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Sample records for model neurons nt2

  1. Integration of human model neurons (NT2) into embryonic chick nervous system.

    PubMed

    Podrygajlo, Grzegorz; Wiegreffe, Christoph; Scaal, Martin; Bicker, Gerd

    2010-02-01

    Postmitotic neurons were generated from the human NT2 teratocarcinoma cell line in a novel cell aggregate differentiation procedure. Approximately a third of the differentiated neurons expressed cell markers related to cholinergic neurotransmission. To examine whether this human cell model system can be directed toward a motoneuronal fate, postmitotic neurons were co-cultured with mouse myotubes. Outgrowing neuronal processes established close contact with the myotubes and formed neuromuscular junction-like structures that bound alpha-bungarotoxin. To determine how grafted precursor cells and neurons respond to embryonic nerve tissue, NT2 cells at different stages of neural development were injected into chick embryo neural tube and brain. Grafted NT2 neurons populated both parts of the nervous system, sometimes migrating away from the site of injection. The neural tube appeared to be more permissive for neurite extensions than the brain. Moreover, extending neurites of spinal grafts were approaching the ventral roots, thus resembling motoneuronal projections.

  2. Potential for Cell-Transplant Therapy with Human Neuronal Precursors to Treat Neuropathic Pain in Models of PNS and CNS Injury: Comparison of hNT2.17 and hNT2.19 Cell Lines

    PubMed Central

    Eaton, Mary J.; Berrocal, Yerko; Wolfe, Stacey Q.

    2012-01-01

    Effective treatment of sensory neuropathies in peripheral neuropathies and spinal cord injury (SCI) is one of the most difficult problems in modern clinical practice. Cell therapy to release antinociceptive agents near the injured spinal cord is a logical next step in the development of treatment modalities. But few clinical trials, especially for chronic pain, have tested the potential of transplant of cells to treat chronic pain. Cell lines derived from the human neuronal NT2 cell line parentage, the hNT2.17 and hNT2.19 lines, which synthesize and release the neurotransmitters gamma-aminobutyric acid (GABA) and serotonin (5HT), respectively, have been used to evaluate the potential of cell-based release of antinociceptive agents near the lumbar dorsal (horn) spinal sensory cell centers to relieve neuropathic pain after PNS (partial nerve and diabetes-related injury) and CNS (spinal cord injury) damage in rat models. Both cell lines transplants potently and permanently reverse behavioral hypersensitivity without inducing tumors or other complications after grafting. Functioning as cellular minipumps for antinociception, human neuronal precursors, like these NT2-derived cell lines, would likely provide a useful adjuvant or replacement for current pharmacological treatments for neuropathic pain. PMID:22619713

  3. Effect of barbiturates on hydroxyl radicals, lipid peroxidation, and hypoxic cell death in human NT2-N neurons.

    PubMed

    Almaas, R; Saugstad, O D; Pleasure, D; Rootwelt, T

    2000-03-01

    Barbiturates have been shown to be neuroprotective in several animal models, but the underlying mechanisms are unknown. In this study, the authors investigated the effect of barbiturates on free radical scavenging and attempted to correlate this with their neuroprotective effects in a model of hypoxic cell death in human NT2-N neurons. Hydroxyl radicals were generated by ascorbic acid and iron and were measured by conversion of salicylate to 2,3-dihydroxybenzoic acid. The effect of barbiturates on lipid peroxidation measured as malondialdehyde and 4-hydroxynon-2-enal was also investigated. Hypoxia studies were then performed on human NT2-N neurons. The cells were exposed to 10 h of hypoxia or combined oxygen and glucose deprivation for 3 or 5 h in the presence of thiopental (50-600 microM), methohexital (50-400 microM), phenobarbital (10-400 microM), or pentobarbital (10-400 microM), and cell death was evaluated after 24 h by lactate dehydrogenase release. Pentobarbital, phenobarbital, methohexital, and thiopental dose-dependently inhibited formation of 2,3-dihydroxybenzoic acid and iron-stimulated lipid peroxidation. There were significant but moderate differences in antioxidant action between the barbiturates. While phenobarbital (10-400 microM) and pentobarbital (10-50 microM) increased lactate dehydrogenase release after combined oxygen and glucose deprivation, thiopental and methohexital protected the neurons at all tested concentrations. At a higher concentration (400 microM), pentobarbital also significantly protected the neurons. At both 50 and 400 microM, thiopental and methohexital protected the NT2-N neurons significantly better than phenobarbital and pentobarbital. Barbiturates differ markedly in their neuroprotective effects against combined oxygen and glucose deprivation in human NT2-N neurons. The variation in neuroprotective effects could only partly be explained by differences in antioxidant action.

  4. Data for the morphometric characterization of NT2-derived postmitotic neurons

    PubMed Central

    González-Burguera, Imanol; Ricobaraza, Ana; Aretxabala, Xabier; Barrondo, Sergio; García del Caño, Gontzal; López de Jesús, Maider; Sallés, Joan

    2016-01-01

    NTERA2/D1 human teratocarcinoma progenitors induced to differentiate into postmitotic neurons by either long-term treatment with retinoic acid or short-term treatment with the nucleoside analog cytosine β-D-arabinofuranoside were subjected to morphometric analysis and compared. Our data provide a methodological and conceptual framework for future investigations aiming at distinguishing neuronal phenotypes on the basis of morphometric analysis. Data presented here are related to research concurrently published in “Highly Efficient Generation of Glutamatergic/Cholinergic NT2-Derived Postmitotic Human Neurons by Short-Term treatment with the Nucleoside Analogue Cytosine β-D-Arabinofuranoside” [1]. PMID:27158648

  5. A not cytotoxic nickel concentration alters the expression of neuronal differentiation markers in NT2 cells.

    PubMed

    Ceci, Claudia; Barbaccia, Maria Luisa; Pistritto, Giuseppa

    2015-03-01

    Nickel, a known occupational/environmental hazard, may cross the placenta and reach appreciable concentrations in various fetal organs, including the brain. The aim of this study was to investigate whether nickel interferes with the process of neuronal differentiation. Following a 4 week treatment with retinoic acid (10μM), the human teratocarcinoma-derived NTera2/D1 cell line (NT2 cells) terminally differentiate into neurons which recapitulate many features of human fetal neurons. The continuous exposure of the differentiating NT2 cells to a not cytotoxic nickel concentration (10μM) increased the expression of specific neuronal differentiation markers such as neural cell adhesion molecule (NCAM) and microtubule associated protein 2 (MAP2). Furthermore, nickel exposure increased the expression of hypoxia-inducible-factor-1α (HIF-1α) and induced the activation of the AKT/PKB kinase pathway, as shown by the increase of P(Ser-9)-GSK-3β, the inactive form of glycogen synthase kinase-3β (GSK-3β). Intriguingly, by the end of the fourth week the expression of tyrosine hydroxylase (TH) protein, a marker of dopaminergic neurons, was lower in nickel-treated than in control cultures. Thus, likely by partially mimicking hypoxic conditions, a not-cytotoxic nickel concentration appears to alter the process of neuronal differentiation and hinder the expression of the dopaminergic neuronal phenotype. Taken together, these results suggest that nickel, by altering normal brain development, may increase susceptibility to neuro-psychopathology later in life.

  6. Spatiotemporal changes in Cx30 and Cx43 expression during neuronal differentiation of P19 EC and NT2/D1 cells.

    PubMed

    Wan, Carthur K; O'Carroll, Simon J; Kim, Sue-Ling; Green, Colin R; Nicholson, Louise F B

    2013-12-01

    While connexins (Cxs) are thought to be involved in differentiation, their expression and role has yet to be fully elucidated. We investigated the temporal expression of Cx30, Cx36 and Cx43 in two in vitro models of neuronal differentiation: human NT2/D1 and murine P19 cells, and the spatial localisation of Cx30 and Cx43 in these models. A temporal Cx43 downregulation was confirmed in both cell lines during RA-induced neuronal differentiation using RT-PCR (P < 0.05) preceding an increase in neuronal doublecortin protein. RT-PCR showed Cx36 was upregulated twofold in NT2/D1 cells (P < 0.05) and sixfold in P19 cells (P < 0.001) during neuronal differentiation. Cx30 exhibited a transient peak in expression midway through the timecourse of differentiation increasing threefold in NT2/D1 cells (P < 0.001) and eightfold in P19 cells (P < 0.01). Qualitative immunocytochemistry was used to examine spatiotemporal patterns of Cx protein distribution alongside neuronal differentiation markers. The temporal immunolabelling pattern was similar to that seen using RT-PCR. Cx43 was observed intracellularly and on cell surfaces, while Cx30 was seen as puncta. Spatially Cx43 was seen on doublecortin-negative cells, which may indicate Cx43 downregulation is requisite for differentiation in these models. Conversely, Cx30 puncta were observed on doublecortin-positive and -negative cells in NT2/D1 cells and examination of the Cx30 peak showed puncta also localized to nestin-positive cells, with few puncta on MAP2-positive cells. In P19 cells Cx30 was localized on clusters of cells surrounded by MAP2- and doublecortin-positive processes. The expression pattern of Cx30 indicates a role in neuronal differentiation; the nature of that role warrants future investigation.

  7. Stabilization of transcription factor Nrf2 by tBHQ prevents oxidative stress-induced amyloid beta formation in NT2N neurons.

    PubMed

    Eftekharzadeh, Bahareh; Maghsoudi, Nader; Khodagholi, Fariba

    2010-03-01

    Alzheimer's disease (AD) a progressive neurodegenerative disorder of later life, is characterized by brain deposition of amyloid beta-protein (Abeta) plaques, accumulation of intracellular neurofibrillatory tangles, synaptic loss and neuronal cell death. There is significant evidence that oxidative stress is a critical event in the pathogenesis of AD. In the present study Abeta formation was induced in NT2N neurons, one of the most appropriate cell line models in AD. Our results indicate that oxidative stress resulting from the treatment of H(2)O(2)/FeSO(4) and/or 4-hydroxy-2-noenal (HNE) can be inhibited in the presence of tBHQ, a known inducer of nuclear factor-erythroid 2 related factor 2 (Nrf2) in NT2N neurons and can therefore be used to elucidate the relationship between oxidative stress, Abeta formation and Nrf2. The role of Nrf2 was confirmed using retinoic acid as an inhibitor of Nrf2. It provides the first documentation that tBHQ not only protects the neurons against cell death but also decreases amyloid beta formation. Moreover, the results indicate that oxidative stress fosters Abeta formation in NT2N neurons, creating a vicious neurodegenerative loop. Copyright (c) 2009 Elsevier Masson SAS. All rights reserved.

  8. Alterations in Glutamate Uptake in NT2-Derived Neurons and Astrocytes after Exposure to Gamma Radiation

    PubMed Central

    Sanchez, Martha C.; Benitez, Abigail; Ortloff, Leticia; Green, Lora M.

    2009-01-01

    Currently, the cellular and molecular mechanisms that underlie radiation-induced damage in the CNS are unclear. The present study began investigations of the underlying mechanism(s) for radiation-induced neurotoxicity by characterizing glutamate transport expression and function in neurons and astrocytes after exposure to γ rays. NTera2-derived neurons and astrocytes, isolated as pure cultures, were exposed to doses of 10 cGy, 50 cGy and 2 Gy γ rays, and transporter expression and function were assessed 3 h, 2 days and 7 days after exposure. In neurons, at 7 days after exposure, a significant increase was detected in EAAT3 after 50 cGy (P < 0.05) and a dose-dependent increase in GLT-1 expression was seen between doses of 10 and 50 cGy (P < 0.05). Functional assays of glutamate uptake revealed that neurons and astrocytes respond in a reciprocal manner after irradiation. Neurons responded to radiation exposure by increased glutamate uptake, an effect still evident at our last time (7 days) after exposure (P < 0.05). The astrocyte response to γ radiation was an initial decrease in uptake followed by recovery to baseline levels at 2 days after exposure (P < 0.05). The observations made in this study demonstrate that neurons and astrocytes, while part of the same multifunctional unit, have distinct functional and reciprocal responses. The response in neurons appears to indicate a protracted response with potential long-term effects after irradiation. PMID:19138048

  9. ER-mediated stress induces mitochondrial-dependent caspases activation in NT2 neuron-like cells.

    PubMed

    Arduino, Daniela M; Esteves, A Raquel; Domingues, A Filipa; Pereira, Claudia M F; Cardoso, Sandra M; Oliveira, Catarina R

    2009-11-30

    Recent studies have revealed that endoplasmic reticulum (ER) disturbance is involved in the pathophysiology of neurodegenerative disorders, contributing to the activation of the ER stress-mediated apoptotic pathway. Therefore, we investigated here the molecular mechanisms underlying the ER-mitochondria axis, focusing on calcium as a potential mediator of cell death signals. Using NT2 cells treated with brefeldin A or tunicamycin, we observed that ER stress induces changes in the mitochondrial function, impairing mitochondrial membrane potential and distressing mitochondrial respiratory chain complex Moreover, stress stimuli at ER level evoked calcium fluxes between ER and mitochondria. Under these conditions, ER stress activated the unfolded protein response by an overexpression of GRP78, and also caspase-4 and-2, both involved upstream of caspase-9. Our findings show that ER and mitochondria interconnection plays a prominent role in the induction of neuronal cell death under particular stress circumstances.

  10. Non ionising radiation as a non chemical strategy in regenerative medicine: Ca(2+)-ICR "In Vitro" effect on neuronal differentiation and tumorigenicity modulation in NT2 cells.

    PubMed

    Ledda, Mario; Megiorni, Francesca; Pozzi, Deleana; Giuliani, Livio; D'Emilia, Enrico; Piccirillo, Sara; Mattei, Cristiana; Grimaldi, Settimio; Lisi, Antonella

    2013-01-01

    In regenerative medicine finding a new method for cell differentiation without pharmacological treatment or gene modification and minimal cell manipulation is a challenging goal. In this work we reported a neuronal induced differentiation and consequent reduction of tumorigenicity in NT2 human pluripotent embryonal carcinoma cells exposed to an extremely low frequency electromagnetic field (ELF-EMF), matching the cyclotron frequency corresponding to the charge/mass ratio of calcium ion (Ca(2+)-ICR). These cells, capable of differentiating into post-mitotic neurons following treatment with Retinoic Acid (RA), were placed in a solenoid and exposed for 5 weeks to Ca(2+)-ICR. The solenoid was installed in a μ-metal shielded room to avoid the effect of the geomagnetic field and obtained totally controlled and reproducible conditions. Contrast microscopy analysis reveled, in the NT2 exposed cells, an important change in shape and morphology with the outgrowth of neuritic-like structures together with a lower proliferation rate and metabolic activity alike those found in the RA treated cells. A significant up-regulation of early and late neuronal differentiation markers and a significant down-regulation of the transforming growth factor-α (TGF-α) and the fibroblast growth factor-4 (FGF-4) were also observed in the exposed cells. The decreased protein expression of the transforming gene Cripto-1 and the reduced capability of the exposed NT2 cells to form colonies in soft agar supported these last results. In conclusion, our findings demonstrate that the Ca(2+)-ICR frequency is able to induce differentiation and reduction of tumorigenicity in NT2 exposed cells suggesting a new potential therapeutic use in regenerative medicine.

  11. Cyanide preconditioning protects brain endothelial and NT2 neuron-like cells against glucotoxicity: role of mitochondrial reactive oxygen species and HIF-1α.

    PubMed

    Correia, Sónia C; Santos, Renato X; Cardoso, Sandra M; Santos, Maria S; Oliveira, Catarina R; Moreira, Paula I

    2012-01-01

    The current study was undertaken to address the role of mitochondrial reactive oxygen species (ROS), and hypoxia inducible factor-1 alpha (HIF-1α) signaling pathway in the protection against high glucose levels in brain endothelial and NT2 neuron-like cells. Rat brain endothelial cells (RBE4) treated with non-toxic concentrations of cyanide (≤1 μM; 1h) exhibited an increase in ROS levels, particularly hydrogen peroxide (H(2)O(2)). Cyanide also induced a modest mitochondrial depolarization, an increase in oxygen consumption and a structural (smaller mitochondria) and spatial (perinuclear region) reorganization of mitochondrial network. The stabilization and nuclear activation of HIF-1α in the presence of cyanide were also observed, which resulted in an increase in vascular endothelial growth factor (VEGF), endothelial nitric oxide synthase (eNOS) and erythropoietin (EPO) protein levels reflecting an adaptive response. Importantly, preconditioning induced by cyanide protected brain endothelial cells against high glucose-mediated damage by the prevention of apoptotic cell death. In mitochondrial DNA-depleted NT2 (NT2 ρ0) cells, cyanide (0.1 μM) was unable to stimulate ROS production and, consequently, protect against glucotoxicity. Conversely, in NT2 cells, the parental cells with functional mitochondria, cyanide significantly increased ROS levels protecting against high glucose-induced neuronal cell loss and activation of caspase-3. The free radical scavenger N-acetyl-L-cysteine and the specific HIF-1α inhibitor 2-methoxyestradiol completely abolished the protective effects of cyanide preconditioning. Altogether our results demonstrate that mitochondrial preconditioning induced by cyanide triggers a protective response mediated by mitochondrial ROS and HIF-1α activation and signaling, which render brain endothelial and neuronal cells resistant against glucotoxicity.

  12. Transcriptional activation of the human brain-derived neurotrophic factor gene promoter III by dopamine signaling in NT2/N neurons.

    PubMed

    Fang, Hung; Chartier, Joanne; Sodja, Caroline; Desbois, Angele; Ribecco-Lutkiewicz, Maria; Walker, P Roy; Sikorska, Marianna

    2003-07-18

    We have identified a functional cAMP-response element (CRE) in the human brain-derived neurotrophic factor (BDNF) gene promoter III and established that it participated in the modulation of BDNF expression in NT2/N neurons via downstream signaling from the D1 class of dopamine (DA) receptors. The up-regulation of BDNF expression, in turn, produced neuroprotective signals through receptor tyrosine kinase B (TrkB) and promoted cell survival under the conditions of oxygen and glucose deprivation. To our knowledge this is the first evidence showing the presence of a functional CRE in the human BDNF gene and the role of DA signaling in establishing transcriptional competence of CRE in post-mitotic NT2/N neurons. This ability of DA to regulate the expression of the BDNF survival factor has a profound significance for the nigrostriatal pathway, because it indicates the existence of a feedback loop between the neutrophin, which promotes both the maturation and survival of dopaminergic neurons, and the neurotransmitter, which the mature neurons ultimately produce and release.

  13. Expression of glycogenes in differentiating human NT2N neurons. Downregulation of fucosyltransferase 9 leads to decreased Lewis(x) levels and impaired neurite outgrowth.

    PubMed

    Gouveia, Ricardo; Schaffer, Lana; Papp, Suzanne; Grammel, Nicolas; Kandzia, Sebastian; Head, Steven R; Kleene, Ralf; Schachner, Melitta; Conradt, Harald S; Costa, Júlia

    2012-12-01

    Several glycan structures are functionally relevant in biological events associated with differentiation and regeneration which occur in the central nervous system. Here we have analysed the glycogene expression and glycosylation patterns during human NT2N neuron differentiation. We have further studied the impact of downregulating fucosyltransferase 9 (FUT9) on neurite outgrowth. The expression of glycogenes in human NT2N neurons differentiating from teratocarcinoma NTERA-2/cl.D1 cells has been analysed using the GlycoV4 GeneChip expression microarray. Changes in glycosylation have been monitored by immunoblot, immunofluorescence microscopy, HPLC and MALDI-TOF MS. Peptide mass fingerprinting and immunoprecipitation have been used for protein identification. FUT9 was downregulated using silencing RNA. One hundred twelve mRNA transcripts showed statistically significant up-regulation, including the genes coding for proteins involved in the synthesis of the Lewis(x) motif (FUT9), polysialic acid (ST8SIA2 and ST8SIA4) and HNK-1 (B3GAT2). Accordingly, increased levels of the corresponding carbohydrate epitopes have been observed. The Lewis(x) structure was found in a carrier glycoprotein that was identified as the CRA-a isoform of human neural cell adhesion molecule 1. Downregulation of FUT9 caused significant decreases in the levels of Lewis(x), as well as GAP-43, a marker of neurite outgrowth. Concomitantly, a reduction in neurite formation and outgrowth has been observed that was reversed by FUT9 overexpression. These results provided information about the regulation of glycogenes during neuron differentiation and they showed that the Lewis(x) motif plays a functional role in neurite outgrowth from human neurons. Copyright © 2012 Elsevier B.V. All rights reserved.

