medicines for motor neuron diseases.
APA, Harvard, Vancouver, ISO, and other styles
7

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 (April 1, 1997): 2007–26. http://dx.doi.org/10.1152/jn.1997.77.4.2007.

Full text
Abstract:
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 expiratory; stage II constant firing expiratory; preinspiratory) and simplified models of lung and pulmonary stretch receptors (PSR), which provide feedback to the respiratory network. The used models of single respiratory neurons were developed in the Hodgkin-Huxley style as described in the previous paper. The mechanism for termination of inspiration (the inspiratory off-switch) in all models operates via late-I neuron, which is considered to be the inspiratory off-switching neuron. Several two- and three-phase CRPG models have been developed using different accepted hypotheses of the mechanism for termination of expiration. The key elements in the two-phase models are the early-I and dec-E neurons. The expiratory off-switch mechanism in these models is based on the mutual inhibitory connections between early-I and dec-E and adaptive properties of the dec-E neuron. The difference between the two-phase models concerns the mechanism for ramp firing patterns of E2 neurons resulting either from the intrinsic neuronal properties of the E2 neuron or from disinhibition from the adapting dec-E neuron. The key element of the three-phase models is the pre-I neuron, which acts as the expiratory off-switching neuron. The three-phase models differ by the mechanisms used for termination of expiration and for the ramp firing patterns of E2 neurons. Additional CRPG models were developed employing a dual switching neuron that generates two bursts per respiratory cycle to terminate both inspiration and expiration. Although distinctly different each model generates a stable respiratory rhythm and shows physiologically plausible firing patterns of respiratory neurons with and without PSR feedback. Using our models, we analyze the roles of different respiratory neuron types and their interconnections for the respiratory rhythm and pattern generation. We also investigate the possible roles of intrinsic biophysical properties of different respiratory neurons in controlling the duration of respiratory phases and timing of switching between them. We show that intrinsic membrane properties of respiratory neurons are integrated with network properties of the CRPG at three hierarchical levels: at the cellular level to provide the specific firing patterns of respiratory neurons (e.g., ramp firing patterns); at the network level to provide switching between the respiratory phases; and at the systems level to control the duration of inspiration and expiration under different conditions (e.g., lack of PSR feedback).
APA, Harvard, Vancouver, ISO, and other styles
8

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

Full text
Abstract:
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 efficient than the VSSN simulator and allow routine simulations of networks of some 105 neurons and 109 connections on moderate computer clusters.
APA, Harvard, Vancouver, ISO, and other styles
9

Harrison, L. M., O. David, and K. J. Friston. "Stochastic models of neuronal dynamics." Philosophical Transactions of the Royal Society B: Biological Sciences 360, no. 1457 (May 29, 2005): 1075–91. http://dx.doi.org/10.1098/rstb.2005.1648.

Full text
Abstract:
Cortical activity is the product of interactions among neuronal populations. Macroscopic electrophysiological phenomena are generated by these interactions. In principle, the mechanisms of these interactions afford constraints on biologically plausible models of electrophysiological responses. In other words, the macroscopic features of cortical activity can be modelled in terms of the microscopic behaviour of neurons. An evoked response potential (ERP) is the mean electrical potential measured from an electrode on the scalp, in response to some event. The purpose of this paper is to outline a population density approach to modelling ERPs. We propose a biologically plausible model of neuronal activity that enables the estimation of physiologically meaningful parameters from electrophysiological data. The model encompasses four basic characteristics of neuronal activity and organization: (i) neurons are dynamic units, (ii) driven by stochastic forces, (iii) organized into populations with similar biophysical properties and response characteristics and (iv) multiple populations interact to form functional networks. This leads to a formulation of population dynamics in terms of the Fokker–Planck equation. The solution of this equation is the temporal evolution of a probability density over state-space, representing the distribution of an ensemble of trajectories. Each trajectory corresponds to the changing state of a neuron. Measurements can be modelled by taking expectations over this density, e.g. mean membrane potential, firing rate or energy consumption per neuron. The key motivation behind our approach is that ERPs represent an average response over many neurons. This means it is sufficient to model the probability density over neurons, because this implicitly models their average state. Although the dynamics of each neuron can be highly stochastic, the dynamics of the density is not. This means we can use Bayesian inference and estimation tools that have already been established for deterministic systems. The potential importance of modelling density dynamics (as opposed to more conventional neural mass models) is that they include interactions among the moments of neuronal states (e.g. the mean depolarization may depend on the variance of synaptic currents through nonlinear mechanisms). Here, we formulate a population model, based on biologically informed model-neurons with spike-rate adaptation and synaptic dynamics. Neuronal sub-populations are coupled to form an observation model, with the aim of estimating and making inferences about coupling among sub-populations using real data. We approximate the time-dependent solution of the system using a bi-orthogonal set and first-order perturbation expansion. For didactic purposes, the model is developed first in the context of deterministic input, and then extended to include stochastic effects. The approach is demonstrated using synthetic data, where model parameters are identified using a Bayesian estimation scheme we have described previously.
APA, Harvard, Vancouver, ISO, and other styles
10