  14. Subarachnoid Transplant of the Human Neuronal hNT2.19 Serotonergic Cell Line Attenuates Behavioral Hypersensitivity without Affecting Motor Dysfunction after Severe Contusive Spinal Cord Injury

    PubMed Central

    Eaton, Mary J.; Widerström-Noga, Eva; Wolfe, Stacey Quintero

    2011-01-01

    Transplant of cells which make biologic agents that can modulate the sensory and motor responses after spinal cord injury (SCI) would be useful to treat pain and paralysis. To address this need for clinically useful human cells, a unique neuronal cell line that synthesizes and secretes/releases the neurotransmitter serotonin (5HT) was isolated. Hind paw tactile allodynia and thermal hyperalgesia induced by severe contusive SCI were potently reversed after lumbar subarachnoid transplant of differentiated cells, but had no effect on open field motor scores, stride length, foot rotation, base of support, or gridwalk footfall errors associated with the SCI. The sensory effects appeared 1 week after transplant and did not diminish during the 8-week course of the experiment when grafts were placed 2 weeks after SCI. Many grafted cells were still present and synthesizing 5HT at the end of the study. These data suggest that the human neuronal serotonergic hNT2.19 cells can be used as a biologic minipump for receiving SCI-related neuropathic pain, but likely requires intraspinal grafts for motor recovery. PMID:21799949

  15. Non Ionising Radiation as a Non Chemical Strategy in Regenerative Medicine: Ca2+-ICR “In Vitro” Effect on Neuronal Differentiation and Tumorigenicity Modulation in NT2 Cells

    PubMed Central

    Ledda, Mario; Megiorni, Francesca; Pozzi, Deleana; Giuliani, Livio; D’Emilia, Enrico; Piccirillo, Sara; Mattei, Cristiana; Grimaldi, Settimio; Lisi, Antonella

    2013-01-01

    In regenerative medicine finding a new method for cell differentiation without pharmacological treatment or gene modification and minimal cell manipulation is a challenging goal. In this work we reported a neuronal induced differentiation and consequent reduction of tumorigenicity in NT2 human pluripotent embryonal carcinoma cells exposed to an extremely low frequency electromagnetic field (ELF-EMF), matching the cyclotron frequency corresponding to the charge/mass ratio of calcium ion (Ca2+-ICR). These cells, capable of differentiating into post-mitotic neurons following treatment with Retinoic Acid (RA), were placed in a solenoid and exposed for 5 weeks to Ca2+-ICR. The solenoid was installed in a μ-metal shielded room to avoid the effect of the geomagnetic field and obtained totally controlled and reproducible conditions. Contrast microscopy analysis reveled, in the NT2 exposed cells, an important change in shape and morphology with the outgrowth of neuritic-like structures together with a lower proliferation rate and metabolic activity alike those found in the RA treated cells. A significant up-regulation of early and late neuronal differentiation markers and a significant down-regulation of the transforming growth factor-α (TGF-α) and the fibroblast growth factor-4 (FGF-4) were also observed in the exposed cells. The decreased protein expression of the transforming gene Cripto-1 and the reduced capability of the exposed NT2 cells to form colonies in soft agar supported these last results. In conclusion, our findings demonstrate that the Ca2+-ICR frequency is able to induce differentiation and reduction of tumorigenicity in NT2 exposed cells suggesting a new potential therapeutic use in regenerative medicine. PMID:23585910

  16. Statistical modeling and optimization of culture conditions by response surface methodology for 2,4- and 2,6-dinitrotoluene biodegradation using Rhodococcus pyridinivorans NT2.

    PubMed

    Kundu, Debasree; Hazra, Chinmay; Chaudhari, Ambalal

    2016-12-01

    To improve biodegradability (% biodegradation) and specific growth rate of Rhodococcus pyridinivorans NT2, culture medium and environmental parameters were screened and optimized using the statistical design techniques of Plackett-Burman and response surface methodology. Of the process variables screened, DNTs (2,4-DNT and 2,6-DNT), MgSO4·7H2O, temperature and inoculum size (O.D.) were selected as the most important (P value <0.05) factors. In multiresponse analysis of central composite design, medium formulation consisting of 474/470 mg l(-1) 2,4-DNT/2,6-DNT, 0.11 g l(-1) MgSO4·7H2O, 37.5 °C temperature and 1.05 OD inoculum size were found to predict maximum % degradation and specific growth rate of 97.55 % and 0.19 h(-1), respectively. The validity of the optimized variables was verified in shake flasks. The optimized media significantly shortened the time required for biodegradation of DNTs while providing a nearly 30 % (for 2,4-DNT) and 70 % (for 2,6-DNT) increased biodegradation along with 5.64-fold increase in specific growth rate for both DNTs.

  17. Towards in vitro DT/DNT testing: Assaying chemical susceptibility in early differentiating NT2 cells.

    PubMed

    Menzner, Ann-Katrin; Abolpour Mofrad, Sepideh; Friedrich, Oliver; Gilbert, Daniel F

    2015-12-02

    Human pluripotent embryonal carcinoma (NT2) cells are increasingly considered as a suitable model for in vitro toxicity testing, e.g. developmental toxicity and neurotoxicity (DT/DNT) studies, as they undergo neuronal differentiation upon stimulation with retinoic acid (RA) and permit toxicity testing at different stages of maturation. NT2 cells have recently been reported to show specific changes in dielectric resistance profiles during differentiation which can be observed as early as 24h upon RA-stimulation. These observations suggest altered susceptibility to chemicals at an early stage of differentiation. However, chemical susceptibility of early differentiating NT cells has not yet been studied. To address this question, we have established a cell fitness screening assay based on the analysis of intracellular ATP levels and we applied the assay in a large-scale drug screening experiment in NT2 stem cells and early differentiating NT2 cells. Subsequent analysis of ranked fitness phenotypes revealed 19 chemicals with differential toxicity profile in early differentiating NT2 cells. To evaluate whether any of the identified drugs have previously been associated with DT/DNT, we conducted a literature search on the identified molecules and quantified the fraction of chemicals assigned to the FDA (Food and Drug Administration) pregnancy risk categories (PRC) N, A, B, C, D, and X in the hit list and the small molecule library. While the fractions of the categories N and B were decreased (0.81 and 0.35-fold), the classes C, D and X were increased (1.35, 1.47 and 3.27-fold) in the hit list compared to the chemical library. From these data as well as from the literature review, identifying large fractions of chemicals being directly (∼42%) and indirectly associated with DT/DNT (∼32%), we conclude that our method may be beneficial to systematic in vitro-based primary screening for developmental toxicants and neurotoxicants and we propose cell fitness screening in

  18. Involvement of NOS3 in RA-Induced neural differentiation of human NT2/D1 cells.

    PubMed

    Jezierski, Anna; Deb-Rinker, Paromita; Sodja, Caroline; Walker, P Roy; Ly, Dao; Haukenfrers, Julie; Sandhu, Jagdeep K; Bani-Yaghoub, Mahmud; Sikorska, Marianna

    2012-12-01

    Nitric oxide (NO) plays a key role in neurogenesis as a regulator of cell proliferation and differentiation. NO is synthesized from the amino acid L-arginine by nitric oxide synthases (NOS1, NOS2, and NOS3), which are encoded by separate genes and display different tissue distributions. We used an in vitro model of RA-induced neural differentiation of NT2 cells to examine which of the three NO-synthesizing enzymes is involved in this process. The results revealed a transient induction of NOS3 (known as the constitutively expressed endothelial nitric oxide synthase; eNOS) during the time course of the RA treatment. The peak of gene expression and the nuclear presence of NOS3 protein coincided with cell cycle exit of NT2-derived neuronal precursors. The subsequent analysis of cytosine methylation and histone H3 acetylation of the human NOS3 5' regulatory sequences indicated that epigenetic modifications, especially upstream of the proximal promoter (-734 to -989, relative to exon 2 TSS at +1), were also taking place. NOS1 was expressed only in the differentiated neurons (NT2-N), whereas NOS2 was not expressed at all in this cellular model. Thus, a burst of NO production, possibly required to inhibit neural cell proliferation, was generated by the transient expression of NOS3. This pattern of gene expression, in turn, required epigenetic remodeling of its regulatory region. Copyright © 2012 Wiley Periodicals, Inc.

  19. Potential for Cell-Transplant Therapy with Human Neuronal Precursors to Treat Neuropathic Pain in Models of PNS and CNS Injury: Comparison of hNT2.17 and hNT2.19 Cell Lines

    DTIC Science & Technology

    2012-01-01

    eliminate experimental bias; the data were analyzed and unblinded by the statistician at the end of the experiment . 2.5.3. Excitotoxic Spinal Cord...conditions to eliminate experimental bias; the data were analyzed and un-blinded by the statistician at the end of the experiment . 2.6. Sensory Behavioral...buffer, pH 7.4. All immunohistochemistry experiments included the use of a negative control, sub- stitution of specific primary antibody with species IgG

  20. SOX2 overexpression affects neural differentiation of human pluripotent NT2/D1 cells.

    PubMed

    Klajn, A; Drakulic, D; Tosic, M; Pavkovic, Z; Schwirtlich, M; Stevanovic, M

    2014-11-01

    SOX2 is one of the key transcription factors involved in maintenance of neural progenitor identity. However, its function during the process of neural differentiation, including phases of lineage-specification and terminal differentiation, is still poorly understood. Considering growing evidence indicating that SOX2 expression level must be tightly controlled for proper neural development, the aim of this research was to analyze the effects of constitutive SOX2 overexpression on outcome of retinoic acid-induced neural differentiation of pluripotent NT2/D1 cells. We demonstrated that in spite of constitutive SOX2 overexpression, NT2/D1 cells were able to reach final phases of neural differentiation yielding both neuronal and glial cells. However, SOX2 overexpression reduced the number of mature MAP2-positive neurons while no difference in the number of GFAP-positive astrocytes was detected. In-depth analysis at single-cell level showed that SOX2 downregulation was in correlation with both neuronal and glial phenotype acquisitions. Interestingly, while in mature neurons SOX2 was completely downregulated, astrocytes with low level of SOX2 expression were detected. Nevertheless, cells with high level of SOX2 expression were incapable of entering in either of two differentiation pathways, neurogenesis or gliogenesis. Accordingly, our results indicate that fine balance between undifferentiated state and neural differentiation depends on SOX2 expression level. Unlike neurons, astrocytes could maintain low level of SOX2 expression after they acquired glial fate. Further studies are needed to determine whether differences in the level of SOX2 expression in GFAP-positive astrocytes are in correlation with their self-renewal capacity, differentiation status, and/or their phenotypic characteristics.

  1. Spider silk as guiding biomaterial for human model neurons.

    PubMed

    Roloff, Frank; Strauß, Sarah; Vogt, Peter M; Bicker, Gerd; Radtke, Christine

    2014-01-01

    Over the last years, a number of therapeutic strategies have emerged to promote axonal regeneration. An attractive strategy is the implantation of biodegradable and nonimmunogenic artificial scaffolds into injured peripheral nerves. In previous studies, transplantation of decellularized veins filled with spider silk for bridging critical size nerve defects resulted in axonal regeneration and remyelination by invading endogenous Schwann cells. Detailed interaction of elongating neurons and the spider silk as guidance material is unknown. To visualize direct cellular interactions between spider silk and neurons in vitro, we developed an in vitro crossed silk fiber array. Here, we describe in detail for the first time that human (NT2) model neurons attach to silk scaffolds. Extending neurites can bridge gaps between single silk fibers and elongate afterwards on the neighboring fiber. Culturing human neurons on the silk arrays led to an increasing migration and adhesion of neuronal cell bodies to the spider silk fibers. Within three to four weeks, clustered somata and extending neurites formed ganglion-like cell structures. Microscopic imaging of human neurons on the crossed fiber arrays in vitro will allow for a more efficient development of methods to maximize cell adhesion and neurite growth on spider silk prior to transplantation studies.

  2. A Predictive In Vitro Model of the Impact of Drugs with Anticholinergic Properties on Human Neuronal and Astrocytic Systems

    PubMed Central

    Woehrling, Elizabeth K.; Parri, H. Rheinallt; Tse, Erin H. Y.; Hill, Eric J.; Maidment, Ian D.; Fox, G. Christopher; Coleman, Michael D.

    2015-01-01

    The link between off-target anticholinergic effects of medications and acute cognitive impairment in older adults requires urgent investigation. We aimed to determine whether a relevant in vitro model may aid the identification of anticholinergic responses to drugs and the prediction of anticholinergic risk during polypharmacy. In this preliminary study we employed a co-culture of human-derived neurons and astrocytes (NT2.N/A) derived from the NT2 cell line. NT2.N/A cells possess much of the functionality of mature neurons and astrocytes, key cholinergic phenotypic markers and muscarinic acetylcholine receptors (mAChRs). The cholinergic response of NT2 astrocytes to the mAChR agonist oxotremorine was examined using the fluorescent dye fluo-4 to quantitate increases in intracellular calcium [Ca2+]i. Inhibition of this response by drugs classified as severe (dicycloverine, amitriptyline), moderate (cyclobenzaprine) and possible (cimetidine) on the Anticholinergic Cognitive Burden (ACB) scale, was examined after exposure to individual and pairs of compounds. Individually, dicycloverine had the most significant effect regarding inhibition of the astrocytic cholinergic response to oxotremorine, followed by amitriptyline then cyclobenzaprine and cimetidine, in agreement with the ACB scale. In combination, dicycloverine with cyclobenzaprine had the most significant effect, followed by dicycloverine with amitriptyline. The order of potency of the drugs in combination frequently disagreed with predicted ACB scores derived from summation of the individual drug scores, suggesting current scales may underestimate the effect of polypharmacy. Overall, this NT2.N/A model may be appropriate for further investigation of adverse anticholinergic effects of multiple medications, in order to inform clinical choices of suitable drug use in the elderly. PMID:25738989

  3. Crosstalk between SOXB1 proteins and WNT/β-catenin signaling in NT2/D1 cells.

    PubMed

    Mojsin, Marija; Topalovic, Vladanka; Vicentic, Jelena Marjanovic; Schwirtlich, Marija; Stanisavljevic, Danijela; Drakulic, Danijela; Stevanovic, Milena

    2015-11-01

    During early vertebrate embryogenesis, the expression of SOXB1 proteins is precisely regulated by a number of different mechanisms, including Wnt/β-catenin signaling. This is essential for controlling the balance between stemness and differentiation in embryonic stem cells. In the present study, we analyzed the molecular mechanism of LiCl action in NT2/D1 cells and examined the crosstalk between SOXB1 proteins and Wnt signaling in this model system. We have shown that LiCl increases β-catenin level, induces its translocation to the nucleus and consequently up-regulates β-catenin/Tcf-dependent transcription in NT2/D1 cells. Our results also suggest that LiCl treatment leads to increased expression of SOX2 and SOX3 proteins in NT2/D1 cells through activation of canonical Wnt signaling. Finally, we have detected a negative feedback loop between β-catenin and SOX2 expression in NT2/D1 cells. Since β-catenin and SOX2 have been linked to processes of self-renewal and pluripotency, our results have implications for future research on the maintenance of stemness and lineage commitment of embryonic stem cells.

  4. Is realistic neuronal modeling realistic?

    PubMed

    Almog, Mara; Korngreen, Alon

    2016-11-01

    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 that some neurons don't merely sum their inputs and fire if the sum exceeds some threshold. Thus researchers have asked what are the computational abilities of single neurons and attempted to give answers using realistic models. We briefly review the state of the art of compartmental modeling highlighting recent progress and intrinsic flaws. We then attempt to address two fundamental questions. Practically, can we realistically model single neurons? Philosophically, should we realistically model single neurons? We use layer 5 neocortical pyramidal neurons as a test case to examine these issues. We subject three publically available models of layer 5 pyramidal neurons to three simple computational challenges. Based on their performance and a partial survey of published models, we conclude that current compartmental models are ad hoc, unrealistic models functioning poorly once they are stretched beyond the specific problems for which they were designed. We then attempt to plot possible paths for generating realistic single neuron models. Copyright © 2016 the American Physiological Society.

  5. Evaluation of Human and Nonhuman Primate Antibody Binding to Pig Cells Lacking GGTA1/CMAH/β4GalNT2 Genes

    PubMed Central

    Estrada, J; Martens, G; Li, P; Adams, AB; Newell, KA; Ford, ML; Butler, JR; Sidner, RA; Tector, M; Tector, AJ

    2015-01-01

    Background Simultaneous inactivation of pig GGTA1 and CMAH genes eliminates carbohydrate xenoantigens recognized by human antibodies. The β4GalNT2 glycosyltransferase may also synthesize xenoantigens. To further characterize glycan-based species incompatibilities, we examined human and non-human primate antibody binding to cells derived from genetically modified pigs lacking these carbohydrate-modifying genes. Methods The Cas9 endonuclease and gRNA were used to create pigs lacking GGTA1, GGTA1/CMAH, or GGTA1/CMAH/β4GalNT2 genes. Peripheral blood mononuclear cells were isolated from these animals and examined for binding to IgM and IgG from humans, rhesus macaques, and baboons. Results Cells from GGTA1/CMAH/β4GalNT2 deficient pigs exhibited reduced human IgM and IgG binding compared to cells lacking both GGTA1 and CMAH. Nonhuman primate antibody reactivity with cells from the various pigs exhibited a slightly different pattern of reactivity than that seen in humans. Simultaneous inactivation of the GGTA1 and CMAH genes increased nonhuman primate antibody binding compared to cells lacking either GGTA1 only or to those deficient in GGTA1/CMAH/β4GalNT2. Conclusions Inactivation of the β4GalNT2 gene reduces human and nonhuman primate antibody binding resulting in diminished porcine xenoantigenicity. The increased humoral immunity of nonhuman primates towards GGTA1/CMAH-deficient cells compared to pigs lacking either GGTA1 or GGTA1/CMAH/β4GalNT2 highlights the complexities of carbohydrate xenoantigens and suggests potential limitations of the nonhuman primate model for examining some genetic modifications. The progressive reduction of swine xenoantigens recognized by human immunoglobulin through inactivation of pig GGTA1/CMAH/β4GalNT2 genes demonstrates that the antibody barrier to xenotransplantation can be minimized by genetic engineering. PMID:25728481

  6. Energy Model of Neuron Activation.

    PubMed

    Romanyshyn, Yuriy; Smerdov, Andriy; Petrytska, Svitlana

    2017-02-01

    On the basis of the neurophysiological strength-duration (amplitude-duration) curve of neuron activation (which relates the threshold amplitude of a rectangular current pulse of neuron activation to the pulse duration), as well as with the use of activation energy constraint (the threshold curve corresponds to the energy threshold of neuron activation by a rectangular current pulse), an energy model of neuron activation by a single current pulse has been constructed. The constructed model of activation, which determines its spectral properties, is a bandpass filter. Under the condition of minimum-phase feature of the neuron activation model, on the basis of Hilbert transform, the possibilities of phase-frequency response calculation from its amplitude-frequency response have been considered. Approximation to the amplitude-frequency response by the response of the Butterworth filter of the first order, as well as obtaining the pulse response corresponding to this approximation, give us the possibility of analyzing the efficiency of activating current pulses of various shapes, including analysis in accordance with the energy constraint.

  7. Elevated caspase 3 activity and cytosolic cytochrome c in NT2 cybrids containing amyotrophic lateral sclerosis subject mtDNA.

    PubMed

    Shrivastava, Mohita; Subbiah, Vivekanandhan

    2016-09-01

    Apoptosis of motor neurons is an important feature in amyotrophic lateral sclerosis (ALS). A vital role of mitochondria in apoptosis and cell survival is well documented. Eventually mitochondria have shown to be an early target in the pathogenesis of ALS. On account of these facts, we investigated the involvement of mitochondrial-dependent apoptosis in ALS and control (CTR) cybrids, generated fusing human platelets with mitochondrial DNA-depleted NT2-neuroteratocarcinoma cells. After a 6 week selection process during which transferred subject mtDNA repopulated the NT2 cells and restored mitochondrial oxygen consumption, we assessed cell viability and two programmed cell death parameters, caspase 3 activity and cytosolic cytochrome c levels. Compared to the control cybrid lines (n = 5), the ALS cybrid lines (n = 10) showed 45% less XTT reduction and higher caspase 3 activity ( p < 0.05, two-way Student's t test) exhibiting lesser cell viability and execution of apoptosis. Elevated cytosolic cytochrome c levels in ALS cybrid lines (n = 8) than in CTR (n = 4) ( p < 0.05, two-way Student's t-test) indicating its mitochondrial release and initiation of apoptosis. This indicates apoptosis as one of the possible mechanisms of cell death in ALS. Our findings support the view that in ALS, subject's mitochondria are altered in non-degenerating tissues in such a way that intrinsic apoptotic pathway activity is relatively increased.

  8. Expression of a novel splice variant of FRMD7 in developing human fetal brains that is upregulated upon the differentiation of NT2 cells

    PubMed Central

    LI, YINGZHI; PU, JIALI; ZHANG, BAORONG

    2014-01-01

    FRMD7 mutations are associated with X-linked idiopathic congenital nystagmus (ICN); however, the underlying mechanisms whereby mutations of FRMD7 lead to ICN remain unclear. In a previous study, the first FRMD7 splice variant (FRMD7-S) was cloned and identified, and FRMD7-S was hypothesized to play a significant role in neuronal differentiation and development. The present study investigated a novel multiple exon-skipping mRNA splice variant of FRMD7, termed FRMD7_SV2, which was detected in NT2 cells using northern blotting. The mRNA expression levels of FRMD7_SV2 in the developing human fetal brain were examined using reverse transcription polymerase chain reaction (PCR), while the expression levels in NT2 cells treated with retinoid acid (RA) or bone morphogenetic protein-2 were investigated using quantitative PCR. The results revealed that the expression of FRMD7_SV2 was spatially and temporally restricted in human fetal brain development, and was upregulated upon RA-induced neuronal differentiation of the NT2 cells. These results indicated that as a novel splice variant of FRMD7, FRMD7_SV2 may play a role in neuronal development. PMID:25187810

  9. Neuron Model with Simplified Memristive Ionic Channels

    NASA Astrophysics Data System (ADS)

    Hegab, Almoatazbellah M.; Salem, Noha M.; Radwan, Ahmed G.; Chua, Leon

    2015-06-01

    A simplified neuron model is introduced to mimic the action potential generated by the famous Hodgkin-Huxley equations by using the genetic optimization algorithm. Comparison with different neuron models is investigated, and it is confirmed that the sodium and potassium channels in our simplified neuron model are made out of memristors. In addition, the channel equations in the simplified model may be adjusted to introduce a simplified memristor model that is in accordance with the theoretical conditions of the memristive systems.