Sajjad, Hassan, Nadir Durrani, and Fahim Dalvi. "Neuron-level Interpretation of Deep NLP Models: A Survey." Transactions of the Association for Computational Linguistics 10 (2022): 1285–303. http://dx.doi.org/10.1162/tacl_a_00519.

Full text
Abstract:
Abstract The proliferation of Deep Neural Networks in various domains has seen an increased need for interpretability of these models. Preliminary work done along this line, and papers that surveyed such, are focused on high-level representation analysis. However, a recent branch of work has concentrated on interpretability at a more granular level of analyzing neurons within these models. In this paper, we survey the work done on neuron analysis including: i) methods to discover and understand neurons in a network; ii) evaluation methods; iii) major findings including cross architectural comparisons that neuron analysis has unraveled; iv) applications of neuron probing such as: controlling the model, domain adaptation, and so forth; and v) a discussion on open issues and future research directions.
APA, Harvard, Vancouver, ISO, and other styles
More sources

Dissertations / Theses on the topic "Neurons Models"

1

Boatin, William. "Characterization of neuron models." Thesis, Available online, Georgia Institute of Technology, 2005, 2005. http://etd.gatech.edu/theses/available/etd-04182005-181732/.

Full text
Abstract:
Thesis (M. S.)--Electrical and Computer Engineering, Georgia Institute of Technology, 2006.
Dr. Robert H. Lee, Committee Member ; Dr. Kurt Wiesenfeld, Committee Member ; Dr Robert J. Butera, Committee Member.
APA, Harvard, Vancouver, ISO, and other styles
2

Dobbins, Allan C. (Allan Charles). "Difference models of visual cortical neurons." Thesis, McGill University, 1992. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=39539.

Full text
Abstract:
Difference operations are ubiquitous in the visual cortex. The central hypothesis in this thesis is that a nonlinear difference model can account for the functional properties of three different classes of visual cortical neurons. Abstractly each of the different neurons can be understood in terms of the same difference model although the computations they perform can be entirely different.
Endstopped neurons respond to short or highly curved oriented patterns. Their behaviour results from the difference in activation of their classical receptive field and inhibitory endzones. Two models of endstopped neurons are evaluated mathematically and by computer simulation. It is concluded that a model with displaced complex cell-like endzones is both more computationally robust and more consistent with the physiological evidence.
Other visual cortical neurons have inhibitory zones which are displaced normally rather than tangentially with respect to the neuron's receptive field orientation. These sidestopped cells are selective for narrow patterns. In other visual cortical neurons the side inhibition is derived from a different eye than the classical receptive field. Because of the geometry of projection these are referred to as binocular Near and Far cells. A difference model of sidestopped and Near and Far neurons is developed which captures their principal features.
Neurons in visual cortical area MT of primates have been shown to exhibit a velocity-specific antagonism between the receptive field and a surrounding region. It is argued that center-surround antagonism is an attempt to resolve competing constraints. Signal reliability increases with spatial averaging, but the variation of the flow field invariably increases with area. A unifying perspective is that difference models provide a means of estimating the range over which a visual quantity is constant or linear. Varieties of these models exist with a more refined property--selectivity for sign of contour curvature or, under certain circumstances, the sign of convexity of the surface generating a binocular disparity or motion field.
APA, Harvard, Vancouver, ISO, and other styles
3

戚大衛 and Tai-wai David Chik. "A numerical study of Hodgkin-Huxley neurons." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2000. http://hub.hku.hk/bib/B31224210.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

Quadroni, Reto. "Realistic models of medial vestibular nuclei neurons /." [S.l.] : [s.n.], 1993. http://e-collection.ethbib.ethz.ch/show?type=diss&nr=10255.

Full text
APA, Harvard, Vancouver, ISO, and other styles
5

Huss, Mikael. "Computational models of lamprey locomotor network neurons." Licentiate thesis, Stockholm : KTH Numerical Analysis and Computer Science, 2005. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-304.

Full text
APA, Harvard, Vancouver, ISO, and other styles
6

Shepardson, Dylan. "Algorithms for inverting Hodgkin-Huxley type neuron models." Diss., Atlanta, Ga. : Georgia Institute of Technology, 2009. http://hdl.handle.net/1853/31686.

Full text
Abstract:
Thesis (Ph.D)--Algorithms, Combinatorics, and Optimization, Georgia Institute of Technology, 2010.
Committee Chair: Tovey, Craig; Committee Member: Butera, Rob; Committee Member: Nemirovski, Arkadi; Committee Member: Prinz, Astrid; Committee Member: Sokol, Joel. Part of the SMARTech Electronic Thesis and Dissertation Collection.
APA, Harvard, Vancouver, ISO, and other styles
7

Shaw, Ivan Ting-kun. "Cell death in motor neurons, two complementary models." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1998. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape11/PQDD_0028/NQ50259.pdf.

Full text
APA, Harvard, Vancouver, ISO, and other styles
8

Shaw, Ivan Ting-kun 1966. "Cell death in motor neurons : two complementary models." Thesis, McGill University, 1998. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=35486.

Full text
Abstract:
Target-dependent cell death is an important embryogenic mechanism for regulating and sculpting the developing motor system. Efficient characterization of apoptosis has been more difficult in the nervous system than in other systems due to the use of several different primary culture systems as well as with heterogeneity of neuronal cell populations. We have developed a simple in vitro model of apoptosis with the motor neuron hybrid NSC34, a cell line which expresses much of the motor neuron phenotype (Cashman et al. 1992). Serum-deprived NSC 34 cells in bulk culture undergo cell death, likely from the withdrawal of the growth factors and/or hormones present in fetal calf serum medium supplements. This cell death is accompanied by fragmentation of chromatin into nucleosome multimers, heterochromatization of the nucleus, and other ultrastructural changes reminiscent of apoptotic death. Cell death is inhibitable by addition of agents which block new gene expression ( e.g. cycloheximide) or inhibit endonuclease activity (e.g. aurintricarboxylic acid).
We report similar findings with primary embryonic rat motor neurons identified by surface immunoreactivity for p75 LA NGFR, the low-affinity neurotrophin receptor (Bloch-Gallego et al. 1991; Camu and Henderson 1992; Chao and Hempstead 1995). The p75+ motor neuron population could be maintained for more than 48 hours in mixed suspension cultures supplemented with 10% fetal calf serum. However, the p75+ cell population was rapidly depleted in serum-deprived cultures, a phenomenon accompanied by the appearance of oligonucleosomal ladders. Serum-deprived p75+ cells were supported by the motor neuron-relevant factors BDNF, CNTF, GDNF and IGF-1, but not the non-relevant factor NGF. Serum-deprived p75 + cells were also protected by cycloheximide, suggesting a role for apoptosis in the cell death.
We have investigated the role of reactive oxygen species in acquired and genetic motor neuron diseases. Interestingly, a rapid burst of reactive oxygen species is observable within one hour of serum deprivation in both NSC34 and rat motor neuron systems. This burst precedes measurable cell death by at least one day, indicating that oxygen species generation may be an initial hallmark of target-dependent death. The amplitude and temporal nature of this burst may be altered by manipulating various cellular ROS defence mechanisms. Such manipulations also alter cell death progression, suggesting that the apoptotic cascade may be dependent upon this early ROS burst. The identity, source and activity of the relevant ROS may provide insight into the etiology and treatment of human motor neuron diseases.
APA, Harvard, Vancouver, ISO, and other styles
9

Clay, Robert Christopher. "Computer models to simulate ion flow in neurons." Thesis, University of Nottingham, 2017. http://eprints.nottingham.ac.uk/42951/.

Full text
Abstract:
In this thesis the Drift Diffusion enhanced Hodgkin Huxley model is developed. This model uses the Drift Diffusion equations to model the bulk solutions both within a neuron and in the surrounding extracellular media. The Hodgkin Huxley ion channel behaviour is incorporated into the membrane regions through the use of an altered diffusion coefficient. Firstly the model is applied to the case of intracellular and extracellular media separated by a single membrane. Secondly the model is applied to a cell within a restricted extracellular space. This takes a slice through a cell and is therefore termed a double membrane model, since there are two membrane layers. Finally the model is used to determine whether there is any charge and field buildup on a gold surface located 100 nm from the cell. The results from this could then be used in future to model Surface Plasmon Resonance experiments which may form the basis of novel neuronal activity detectors.
APA, Harvard, Vancouver, ISO, and other styles
10

Mo, Mimi Shin Ning. "Neural vulnerability in models of Parkinson's disease." Thesis, University of Oxford, 2007. http://ora.ox.ac.uk/objects/uuid:ac82e1c1-5d9f-473f-97ac-fcb70b2587ca.