  10. Neuronal cell lines as model dorsal root ganglion neurons

    PubMed Central

    Yin, Kathleen; Baillie, Gregory J

    2016-01-01

    Background Dorsal root ganglion neuron-derived immortal cell lines including ND7/23 and F-11 cells have been used extensively as in vitro model systems of native peripheral sensory neurons. However, while it is clear that some sensory neuron-specific receptors and ion channels are present in these cell lines, a systematic comparison of the molecular targets expressed by these cell lines with those expressed in intact peripheral neurons is lacking. Results In this study, we examined the expression of RNA transcripts in the human neuroblastoma-derived cell line, SH-SY5Y, and two dorsal root ganglion hybridoma cell lines, F-11 and ND7/23, using Illumina next-generation sequencing, and compared the results with native whole murine dorsal root ganglions. The gene expression profiles of these three cell lines did not resemble any specific defined dorsal root ganglion subclass. The cell lines lacked many markers for nociceptive sensory neurons, such as the Transient receptor potential V1 gene, but expressed markers for both myelinated and unmyelinated neurons. Global gene ontology analysis on whole dorsal root ganglions and cell lines showed similar enrichment of biological process terms across all samples. Conclusions This paper provides insights into the receptor repertoire expressed in common dorsal root ganglion neuron-derived cell lines compared with whole murine dorsal root ganglions, and illustrates the limits and potentials of these cell lines as tools for neuropharmacological exploration. PMID:27130590

  11. Evaluation of human and non-human primate antibody binding to pig cells lacking GGTA1/CMAH/β4GalNT2 genes.

    PubMed

    Estrada, Jose L; Martens, Greg; Li, Ping; Adams, Andrew; Newell, Kenneth A; Ford, Mandy L; Butler, James R; Sidner, Richard; Tector, Matt; Tector, Joseph

    2015-01-01

    Simultaneous inactivation of pig GGTA1 and CMAH genes eliminates carbohydrate xenoantigens recognized by human antibodies. The β4GalNT2 glycosyltransferase may also synthesize xenoantigens. To further characterize glycan-based species incompatibilities, we examined human and non-human primate antibody binding to cells derived from genetically modified pigs lacking these carbohydrate-modifying genes. The Cas9 endonuclease and gRNA were used to create pigs lacking GGTA1, GGTA1/CMAH, or GGTA1/CMAH/β4GalNT2 genes. Peripheral blood mononuclear cells were isolated from these animals and examined for binding to IgM and IgG from humans, rhesus macaques, and baboons. Cells from GGTA1/CMAH/β4GalNT2 deficient pigs exhibited reduced human IgM and IgG binding compared to cells lacking both GGTA1 and CMAH. Non-human primate antibody reactivity with cells from the various pigs exhibited a slightly different pattern of reactivity than that seen in humans. Simultaneous inactivation of the GGTA1 and CMAH genes increased non-human primate antibody binding compared to cells lacking either GGTA1 only or to those deficient in GGTA1/CMAH/β4GalNT2. Inactivation of the β4GalNT2 gene reduces human and non-human primate antibody binding resulting in diminished porcine xenoantigenicity. The increased humoral immunity of non-human primates toward GGTA1-/CMAH-deficient cells compared to pigs lacking either GGTA1 or GGTA1/CMAH/β4GalNT2 highlights the complexities of carbohydrate xenoantigens and suggests potential limitations of the non-human primate model for examining some genetic modifications. The progressive reduction of swine xenoantigens recognized by human immunoglobulin through inactivation of pig GGTA1/CMAH/β4GalNT2 genes demonstrates that the antibody barrier to xenotransplantation can be minimized by genetic engineering. © 2015 The Authors. Xenotransplantation Published by John Wiley & Sons Ltd.

  12. Establishment and initial characterization of SOX2-overexpressing NT2/D1 cell clones.

    PubMed

    Drakulic, D; Krstic, A; Stevanovic, M

    2012-05-15

    SOX2, a universal marker of pluripotent stem cells, is a transcription factor that helps control embryonic development in vertebrates; its expression persists in neural stem/progenitor cells into adulthood. Considering the critical role of the SOX2 transcription factor in the regulation of genes required for self-renewal and pluripotency of stem cells, we developed and characterized SOX2-overexpressing NT2/D1 cell clones. Using Southern blot and semi-quantitative RT-PCR, we confirmed integration and expression of exogenous SOX2 in three NT2/D1 cell clones. Overexpression of the SOX2 gene was detected in two of these clones. SOX2 overexpression in NT2/D1 cell clones resulted in altered expression of key pluripotency genes OCT4 and NANOG. Furthermore, SOX2-overexpressing NT2/D1 cell clones entered into retinoic acid-dependent neural differentiation, even when there was elevated SOX2 expression. After 21 days of induction by retinoic acid, expression of neural markers (neuroD1 and synaptophysin) was higher in induced cell clones than in induced parental cells. The cell clone with SOX2 overexpression had an approximately 1.3-fold higher growth rate compared to parental cells. SOX2 overexpression did not increase the population of cells undergoing apoptosis. Taken together, we developed two SOX2-overexpressing cell clones, with constitutive SOX2 expression after three weeks of retinoic acid treatment. SOX2 overexpression resulted in altered expression of pluripotency-related genes, increased proliferation, and altered expression of neural markers after three weeks of retinoic acid treatment.

  13. Results on a binding neuron model and their implications for modified hourglass model for neuronal network.

    PubMed

    Arunachalam, Viswanathan; Akhavan-Tabatabaei, Raha; Lopez, Cristina

    2013-01-01

    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 like the firing time distribution and other statistical characteristics. We also discuss the applicability of the developed results in constructing a modified hourglass network model in which there are interconnected neurons with excitatory as well as inhibitory inputs. Limited simulation results of the hourglass network are presented.

  14. MRI of neuronal plasticity in rodent models.

    PubMed

    Pelled, Galit

    2011-01-01

    Modifications in the behavior and architecture of neuronal networks are well documented to occur in association with learning and memory, as well as following injury. These plasticity mechanisms are crucial to ensure adequate processing of stimuli, and they also dictate the degree of recovery following peripheral or central nervous system injury. Nevertheless, the underlying neuronal mechanisms that determine the degree of plasticity of neuronal pathways are not fully understood. Recent developments in animal-dedicated magnetic resonance imaging (MRI) scanners and related hardware afford a high spatial and temporal resolution, making functional MRI and manganese-enhanced MRI emerging tools for studying reorganization of neuronal pathways in rodent models. Many of the observed changes in neuronal functions in rodent's brains following injury discussed here agree with clinical human fMRI findings. This demonstrates that animal model imaging can have a significant clinical impact in the neuronal plasticity and rehabilitation arenas.

  15. Fitting Neuron Models to Spike Trains

    PubMed Central

    Rossant, Cyrille; Goodman, Dan F. M.; Fontaine, Bertrand; Platkiewicz, Jonathan; Magnusson, Anna K.; Brette, Romain

    2011-01-01

    Computational modeling is increasingly used to understand the function of neural circuits in systems neuroscience. These studies require models of individual neurons with realistic input–output properties. Recently, it was found that spiking models can accurately predict the precisely timed spike trains produced by cortical neurons in response to somatically injected currents, if properly fitted. This requires fitting techniques that are efficient and flexible enough to easily test different candidate models. We present a generic solution, based on the Brian simulator (a neural network simulator in Python), which allows the user to define and fit arbitrary neuron models to electrophysiological recordings. It relies on vectorization and parallel computing techniques to achieve efficiency. We demonstrate its use on neural recordings in the barrel cortex and in the auditory brainstem, and confirm that simple adaptive spiking models can accurately predict the response of cortical neurons. Finally, we show how a complex multicompartmental model can be reduced to a simple effective spiking model. PMID:21415925

  16. An overview of the neuron ring model

    NASA Technical Reports Server (NTRS)

    Taber, Rod

    1991-01-01

    The Neuron Ring model employs an avalanche structure with two important distinctions at the neuron level. Each neuron has two memory latches; one traps maximum neuronal activation during pattern presentation, and the other records the time of latch content change. The latches filter short term memory. In the process, they preserve length 1 snapshots of activation theory history. The model finds utility in pattern classification. Its synaptic weights are first conditioned with sample spectra. The model then receives a test or unknown signal. The objective is to identify the sample closest to the test signal. Class decision follows complete presentation of the test data. The decision maker relies exclusively on the latch contents. Presented here is an overview of the Neuron Ring at the seminar level.

  17. Effective Stimuli for Constructing Reliable Neuron Models

    PubMed Central

    Druckmann, Shaul; Berger, Thomas K.; Schürmann, Felix; Hill, Sean; Markram, Henry; Segev, Idan

    2011-01-01

    The rich dynamical nature of neurons poses major conceptual and technical challenges for unraveling their nonlinear membrane properties. Traditionally, various current waveforms have been injected at the soma to probe neuron dynamics, but the rationale for selecting specific stimuli has never been rigorously justified. The present experimental and theoretical study proposes a novel framework, inspired by learning theory, for objectively selecting the stimuli that best unravel the neuron's dynamics. The efficacy of stimuli is assessed in terms of their ability to constrain the parameter space of biophysically detailed conductance-based models that faithfully replicate the neuron's dynamics as attested by their ability to generalize well to the neuron's response to novel experimental stimuli. We used this framework to evaluate a variety of stimuli in different types of cortical neurons, ages and animals. Despite their simplicity, a set of stimuli consisting of step and ramp current pulses outperforms synaptic-like noisy stimuli in revealing the dynamics of these neurons. The general framework that we propose paves a new way for defining, evaluating and standardizing effective electrical probing of neurons and will thus lay the foundation for a much deeper understanding of the electrical nature of these highly sophisticated and non-linear devices and of the neuronal networks that they compose. PMID:21876663

  18. A computational model of motor neuron degeneration.

    PubMed

    Le Masson, Gwendal; Przedborski, Serge; Abbott, L F

    2014-08-20

    To explore the link between bioenergetics and motor neuron degeneration, we used a computational model in which detailed morphology and ion conductance are paired with intracellular ATP production and consumption. We found that reduced ATP availability increases the metabolic cost of a single action potential and disrupts K+/Na+ homeostasis, resulting in a chronic depolarization. The magnitude of the ATP shortage at which this ionic instability occurs depends on the morphology and intrinsic conductance characteristic of the neuron. If ATP shortage is confined to the distal part of the axon, the ensuing local ionic instability eventually spreads to the whole neuron and involves fasciculation-like spiking events. A shortage of ATP also causes a rise in intracellular calcium. Our modeling work supports the notion that mitochondrial dysfunction can account for salient features of the paralytic disorder amyotrophic lateral sclerosis, including motor neuron hyperexcitability, fasciculation, and differential vulnerability of motor neuron subpopulations. Copyright © 2014 Elsevier Inc. All rights reserved.

  19. A COMPUTATIONAL MODEL OF MOTOR NEURON DEGENERATION

    PubMed Central

    Le Masson, Gwendal; Przedborski, Serge; Abbott, L.F.

    2014-01-01

    SUMMARY To explore the link between bioenergetics and motor neuron degeneration, we used a computational model in which detailed morphology and ion conductance are paired with intracellular ATP production and consumption. We found that reduced ATP availability increases the metabolic cost of a single action potential and disrupts K+/Na+ homeostasis, resulting in a chronic depolarization. The magnitude of the ATP shortage at which this ionic instability occurs depends on the morphology and intrinsic conductance characteristic of the neuron. If ATP shortage is confined to the distal part of the axon, the ensuing local ionic instability eventually spreads to the whole neuron and involves fasciculation-like spiking events. A shortage of ATP also causes a rise in intracellular calcium. Our modeling work supports the notion that mitochondrial dysfunction can account for salient features of the paralytic disorder amyotrophic lateral sclerosis, including motor neuron hyperexcitability, fasciculation, and differential vulnerability of motor neuron subpopulations. PMID:25088365

  20. Mirror neurons: functions, mechanisms and models.

    PubMed

    Oztop, Erhan; Kawato, Mitsuo; Arbib, Michael A

    2013-04-12

    Mirror neurons for manipulation fire both when the animal manipulates an object in a specific way and when it sees another animal (or the experimenter) perform an action that is more or less similar. Such neurons were originally found in macaque monkeys, in the ventral premotor cortex, area F5 and later also in the inferior parietal lobule. Recent neuroimaging data indicate that the adult human brain is endowed with a "mirror neuron system," putatively containing mirror neurons and other neurons, for matching the observation and execution of actions. Mirror neurons may serve action recognition in monkeys as well as humans, whereas their putative role in imitation and language may be realized in human but not in monkey. This article shows the important role of computational models in providing sufficient and causal explanations for the observed phenomena involving mirror systems and the learning processes which form them, and underlines the need for additional circuitry to lift up the monkey mirror neuron circuit to sustain the posited cognitive functions attributed to the human mirror neuron system. Copyright © 2012 Elsevier Ireland Ltd. All rights reserved.

  1. Fractional Cable Models for Spiny Neuronal Dendrites

    NASA Astrophysics Data System (ADS)

    Henry, B. I.; Langlands, T. A. M.; Wearne, S. L.

    2008-03-01

    Cable equations with fractional order temporal operators are introduced to model electrotonic properties of spiny neuronal dendrites. These equations are derived from Nernst-Planck equations with fractional order operators to model the anomalous subdiffusion that arises from trapping properties of dendritic spines. The fractional cable models predict that postsynaptic potentials propagating along dendrites with larger spine densities can arrive at the soma faster and be sustained at higher levels over longer times. Calibration and validation of the models should provide new insight into the functional implications of altered neuronal spine densities, a hallmark of normal aging and many neurodegenerative disorders.

  2. Human motor neuron progenitor transplantation leads to endogenous neuronal sparing in 3 models of motor neuron loss.

    PubMed

    Wyatt, Tanya J; Rossi, Sharyn L; Siegenthaler, Monica M; Frame, Jennifer; Robles, Rockelle; Nistor, Gabriel; Keirstead, Hans S

    2011-01-01

    Motor neuron loss is characteristic of many neurodegenerative disorders and results in rapid loss of muscle control, paralysis, and eventual death in severe cases. In order to investigate the neurotrophic effects of a motor neuron lineage graft, we transplanted human embryonic stem cell-derived motor neuron progenitors (hMNPs) and examined their histopathological effect in three animal models of motor neuron loss. Specifically, we transplanted hMNPs into rodent models of SMA (Δ7SMN), ALS (SOD1 G93A), and spinal cord injury (SCI). The transplanted cells survived and differentiated in all models. In addition, we have also found that hMNPs secrete physiologically active growth factors in vivo, including NGF and NT-3, which significantly enhanced the number of spared endogenous neurons in all three animal models. The ability to maintain dying motor neurons by delivering motor neuron-specific neurotrophic support represents a powerful treatment strategy for diseases characterized by motor neuron loss.

  3. Activity-Dependent Model for Neuronal Avalanches

    NASA Astrophysics Data System (ADS)

    de Arcangelis, L.

    Networks of living neurons represent one of the most fascinating systems of modern biology. If the physical and chemical mechanisms at the basis of the functioning of a single neuron are quite well understood, the collective behavior of a system of many neurons is an extremely intriguing subject. Crucial ingredient of this complex behavior is the plasticity property of the network, namely the capacity to adapt and evolve depending on the level of activity. This plastic ability is believed, nowadays, to be at the basis of learning and memory in real brains. This fundamental problem in neurobiology has recently shown a number of features in common to other complex systems. These features mainly concern the morphology of the network, namely the spatial organization of the established connections, and a novel kind of neuronal activity. Experimental data have, in fact, shown that electrical information propagates in a cortex slice via an avalanche mode. Both features have been found in other problems in the context of the physics of complex systems and successful models have been developed to describe their behavior. In this contribution, we apply a statistical mechanical model to describe the complex activity in a neuronal network. The network is chosen to have a number of connections in long range, as found for neurons in vitro. The model implements the main physiological properties of living neurons and is able to reproduce recent experimental results. The numerical power spectra for electrical activity reproduces also the power law behavior measured in an EEG of man resting with the eyes closed.

  4. Dressed Neurons: Modeling the Tripartite Synapse

    NASA Astrophysics Data System (ADS)

    Jung, Peter

    2004-03-01

    The vast majority of cells in the brain are glial cells, of which astrocytes are the most numerous. Besides providing structural and metabolic support for the neurons, they listen to the neuronal chatter and modulate synaptic transmission at the synapse through extended processes that enwrap partially or fully neuronal synapses. The interaction of neurons with astrocytes involves metabolic pathways that are much slower than neuronal processes, typically of the order of seconds to minutes, resulting in complex neural-glial circuits that are at the forefront of research in neurobiology. In this talk I will give a brief introduction into the neurobiology of neural-glial circuitry and then discuss first attempts to mould these into mathematical models. One of the main signaling mechanisms within the astrocytes is through calcium release from internal stores. Neurotransmitter, released at synapses triggers astrocytic calcium events that can travel intra- and intercellularly to modify nearby or remote synapses through co-released glutamate. We discuss a few simple neural-glial circuits and the fingerprints of the astrocytic environment on neuronal dynamics. We further explore extreme parameter ranges that are consistent with conditions found in epileptic tissue and discuss the possible role of astrocytes for epilepsy.

  5. A stochastic model for interconnected neurons.

    PubMed

    Cottrell, M; Piat, F; Rospars, J P

    1997-01-01

    A model is proposed to describe the collective behavior of a biologically plausible neural network, composed of interconnected spiking neurons which separately receive external stationary stimulations. The spiking dynamics of each neuron is represented by an hourglass metaphor. This network model was first studied in a special case where the connections are only inhibitory (Cottrell, 1988, 1992). We study the network dynamics as a function of the parameters which quantify the strengths of both inhibitory and excitatory connections. We show that the model exhibits two kinds of limit states. In the first states (convergent case), the system is ergodic and all neurons have a positive mean firing rate. In the other states (divergent case), some neurons become definitively inactive while the sub-network of the active neurons is ergodic. The patterns which result from these divergent states can be seen as a neural coding of the external stimulation by the network. This property is applied to the olfactory system to produce a code for an odor. The role of inhibitory connections in odor discrimination is studied.

  6. Towards Reproducible Descriptions of Neuronal Network Models

    PubMed Central

    Nordlie, Eilen; Gewaltig, Marc-Oliver; Plesser, Hans Ekkehard

    2009-01-01

    Progress in science depends on the effective exchange of ideas among scientists. New ideas can be assessed and criticized in a meaningful manner only if they are formulated precisely. This applies to simulation studies as well as to experiments and theories. But after more than 50 years of neuronal network simulations, we still lack a clear and common understanding of the role of computational models in neuroscience as well as established practices for describing network models in publications. This hinders the critical evaluation of network models as well as their re-use. We analyze here 14 research papers proposing neuronal network models of different complexity and find widely varying approaches to model descriptions, with regard to both the means of description and the ordering and placement of material. We further observe great variation in the graphical representation of networks and the notation used in equations. Based on our observations, we propose a good model description practice, composed of guidelines for the organization of publications, a checklist for model descriptions, templates for tables presenting model structure, and guidelines for diagrams of networks. The main purpose of this good practice is to trigger a debate about the communication of neuronal network models in a manner comprehensible to humans, as opposed to machine-readable model description languages. We believe that the good model description practice proposed here, together with a number of other recent initiatives on data-, model-, and software-sharing, may lead to a deeper and more fruitful exchange of ideas among computational neuroscientists in years to come. We further hope that work on standardized ways of describing—and thinking about—complex neuronal networks will lead the scientific community to a clearer understanding of high-level concepts in network dynamics, and will thus lead to deeper insights into the function of the brain. PMID:19662159

  7. Towards reproducible descriptions of neuronal network models.

    PubMed

    Nordlie, Eilen; Gewaltig, Marc-Oliver; Plesser, Hans Ekkehard

    2009-08-01

    Progress in science depends on the effective exchange of ideas among scientists. New ideas can be assessed and criticized in a meaningful manner only if they are formulated precisely. This applies to simulation studies as well as to experiments and theories. But after more than 50 years of neuronal network simulations, we still lack a clear and common understanding of the role of computational models in neuroscience as well as established practices for describing network models in publications. This hinders the critical evaluation of network models as well as their re-use. We analyze here 14 research papers proposing neuronal network models of different complexity and find widely varying approaches to model descriptions, with regard to both the means of description and the ordering and placement of material. We further observe great variation in the graphical representation of networks and the notation used in equations. Based on our observations, we propose a good model description practice, composed of guidelines for the organization of publications, a checklist for model descriptions, templates for tables presenting model structure, and guidelines for diagrams of networks. The main purpose of this good practice is to trigger a debate about the communication of neuronal network models in a manner comprehensible to humans, as opposed to machine-readable model description languages. We believe that the good model description practice proposed here, together with a number of other recent initiatives on data-, model-, and software-sharing, may lead to a deeper and more fruitful exchange of ideas among computational neuroscientists in years to come. We further hope that work on standardized ways of describing--and thinking about--complex neuronal networks will lead the scientific community to a clearer understanding of high-level concepts in network dynamics, and will thus lead to deeper insights into the function of the brain.

  8. Determining the Oxidation States of Manganese in NT2 Cells and Cultured Astrocytes

    SciTech Connect

    Gunter,K.; Aschner, M.; Miller, L.; Eliseev, R.; Salter, J.; Andersen, K.; Gunter, T.

    2006-01-01

    Excessive brain manganese (Mn) can produce a syndrome called 'manganism', which correlates with loss of striatal dopamine and cell death in the striatum and globus pallidus. The prevalent hypothesis for the cause of this syndrome has been oxidation of cell components by the strong oxidizing agent, Mn{sup 3+}, either formed by oxidation of intracellular Mn{sup 2+} or transported into the cell as Mn{sup 3+}. We have recently used X-ray absorption near edge structure spectroscopy (XANES) to determine the oxidation states of manganese complexes in brain and liver mitochondria and in nerve growth factor (NGF)-induced and non-induced PC12 cells. No evidence was found for stabilization or accumulation of Mn{sup 3+} complexes because of oxidation of Mn{sup 2+} by reactive oxygen species in these tissues. Here we extend these studies of manganese oxidation state to cells of brain origin, human neuroteratocarcinoma (NT2) cells and primary cultures of rat astrocytes. Again we find no evidence for stabilization or accumulation of any Mn{sup 3+} complex derived from oxidation of Mn{sup 2+} under a range of conditions.

  9. Nonsmooth dynamics in spiking neuron models

    NASA Astrophysics Data System (ADS)

    Coombes, S.; Thul, R.; Wedgwood, K. C. A.

    2012-11-01

    Large scale studies of spiking neural networks are a key part of modern approaches to understanding the dynamics of biological neural tissue. One approach in computational neuroscience has been to consider the detailed electrophysiological properties of neurons and build vast computational compartmental models. An alternative has been to develop minimal models of spiking neurons with a reduction in the dimensionality of both parameter and variable space that facilitates more effective simulation studies. In this latter case the single neuron model of choice is often a variant of the classic integrate-and-fire model, which is described by a nonsmooth dynamical system. In this paper we review some of the more popular spiking models of this class and describe the types of spiking pattern that they can generate (ranging from tonic to burst firing). We show that a number of techniques originally developed for the study of impact oscillators are directly relevant to their analysis, particularly those for treating grazing bifurcations. Importantly we highlight one particular single neuron model, capable of generating realistic spike trains, that is both computationally cheap and analytically tractable. This is a planar nonlinear integrate-and-fire model with a piecewise linear vector field and a state dependent reset upon spiking. We call this the PWL-IF model and analyse it at both the single neuron and network level. The techniques and terminology of nonsmooth dynamical systems are used to flesh out the bifurcation structure of the single neuron model, as well as to develop the notion of Lyapunov exponents. We also show how to construct the phase response curve for this system, emphasising that techniques in mathematical neuroscience may also translate back to the field of nonsmooth dynamical systems. The stability of periodic spiking orbits is assessed using a linear stability analysis of spiking times. At the network level we consider linear coupling between voltage

  10. Estimating Neuronal Ageing with Hidden Markov Models

    NASA Astrophysics Data System (ADS)

    Wang, Bing; Pham, Tuan D.

    2011-06-01

    Neuronal degeneration is widely observed in normal ageing, meanwhile the neurode-generative disease like Alzheimer's disease effects neuronal degeneration in a faster way which is considered as faster ageing. Early intervention of such disease could benefit subjects with potentials of positive clinical outcome, therefore, early detection of disease related brain structural alteration is required. In this paper, we propose a computational approach for modelling the MRI-based structure alteration with ageing using hidden Markov model. The proposed hidden Markov model based brain structural model encodes intracortical tissue/fluid distribution using discrete wavelet transformation and vector quantization. Further, it captures gray matter volume loss, which is capable of reflecting subtle intracortical changes with ageing. Experiments were carried out on healthy subjects to validate its accuracy and robustness. Results have shown its ability of predicting the brain age with prediction error of 1.98 years without training data, which shows better result than other age predition methods.

  11. A drive-reinforcement model of single neuron function: An alternative to the Hebbian neuronal model

    NASA Astrophysics Data System (ADS)

    Klopf, A. Harry

    1986-08-01

    A neuronal learning mechanism is proposed that accounts for the basic animal learning phenomena that have been observed. Among the classical conditioning phenomena predicted by the neuronal model are delay conditioning, trace conditioning, simultaneous conditioning, conditioned stimulus duration and amplitude effects, unconditioned stimulus amplitude effects, interstimulus interval effects, second and higher order conditioning, conditioned inhibition, habituation and extinction, reacquisition effects, backward conditioning, blocking, overshadowing and serial compound conditioning. The proposed neuronal model and learning mechanism offer a new building block for constructing neural network-like computer arthitectures for artificial intelligence.

  12. Model reduction of strong-weak neurons.

    PubMed

    Du, Bosen; Sorensen, Danny; Cox, Steven J

    2014-01-01

    We consider neurons with large dendritic trees that are weakly excitable in the sense that back propagating action potentials are severly attenuated as they travel from the small, strongly excitable, spike initiation zone. In previous work we have shown that the computational size of weakly excitable cell models may be reduced by two or more orders of magnitude, and that the size of strongly excitable models may be reduced by at least one order of magnitude, without sacrificing the spatio-temporal nature of its inputs (in the sense we reproduce the cell's precise mapping of inputs to outputs). We combine the best of these two strategies via a predictor-corrector decomposition scheme and achieve a drastically reduced highly accurate model of a caricature of the neuron responsible for collision detection in the locust.

  13. Model reduction of strong-weak neurons

    PubMed Central

    Du, Bosen; Sorensen, Danny; Cox, Steven J.

    2014-01-01

    We consider neurons with large dendritic trees that are weakly excitable in the sense that back propagating action potentials are severly attenuated as they travel from the small, strongly excitable, spike initiation zone. In previous work we have shown that the computational size of weakly excitable cell models may be reduced by two or more orders of magnitude, and that the size of strongly excitable models may be reduced by at least one order of magnitude, without sacrificing the spatio–temporal nature of its inputs (in the sense we reproduce the cell's precise mapping of inputs to outputs). We combine the best of these two strategies via a predictor-corrector decomposition scheme and achieve a drastically reduced highly accurate model of a caricature of the neuron responsible for collision detection in the locust. PMID:25566048

  14. Simple models for reading neuronal population codes.

    PubMed Central

    Seung, H S; Sompolinsky, H

    1993-01-01

    In many neural systems, sensory information is distributed throughout a population of neurons. We study simple neural network models for extracting this information. The inputs to the networks are the stochastic responses of a population of sensory neurons tuned to directional stimuli. The performance of each network model in psychophysical tasks is compared with that of the optimal maximum likelihood procedure. As a model of direction estimation in two dimensions, we consider a linear network that computes a population vector. Its performance depends on the width of the population tuning curves and is maximal for width, which increases with the level of background activity. Although for narrowly tuned neurons the performance of the population vector is significantly inferior to that of maximum likelihood estimation, the difference between the two is small when the tuning is broad. For direction discrimination, we consider two models: a perceptron with fully adaptive weights and a network made by adding an adaptive second layer to the population vector network. We calculate the error rates of these networks after exhaustive training to a particular direction. By testing on the full range of possible directions, the extent of transfer of training to novel stimuli can be calculated. It is found that for threshold linear networks the transfer of perceptual learning is nonmonotonic. Although performance deteriorates away from the training stimulus, it peaks again at an intermediate angle. This nonmonotonicity provides an important psychophysical test of these models. PMID:8248166

  15. Biophysically realistic minimal model of dopamine neuron

    NASA Astrophysics Data System (ADS)

    Oprisan, Sorinel

    2008-03-01

    We proposed and studied a new biophysically relevant computational model of dopaminergic neurons. Midbrain dopamine neurons are involved in motivation and the control of movement, and have been implicated in various pathologies such as Parkinson's disease, schizophrenia, and drug abuse. The model we developed is a single-compartment Hodgkin-Huxley (HH)-type parallel conductance membrane model. The model captures the essential mechanisms underlying the slow oscillatory potentials and plateau potential oscillations. The main currents involved are: 1) a voltage-dependent fast calcium current, 2) a small conductance potassium current that is modulated by the cytosolic concentration of calcium, and 3) a slow voltage-activated potassium current. We developed multidimensional bifurcation diagrams and extracted the effective domains of sustained oscillations. The model includes a calcium balance due to the fundamental importance of calcium influx as proved by simultaneous electrophysiological and calcium imaging procedure. Although there are significant evidences to suggest a partially electrogenic calcium pump, all previous models considered only elecrtogenic pumps. We investigated the effect of the electrogenic calcium pump on the bifurcation diagram of the model and compared our findings against the experimental results.

  16. Modeling autism spectrum disorders with human neurons.

    PubMed

    Beltrão-Braga, Patricia C B; Muotri, Alysson R

    2017-02-01

    Autism spectrum disorder (ASD) is a group of neurodevelopmental disorders characterized by impaired social communication and interactions and by restricted and repetitive behaviors. Although ASD is suspected to have a heritable or sporadic genetic basis, its underlying etiology and pathogenesis are not well understood. Therefore, viable human neurons and glial cells produced using induced pluripotent stem cells (iPSC) to reprogram cells from individuals affected with ASD provide an unprecedented opportunity to elucidate the pathophysiology of these disorders, providing novel insights regarding ASD and a potential platform to develop and test therapeutic compounds. Herein, we discuss the state of art with regards to ASD modeling, including limitations of this technology, as well as potential future directions. This article is part of a Special Issue entitled SI: Exploiting human neurons.

  17. Qualitative-Modeling-Based Silicon Neurons and Their Networks.

    PubMed

    Kohno, Takashi; Sekikawa, Munehisa; Li, Jing; Nanami, Takuya; Aihara, Kazuyuki

    2016-01-01

    The ionic conductance models of neuronal cells can finely reproduce a wide variety of complex neuronal activities. However, the complexity of these models has prompted the development of qualitative neuron models. They are described by differential equations with a reduced number of variables and their low-dimensional polynomials, which retain the core mathematical structures. Such simple models form the foundation of a bottom-up approach in computational and theoretical neuroscience. We proposed a qualitative-modeling-based approach for designing silicon neuron circuits, in which the mathematical structures in the polynomial-based qualitative models are reproduced by differential equations with silicon-native expressions. This approach can realize low-power-consuming circuits that can be configured to realize various classes of neuronal cells. In this article, our qualitative-modeling-based silicon neuron circuits for analog and digital implementations are quickly reviewed. One of our CMOS analog silicon neuron circuits can realize a variety of neuronal activities with a power consumption less than 72 nW. The square-wave bursting mode of this circuit is explained. Another circuit can realize Class I and II neuronal activities with about 3 nW. Our digital silicon neuron circuit can also realize these classes. An auto-associative memory realized on an all-to-all connected network of these silicon neurons is also reviewed, in which the neuron class plays important roles in its performance.

  18. Qualitative-Modeling-Based Silicon Neurons and Their Networks

    PubMed Central

    Kohno, Takashi; Sekikawa, Munehisa; Li, Jing; Nanami, Takuya; Aihara, Kazuyuki

    2016-01-01

    The ionic conductance models of neuronal cells can finely reproduce a wide variety of complex neuronal activities. However, the complexity of these models has prompted the development of qualitative neuron models. They are described by differential equations with a reduced number of variables and their low-dimensional polynomials, which retain the core mathematical structures. Such simple models form the foundation of a bottom-up approach in computational and theoretical neuroscience. We proposed a qualitative-modeling-based approach for designing silicon neuron circuits, in which the mathematical structures in the polynomial-based qualitative models are reproduced by differential equations with silicon-native expressions. This approach can realize low-power-consuming circuits that can be configured to realize various classes of neuronal cells. In this article, our qualitative-modeling-based silicon neuron circuits for analog and digital implementations are quickly reviewed. One of our CMOS analog silicon neuron circuits can realize a variety of neuronal activities with a power consumption less than 72 nW. The square-wave bursting mode of this circuit is explained. Another circuit can realize Class I and II neuronal activities with about 3 nW. Our digital silicon neuron circuit can also realize these classes. An auto-associative memory realized on an all-to-all connected network of these silicon neurons is also reviewed, in which the neuron class plays important roles in its performance. PMID:27378842

  19. A neuron-astrocyte transistor-like model for neuromorphic dressed neurons.

    PubMed

    Valenza, G; Pioggia, G; Armato, A; Ferro, M; Scilingo, E P; De Rossi, D

    2011-09-01

    Experimental evidences on the role of the synaptic glia as an active partner together with the bold synapse in neuronal signaling and dynamics of neural tissue strongly suggest to investigate on a more realistic neuron-glia model for better understanding human brain processing. Among the glial cells, the astrocytes play a crucial role in the tripartite synapsis, i.e. the dressed neuron. A well-known two-way astrocyte-neuron interaction can be found in the literature, completely revising the purely supportive role for the glia. The aim of this study is to provide a computationally efficient model for neuron-glia interaction. The neuron-glia interactions were simulated by implementing the Li-Rinzel model for an astrocyte and the Izhikevich model for a neuron. Assuming the dressed neuron dynamics similar to the nonlinear input-output characteristics of a bipolar junction transistor, we derived our computationally efficient model. This model may represent the fundamental computational unit for the development of real-time artificial neuron-glia networks opening new perspectives in pattern recognition systems and in brain neurophysiology.

  20. The overexpression of SOX2 affects the migration of human teratocarcinoma cell line NT2/D1.

    PubMed

    Drakulic, Danijela; Vicentic, Jelena Marjanovic; Schwirtlich, Marija; Tosic, Jelena; Krstic, Aleksandar; Klajn, Andrijana; Stevanovic, Milena

    2015-03-01

    The altered expression of the SOX2 transcription factor is associated with oncogenic or tumor suppressor functions in human cancers. This factor regulates the migration and invasion of different cancer cells. In this study we investigated the effect of constitutive SOX2 overexpression on the migration and adhesion capacity of embryonal teratocarcinoma NT2/D1 cells derived from a metastasis of a human testicular germ cell tumor. We detected that increased SOX2 expression changed the speed, mode and path of cell migration, but not the adhesion ability of NT2/D1 cells. Additionally, we demonstrated that SOX2 overexpression increased the expression of the tumor suppressor protein p53 and the HDM2 oncogene. Our results contribute to the better understanding of the effect of SOX2 on the behavior of tumor cells originating from a human testicular germ cell tumor. Considering that NT2/D1 cells resemble cancer stem cells in many features, our results could contribute to the elucidation of the role of SOX2 in cancer stem cells behavior and the process of metastasis.

  1. Problems with neuronal models in temperature regulation.

    PubMed Central

    Jessen, C.

    1986-01-01

    Neuronal models in temperature regulation are primarily considered explicit statements of assumptions and premises used in design of experiments and development of descriptive equations concerning the relationships between thermal inputs and control actions. Some of the premises of current multiplicative models are discussed in relation to presently available experimental evidence. The results of these experiments suggest that there is no skin temperature compatible with life which completely suppresses a rise of heat production in response to low internal temperature. The slope of heat production versus internal temperature at a given skin temperature is not constant but depends on internal temperature and the level of heat production. Therefore, a concept involving additive interaction of central and peripheral temperature signals appears more flexible in accepting data obtained even under extreme conditions. PMID:3751140

  2. Functionalized Anatomical Models for EM-Neuron Interaction Modeling

    PubMed Central

    Neufeld, Esra; Cassará, Antonino Mario; Montanaro, Hazael; Kuster, Niels; Kainz, Wolfgang

    2017-01-01

    The understanding of interactions between electromagnetic (EM) fields and nerves are crucial in contexts ranging from therapeutic neurostimulation to low frequency EM exposure safety. To properly consider the impact of in-vivo induced field inhomogeneity on non-linear neuronal dynamics, coupled EM-neuronal dynamics modeling is required. For that purpose, novel functionalized computable human phantoms have been developed. Their implementation and the systematic verification of the integrated anisotropic quasi-static EM solver and neuronal dynamics modeling functionality, based on the method of manufactured solutions and numerical reference data, is described. Electric and magnetic stimulation of the ulnar and sciatic nerve were modeled to help understanding a range of controversial issues related to the magnitude and optimal determination of strength-duration (SD) time constants. The results indicate the importance of considering the stimulation-specific inhomogeneous field distributions (especially at tissue interfaces), realistic models of non-linear neuronal dynamics, very short pulses, and suitable SD extrapolation models. These results and the functionalized computable phantom will influence and support the development of safe and effective neuroprosthetic devices and novel electroceuticals. Furthermore they will assist the evaluation of existing low frequency exposure standards for the entire population under all exposure conditions. PMID:27224508

  3. Functionalized anatomical models for EM-neuron Interaction modeling

    NASA Astrophysics Data System (ADS)

    Neufeld, Esra; Cassará, Antonino Mario; Montanaro, Hazael; Kuster, Niels; Kainz, Wolfgang

    2016-06-01

    The understanding of interactions between electromagnetic (EM) fields and nerves are crucial in contexts ranging from therapeutic neurostimulation to low frequency EM exposure safety. To properly consider the impact of in vivo induced field inhomogeneity on non-linear neuronal dynamics, coupled EM-neuronal dynamics modeling is required. For that purpose, novel functionalized computable human phantoms have been developed. Their implementation and the systematic verification of the integrated anisotropic quasi-static EM solver and neuronal dynamics modeling functionality, based on the method of manufactured solutions and numerical reference data, is described. Electric and magnetic stimulation of the ulnar and sciatic nerve were modeled to help understanding a range of controversial issues related to the magnitude and optimal determination of strength-duration (SD) time constants. The results indicate the importance of considering the stimulation-specific inhomogeneous field distributions (especially at tissue interfaces), realistic models of non-linear neuronal dynamics, very short pulses, and suitable SD extrapolation models. These results and the functionalized computable phantom will influence and support the development of safe and effective neuroprosthetic devices and novel electroceuticals. Furthermore they will assist the evaluation of existing low frequency exposure standards for the entire population under all exposure conditions.

  4. A hidden Markov model approach to neuron firing patterns.

    PubMed Central

    Camproux, A C; Saunier, F; Chouvet, G; Thalabard, J C; Thomas, G

    1996-01-01

    Analysis and characterization of neuronal discharge patterns are of interest to neurophysiologists and neuropharmacologists. In this paper we present a hidden Markov model approach to modeling single neuron electrical activity. Basically the model assumes that each interspike interval corresponds to one of several possible states of the neuron. Fitting the model to experimental series of interspike intervals by maximum likelihood allows estimation of the number of possible underlying neuron states, the probability density functions of interspike intervals corresponding to each state, and the transition probabilities between states. We present an application to the analysis of recordings of a locus coeruleus neuron under three pharmacological conditions. The model distinguishes two states during halothane anesthesia and during recovery from halothane anesthesia, and four states after administration of clonidine. The transition probabilities yield additional insights into the mechanisms of neuron firing. Images FIGURE 3 PMID:8913581

  5. Biodegradation of 4-nitrotoluene with biosurfactant production by Rhodococcus pyridinivorans NT2: metabolic pathway, cell surface properties and toxicological characterization.

    PubMed

    Kundu, Debasree; Hazra, Chinmay; Dandi, Navin; Chaudhari, Ambalal

    2013-11-01

    A novel 4-nitrotoluene-degrading bacterial strain was isolated from pesticides contaminated effluent-sediment and identified as Rhodococcus pyridinivorans NT2 based on morphological and biochemical properties and 16S rDNA sequencing. The strain NT2 degraded 4-NT (400 mg l(-1)) with rapid growth at the end of 120 h, reduced surface tension of the media from 71 to 29 mN m(-1) and produced glycolipidic biosurfactants (45 mg l(-1)). The biosurfactant was purified and characterized as trehalose lipids. The biosurfactant was stable in high salinity (10 % w/v NaCl), elevated temperatures (120 °C for 15 min) and a wide pH range (2.0-10.0). The noticeable changes during biodegradation were decreased hydrophobicity; an increase in degree of fatty acid saturation, saturated/unsaturated ratio and cyclopropane fatty acid. Biodegradation of 4-NT was accompanied by the accumulation of ammonium (NH4 (+)) and negligible amount of nitrite ion (NO2 (-)). Product stoichiometry showed a carbon (C) and nitrogen (N) mass balance of 37 and 35 %, respectively. Biodegradation of 4-NT proceeded by oxidation at the methyl group to form 4-nitrobenzoate, followed by reduction and hydrolytic deamination yielding protocatechuate, which was metabolized through β-ketoadipate pathway. In vitro and in vivo acute toxicity assays in adult rat (Rattus norvegicus) showed sequential detoxification and the order of toxicity was 4-NT >4-nitrobenzyl alcohol >4-nitrobenzaldehyde >4-nitrobenzoate > protocatechuate. Taken together, the strain NT2 could be used as a potential bioaugmentation candidate for the bioremediation of contaminated sites.