Full text
Abstract:
Parkinson's disease (PD) is a neurodegenerative disorder with no known cure. This thesis explores the degenerative process in two neurotoxin-based models, the 6-hydroxydopamine and the chronic 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine(MPTP)/probenecid mouse models, to yield important information about the pathogenesis of PD. Neuronal survival patterns in Parkinsonian patients and animals are heterogeneous. More dopaminergic neurons are lost from the ventral tier of the substantia nigra (SN) than from the dorsal tier or the adjacent ventral tegmental area, possibly due to differential expression of the calcium-binding protein, calbindin D28K. Brain sections were processed for tyrosine hydroxylase (TH) and calbindin (CB) immunocytochemistry to distinguish the dopaminergic subpopulations. I show that more TH+/CB- and TH-/CB+ than TH+/CB+ neurons are lost in both models, suggesting that CB confers some degree of protection for dopaminergic neurons. With respect to connectivity, I show that both TH+ and CB+ neurons receive striatal and dorsal raphe inputs. I investigated the possibility of a progressive loss in midbrain neurons by prolonging the post-lesion survival period. In both models, there is an irreversible neuronal cell loss of TH+, CB+ and TH+/CB+ neurons but the effects of survival time and lesion treatments differ for the three neuronal types. The lesions also appear to be toxic to GABAergic neurons. I explore whether, once neurodegeneration has started, neurons can be rescued by pharmacological intervention. Salicylic acid appears both to reduce microglial activation and significantly improve TH+, but not CB+ or TH+/CB+ neuronal survival. PD appears multifactorial in origin and may involve complex interactions between genetic and environmental influences. I show that a xenobiotic-metabolising enzyme, arylamine N-acetyltransferase may fulfil a neuroprotective role in the SN by limiting the environmental risks. Taken together, this study provides a body of information on two different mouse PD models and highlights possible genetic predispositions to PD neuropathology.
APA, Harvard, Vancouver, ISO, and other styles
More sources

Books on the topic "Neurons Models"

1

Gerstner, Wulfram. Spiking neuron models: Single neurons, populations, plasticity. Cambridge, U.K: Cambridge University Press, 2002.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
2

An introduction to the mathematics of neurons. Cambridge: Cambridge University Press, 1986.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
3

Borkowski, Lech S. Nonlinear dynamics of Hodgkin-Huxley neurons. Poznań: Wydawn. Nauk. UAM, 2010.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
4

Bielecki, Andrzej. Models of Neurons and Perceptrons: Selected Problems and Challenges. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-319-90140-4.

Full text
APA, Harvard, Vancouver, ISO, and other styles
5

Mira, José, and Alberto Prieto, eds. Connectionist Models of Neurons, Learning Processes, and Artificial Intelligence. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/3-540-45720-8.

Full text
APA, Harvard, Vancouver, ISO, and other styles
6

John, Milton. Dynamics of small neural populations. Providence, R.I: American Mathematical Society, 1996.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
7

John, Milton. Dynamics of small neural populations. Providence, R.I: American Mathematical Society, 1996.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
8

Friesen, W. Otto. NeuroDynamix: Computer models for neurophysiology. New York: Oxford University Press, 1994.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
9

1973-, Friesen Jonathon A., ed. NeuroDynamix: Computer-based neuronal models for neurophysiology. Oxford: Oxford University Press, 1994.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
10

An introduction to the mathematics of neurons: Modeling in the frequency domain. 2nd ed. Cambridge, U.K: Cambridge University Press, 1997.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
More sources

Book chapters on the topic "Neurons Models"

1

De Wilde, Philippe. "Neurons in the Brain." In Neural Network Models, 53–70. London: Springer London, 1997. http://dx.doi.org/10.1007/978-1-84628-614-8_3.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

Negrello, Mario. "Neurons, Models, and Invariants." In Invariants of Behavior, 101–21. New York, NY: Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4419-8804-1_6.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

Bhalla, Upinder S. "Multi-compartmental Models of Neurons." In Computational Systems Neurobiology, 193–225. Dordrecht: Springer Netherlands, 2012. http://dx.doi.org/10.1007/978-94-007-3858-4_7.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

Tsur, Elishai Ezra. "Models of morphologically detailed neurons." In Neuromorphic Engineering, 99–114. Boca Raton: CRC Press, 2021. http://dx.doi.org/10.1201/9781003143499-8.

Full text
APA, Harvard, Vancouver, ISO, and other styles
5

Guo, Liang. "Equivalent Circuit Models of Neurons." In Principles of Electrical Neural Interfacing, 9–15. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-77677-0_2.

Full text
APA, Harvard, Vancouver, ISO, and other styles
6

Rigatos, Gerasimos G. "Oscillatory Dynamics in Biological Neurons." In Advanced Models of Neural Networks, 75–106. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-662-43764-3_4.