  6. Role of Transmembrane Domain 4 in Ligand Permeation by Crithidia fasciculata Equilibrative Nucleoside Transporter 2 (CfNT2)*

    PubMed Central

    Arendt, Cassandra S.; Ullman, Buddy

    2010-01-01

    Equilibrative nucleoside transporters play essential roles in nutrient uptake, cardiovascular and renal function, and purine analog drug chemotherapies. Limited structural information is available for this family of transporters; however, residues in transmembrane domains 1, 2, 4, and 5 appear to be important for ligand and inhibitor binding. In order to identify regions of the transporter that are important for ligand specificity, a genetic selection for mutants of the inosine-guanosine-specific Crithidia fasciculata nucleoside transporter 2 (CfNT2) that had gained the ability to transport adenosine was carried out in the yeast Saccharomyces cerevisiae. Nearly all positive clones from the genetic selection carried mutations at lysine 155 in transmembrane domain 4, highlighting lysine 155 as a pivotal residue governing the ligand specificity of CfNT2. Mutation of lysine 155 to asparagine conferred affinity for adenosine on the mutant transporter at the expense of inosine and guanosine affinity due to weakened contacts to the purine ring of the ligand. Following systematic cysteine-scanning mutagenesis, thiol-specific modification of several positions within transmembrane domain 4 was found to interfere with inosine transport capability, indicating that this helix lines the water-filled ligand translocation channel. Additionally, the pattern of modification of transmembrane domain 4 suggested that it may deviate from helicity in the vicinity of residue 155. Position 155 was also protected from modification in the presence of ligand, suggesting that lysine 155 is in or near the ligand binding site. Transmembrane domain 4 and particularly lysine 155 appear to play key roles in ligand discrimination and translocation by CfNT2. PMID:20037157

  7. Neuronal models for studying lipid metabolism and transport.

    PubMed

    Karten, Barbara; Hayashi, Hideki; Campenot, Robert B; Vance, Dennis E; Vance, Jean E

    2005-06-01

    New methods have been developed for studying lipid metabolism and transport in primary cultures of neurons. Sympathetic neurons from rats and mice, as well as retinal ganglion neurons from rats, can be cultured in three-compartmented culture dishes in which the cell bodies reside in a compartment separate from that housing the distal axons. In addition, the three compartments contain completely independent fluid environments. Consequently, these neuronal cultures represent an excellent model for studying the intra-neuronal transport of lipids and proteins between cell bodies and distal axons. In addition, compartmented neuron cultures are particularly appropriate for investigating factors that regulate axonal growth and neuronal survival. The application of the compartmented culture model for use with murine neurons has opened up many new possibilities for studying lipid metabolism in neurons derived from genetically modified mice. Examples are given in which compartmented cultures of primary neurons have been used in studies on (i) lipid analysis of distal axons and cell bodies/proximal axons, (ii) immunoblotting of neuronal proteins involved in lipid metabolism, (iii) the compartmentalization of lipid metabolism, (iv) the role of lipids in axonal growth and survival, and (v) intracellular lipid transport.

  8. From spiking neuron models to linear-nonlinear models.

    PubMed

    Ostojic, Srdjan; Brunel, Nicolas

    2011-01-20

    Neurons transform time-varying inputs into action potentials emitted stochastically at a time dependent rate. The mapping from current input to output firing rate is often represented with the help of phenomenological models such as the linear-nonlinear (LN) cascade, in which the output firing rate is estimated by applying to the input successively a linear temporal filter and a static non-linear transformation. These simplified models leave out the biophysical details of action potential generation. It is not a priori clear to which extent the input-output mapping of biophysically more realistic, spiking neuron models can be reduced to a simple linear-nonlinear cascade. Here we investigate this question for the leaky integrate-and-fire (LIF), exponential integrate-and-fire (EIF) and conductance-based Wang-Buzsáki models in presence of background synaptic activity. We exploit available analytic results for these models to determine the corresponding linear filter and static non-linearity in a parameter-free form. We show that the obtained functions are identical to the linear filter and static non-linearity determined using standard reverse correlation analysis. We then quantitatively compare the output of the corresponding linear-nonlinear cascade with numerical simulations of spiking neurons, systematically varying the parameters of input signal and background noise. We find that the LN cascade provides accurate estimates of the firing rates of spiking neurons in most of parameter space. For the EIF and Wang-Buzsáki models, we show that the LN cascade can be reduced to a firing rate model, the timescale of which we determine analytically. Finally we introduce an adaptive timescale rate model in which the timescale of the linear filter depends on the instantaneous firing rate. This model leads to highly accurate estimates of instantaneous firing rates.

  9. Modelling small-patterned neuronal networks coupled to microelectrode arrays

    NASA Astrophysics Data System (ADS)

    Massobrio, Paolo; Martinoia, Sergio

    2008-09-01

    Cultured neurons coupled to planar substrates which exhibit 'well-defined' two-dimensional network architectures can provide valuable insights into cell-to-cell communication, network dynamics versus topology, and basic mechanisms of synaptic plasticity and learning. In the literature several approaches were presented to drive neuronal growth, such as surface modification by silane chemistry, photolithographic techniques, microcontact printing, microfluidic channel flow patterning, microdrop patterning, etc. This work presents a computational model fit for reproducing and explaining the dynamics exhibited by small-patterned neuronal networks coupled to microelectrode arrays (MEAs). The model is based on the concept of meta-neuron, i.e., a small spatially confined number of actual neurons which perform single macroscopic functions. Each meta-neuron is characterized by a detailed morphology, and the membrane channels are modelled by simple Hodgkin-Huxley and passive kinetics. The two main findings that emerge from the simulations can be summarized as follows: (i) the increasing complexity of meta-neuron morphology reflects the variations of the network dynamics as a function of network development; (ii) the dynamics displayed by the patterned neuronal networks considered can be explained by hypothesizing the presence of several short- and a few long-term distance interactions among small assemblies of neurons (i.e., meta-neurons).

  10. Modelling small-patterned neuronal networks coupled to microelectrode arrays.

    PubMed

    Massobrio, Paolo; Martinoia, Sergio

    2008-09-01

    Cultured neurons coupled to planar substrates which exhibit 'well-defined' two-dimensional network architectures can provide valuable insights into cell-to-cell communication, network dynamics versus topology, and basic mechanisms of synaptic plasticity and learning. In the literature several approaches were presented to drive neuronal growth, such as surface modification by silane chemistry, photolithographic techniques, microcontact printing, microfluidic channel flow patterning, microdrop patterning, etc. This work presents a computational model fit for reproducing and explaining the dynamics exhibited by small-patterned neuronal networks coupled to microelectrode arrays (MEAs). The model is based on the concept of meta-neuron, i.e., a small spatially confined number of actual neurons which perform single macroscopic functions. Each meta-neuron is characterized by a detailed morphology, and the membrane channels are modelled by simple Hodgkin-Huxley and passive kinetics. The two main findings that emerge from the simulations can be summarized as follows: (i) the increasing complexity of meta-neuron morphology reflects the variations of the network dynamics as a function of network development; (ii) the dynamics displayed by the patterned neuronal networks considered can be explained by hypothesizing the presence of several short- and a few long-term distance interactions among small assemblies of neurons (i.e., meta-neurons).

  11. Chaotic Resonance in Coupled Inferior Olive Neurons with the Llinás Approach Neuron Model.

    PubMed

    Nobukawa, Sou; Nishimura, Haruhiko

    2016-09-14

    It is well known that cerebellar motor control is fine-tuned by the learning process adjusted according to rich error signals from inferior olive (IO) neurons. Schweighofer and colleagues proposed that these signals can be produced by chaotic irregular firing in the IO neuron assembly; such chaotic resonance (CR) was replicated in their computer demonstration of a Hodgkin-Huxley (HH)-type compartment model. In this study, we examined the response of CR to a periodic signal in the IO neuron assembly comprising the Llinás approach IO neuron model. This system involves empirically observed dynamics of the IO membrane potential and is simpler than the HH-type compartment model. We then clarified its dependence on electrical coupling strength, input signal strength, and frequency. Furthermore, we compared the physiological validity for IO neurons such as low firing rate and sustaining subthreshold oscillation between CR and conventional stochastic resonance (SR) and examined the consistency with asynchronous firings indicated by the previous model-based studies in the cerebellar learning process. In addition, the signal response of CR and SR was investigated in a large neuron assembly. As the result, we confirmed that CR was consistent with the above IO neuron's characteristics, but it was not as easy for SR.

  12. Evaluation of the importance of astrocytes when screening for acute toxicity in neuronal cell systems.

    PubMed

    Woehrling, E K; Hill, E J; Coleman, M D

    2010-02-01

    Reliable, high throughput, in vitro preliminary screening batteries have the potential to greatly accelerate the rate at which regulatory neurotoxicity data is generated. This study evaluated the importance of astrocytes when predicting acute toxic potential using a neuronal screening battery of pure neuronal (NT2.N) and astrocytic (NT2.A) and integrated neuronal/astrocytic (NT2.N/A) cell systems derived from the human NT2.D1 cell line, using biochemical endpoints (mitochondrial membrane potential (MMP) depolarisation and ATP and GSH depletion). Following exposure for 72 h, the known acute human neurotoxicants trimethyltin-chloride, chloroquine and 6-hydroxydopamine were frequently capable of disrupting biochemical processes in all of the cell systems at non-cytotoxic concentrations. Astrocytes provide key metabolic and protective support to neurons during toxic challenge in vivo and generally the astrocyte containing cell systems showed increased tolerance to toxicant insult compared with the NT2.N mono-culture in vitro. Whilst there was no consistent relationship between MMP, ATP and GSH log IC(50) values for the NT2.N/A and NT2.A cell systems, these data did provide preliminary evidence of modulation of the acute neuronal toxic response by astrocytes. In conclusion, the suitability of NT2 neurons and astrocytes as cell systems for acute toxicity screening deserves further investigation.

  13. Mathematical modeling of the neuron morphology using two dimensional images.

    PubMed

    Rajković, Katarina; Marić, Dušica L; Milošević, Nebojša T; Jeremic, Sanja; Arsenijević, Valentina Arsić; Rajković, Nemanja

    2016-02-07

    In this study mathematical analyses such as the analysis of area and length, fractal analysis and modified Sholl analysis were applied on two dimensional (2D) images of neurons from adult human dentate nucleus (DN). Using mathematical analyses main morphological properties were obtained including the size of neuron and soma, the length of all dendrites, the density of dendritic arborization, the position of the maximum density and the irregularity of dendrites. Response surface methodology (RSM) was used for modeling the size of neurons and the length of all dendrites. However, the RSM model based on the second-order polynomial equation was only possible to apply to correlate changes in the size of the neuron with other properties of its morphology. Modeling data provided evidence that the size of DN neurons statistically depended on the size of the soma, the density of dendritic arborization and the irregularity of dendrites. The low value of mean relative percent deviation (MRPD) between the experimental data and the predicted neuron size obtained by RSM model showed that model was suitable for modeling the size of DN neurons. Therefore, RSM can be generally used for modeling neuron size from 2D images.

  14. Population model of hippocampal pyramidal neurons, linking a refractory density approach to conductance-based neurons

    NASA Astrophysics Data System (ADS)

    Chizhov, Anton V.; Graham, Lyle J.

    2007-01-01

    We propose a macroscopic approach toward realistic simulations of the population activity of hippocampal pyramidal neurons, based on the known refractory density equation with a different hazard function and on a different single-neuron threshold model. The threshold model is a conductance-based model taking into account adaptation-providing currents, which is reduced by omitting the fast sodium current and instead using an explicit threshold criterion for action potential events. Compared to the full pyramidal neuron model, the threshold model well approximates spike-time moments, postspike refractory states, and postsynaptic current integration. The dynamics of a neural population continuum are described by a set of one-dimensional partial differential equations in terms of the distributions of the refractory density (where the refractory state is defined by the time elapsed since the last action potential), the membrane potential, and the gating variables of the voltage-dependent channels, across the entire population. As the source term in the density equation, the probability density of firing, or hazard function, is derived from the Fokker-Planck (FP) equation, assuming that a single neuron is governed by a deterministic average-across-population input and a noise term. A self-similar solution of the FP equation in the subthreshold regime is obtained. Responses of the ensemble to stimulation by a current step and oscillating current are simulated and compared with individual neuron simulations. An example of interictal-like activity of a population of all-to-all connected excitatory neurons is presented.

  15. Modeling of inter-neuronal coupling medium and its impact on neuronal synchronization

    PubMed Central

    Iqbal, Muhammad; Hong, Keum-Shik

    2017-01-01

    In this paper, modeling of the coupling medium between two neurons, the effects of the model parameters on the synchronization of those neurons, and compensation of coupling strength deficiency in synchronization are studied. Our study exploits the inter-neuronal coupling medium and investigates its intrinsic properties in order to get insight into neuronal-information transmittance and, there from, brain-information processing. A novel electrical model of the coupling medium that represents a well-known RLC circuit attributable to the coupling medium’s intrinsic resistive, inductive, and capacitive properties is derived. Surprisingly, the integration of such properties reveals the existence of a natural three-term control strategy, referred to in the literature as the proportional integral derivative (PID) controller, which can be responsible for synchronization between two neurons. Consequently, brain-information processing can rely on a large number of PID controllers based on the coupling medium properties responsible for the coherent behavior of neurons in a neural network. Herein, the effects of the coupling model (or natural PID controller) parameters are studied and, further, a supervisory mechanism is proposed that follows a learning and adaptation policy based on the particle swarm optimization algorithm for compensation of the coupling strength deficiency. PMID:28486505

  16. Modeling schizophrenia using hiPSC neurons

    PubMed Central

    Brennand, Kristen; Simone, Anthony; Jou, Jessica; Gelboin-Burkhart, Chelsea; Tran, Ngoc; Sangar, Sarah; Li, Yan; Mu, Yangling; Chen, Gong; Yu, Diana; McCarthy, Shane; Sebat, Jonathan; Gage, Fred H.

    2012-01-01

    SUMMARY Schizophrenia (SCZD) is a debilitating neurological disorder with a world-wide prevalence of 1%; there is a strong genetic component, with an estimated heritability of 80–85%1. Though postmortem studies have revealed reduced brain volume, cell size, spine density and abnormal neural distribution in the prefrontal cortex and hippocampus of SCZD brain tissue2 and neuropharmacological studies have implicated dopaminergic, glutamatergic and GABAergic activity in SCZD3, the cell types affected in SCZD and the molecular mechanisms underlying the disease state remain unclear. To elucidate the cellular and molecular defects of SCZD, we directly reprogrammed fibroblasts from SCZD patients into human induced pluripotent stem cells (hiPSCs) and subsequently differentiated these disorder-specific hiPSCs into neurons (SI Fig. 1). SCZD hiPSC neurons showed diminished neuronal connectivity in conjunction with decreased neurite number, PSD95-protein levels and glutamate receptor expression. Gene expression profiles of SCZD hiPSC neurons identified altered expression of many components of the cAMP and WNT signaling pathways. Key cellular and molecular elements of the SCZD phenotype were ameliorated following treatment of SCZD hiPSC neurons with the antipsychotic Loxapine. To date, hiPSC neuronal pathology has only been demonstrated in diseases characterized by both the loss of function of a single gene product and rapid disease progression in early childhood4–6. We now report hiPSC neuronal phenotypes and gene expression changes associated with SCZD, a complex genetic psychiatric disorder (SI Table 1). PMID:21490598

  17. Small is beautiful: models of small neuronal networks

    PubMed Central

    Lamb, Damon G; Calabrese, Ronald L

    2013-01-01

    Modeling has contributed a great deal to our understanding of how individual neurons and neuronal networks function. In this review, we focus on models of the small neuronal networks of invertebrates, especially rhythmically active CPG networks. Models have elucidated many aspects of these networks, from identifying key interacting membrane properties to pointing out gaps in our understanding, for example missing neurons. Even the complex CPGs of vertebrates, such as those that underlie respiration, have been reduced to small network models to great effect. Modeling of these networks spans from simplified models, which are amenable to mathematical analyses, to very complicated biophysical models. Some researchers have now adopted a population approach, where they generate and analyze many related models that differ in a few to several judiciously chosen free parameters; often these parameters show variability across animals and thus justify the approach. Models of small neuronal networks will continue to expand and refine our understanding of how neuronal networks in all animals program motor output, process sensory information and learn. PMID:22364687

  18. Small is beautiful: models of small neuronal networks.

    PubMed

    Lamb, Damon G; Calabrese, Ronald L

    2012-08-01

    Modeling has contributed a great deal to our understanding of how individual neurons and neuronal networks function. In this review, we focus on models of the small neuronal networks of invertebrates, especially rhythmically active CPG networks. Models have elucidated many aspects of these networks, from identifying key interacting membrane properties to pointing out gaps in our understanding, for example missing neurons. Even the complex CPGs of vertebrates, such as those that underlie respiration, have been reduced to small network models to great effect. Modeling of these networks spans from simplified models, which are amenable to mathematical analyses, to very complicated biophysical models. Some researchers have now adopted a population approach, where they generate and analyze many related models that differ in a few to several judiciously chosen free parameters; often these parameters show variability across animals and thus justify the approach. Models of small neuronal networks will continue to expand and refine our understanding of how neuronal networks in all animals program motor output, process sensory information and learn.

  19. Unsupervised learnable neuron model with nonlinear interaction on dendrites.

    PubMed

    Todo, Yuki; Tamura, Hiroki; Yamashita, Kazuya; Tang, Zheng

    2014-12-01

    Recent researches have provided strong circumstantial support to dendrites playing a key and possibly essential role in computations. In this paper, we propose an unsupervised learnable neuron model by including the nonlinear interactions between excitation and inhibition on dendrites. The model neuron self-adjusts its synaptic parameters, so that the synapse to dendrite, according to a generalized delta-rule-like algorithm. The model is used to simulate directionally selective cells by the unsupervised learning algorithm. In the simulations, we initialize the interaction and dendrite of the neuron randomly and use the generalized delta-rule-like unsupervised learning algorithm to learn the two-dimensional multi-directional selectivity problem without an external teacher's signals. Simulation results show that the directionally selective cells can be formed by unsupervised learning, acquiring the required number of dendritic branches, and if needed, enhanced and if not, eliminated. Further, the results show whether a synapse exists; if it exists, where and what type (excitatory or inhibitory) of synapse it is. This leads us to believe that the proposed neuron model may be considerably more powerful on computations than the McCulloch-Pitts model because theoretically a single neuron or a single layer of such neurons is capable of solving any complex problem. These may also lead to a completely new technique for analyzing the mechanisms and principles of neurons, dendrites, and synapses.

  20. A spiking neuron model for synchronous flashing of fireflies.

    PubMed

    Kim, DaeEun

    2004-01-01

    Certain species of fireflies show a group behavior of synchronous flashing. Their synchronized and rhythmic flashing has received much attention among many researchers, and there has been a study of biological models for their entrainment of flashing. The synchronous behavior of fireflies resembles the firing synchrony of integrate-and-fire neurons with excitatory or inhibitory connections. This paper shows an analysis of spiking neurons specialized for a firefly flashing model, and provides simulation results of multiple neurons with various transmission delays and coupling strengths. It also explains flashing patterns of some firefly species and examines the synchrony conditions depending on transmission delays and coupling strengths.

  1. A bi-stable neuronal model of Gibbs distribution

    NASA Astrophysics Data System (ADS)

    Gross, Eitan

    2015-07-01

    In this paper we present a bi-stable neuronal model consistent with the Gibbs distribution. Our approach utilizes a formalism used in stochastic (Boltzmann) machines with a bistable-neuron algorithm in which each neuron can exist in either an ON or an OFF state. The transition between the system's states is composed of two random processes, the first one decides which state transition should be attempted and the second one decides if the transition is accepted or not. Our model can be easily extended to systems with asymmetrical weight matrices.

  2. GABAergic pathway in a rat model of chronic neuropathic pain: modulation after intrathecal transplantation of a human neuronal cell line.

    PubMed

    Vaysse, L; Sol, J C; Lazorthes, Y; Courtade-Saidi, M; Eaton, M J; Jozan, S

    2011-02-01

    Current understanding of chronic pain points a decrease in level of the inhibitory neurotransmitter GABA, in the spinal dorsal horn, leading to an imbalance between excitatory and inhibitory pathways. A subcloned derivative of the human NT2 cell line (hNT2.17) which, after neuronal differentiation, secretes different inhibitory neurotransmitters such as GABA and glycine has been recently isolated. In this study, we have investigated the effect of this new cell line on peripheral nerve injury induced by chronic constriction (CCI) and notably the effect on the cellular GABAergic pathway. Our data show that the decrease in GABA expression in the spinal dorsal horn of injured animals is concomitant with a decline of its synthetic enzyme GAD67-Ir and mRNA but not GAD65. Interestingly, in transplanted animals we observed a strong induction of GAD67 mRNA with one week after graft, which is followed by a recovery of GAD67 and GABA Ir. This effect paralleled a reduction of hindpaw hypersensitivity and thermal hyperalgesia induced by CCI. These results suggest that hNT2.17 GABA cells can modulate neuropathic pain after CCI certainly by minimizing the imbalance and restoring the cellular GABAergic pathway.