Full text
APA, Harvard, Vancouver, ISO, and other styles
7

Buccino, Alessio Paolo, Miroslav Kuchta, Jakob Schreiner, and Kent-André Mardal. "Improving Neural Simulations with the EMI Model." In Modeling Excitable Tissue, 87–98. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-61157-6_7.

Full text
Abstract:
Abstract Mathematical modeling of neurons is an essential tool to investigate neuronal activity alongside with experimental approaches. However, the conventional modeling framework to simulate neuronal dynamics and extracellular potentials makes several assumptions that might need to be revisited for some applications. In this chapter we apply the EMI model to investigate the ephaptic effect and the effect of the extracellular probes on the measured potential. Finally, we introduce reduced EMI models, which provide a more computationally efficient framework for simulating neurons with complex morphologies.
APA, Harvard, Vancouver, ISO, and other styles
8

Strisciuglio, Nicola, and Nicolai Petkov. "Brain-Inspired Algorithms for Processing of Visual Data." In Lecture Notes in Computer Science, 105–15. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-82427-3_8.

Full text
Abstract:
AbstractThe study of the visual system of the brain has attracted the attention and interest of many neuro-scientists, that derived computational models of some types of neuron that compose it. These findings inspired researchers in image processing and computer vision to deploy such models to solve problems of visual data processing.In this paper, we review approaches for image processing and computer vision, the design of which is based on neuro-scientific findings about the functions of some neurons in the visual cortex. Furthermore, we analyze the connection between the hierarchical organization of the visual system of the brain and the structure of Convolutional Networks (ConvNets). We pay particular attention to the mechanisms of inhibition of the responses of some neurons, which provide the visual system with improved stability to changing input stimuli, and discuss their implementation in image processing operators and in ConvNets.
APA, Harvard, Vancouver, ISO, and other styles
9

Maass, Wolfgang. "Paradigms for Computing with Spiking Neurons." In Models of Neural Networks IV, 373–402. New York, NY: Springer New York, 2002. http://dx.doi.org/10.1007/978-0-387-21703-1_9.

Full text
APA, Harvard, Vancouver, ISO, and other styles
10

Davis, Kevin A., Kenneth E. Hancock, and Bertrand Delgutte. "Computational Models of Inferior Colliculus Neurons." In Computational Models of the Auditory System, 129–76. Boston, MA: Springer US, 2010. http://dx.doi.org/10.1007/978-1-4419-5934-8_6.

Full text
APA, Harvard, Vancouver, ISO, and other styles

Conference papers on the topic "Neurons Models"

1

Gopakumar, Manu, Jiaming Cao, Shawn K. Kelly, and Pulkit Grover. "Cell-type Selective Stimulation of Neurons Based on Single Neuron Models." In 2019 9th International IEEE/EMBS Conference on Neural Engineering (NER). IEEE, 2019. http://dx.doi.org/10.1109/ner.2019.8716976.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

Pitta, Marina Galdino da Rocha, Jordy Silva de Carvalho, Luzilene Pereira de Lima, and Ivan da Rocha Pitta. "iPSC therapies applied to rehabilitation in parkinson’s disease." In XIII Congresso Paulista de Neurologia. Zeppelini Editorial e Comunicação, 2021. http://dx.doi.org/10.5327/1516-3180.022.

Full text
Abstract:
Background: Parkinson’s disease (PD) is a neurological disorder that affects movement, mainly due to damage and degeneration of the nigrostriatal dopaminergic pathway. The diagnosis is made through a clinical neurological analysis where motor characteristics are considered. There is still no cure, and treatment strategies are focused on symptoms control. Cell replacement therapies emerge as an alternative. Objective: This review focused on current techniques of induced pluripotent stem cells (iPSCs). Methods: The search terms used were: “Parkinson’s Disease”, “Stem cells” and “iPSC”. Open articles written in English, from 2016-21 were selected in the Pubmed database, 10 publications were identified. Results: With the modernization of iPSC, it was possible to reprogram pluripotent human somatic cells and generate dopaminergic neurons and individual-specific glial cells. To understand the molecular basis, cell and animal models of neurons and organelles are currently being employed. Organoids are derived from stem cells in a three-dimensional matrix, such as matrigel or hydrogels derived from animals. The neuronal models are: α-synuclein (SNCA), leucine-rich repeat kinase2 (LRRK2), PARK2, putative kinase1 induced by phosphatase and tensin homolog (PINK1), DJ-1. Both models offer opportunities to investigate pathogenic mechanisms of PD and test compounds on human neurons. Conclusions: Cell replacement therapy is promising and has great capacity for the treatment of neurodegenerative diseases. Studies using iPSC neuron and PD organoid modeling is highly valuable in elucidating relevants neuronal pathways and therapeutic targets, moreover providing important models for testing future therapies.
APA, Harvard, Vancouver, ISO, and other styles
3

WANG, RUBIN, and ZHIKANG ZHANG. "NONLINEAR STOCHASTIC MODELS OF NEURONS ACTIVITIES." In Proceedings of the 16th International Conference. WORLD SCIENTIFIC, 2001. http://dx.doi.org/10.1142/9789812811165_0092.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

Ishlam Nazrul, Mohammad Nazrul, Carl Tropper, Robert A. McDougal, and William W. Lytton. "Optimizations for Neuron Time Warp(NTW) for stochastic reaction-diffusion models of neurons." In 2017 Winter Simulation Conference (WSC). IEEE, 2017. http://dx.doi.org/10.1109/wsc.2017.8247871.

Full text
APA, Harvard, Vancouver, ISO, and other styles
5

Cao, Guoxin, You Zhou, Jeong Soon Lee, Jung Yul Lim, and Namas Chandra. "Mechanical Model of Neuronal Function Loss." In ASME 2010 International Mechanical Engineering Congress and Exposition. ASMEDC, 2010. http://dx.doi.org/10.1115/imece2010-39447.

Full text
Abstract:
The mechanism of mild traumatic brain injury (mTBI) is directly related to the relationship between the mechanical response of neurons and their biological/chemical functions since the neuron is the main functional component of brain.1 The hypotheses is that the external mechanical load will firstly cause the mechanical deformation of neurons, and then, when the mechanical deformation of neurons reaches to a critical point (the mechanical deformation threshold), it will initiate the chemical/biological response (e.g. neuronal function loss). Therefore, defining and measuring the mechanical deformation threshold for the neuronal cell injury is an important first step to understand the mechanism of mTBI. Typically, the mechanical response of neurons is investigated based on the deformation of in vitro model, in which the neurons are cultured on the elastic substrate (e.g. PDMS membranes). The elastic membrane is deformed by the external load, e.g. equibiaxial stretching. The substrate deformation is considered to be the deformation of neurons since the substrate is several orders stiffer than the neurons and the neurons are perfectly bonded with the substrate. The fluoresce method is typically used to test the cell injury, e.g. the cell vitality and the neuron internal ROS level.1, 2
APA, Harvard, Vancouver, ISO, and other styles
6

Zhang, Hao, Yajie Miao, and Florian Metze. "Regularizing DNN acoustic models with Gaussian stochastic neurons." In ICASSP 2015 - 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2015. http://dx.doi.org/10.1109/icassp.2015.7178915.

Full text
APA, Harvard, Vancouver, ISO, and other styles
7

Durrani, Nadir, Hassan Sajjad, Fahim Dalvi, and Yonatan Belinkov. "Analyzing Individual Neurons in Pre-trained Language Models." In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Stroudsburg, PA, USA: Association for Computational Linguistics, 2020. http://dx.doi.org/10.18653/v1/2020.emnlp-main.395.

Full text
APA, Harvard, Vancouver, ISO, and other styles
8

FILO, G. "Analysis of Neural Network Structure for Implementation of the Prescriptive Maintenance Strategy." In Terotechnology XII. Materials Research Forum LLC, 2022. http://dx.doi.org/10.21741/9781644902059-40.

Full text
Abstract:
Abstract. This paper provides an initial analysis of neural network implementation possibilities in practical implementations of the prescriptive maintenance strategy. The main issues covered are the preparation and processing of input data, the choice of artificial neural network architecture and the models of neurons used in each layer. The methods of categorisation and normalisation within each distinguished category were proposed in input data. Based on the normalisation results, it was suggested to use specific neuron activation functions. As part of the network structure, the applied solutions were analysed, including the number of neuron layers used and the number of neurons in each layer. In further work, the proposed structures of neural networks may undergo a process of supervised or partially supervised training to verify the accuracy and confidence level of the results they generate.
APA, Harvard, Vancouver, ISO, and other styles
9

Leugering, Johannes. "Making spiking neurons more succinct with multi-compartment models." In NICE '20: Neuro-inspired Computational Elements Workshop. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3381755.3381763.

Full text
APA, Harvard, Vancouver, ISO, and other styles
10

Ning, Ning, Kejie Huang, and Luping Shi. "Artificial neuron with somatic and axonal computation units: Mathematical and neuromorphic models of persistent firing neurons." In 2012 International Joint Conference on Neural Networks (IJCNN 2012 - Brisbane). IEEE, 2012. http://dx.doi.org/10.1109/ijcnn.2012.6252428.