  3. A Neuron-Based Model of Sleep-Wake Cycles

    NASA Astrophysics Data System (ADS)

    Postnova, Svetlana; Peters, Achim; Braun, Hans

    2008-03-01

    In recent years it was discovered that a neuropeptide orexin/hypocretin plays a main role in sleep processes. This peptide is produced by the neurons in the lateral hypothalamus, which project to almost all brain areas. We present a computational model of sleep-wake cycles, which is based on the Hodgkin-Huxley type neurons and considers reciprocal glutaminergic projections between the lateral hypothalamus and the prefrontal cortex. Orexin is released as a neuromodulator and is required to keep the neurons firing, which corresponds to the wake state. When orexin is depleted the neurons are getting silent as observed in the sleep state. They can be reactivated by the circadian signal from the suprachiasmatic nucleus and/or external stimuli (alarm clock). Orexin projections to the thalamocortical neurons also can account for their transition from tonic firing activity during wakefulness to synchronized burst discharges during sleep.

  4. A Statistical Model for In Vivo Neuronal Dynamics.

    PubMed

    Surace, Simone Carlo; Pfister, Jean-Pascal

    2015-01-01

    Single neuron models have a long tradition in computational neuroscience. Detailed biophysical models such as the Hodgkin-Huxley model as well as simplified neuron models such as the class of integrate-and-fire models relate the input current to the membrane potential of the neuron. Those types of models have been extensively fitted to in vitro data where the input current is controlled. Those models are however of little use when it comes to characterize intracellular in vivo recordings since the input to the neuron is not known. Here we propose a novel single neuron model that characterizes the statistical properties of in vivo recordings. More specifically, we propose a stochastic process where the subthreshold membrane potential follows a Gaussian process and the spike emission intensity depends nonlinearly on the membrane potential as well as the spiking history. We first show that the model has a rich dynamical repertoire since it can capture arbitrary subthreshold autocovariance functions, firing-rate adaptations as well as arbitrary shapes of the action potential. We then show that this model can be efficiently fitted to data without overfitting. We finally show that this model can be used to characterize and therefore precisely compare various intracellular in vivo recordings from different animals and experimental conditions.

  5. A Statistical Model for In Vivo Neuronal Dynamics

    PubMed Central

    Surace, Simone Carlo; Pfister, Jean-Pascal

    2015-01-01

    Single neuron models have a long tradition in computational neuroscience. Detailed biophysical models such as the Hodgkin-Huxley model as well as simplified neuron models such as the class of integrate-and-fire models relate the input current to the membrane potential of the neuron. Those types of models have been extensively fitted to in vitro data where the input current is controlled. Those models are however of little use when it comes to characterize intracellular in vivo recordings since the input to the neuron is not known. Here we propose a novel single neuron model that characterizes the statistical properties of in vivo recordings. More specifically, we propose a stochastic process where the subthreshold membrane potential follows a Gaussian process and the spike emission intensity depends nonlinearly on the membrane potential as well as the spiking history. We first show that the model has a rich dynamical repertoire since it can capture arbitrary subthreshold autocovariance functions, firing-rate adaptations as well as arbitrary shapes of the action potential. We then show that this model can be efficiently fitted to data without overfitting. We finally show that this model can be used to characterize and therefore precisely compare various intracellular in vivo recordings from different animals and experimental conditions. PMID:26571371

  6. A Semiparametric Bayesian Model for Detecting Synchrony Among Multiple Neurons

    PubMed Central

    Shahbaba, Babak; Zhou, Bo; Lan, Shiwei; Ombao, Hernando; Moorman, David; Behseta, Sam

    2015-01-01

    We propose a scalable semiparametric Bayesian model to capture dependencies among multiple neurons by detecting their co-firing (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 1’s (spike) and 0’s (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 are as follows: the nonparametric component (i.e., the Gaussian process model) provides a flexible framework for modeling the underlying firing rates; the parametric component (i.e., the copula model) allows us to make inference regarding both contemporaneous and lagged relationships among neurons; using the copula model, we construct multivariate probabilistic models by separating the modeling of univariate marginal distributions from the modeling of dependence structure among variables; our method is easy to implement using a computationally efficient sampling algorithm that can be easily extended to high dimensional problems. Using simulated data, we show that our approach could correctly capture temporal dependencies in firing rates and identify synchronous neurons. We also apply our model to spike train data obtained from prefrontal cortical areas. PMID:24922500

  7. Blind Deconvolution of Hodgkin-Huxley neuronal model.

    PubMed

    Lankarany, M; Zhu, W-P; Swamy, M N S; Toyoizumi, Taro

    2013-01-01

    Neuron transforms information via a complex interaction between its previous states, its intrinsic properties, and the synaptic input it receives from other neurons. Inferring synaptic input of a neuron only from its membrane potential (output) that contains both sub-threshold and action potentials can effectively elucidate the information processing mechanism of a neuron. The term coined blind deconvolution of Hodgkin-Huxley (HH) neuronal model is defined, for the first time in this paper, to address the problem of reconstructing the hidden dynamics and synaptic input of a single neuron modeled by the HH model as well as estimating its intrinsic parameters only from single trace of noisy membrane potential. The blind deconvolution is accomplished via a recursive algorithm whose iterations contain running an extended Kalman filtering followed by the expectation maximization (EM) algorithm. The accuracy and robustness of the proposed algorithm have been demonstrated by our simulations. The capability of the proposed algorithm makes it particularly useful to understand the neural coding mechanism of a neuron.

  8. Dynamical Neural Network Model of Hippocampus with Excitatory and Inhibitory Neurons

    NASA Astrophysics Data System (ADS)

    Omori, Toshiaki; Horiguchi, Tsuyoshi

    2004-03-01

    We propose a dynamical neural network model with excitatory neurons and inhibitory neurons for memory function in hippocampus and investigate the effect of inhibitory neurons on memory recall. The results by numerical simulations show that the introduction of inhibitory neurons improves the stability of the memory recall in the proposed model by suppressing the bursting of neurons.

  9. Iterative learning control algorithm for spiking behavior of neuron model

    NASA Astrophysics Data System (ADS)

    Li, Shunan; Li, Donghui; Wang, Jiang; Yu, Haitao

    2016-11-01

    Controlling neurons to generate a desired or normal spiking behavior is the fundamental building block of the treatment of many neurologic diseases. The objective of this work is to develop a novel control method-closed-loop proportional integral (PI)-type iterative learning control (ILC) algorithm to control the spiking behavior in model neurons. In order to verify the feasibility and effectiveness of the proposed method, two single-compartment standard models of different neuronal excitability are specifically considered: Hodgkin-Huxley (HH) model for class 1 neural excitability and Morris-Lecar (ML) model for class 2 neural excitability. ILC has remarkable advantages for the repetitive processes in nature. To further highlight the superiority of the proposed method, the performances of the iterative learning controller are compared to those of classical PI controller. Either in the classical PI control or in the PI control combined with ILC, appropriate background noises are added in neuron models to approach the problem under more realistic biophysical conditions. Simulation results show that the controller performances are more favorable when ILC is considered, no matter which neuronal excitability the neuron belongs to and no matter what kind of firing pattern the desired trajectory belongs to. The error between real and desired output is much smaller under ILC control signal, which suggests ILC of neuron’s spiking behavior is more accurate.

  10. Modeling the Development of Goal-Specificity in Mirror Neurons.

    PubMed

    Thill, Serge; Svensson, Henrik; Ziemke, Tom

    2011-12-01

    Neurophysiological studies have shown that parietal mirror neurons encode not only actions but also the goal of these actions. Although some mirror neurons will fire whenever a certain action is perceived (goal-independently), most will only fire if the motion is perceived as part of an action with a specific goal. This result is important for the action-understanding hypothesis as it provides a potential neurological basis for such a cognitive ability. It is also relevant for the design of artificial cognitive systems, in particular robotic systems that rely on computational models of the mirror system in their interaction with other agents. Yet, to date, no computational model has explicitly addressed the mechanisms that give rise to both goal-specific and goal-independent parietal mirror neurons. In the present paper, we present a computational model based on a self-organizing map, which receives artificial inputs representing information about both the observed or executed actions and the context in which they were executed. We show that the map develops a biologically plausible organization in which goal-specific mirror neurons emerge. We further show that the fundamental cause for both the appearance and the number of goal-specific neurons can be found in geometric relationships between the different inputs to the map. The results are important to the action-understanding hypothesis as they provide a mechanism for the emergence of goal-specific parietal mirror neurons and lead to a number of predictions: (1) Learning of new goals may mostly reassign existing goal-specific neurons rather than recruit new ones; (2) input differences between executed and observed actions can explain observed corresponding differences in the number of goal-specific neurons; and (3) the percentage of goal-specific neurons may differ between motion primitives.

  11. 3D-printer visualization of neuron models

    PubMed Central

    McDougal, Robert A.; Shepherd, Gordon M.

    2015-01-01

    Neurons come in a wide variety of shapes and sizes. In a quest to understand this neuronal diversity, researchers have three-dimensionally traced tens of thousands of neurons; many of these tracings are freely available through online repositories like NeuroMorpho.Org and ModelDB. Tracings can be visualized on the computer screen, used for statistical analysis of the properties of different cell types, used to simulate neuronal behavior, and more. We introduce the use of 3D printing as a technique for visualizing traced morphologies. Our method for generating printable versions of a cell or group of cells is to expand dendrite and axon diameters and then to transform the tracing into a 3D object with a neuronal surface generating algorithm like Constructive Tessellated Neuronal Geometry (CTNG). We show that 3D printed cells can be readily examined, manipulated, and compared with other neurons to gain insight into both the biology and the reconstruction process. We share our printable models in a new database, 3DModelDB, and encourage others to do the same with cells that they generate using our code or other methods. To provide additional context, 3DModelDB provides a simulatable version of each cell, links to papers that use or describe it, and links to associated entries in other databases. PMID:26175684

  12. 3D-printer visualization of neuron models.

    PubMed

    McDougal, Robert A; Shepherd, Gordon M

    2015-01-01

    Neurons come in a wide variety of shapes and sizes. In a quest to understand this neuronal diversity, researchers have three-dimensionally traced tens of thousands of neurons; many of these tracings are freely available through online repositories like NeuroMorpho.Org and ModelDB. Tracings can be visualized on the computer screen, used for statistical analysis of the properties of different cell types, used to simulate neuronal behavior, and more. We introduce the use of 3D printing as a technique for visualizing traced morphologies. Our method for generating printable versions of a cell or group of cells is to expand dendrite and axon diameters and then to transform the tracing into a 3D object with a neuronal surface generating algorithm like Constructive Tessellated Neuronal Geometry (CTNG). We show that 3D printed cells can be readily examined, manipulated, and compared with other neurons to gain insight into both the biology and the reconstruction process. We share our printable models in a new database, 3DModelDB, and encourage others to do the same with cells that they generate using our code or other methods. To provide additional context, 3DModelDB provides a simulatable version of each cell, links to papers that use or describe it, and links to associated entries in other databases.

  13. Spiking neuron model for temporal sequence recognition.

    PubMed

    Byrnes, Sean; Burkitt, Anthony N; Grayden, David B; Meffin, Hamish

    2010-01-01

    A biologically inspired neuronal network that stores and recognizes temporal sequences of symbols is described. Each symbol is represented by excitatory input to distinct groups of neurons (symbol pools). Unambiguous storage of multiple sequences with common subsequences is ensured by partitioning each symbol pool into subpools that respond only when the current symbol has been preceded by a particular sequence of symbols. We describe synaptic structure and neural dynamics that permit the selective activation of subpools by the correct sequence. Symbols may have varying durations of the order of hundreds of milliseconds. Physiologically plausible plasticity mechanisms operate on a time scale of tens of milliseconds; an interaction of the excitatory input with periodic global inhibition bridges this gap so that neural events representing successive symbols occur on this much faster timescale. The network is shown to store multiple overlapping sequences of events. It is robust to variation in symbol duration, it is scalable, and its performance degrades gracefully with perturbation of its parameters.

  14. An electromechanical model of neuronal dynamics using Hamilton's principle

    PubMed Central

    Drapaca, Corina S.

    2015-01-01

    Damage of the brain may be caused by mechanical loads such as penetration, blunt force, shock loading from blast, and by chemical imbalances due to neurological diseases and aging that trigger not only neuronal degeneration but also changes in the mechanical properties of brain tissue. An understanding of the interconnected nature of the electro-chemo-mechanical processes that result in brain damage and ultimately loss of functionality is currently lacking. While modern mathematical models that focus on how to link brain mechanics to its biochemistry are essential in enhancing our understanding of brain science, the lack of experimental data required by these models as well as the complexity of the corresponding computations render these models hard to use in clinical applications. In this paper we propose a unified variational framework for the modeling of neuronal electromechanics. We introduce a constrained Lagrangian formulation that takes into account Newton's law of motion of a linear viscoelastic Kelvin–Voigt solid-state neuron as well as the classic Hodgkin–Huxley equations of the electronic neuron. The system of differential equations describing neuronal electromechanics is obtained by applying Hamilton's principle. Numerical simulations of possible damage dynamics in neurons will be presented. PMID:26236195

  15. Visual Attention Model Based on Statistical Properties of Neuron Responses

    PubMed Central

    Duan, Haibin; Wang, Xiaohua

    2015-01-01

    Visual attention is a mechanism of the visual system that can select relevant objects from a specific scene. Interactions among neurons in multiple cortical areas are considered to be involved in attentional allocation. However, the characteristics of the encoded features and neuron responses in those attention related cortices are indefinite. Therefore, further investigations carried out in this study aim at demonstrating that unusual regions arousing more attention generally cause particular neuron responses. We suppose that visual saliency is obtained on the basis of neuron responses to contexts in natural scenes. A bottom-up visual attention model is proposed based on the self-information of neuron responses to test and verify the hypothesis. Four different color spaces are adopted and a novel entropy-based combination scheme is designed to make full use of color information. Valuable regions are highlighted while redundant backgrounds are suppressed in the saliency maps obtained by the proposed model. Comparative results reveal that the proposed model outperforms several state-of-the-art models. This study provides insights into the neuron responses based saliency detection and may underlie the neural mechanism of early visual cortices for bottom-up visual attention. PMID:25747859

  16. An electromechanical model of neuronal dynamics using Hamilton's principle.

    PubMed

    Drapaca, Corina S

    2015-01-01

    Damage of the brain may be caused by mechanical loads such as penetration, blunt force, shock loading from blast, and by chemical imbalances due to neurological diseases and aging that trigger not only neuronal degeneration but also changes in the mechanical properties of brain tissue. An understanding of the interconnected nature of the electro-chemo-mechanical processes that result in brain damage and ultimately loss of functionality is currently lacking. While modern mathematical models that focus on how to link brain mechanics to its biochemistry are essential in enhancing our understanding of brain science, the lack of experimental data required by these models as well as the complexity of the corresponding computations render these models hard to use in clinical applications. In this paper we propose a unified variational framework for the modeling of neuronal electromechanics. We introduce a constrained Lagrangian formulation that takes into account Newton's law of motion of a linear viscoelastic Kelvin-Voigt solid-state neuron as well as the classic Hodgkin-Huxley equations of the electronic neuron. The system of differential equations describing neuronal electromechanics is obtained by applying Hamilton's principle. Numerical simulations of possible damage dynamics in neurons will be presented.

  17. Modelling of the enteric nervous network: 3. Adrenergic neuron.

    PubMed

    Miftakhov, R N; Wingate, D L

    1994-11-01

    A mathematical model is developed to investigate the coupled electrochemical processes of nerve-pulse transmission via adrenergic synapse. Based on pharmacological and morphophysiological data, the model describes the dynamics of the propagation of the electric signal along the unmyelinated geometrically non-uniform axon of the neuron and the chemical mechanisms of the transformation of the electrical signal in the synaptic zone into the post-synaptic output. The combined nonlinear system of partial and ordinary differential equations has been obtained and solved numerically. The results of computer simulation of the function of the idealized adrenergic neuron quantitatively and qualitatively describe the dynamics of Ca2+ ion influx into the terminal, noradrenaline release from the free 'releasable' store, its diffusion into the synaptic cleft, binding with the adrenoceptors on the pre- and post-synaptic structures with the generation of the inhibitory post-synaptic potential, and utilization of noradrenaline by neuronal and non-neuronal capture mechanisms.

  18. Tunable Neuromimetic Integrated System for Emulating Cortical Neuron Models

    PubMed Central

    Grassia, Filippo; Buhry, Laure; Lévi, Timothée; Tomas, Jean; Destexhe, Alain; Saïghi, Sylvain

    2011-01-01

    Nowadays, many software solutions are currently available for simulating neuron models. Less conventional than software-based systems, hardware-based solutions generally combine digital and analog forms of computation. In previous work, we designed several neuromimetic chips, including the Galway chip that we used for this paper. These silicon neurons are based on the Hodgkin–Huxley formalism and they are optimized for reproducing a large variety of neuron behaviors thanks to tunable parameters. Due to process variation and device mismatch in analog chips, we use a full-custom fitting method in voltage-clamp mode to tune our neuromimetic integrated circuits. By comparing them with experimental electrophysiological data of these cells, we show that the circuits can reproduce the main firing features of cortical cell types. In this paper, we present the experimental measurements of our system which mimic the four most prominent biological cells: fast spiking, regular spiking, intrinsically bursting, and low-threshold spiking neurons into analog neuromimetic integrated circuit dedicated to cortical neuron simulations. This hardware and software platform will allow to improve the hybrid technique, also called “dynamic-clamp,” that consists of connecting artificial and biological neurons to study the function of neuronal circuits. PMID:22163213

  19. Nonlinear Modeling of Causal Interrelationships in Neuronal Ensembles

    PubMed Central

    Zanos, Theodoros P.; Courellis, Spiros H.; Berger, Theodore W.; Hampson, Robert E.; Deadwyler, Sam A.; Marmarelis, Vasilis Z.

    2009-01-01

    The increasing availability of multiunit recordings gives new urgency to the need for effective analysis of “multidimensional” time-series data that are derived from the recorded activity of neuronal ensembles in the form of multiple sequences of action potentials—treated mathematically as point-processes and computationally as spike-trains. Whether in conditions of spontaneous activity or under conditions of external stimulation, the objective is the identification and quantification of possible causal links among the neurons generating the observed binary signals. A multiple-input/multiple-output (MIMO) modeling methodology is presented that can be used to quantify the neuronal dynamics of causal interrelationships in neuronal ensembles using spike-train data recorded from individual neurons. These causal interrelationships are modeled as transformations of spike-trains recorded from a set of neurons designated as the “inputs” into spike-trains recorded from another set of neurons designated as the “outputs.” The MIMO model is composed of a set of multiinput/single-output (MISO) modules, one for each output. Each module is the cascade of a MISO Volterra model and a threshold operator generating the output spikes. The Laguerre expansion approach is used to estimate the Volterra kernels of each MISO module from the respective input–output data using the least-squares method. The predictive performance of the model is evaluated with the use of the receiver operating characteristic (ROC) curve, from which the optimum threshold is also selected. The Mann–Whitney statistic is used to select the significant inputs for each output by examining the statistical significance of improvements in the predictive accuracy of the model when the respective inputs is included. Illustrative examples are presented for a simulated system and for an actual application using multiunit data recordings from the hippocampus of a behaving rat. PMID:18701382

  20. Avalanches in a Stochastic Model of Spiking Neurons

    PubMed Central

    Cowan, Jack D.; van Drongelen, Wim

    2010-01-01

    Neuronal avalanches are a form of spontaneous activity widely observed in cortical slices and other types of nervous tissue, both in vivo and in vitro. They are characterized by irregular, isolated population bursts when many neurons fire together, where the number of spikes per burst obeys a power law distribution. We simulate, using the Gillespie algorithm, a model of neuronal avalanches based on stochastic single neurons. The network consists of excitatory and inhibitory neurons, first with all-to-all connectivity and later with random sparse connectivity. Analyzing our model using the system size expansion, we show that the model obeys the standard Wilson-Cowan equations for large network sizes ( neurons). When excitation and inhibition are closely balanced, networks of thousands of neurons exhibit irregular synchronous activity, including the characteristic power law distribution of avalanche size. We show that these avalanches are due to the balanced network having weakly stable functionally feedforward dynamics, which amplifies some small fluctuations into the large population bursts. Balanced networks are thought to underlie a variety of observed network behaviours and have useful computational properties, such as responding quickly to changes in input. Thus, the appearance of avalanches in such functionally feedforward networks indicates that avalanches may be a simple consequence of a widely present network structure, when neuron dynamics are noisy. An important implication is that a network need not be “critical” for the production of avalanches, so experimentally observed power laws in burst size may be a signature of noisy functionally feedforward structure rather than of, for example, self-organized criticality. PMID:20628615

  1. Avalanches in a stochastic model of spiking neurons.

    PubMed

    Benayoun, Marc; Cowan, Jack D; van Drongelen, Wim; Wallace, Edward

    2010-07-08

    Neuronal avalanches are a form of spontaneous activity widely observed in cortical slices and other types of nervous tissue, both in vivo and in vitro. They are characterized by irregular, isolated population bursts when many neurons fire together, where the number of spikes per burst obeys a power law distribution. We simulate, using the Gillespie algorithm, a model of neuronal avalanches based on stochastic single neurons. The network consists of excitatory and inhibitory neurons, first with all-to-all connectivity and later with random sparse connectivity. Analyzing our model using the system size expansion, we show that the model obeys the standard Wilson-Cowan equations for large network sizes ( neurons). When excitation and inhibition are closely balanced, networks of thousands of neurons exhibit irregular synchronous activity, including the characteristic power law distribution of avalanche size. We show that these avalanches are due to the balanced network having weakly stable functionally feedforward dynamics, which amplifies some small fluctuations into the large population bursts. Balanced networks are thought to underlie a variety of observed network behaviours and have useful computational properties, such as responding quickly to changes in input. Thus, the appearance of avalanches in such functionally feedforward networks indicates that avalanches may be a simple consequence of a widely present network structure, when neuron dynamics are noisy. An important implication is that a network need not be "critical" for the production of avalanches, so experimentally observed power laws in burst size may be a signature of noisy functionally feedforward structure rather than of, for example, self-organized criticality.