Full text
APA, Harvard, Vancouver, ISO, and other styles

Reports on the topic "Neurons Models"

1

Wynshaw-Boris, Anthony. Testing Brain Overgrowth and Synaptic Models of Autism Using NPCs and Neurons from Patient-Derived iPS Cells. Fort Belvoir, VA: Defense Technical Information Center, October 2014. http://dx.doi.org/10.21236/ada613860.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

Zigmond, Michael J., Amanda Smith, and Anthony Liou. The Impact of Exercise on the Vulnerability of Dopamine Neurons to Cell Death in Animal Models of Parkinson's Disease. Fort Belvoir, VA: Defense Technical Information Center, July 2008. http://dx.doi.org/10.21236/ada501105.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

Gothilf, Yoav, Yonathan Zohar, Susan Wray, and Hanna Rosenfeld. Inducing sterility in farmed fish by disrupting the development of the GnRH System. United States Department of Agriculture, October 2007. http://dx.doi.org/10.32747/2007.7696512.bard.

Full text
Abstract:
Hypothalamic gonadotropinreleasing hormone (GnRH1) is the key hormone in the control of gametogenesis and gonadal growth in vertebrates. Developmentally, hypothalamic GnRHproducing neurons originate from the olfactory placode, migrate along olfactory axons into the forebrain, and continue to the preoptic area and hypothalamus where they function to stimulate gonadotropin secretion from the pituitary gland. An appropriate location of GnRH neurons within the hypothalamus is necessary for normal reproductive function in the adult; abnormal migration and targeting of GnRH neurons during embryogenesis results in hypogonadism and infertility. The developmental migration of GnRH neurons and axonal pathfinding in mammals are modulated by a plethora of factors, including receptors, secreted molecules, adhesion molecules, etc. Yet the exact mechanism that controls these developmental events is still unknown. We investigated these developmental events and the underlying mechanisms using a transgenic zebrafish model, Tg(gnrh1: EGFP), in which GnRH1 neurons and axons are fluorescently labeled. The role of factors that potentially affect the development of this system was investigated by testing the effect of their knockdown and mutation on the development of the GnRH1 system. In addition, their localization in relation to GnRH1 was described during development. These studies are expected to generate the scientific foundation that will lead to developing innovative technologies, based on the disruption of the early establishment of the GnRH system, for inducing sterility in farmed fish, which is highly desirable for economical and environmental reasons.
APA, Harvard, Vancouver, ISO, and other styles
4

Ori, Naomi, and Sarah Hake. Similarities and differences in KNOX function. United States Department of Agriculture, March 2008. http://dx.doi.org/10.32747/2008.7696516.bard.

Full text
Abstract:
Hypothalamic gonadotropinreleasing hormone (GnRH1) is the key hormone in the control of gametogenesis and gonadal growth in vertebrates. Developmentally, hypothalamic GnRHproducing neurons originate from the olfactory placode, migrate along olfactory axons into the forebrain, and continue to the preoptic area and hypothalamus where they function to stimulate gonadotropin secretion from the pituitary gland. An appropriate location of GnRH neurons within the hypothalamus is necessary for normal reproductive function in the adult; abnormal migration and targeting of GnRH neurons during embryogenesis results in hypogonadism and infertility. The developmental migration of GnRH neurons and axonal pathfinding in mammals are modulated by a plethora of factors, including receptors, secreted molecules, adhesion molecules, etc. Yet the exact mechanism that controls these developmental events is still unknown. We investigated these developmental events and the underlying mechanisms using a transgenic zebrafish model, Tg(gnrh1: EGFP), in which GnRH1 neurons and axons are fluorescently labeled. The role of factors that potentially affect the development of this system was investigated by testing the effect of their knockdown and mutation on the development of the GnRH1 system. In addition, their localization in relation to GnRH1 was described during development. These studies are expected to generate the scientific foundation that will lead to developing innovative technologies, based on the disruption of the early establishment of the GnRH system, for inducing sterility in farmed fish, which is highly desirable for economical and environmental reasons.
APA, Harvard, Vancouver, ISO, and other styles
5

Elmann, Anat, Orly Lazarov, Joel Kashman, and Rivka Ofir. therapeutic potential of a desert plant and its active compounds for Alzheimer's Disease. United States Department of Agriculture, March 2015. http://dx.doi.org/10.32747/2015.7597913.bard.

Full text
Abstract:
We chose to focus our investigations on the effect of the active forms, TTF and AcA, rather than the whole (crude) extract. 1. To establish cultivation program designed to develop lead cultivar/s (which will be selected from the different Af accessions) with the highest yield of the active compounds TTF and/or achillolide A (AcA). These cultivar/s will be the source for the purification of large amounts of the active compounds when needed in the future for functional foods/drug development. This task was completed. 2. To determine the effect of the Af extract, TTF and AcA on neuronal vulnerability to oxidative stress in cultured neurons expressing FAD-linked mutants.Compounds were tested in N2a neuroblastoma cell line. In addition, we have tested the effects of TTF and AcA on signaling events promoted by H₂O₂ in astrocytes and by β-amyloid in neuronal N2a cells. 3. To determine the effect of the Af extract, TTF and AcA on neuropathology (amyloidosis and tau phosphorylation) in cultured neurons expressing FAD-linked mutants. 4. To determine the effect of A¦ extract, AcA and TTF on FAD-linked neuropathology (amyloidosis, tau phosphorylation and inflammation) in transgenic mice. 5. To examine whether A¦ extract, TTF and AcA can reverse behavioral deficits in APPswe/PS1DE9 mice, and affect learning and memory and cognitive performance in these FAD-linked transgenic mice. Background to the topic.Neuroinflammation, oxidative stress, glutamate toxicity and amyloid beta (Ab) toxicity are involved in the pathogenesis of Alzheimer's diseases. We have previously purified from Achilleafragrantissimatwo active compounds: a protective flavonoid named 3,5,4’-trihydroxy-6,7,3’-trimethoxyflavone (TTF, Fl-72/2) and an anti-inflammatory sesquiterpenelactone named achillolide A (AcA). Major conclusions, solutions, achievements. In this study we could show that TTF and AcA protected cultured astrocytes from H₂O₂ –induced cell death via interference with cell signaling events. TTF inhibited SAPK/JNK, ERK1/2, MEK1 and CREBphosphorylation, while AcA inhibited only ERK1/2 and MEK1 phosphorylation. In addition to its protective activities, TTF had also anti-inflammatory activities, and inhibited the LPS-elicited secretion of the proinflammatorycytokinesInterleukin 6 (IL-6) and IL-1b from cultured microglial cells. Moreover, TTF and AcA protected neuronal cells from glutamate and Abcytotoxicity by reducing the glutamate and amyloid beta induced levels of intracellular reactive oxygen species (ROS) and via interference with cell signaling events induced by Ab. These compounds also reduced amyloid precursor protein net processing in vitro and in vivo in a mouse model for Alzheimer’s disease and improvedperformance in the novel object recognition learning and memory task. Conclusion: TTF and AcA are potential candidates to be developed as drugs or food additives to prevent, postpone or ameliorate Alzheimer’s disease. Implications, both scientific and agricultural.The synthesis ofAcA and TTF is very complicated. Thus, the plant itself will be the source for the isolation of these compounds or their precursors for synthesis. Therefore, Achilleafragrantissima could be developed into a new crop with industrial potential for the Arava-Negev area in Israel, and will generate more working places in this region.
APA, Harvard, Vancouver, ISO, and other styles
6

Rulkov, Nikolai. Nonlinear Maps for Design of Discrete Time Models of Neuronal Network Dynamics. Fort Belvoir, VA: Defense Technical Information Center, February 2016. http://dx.doi.org/10.21236/ad1004577.

Full text
APA, Harvard, Vancouver, ISO, and other styles
7

Rulkov, Nikolai. Nonlinear Maps for Design of Discrete-Time Models of Neuronal Network Dynamics. Fort Belvoir, VA: Defense Technical Information Center, March 2016. http://dx.doi.org/10.21236/ad1007639.

Full text
APA, Harvard, Vancouver, ISO, and other styles
8

Pearson, John, and David Sarnoff. Models of the Neuronal Mechanisms of Target Localization of the Barn Owl. Fort Belvoir, VA: Defense Technical Information Center, December 1990. http://dx.doi.org/10.21236/ada230410.

Full text
APA, Harvard, Vancouver, ISO, and other styles
9

Forger, Daniel B. Information Processing and Collective Behavior in a Model Neuronal System. Fort Belvoir, VA: Defense Technical Information Center, March 2014. http://dx.doi.org/10.21236/ada601965.

Full text
APA, Harvard, Vancouver, ISO, and other styles
10

Rothstein, Jeffrey D. Anti-Excitotoxic and Antioxidant TGF-Beta Family Neurotrophic Factors: In Vitro Screening Models of Motor Neuron Degeneration. Fort Belvoir, VA: Defense Technical Information Center, July 2002. http://dx.doi.org/10.21236/ada405360.

Full text
APA, Harvard, Vancouver, ISO, and other styles
You might also be interested in the extended bibliographies on the topic 'Neurons Models' for particular source types:
Journal articles Dissertations / Theses Books
We offer discounts on all premium plans for authors whose works are included in thematic literature selections. Contact us to get a unique promo code!

To the bibliography