  2. Selective loss of alpha motor neurons with sparing of gamma motor neurons and spinal cord cholinergic neurons in a mouse model of spinal muscular atrophy.

    PubMed

    Powis, Rachael A; Gillingwater, Thomas H

    2016-03-01

    Spinal muscular atrophy (SMA) is a neuromuscular disease characterised primarily by loss of lower motor neurons from the ventral grey horn of the spinal cord and proximal muscle atrophy. Recent experiments utilising mouse models of SMA have demonstrated that not all motor neurons are equally susceptible to the disease, revealing that other populations of neurons can also be affected. Here, we have extended investigations of selective vulnerability of neuronal populations in the spinal cord of SMA mice to include comparative assessments of alpha motor neuron (α-MN) and gamma motor neuron (γ-MN) pools, as well as other populations of cholinergic neurons. Immunohistochemical analyses of late-symptomatic SMA mouse spinal cord revealed that numbers of α-MNs were significantly reduced at all levels of the spinal cord compared with controls, whereas numbers of γ-MNs remained stable. Likewise, the average size of α-MN cell somata was decreased in SMA mice with no change occurring in γ-MNs. Evaluation of other pools of spinal cord cholinergic neurons revealed that pre-ganglionic sympathetic neurons, central canal cluster interneurons, partition interneurons and preganglionic autonomic dorsal commissural nucleus neuron numbers all remained unaffected in SMA mice. Taken together, these findings indicate that α-MNs are uniquely vulnerable among cholinergic neuron populations in the SMA mouse spinal cord, with γ-MNs and other cholinergic neuronal populations being largely spared.

  3. Multisynaptic activity in a pyramidal neuron model and neural code.

    PubMed

    Ventriglia, Francesco; Di Maio, Vito

    2006-01-01

    The highly irregular firing of mammalian cortical pyramidal neurons is one of the most striking observation of the brain activity. This result affects greatly the discussion on the neural code, i.e. how the brain codes information transmitted along the different cortical stages. In fact it seems to be in favor of one of the two main hypotheses about this issue, named the rate code. But the supporters of the contrasting hypothesis, the temporal code, consider this evidence inconclusive. We discuss here a leaky integrate-and-fire model of a hippocampal pyramidal neuron intended to be biologically sound to investigate the genesis of the irregular pyramidal firing and to give useful information about the coding problem. To this aim, the complete set of excitatory and inhibitory synapses impinging on such a neuron has been taken into account. The firing activity of the neuron model has been studied by computer simulation both in basic conditions and allowing brief periods of over-stimulation in specific regions of its synaptic constellation. Our results show neuronal firing conditions similar to those observed in experimental investigations on pyramidal cortical neurons. In particular, the variation coefficient (CV) computed from the inter-spike intervals (ISIs) in our simulations for basic conditions is close to the unity as that computed from experimental data. Our simulation shows also different behaviors in firing sequences for different frequencies of stimulation.

  4. Mathematical Modeling of Subthreshold Resonant Properties in Pyloric Dilator Neurons

    PubMed Central

    Vazifehkhah Ghaffari, Babak; Kouhnavard, Mojgan; Aihara, Takeshi; Kitajima, Tatsuo

    2015-01-01

    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 calcium (ICa) and calcium-dependent potassium (IKCa) currents on resonant properties remain unclear. By taking advantage of Hodgkin-Huxley-type model of neuron and its equivalent RLC circuit, we examine the effects of changing resting membrane potential and those ionic currents on the resonance. Results show that changing the resting membrane potential influences the amplitude and frequency of resonance so that the strength of resonance (Q-value) increases by both depolarization and hyperpolarization of the resting membrane potential. Moreover, hyperpolarization-activated inward current (Ih) and ICa (in association with IKCa) are dominant factors on resonant properties at hyperpolarized and depolarized potentials, respectively. Through mathematical analysis, results indicate that Ih and IKCa affect the resonant properties of PD neurons. However, ICa only has an amplifying effect on the resonance amplitude of these neurons. PMID:25960999

  5. Mathematical modeling of subthreshold resonant properties in pyloric dilator neurons.

    PubMed

    Vazifehkhah Ghaffari, Babak; Kouhnavard, Mojgan; Aihara, Takeshi; Kitajima, Tatsuo

    2015-01-01

    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 calcium (I(Ca)) and calcium-dependent potassium (I(KCa)) currents on resonant properties remain unclear. By taking advantage of Hodgkin-Huxley-type model of neuron and its equivalent RLC circuit, we examine the effects of changing resting membrane potential and those ionic currents on the resonance. Results show that changing the resting membrane potential influences the amplitude and frequency of resonance so that the strength of resonance (Q-value) increases by both depolarization and hyperpolarization of the resting membrane potential. Moreover, hyperpolarization-activated inward current (I(h)) and I(Ca) (in association with I(KCa)) are dominant factors on resonant properties at hyperpolarized and depolarized potentials, respectively. Through mathematical analysis, results indicate that I h and I(KCa) affect the resonant properties of PD neurons. However, I(Ca) only has an amplifying effect on the resonance amplitude of these neurons.

  6. Comparison of single neuron models in terms of synchronization propensity

    NASA Astrophysics Data System (ADS)

    Sungar, N.; Allaria, E.; Leyva, I.; Arecchi, F. T.

    2008-09-01

    A plausible model for coherent perception is the synchronization of chaotically distributed neural spike trains over wide cortical areas. A recently introduced propensity criterion provides a tool for a quantitative comparison of different neuron models in terms of their ability to synchronize to an applied perturbation. We explore the propensity of several systems and indicate the requirements to be satisfied by a plausible candidate for modeling neuronal activity. Our results show that the conflicting requirements of stability and sensitivity leading to high propensity to synchronization can be satisfied by a strongly nonuniform attractor made of two distinct regions: a saddle focus plus a sufficiently separated saddle node.

  7. Attentional modulation of neuronal variability in circuit models of cortex

    PubMed Central

    Kanashiro, Tatjana; Ocker, Gabriel Koch; Cohen, Marlene R; Doiron, Brent

    2017-01-01

    The circuit mechanisms behind shared neural variability (noise correlation) and its dependence on neural state are poorly understood. Visual attention is well-suited to constrain cortical models of response variability because attention both increases firing rates and their stimulus sensitivity, as well as decreases noise correlations. We provide a novel analysis of population recordings in rhesus primate visual area V4 showing that a single biophysical mechanism may underlie these diverse neural correlates of attention. We explore model cortical networks where top-down mediated increases in excitability, distributed across excitatory and inhibitory targets, capture the key neuronal correlates of attention. Our models predict that top-down signals primarily affect inhibitory neurons, whereas excitatory neurons are more sensitive to stimulus specific bottom-up inputs. Accounting for trial variability in models of state dependent modulation of neuronal activity is a critical step in building a mechanistic theory of neuronal cognition. DOI: http://dx.doi.org/10.7554/eLife.23978.001 PMID:28590902

  8. Modeling ALS and FTD with iPSC-derived neurons.

    PubMed

    Lee, Sebum; Huang, Eric J

    2017-02-01

    Recent advances in genetics and neuropathology support the idea that amyotrophic lateral sclerosis (ALS) and frontotemporal lobar dementia (FTD) are two ends of a disease spectrum. Although several animal models have been developed to investigate the pathogenesis and disease progression in ALS and FTD, there are significant limitations that hamper our ability to connect these models with the neurodegenerative processes in human diseases. With the technical breakthrough in reprogramming biology, it is now possible to generate patient-specific induced pluripotent stem cells (iPSCs) and disease-relevant neuron subtypes. This review provides a comprehensive summary of studies that use iPSC-derived neurons to model ALS and FTD. We discuss the unique capabilities of iPSC-derived neurons that capture some key features of ALS and FTD, and underscore their potential roles in drug discovery. There are, however, several critical caveats that require improvements before iPSC-derived neurons can become highly effective disease models. This article is part of a Special Issue entitled SI: Exploiting human neurons.

  9. Generative modelling of regulated dynamical behavior in cultured neuronal networks

    NASA Astrophysics Data System (ADS)

    Volman, Vladislav; Baruchi, Itay; Persi, Erez; Ben-Jacob, Eshel

    2004-04-01

    The spontaneous activity of cultured in vitro neuronal networks exhibits rich dynamical behavior. Despite the artificial manner of their construction, the networks’ activity includes features which seemingly reflect the action of underlying regulating mechanism rather than arbitrary causes and effects. Here, we study the cultured networks dynamical behavior utilizing a generative modelling approach. The idea is to include the minimal required generic mechanisms to capture the non-autonomous features of the behavior, which can be reproduced by computer modelling, and then, to identify the additional features of biotic regulation in the observed behavior which are beyond the scope of the model. Our model neurons are composed of soma described by the two Morris-Lecar dynamical variables (voltage and fraction of open potassium channels), with dynamical synapses described by the Tsodyks-Markram three variables dynamics. The model neuron satisfies our self-consistency test: when fed with data recorded from a real cultured networks, it exhibits dynamical behavior very close to that of the networks’ “representative” neuron. Specifically, it shows similar statistical scaling properties (approximated by similar symmetric Lévy distribution with finite mean). A network of such M-L elements spontaneously generates (when weak “structured noise” is added) synchronized bursting events (SBEs) similar to the observed ones. Both the neuronal statistical scaling properties within the bursts and the properties of the SBEs time series show generative (a new discussed concept) agreement with the recorded data. Yet, the model network exhibits different structure of temporal variations and does not recover the observed hierarchical temporal ordering, unless fed with recorded special neurons (with much higher rates of activity), thus indicating the existence of self-regulation mechanisms. It also implies that the spontaneous activity is not simply noise-induced. Instead, the

  10. Leaders of neuronal cultures in a quorum percolation model.

    PubMed

    Eckmann, Jean-Pierre; Moses, Elisha; Stetter, Olav; Tlusty, Tsvi; Zbinden, Cyrille

    2010-01-01

    We present a theoretical framework using quorum percolation for describing the initiation of activity in a neural culture. The cultures are modeled as random graphs, whose nodes are excitatory neurons with k(in) inputs and k(out) outputs, and whose input degrees k(in) = k obey given distribution functions p(k). We examine the firing activity of the population of neurons according to their input degree (k) classes and calculate for each class its firing probability Φ(k)(t) as a function of t. The probability of a node to fire is found to be determined by its in-degree k, and the first-to-fire neurons are those that have a high k. A small minority of high-k-classes may be called "Leaders," as they form an interconnected sub-network that consistently fires much before the rest of the culture. Once initiated, the activity spreads from the Leaders to the less connected majority of the culture. We then use the distribution of in-degree of the Leaders to study the growth rate of the number of neurons active in a burst, which was experimentally measured to be initially exponential. We find that this kind of growth rate is best described by a population that has an in-degree distribution that is a Gaussian centered around k = 75 with width σ = 31 for the majority of the neurons, but also has a power law tail with exponent -2 for 10% of the population. Neurons in the tail may have as many as k = 4,700 inputs. We explore and discuss the correspondence between the degree distribution and a dynamic neuronal threshold, showing that from the functional point of view, structure and elementary dynamics are interchangeable. We discuss possible geometric origins of this distribution, and comment on the importance of size, or of having a large number of neurons, in the culture.

  11. Reconstruction of neuronal input through modeling single-neuron dynamics and computations

    NASA Astrophysics Data System (ADS)

    Qin, Qing; Wang, Jiang; Yu, Haitao; Deng, Bin; Chan, Wai-lok

    2016-06-01

    Mathematical models provide a mathematical description of neuron activity, which can better understand and quantify neural computations and corresponding biophysical mechanisms evoked by stimulus. In this paper, based on the output spike train evoked by the acupuncture mechanical stimulus, we present two different levels of models to describe the input-output system to achieve the reconstruction of neuronal input. The reconstruction process is divided into two steps: First, considering the neuronal spiking event as a Gamma stochastic process. The scale parameter and the shape parameter of Gamma process are, respectively, defined as two spiking characteristics, which are estimated by a state-space method. Then, leaky integrate-and-fire (LIF) model is used to mimic the response system and the estimated spiking characteristics are transformed into two temporal input parameters of LIF model, through two conversion formulas. We test this reconstruction method by three different groups of simulation data. All three groups of estimates reconstruct input parameters with fairly high accuracy. We then use this reconstruction method to estimate the non-measurable acupuncture input parameters. Results show that under three different frequencies of acupuncture stimulus conditions, estimated input parameters have an obvious difference. The higher the frequency of the acupuncture stimulus is, the higher the accuracy of reconstruction is.

  12. Reconstruction of neuronal input through modeling single-neuron dynamics and computations

    SciTech Connect

    Qin, Qing; Wang, Jiang; Yu, Haitao; Deng, Bin Chan, Wai-lok

    2016-06-15

    Mathematical models provide a mathematical description of neuron activity, which can better understand and quantify neural computations and corresponding biophysical mechanisms evoked by stimulus. In this paper, based on the output spike train evoked by the acupuncture mechanical stimulus, we present two different levels of models to describe the input-output system to achieve the reconstruction of neuronal input. The reconstruction process is divided into two steps: First, considering the neuronal spiking event as a Gamma stochastic process. The scale parameter and the shape parameter of Gamma process are, respectively, defined as two spiking characteristics, which are estimated by a state-space method. Then, leaky integrate-and-fire (LIF) model is used to mimic the response system and the estimated spiking characteristics are transformed into two temporal input parameters of LIF model, through two conversion formulas. We test this reconstruction method by three different groups of simulation data. All three groups of estimates reconstruct input parameters with fairly high accuracy. We then use this reconstruction method to estimate the non-measurable acupuncture input parameters. Results show that under three different frequencies of acupuncture stimulus conditions, estimated input parameters have an obvious difference. The higher the frequency of the acupuncture stimulus is, the higher the accuracy of reconstruction is.

  13. A Developmental Approach to Predicting Neuronal Connectivity from Small Biological Datasets: A Gradient-Based Neuron Growth Model

    PubMed Central

    Borisyuk, Roman; Azad, Abul Kalam al; Conte, Deborah; Roberts, Alan; Soffe, Stephen R.

    2014-01-01

    Relating structure and function of neuronal circuits is a challenging problem. It requires demonstrating how dynamical patterns of spiking activity lead to functions like cognitive behaviour and identifying the neurons and connections that lead to appropriate activity of a circuit. We apply a “developmental approach” to define the connectome of a simple nervous system, where connections between neurons are not prescribed but appear as a result of neuron growth. A gradient based mathematical model of two-dimensional axon growth from rows of undifferentiated neurons is derived for the different types of neurons in the brainstem and spinal cord of young tadpoles of the frog Xenopus. Model parameters define a two-dimensional CNS growth environment with three gradient cues and the specific responsiveness of the axons of each neuron type to these cues. The model is described by a nonlinear system of three difference equations; it includes a random variable, and takes specific neuron characteristics into account. Anatomical measurements are first used to position cell bodies in rows and define axon origins. Then a generalization procedure allows information on the axons of individual neurons from small anatomical datasets to be used to generate larger artificial datasets. To specify parameters in the axon growth model we use a stochastic optimization procedure, derive a cost function and find the optimal parameters for each type of neuron. Our biologically realistic model of axon growth starts from axon outgrowth from the cell body and generates multiple axons for each different neuron type with statistical properties matching those of real axons. We illustrate how the axon growth model works for neurons with axons which grow to the same and the opposite side of the CNS. We then show how, by adding a simple specification for dendrite morphology, our model “developmental approach” allows us to generate biologically-realistic connectomes. PMID:24586794

  14. Edge detection based on Hodgkin-Huxley neuron model simulation.

    PubMed

    Yedjour, Hayat; Meftah, Boudjelal; Lézoray, Olivier; Benyettou, Abdelkader

    2017-04-03

    In this paper, we propose a spiking neural network model for edge detection in images. The proposed model is biologically inspired by the mechanisms employed by natural vision systems, more specifically by the biologically fulfilled function of simple cells of the human primary visual cortex that are selective for orientation. Several aspects are studied in this model according to three characteristics: feedforward spiking neural structure; conductance-based model of the Hodgkin-Huxley neuron and Gabor receptive fields structure. A visualized map is generated using the firing rate of neurons representing the orientation map of the visual cortex area. We have simulated the proposed model on different images. Successful computer simulation results are obtained. For comparison, we have chosen five methods for edge detection. We finally evaluate and compare the performances of our model toward contour detection using a public dataset of natural images with associated contour ground truths. Experimental results show the ability and high performance of the proposed network model.

  15. A Neuronal Network Model for Pitch Selectivity and Representation

    PubMed Central

    Huang, Chengcheng; Rinzel, John

    2016-01-01

    Pitch is a perceptual correlate of periodicity. Sounds with distinct spectra can elicit the same pitch. Despite the importance of pitch perception, understanding the cellular mechanism of pitch perception is still a major challenge and a mechanistic model of pitch is lacking. A multi-stage neuronal network model is developed for pitch frequency estimation using biophysically-based, high-resolution coincidence detector neurons. The neuronal units respond only to highly coincident input among convergent auditory nerve fibers across frequency channels. Their selectivity for only very fast rising slopes of convergent input enables these slope-detectors to distinguish the most prominent coincidences in multi-peaked input time courses. Pitch can then be estimated from the first-order interspike intervals of the slope-detectors. The regular firing pattern of the slope-detector neurons are similar for sounds sharing the same pitch despite the distinct timbres. The decoded pitch strengths also correlate well with the salience of pitch perception as reported by human listeners. Therefore, our model can serve as a neural representation for pitch. Our model performs successfully in estimating the pitch of missing fundamental complexes and reproducing the pitch variation with respect to the frequency shift of inharmonic complexes. It also accounts for the phase sensitivity of pitch perception in the cases of Schroeder phase, alternating phase and random phase relationships. Moreover, our model can also be applied to stochastic sound stimuli, iterated-ripple-noise, and account for their multiple pitch perceptions. PMID:27378900

  16. Nonlinear multiplicative dendritic integration in neuron and network models

    PubMed Central

    Zhang, Danke; Li, Yuanqing; Rasch, Malte J.; Wu, Si

    2013-01-01

    Neurons receive inputs from thousands of synapses distributed across dendritic trees of complex morphology. It is known that dendritic integration of excitatory and inhibitory synapses can be highly non-linear in reality and can heavily depend on the exact location and spatial arrangement of inhibitory and excitatory synapses on the dendrite. Despite this known fact, most neuron models used in artificial neural networks today still only describe the voltage potential of a single somatic compartment and assume a simple linear summation of all individual synaptic inputs. We here suggest a new biophysical motivated derivation of a single compartment model that integrates the non-linear effects of shunting inhibition, where an inhibitory input on the route of an excitatory input to the soma cancels or “shunts” the excitatory potential. In particular, our integration of non-linear dendritic processing into the neuron model follows a simple multiplicative rule, suggested recently by experiments, and allows for strict mathematical treatment of network effects. Using our new formulation, we further devised a spiking network model where inhibitory neurons act as global shunting gates, and show that the network exhibits persistent activity in a low firing regime. PMID:23658543

  17. Transplantation of post-mitotic human neuroteratocarcinoma-overexpressing Nurr1 cells provides therapeutic benefits in experimental stroke: in vitro evidence of expedited neuronal differentiation and GDNF secretion.

    PubMed

    Hara, Koichi; Matsukawa, Noriyuki; Yasuhara, Takao; Xu, Lin; Yu, Guolong; Maki, Mina; Kawase, Takeshi; Hess, David C; Kim, Seung U; Borlongan, Cesar V

    2007-05-01

    Nurr1 has been implicated as a transcription factor mediating the endogenous neuroprotective mechanism against stroke. We examined the in vivo and in vitro properties of a new human embryonic carcinoma Ntera-2 cell line carrying the human Nurr1 gene (NT2N.Nurr1). Adult Sprague-Dawley rats underwent experimental stroke initially and 14 days later were assigned randomly to receive stereotaxic transplantation of NT2N.Nurr1 cells or infusion of vehicle into their ischemic striatum. Transplantation of NT2N.Nurr1 cells promoted significant attenuation of behavioral impairments over a 56-day period after stroke, characterized by decreased hyperactivity, biased swing activity, and neurologic deficits, as well as significant reduction in ischemic striatal cell loss compared to vehicle-infused stroke animals. Transplanted NT2N.Nurr1 cells survived and expressed neuronal phenotypic markers in the ischemic striatum. In vitro results showed that cultured NT2.Nurr1 cells were already negative for nestin even before retinoic acid treatment, despite strong nestin immunoreactivity in NT2 cells. This indicates Nurr1 triggered a rapid commitment of NT2 cells into a neuronal lineage. Indeed, NT2.Nurr1 cells, at 4 weeks into RA treatment, displayed more abundant tyrosine hydroxylase positive cells than NT2 cells. Parallel ELISA studies showed further that cultured NT2N.Nurr1, but not NT2N cells, secreted glial cell derived neurotrophic factor. The present study shows efficacy of NT2N.Nurr1 cell grafts in ischemic stroke, with in vitro evidence suggesting the cells' excellent neuronal differentiation capability and ability to secrete GDNF as likely mechanisms mediating the observed therapeutic benefits. (c) 2007 Wiley-Liss, Inc.

  18. Apoptosis and in vitro Alzheimer disease neuronal models

    PubMed Central

    Calissano, P; Matrone, C

    2009-01-01

    Alzheimer disease (AD) is a human neurodegenerative disease characterized by co-existence of extracellular senile plaques (SP) and neurofibrillary tangles (NFT) associated with an extensive neuronal loss, primarily in the cerebral cortex and hippocampus. Several studies suggest that caspase(s)-mediated neuronal death occurs in cellular and animal AD models as well as in human brains of affected patients, although an etiologic role of apoptosis in such neurodegenerative disorder is still debated. This review summarizes the experimental evidences corroborating the possible involvement of apoptosis in AD pathogenesis and discusses the usefulness of ad hoc devised in vitro approaches to study how caspase(s), amyloidogenic processing and tau metabolism might reciprocally interact leading to neuronal death. PMID:19513272

  19. Computational modeling of neuronal dynamics for systems analysis: application to neurons of the cardiorespiratory NTS in the rat.

    PubMed

    Schwaber, J S; Graves, E B; Paton, J F

    1993-02-26

    The study constructs computational models of neurons in order to examine the contribution that their response dynamics may make to functional properties at the system level. As described in the accompanying study, neurons in the cardiorespiratory nucleus tractus solitarii (NTS) of the rat were recorded in vitro. When these cells were intracellularly injected with a constant current pulse, spike discharge patterns and subthreshold voltage trajectories were observed that were time- and voltage-dependent. The accompanying manuscript describes these dynamic responses in 4 classes of putative second-order cells that appear to receive direct primary afferent input, and a previous paper described two populations of rhythmically firing interneurons, one of which is intrinsically auto-active. In the present manuscript experimental neuronal voltage response data was collected across a current injection series for the S3 neuron type described in the accompanying study and for the auto-active neuron described previously. Using this data, computational model neurons have been constructed for these two neurons by using membrane ion channels to produce and match the observed neuronal voltage behavior. The channels were those implicated in the dynamic responses observed in the companion study, and include gNafast, gKdr, gKA, gKCa, gKAHP, gKM, gCaT and gCaL. The description of channel kinetics follows the Hodgkin-Huxley form. Different neuronal sources from the literature of channel kinetics were investigated and assembled into a 'channel kinetics library' from which both neuron models were tuned, primarily by adjusting the maximum channel densities, g, and time-dependence of kinetics. Methods are described for tuning the channel kinetics library to match various physiological responses. This approach created neuron models that were able to closely replicate the observed complex voltage and spiking responses of the two very different cardiorespiratory NTS neurons. The interaction

  20. Neuronal modelling of baroreflex response to orthostatic stress

    NASA Astrophysics Data System (ADS)

    Samin, Azfar

    The accelerations experienced in aerial combat can cause pilot loss of consciousness (GLOC) due to a critical reduction in cerebral blood circulation. The development of smart protective equipment requires understanding of how the brain processes blood pressure (BP) information in response to acceleration. We present a biologically plausible model of the Baroreflex to investigate the neural correlates of short-term BP control under acceleration or orthostatic stress. The neuronal network model, which employs an integrate-and-fire representation of a biological neuron, comprises the sensory, motor, and the central neural processing areas that form the Baroreflex. Our modelling strategy is to test hypotheses relating to the encoding mechanisms of multiple sensory inputs to the nucleus tractus solitarius (NTS), the site of central neural processing. The goal is to run simulations and reproduce model responses that are consistent with the variety of available experimental data. Model construction and connectivity are inspired by the available anatomical and neurophysiological evidence that points to a barotopic organization in the NTS, and the presence of frequency-dependent synaptic depression, which provides a mechanism for generating non-linear local responses in NTS neurons that result in quantifiable dynamic global baroreflex responses. The entire physiological range of BP and rate of change of BP variables is encoded in a palisade of NTS neurons in that the spike responses approximate Gaussian 'tuning' curves. An adapting weighted-average decoding scheme computes the motor responses and a compensatory signal regulates the heart rate (HR). Model simulations suggest that: (1) the NTS neurons can encode the hydrostatic pressure difference between two vertically separated sensory receptor regions at +Gz, and use changes in that difference for the regulation of HR; (2) even though NTS neurons do not fire with a cardiac rhythm seen in the afferents, pulse

  1. Noise-induced transition in excitable neuron models.

    PubMed

    Tanabe, S; Pakdaman, K

    2001-10-01

    We studied the influence of noisy stimulation on the Hodgkin-Huxley neuron model. Rather than examining the noise-related variability of the discharge times of the model--as has been done previously--our study focused on the effect of noise on the stationary distributions of the membrane potential and gating variables of the model. We observed that a gradual increase in the noise intensity did not result in a gradual change of the distributions. Instead, we could identify a critical intermediate noise range in which the shapes of the distributions underwent a drastic qualitative change. Namely, they moved from narrow unimodal Gaussian-like shapes associated with low noise intensities to ones that spread widely at large noise intensities. In particular, for the membrane potential and the sodium activation variable, the distributions changed from unimodal to bimodal. Thus, our investigation revealed a noise-induced transition in the Hodgkin-Huxley model. In order to further characterize this phenomenon, we considered a reduced one-dimensional model of an excitable system, namely the active rotator. For this model, our analysis indicated that the noise-induced transition is associated with a deterministic bifurcation of approximate equations governing the dynamics of the mean and variance of the state variable. Finally, we shed light on the possible functional importance of this noise-induced transition in neuronal coding by determining its effect on the spike timing precision in models of neuronal ensembles.

  2. [A model for evoked activity of hippocampal neuronal population].

    PubMed

    Chizhov, A V

    2002-01-01

    A system of equations governing the activity of hippocampal neuron populations is proposed. This continual firing-rate model is aimed to simulate evoked potentials and synchronous wave activity of the neural tissue. The populations of excitatory and inhibitory neurons and the types of synaptic receptors are distinguished. The model is based on the idea of control and averaging of Hodgkin-Huxley equations, a simple model of a threshold elicitation of population action potential bursts, approximations of synaptic currents by the second-order differential equations, and hyperbolic partial derivative equation of axonal excitation propagation. The model was fitted to intracellular cordings of postsynaptic potentials and postsynaptic currents in CA1 of rat hippocampal slices.

  3. Parameter estimation of neuron models using in-vitro and in-vivo electrophysiological data

    PubMed Central

    Lynch, Eoin P.; Houghton, Conor J.

    2015-01-01

    Spiking neuron models can accurately predict the response of neurons to somatically injected currents if the model parameters are carefully tuned. Predicting the response of in-vivo neurons responding to natural stimuli presents a far more challenging modeling problem. In this study, an algorithm is presented for parameter estimation of spiking neuron models. The algorithm is a hybrid evolutionary algorithm which uses a spike train metric as a fitness function. We apply this to parameter discovery in modeling two experimental data sets with spiking neurons; in-vitro current injection responses from a regular spiking pyramidal neuron are modeled using spiking neurons and in-vivo extracellular auditory data is modeled using a two stage model consisting of a stimulus filter and spiking neuron model. PMID:25941485

  4. Optical Computing Based on Neuronal Models

    DTIC Science & Technology

    1988-05-01

    walking, and cognition are far too complex for existing sequential digital computers. Therefore new architectures, hardware, and algorithms modeled...collective behavior, and iterative processing into optical processing and artificial neurodynamical systems. Another intriguing promise of neural nets is...with architectures, implementations, and programming; and material research s -7- called for. Our future research in neurodynamics will continue to

  5. Complex Parameter Landscape for a Complex Neuron Model

    PubMed Central

    Achard, Pablo; De Schutter, Erik

    2006-01-01

    The electrical activity of a neuron is strongly dependent on the ionic channels present in its membrane. Modifying the maximal conductances from these channels can have a dramatic impact on neuron behavior. But the effect of such modifications can also be cancelled out by compensatory mechanisms among different channels. We used an evolution strategy with a fitness function based on phase-plane analysis to obtain 20 very different computational models of the cerebellar Purkinje cell. All these models produced very similar outputs to current injections, including tiny details of the complex firing pattern. These models were not completely isolated in the parameter space, but neither did they belong to a large continuum of good models that would exist if weak compensations between channels were sufficient. The parameter landscape of good models can best be described as a set of loosely connected hyperplanes. Our method is efficient in finding good models in this complex landscape. Unraveling the landscape is an important step towards the understanding of functional homeostasis of neurons. PMID:16848639

  6. Modeling Huntington׳s disease with patient-derived neurons.

    PubMed

    Mattis, Virginia B; Svendsen, Clive N

    2017-02-01

    Huntington׳s Disease (HD) is a fatal neurodegenerative disorder caused by expanded polyglutamine repeats in the Huntingtin (HTT) gene. While the gene was identified over two decades ago, it remains poorly understood why mutant HTT (mtHTT) is initially toxic to striatal medium spiny neurons (MSNs). Models of HD using non-neuronal human patient cells and rodents exhibit some characteristic HD phenotypes. While these current models have contributed to the field, they are limited in disease manifestation and may vary in their response to treatments. As such, human HD patient MSNs for disease modeling could greatly expand the current understanding of HD and facilitate the search for a successful treatment. It is now possible to use pluripotent stem cells, which can generate any tissue type in the body, to study and potentially treat HD. This review covers disease modeling in vitro and, via chimeric animal generation, in vivo using human HD patient MSNs differentiated from embryonic stem cells or induced pluripotent stem cells. This includes an overview of the differentiation of pluripotent cells into MSNs, the established phenotypes found in cell-based models and transplantation studies using these cells. This review not only outlines the advancements in the rapidly progressing field of HD modeling using neurons derived from human pluripotent cells, but also it highlights several remaining controversial issues such as the 'ideal' series of pluripotent lines, the optimal cell types to use and the study of a primarily adult-onset disease in a developmental model. This article is part of a Special Issue entitled SI: Exploiting human neurons. Copyright © 2015 Elsevier B.V. All rights reserved.

  7. Optical Computing Based on Neuronal Models.

    DTIC Science & Technology

    1987-10-01

    Tikhonov , A.N. and V.Y. Arsenin , " Solutions of Ill - Posed Problems ", Winston and Sons, Washington, D.C. 1977 . 11. Poggio, T. and C. Koch, " Ill - Posed ...describe a solution to this problem and to use the solu- tion as a vehicle for pointing out the distinctive features of the neural net model approach to... ill - posedness [11]. The brain’s associative memory capabilities where nearest neighbor searches are performed successfully

  8. Study on dynamic characteristics' change of hippocampal neuron reduced models caused by the Alzheimer's disease.

    PubMed

    Peng, Yueping; Wang, Jue; Zheng, Chongxun

    2016-01-01

    In the paper, based on the electrophysiological experimental data, the Hippocampal neuron reduced model under the pathology condition of Alzheimer's disease (AD) has been built by modifying parameters' values. The reduced neuron model's dynamic characteristics under effect of AD are comparatively studied. Under direct current stimulation, compared with the normal neuron model, the AD neuron model's dynamic characteristics have obviously been changed. The neuron model under the AD condition undergoes supercritical Andronov-Hopf bifurcation from the rest state to the continuous discharge state. It is different from the neuron model under the normal condition, which undergoes saddle-node bifurcation. So, the neuron model changes into a resonator with monostable state from an integrator with bistable state under AD's action. The research reveals the neuron model's dynamic characteristics' changing under effect of AD, and provides some theoretic basis for AD research by neurodynamics theory.

  9. Neuronal and brain morphological changes in animal models of schizophrenia.

    PubMed

    Flores, Gonzalo; Morales-Medina, Julio César; Diaz, Alfonso

    2016-03-15

    Schizophrenia, a severe and debilitating disorder with a high social burden, affects 1% of the adult world population. Available therapies are unable to treat all the symptoms, and result in strong side effects. For this reason, numerous animal models have been generated to elucidate the pathophysiology of this disorder. All these models present neuronal remodeling and abnormalities in spine stability. It is well known that the complexity in dendritic arborization determines the number of receptive synaptic contacts. Also the loss of dendritic spines and arbor stability are strongly associated with schizophrenia. This review evaluates changes in spine density and dendritic arborization in animal models of schizophrenia. By understanding these changes, pharmacological treatments can be designed to target specific neural systems to attenuate neuronal remodeling and associated behavioral deficits.

  10. Activity-Dependent Neuronal Model on Complex Networks

    PubMed Central

    de Arcangelis, Lucilla; Herrmann, Hans J.

    2012-01-01

    Neuronal avalanches are a novel mode of activity in neuronal networks, experimentally found in vitro and in vivo, and exhibit a robust critical behavior: these avalanches are characterized by a power law distribution for the size and duration, features found in other problems in the context of the physics of complex systems. We present a recent model inspired in self-organized criticality, which consists of an electrical network with threshold firing, refractory period, and activity-dependent synaptic plasticity. The model reproduces the critical behavior of the distribution of avalanche sizes and durations measured experimentally. Moreover, the power spectra of the electrical signal reproduce very robustly the power law behavior found in human electroencephalogram (EEG) spectra. We implement this model on a variety of complex networks, i.e., regular, small-world, and scale-free and verify the robustness of the critical behavior. PMID:22470347

  11. Colored noise and memory effects on formal spiking neuron models.

    PubMed

    da Silva, L A; Vilela, R D

    2015-06-01

    Simplified neuronal models capture the essence of the electrical activity of a generic neuron, besides being more interesting from the computational point of view when compared to higher-dimensional models such as the Hodgkin-Huxley one. In this work, we propose a generalized resonate-and-fire model described by a generalized Langevin equation that takes into account memory effects and colored noise. We perform a comprehensive numerical analysis to study the dynamics and the point process statistics of the proposed model, highlighting interesting new features such as (i) nonmonotonic behavior (emergence of peak structures, enhanced by the choice of colored noise characteristic time scale) of the coefficient of variation (CV) as a function of memory characteristic time scale, (ii) colored noise-induced shift in the CV, and (iii) emergence and suppression of multimodality in the interspike interval (ISI) distribution due to memory-induced subthreshold oscillations. Moreover, in the noise-induced spike regime, we study how memory and colored noise affect the coherence resonance (CR) phenomenon. We found that for sufficiently long memory, not only is CR suppressed but also the minimum of the CV-versus-noise intensity curve that characterizes the presence of CR may be replaced by a maximum. The aforementioned features allow to interpret the interplay between memory and colored noise as an effective control mechanism to neuronal variability. Since both variability and nontrivial temporal patterns in the ISI distribution are ubiquitous in biological cells, we hope the present model can be useful in modeling real aspects of neurons.

  12. Colored noise and memory effects on formal spiking neuron models

    NASA Astrophysics Data System (ADS)

    da Silva, L. A.; Vilela, R. D.

    2015-06-01

    Simplified neuronal models capture the essence of the electrical activity of a generic neuron, besides being more interesting from the computational point of view when compared to higher-dimensional models such as the Hodgkin-Huxley one. In this work, we propose a generalized resonate-and-fire model described by a generalized Langevin equation that takes into account memory effects and colored noise. We perform a comprehensive numerical analysis to study the dynamics and the point process statistics of the proposed model, highlighting interesting new features such as (i) nonmonotonic behavior (emergence of peak structures, enhanced by the choice of colored noise characteristic time scale) of the coefficient of variation (CV) as a function of memory characteristic time scale, (ii) colored noise-induced shift in the CV, and (iii) emergence and suppression of multimodality in the interspike interval (ISI) distribution due to memory-induced subthreshold oscillations. Moreover, in the noise-induced spike regime, we study how memory and colored noise affect the coherence resonance (CR) phenomenon. We found that for sufficiently long memory, not only is CR suppressed but also the minimum of the CV-versus-noise intensity curve that characterizes the presence of CR may be replaced by a maximum. The aforementioned features allow to interpret the interplay between memory and colored noise as an effective control mechanism to neuronal variability. Since both variability and nontrivial temporal patterns in the ISI distribution are ubiquitous in biological cells, we hope the present model can be useful in modeling real aspects of neurons.

  13. Derivation of a neural field model from a network of theta neurons.

    PubMed

    Laing, Carlo R

    2014-07-01

    Neural field models are used to study macroscopic spatiotemporal patterns in the cortex. Their derivation from networks of model neurons normally involves a number of assumptions, which may not be correct. Here we present an exact derivation of a neural field model from an infinite network of theta neurons, the canonical form of a type I neuron. We demonstrate the existence of a "bump" solution in both a discrete network of neurons and in the corresponding neural field model.

  14. Constructing Neuronal Network Models in Massively Parallel Environments.

    PubMed

    Ippen, Tammo; Eppler, Jochen M; Plesser, Hans E; Diesmann, Markus

    2017-01-01

    Recent advances in the development of data structures to represent spiking neuron network models enable us to exploit the complete memory of petascale computers for a single brain-scale network simulation. In this work, we investigate how well we can exploit the computing power of such supercomputers for the creation of neuronal networks. Using an established benchmark, we divide the runtime of simulation code into the phase of network construction and the phase during which the dynamical state is advanced in time. We find that on multi-core compute nodes network creation scales well with process-parallel code but exhibits a prohibitively large memory consumption. Thread-parallel network creation, in contrast, exhibits speedup only up to a small number of threads but has little overhead in terms of memory. We further observe that the algorithms creating instances of model neurons and their connections scale well for networks of ten thousand neurons, but do not show the same speedup for networks of millions of neurons. Our work uncovers that the lack of scaling of thread-parallel network creation is due to inadequate memory allocation strategies and demonstrates that thread-optimized memory allocators recover excellent scaling. An analysis of the loop order used for network construction reveals that more complex tests on the locality of operations significantly improve scaling and reduce runtime by allowing construction algorithms to step through large networks more efficiently than in existing code. The combination of these techniques increases performance by an order of magnitude and harnesses the increasingly parallel compute power of the compute nodes in high-performance clusters and supercomputers.

  15. Constructing Neuronal Network Models in Massively Parallel Environments

    PubMed Central

    Ippen, Tammo; Eppler, Jochen M.; Plesser, Hans E.; Diesmann, Markus

    2017-01-01

    Recent advances in the development of data structures to represent spiking neuron network models enable us to exploit the complete memory of petascale computers for a single brain-scale network simulation. In this work, we investigate how well we can exploit the computing power of such supercomputers for the creation of neuronal networks. Using an established benchmark, we divide the runtime of simulation code into the phase of network construction and the phase during which the dynamical state is advanced in time. We find that on multi-core compute nodes network creation scales well with process-parallel code but exhibits a prohibitively large memory consumption. Thread-parallel network creation, in contrast, exhibits speedup only up to a small number of threads but has little overhead in terms of memory. We further observe that the algorithms creating instances of model neurons and their connections scale well for networks of ten thousand neurons, but do not show the same speedup for networks of millions of neurons. Our work uncovers that the lack of scaling of thread-parallel network creation is due to inadequate memory allocation strategies and demonstrates that thread-optimized memory allocators recover excellent scaling. An analysis of the loop order used for network construction reveals that more complex tests on the locality of operations significantly improve scaling and reduce runtime by allowing construction algorithms to step through large networks more efficiently than in existing code. The combination of these techniques increases performance by an order of magnitude and harnesses the increasingly parallel compute power of the compute nodes in high-performance clusters and supercomputers. PMID:28559808

  16. Computational modeling of optogenetic neuronal excitation under complex illumination conditions using a Matlab-Neuron interface (Conference Presentation)

    NASA Astrophysics Data System (ADS)

    Yona, Guy; Weissler, Yonatan; Meitav, Nizan; Guzi, Eliran; Rifold, Dafna D.; Kahn, Itamar; Shoham, Shy

    2016-03-01

    Optogenetics has in recent years become a central tool in neuroscience research. Creating a realistic model of optogenetic neuronal excitation is of crucial importance for controlling the activation levels of various neuronal populations in different depths, predicting experimental results and designing the optical systems. However, current approaches to modeling light propagation through rodents' brain tissue suffer from major shortcomings and comprehensive modeling of local illumination levels together with other important factors governing excitation (i.e., cellular morphology, channel dynamics and expression), are still lacking. To address this challenge we introduce a new simulation tool for optogenetic neuronal excitation under complex and realistic illumination conditions that implements a detailed physical model for light scattering (in MATLAB) together with neuron morphology and channelrhodopsin-2 model (in NEURON). These two disparate simulation environments were interconnected using a newly developed generic interface termed 'NeuroLab'. Applying this method, we show that in a layer-V cortical neuron, the relative contribution of the apical dendrites to neuronal excitation is considerably greater than that of the soma or basal dendrites, when illuminated from the surface.

  17. Optical computing based on neuronal models

    NASA Astrophysics Data System (ADS)

    Farhat, Nabil H.

    1987-10-01

    Ever since the fit between what neural net models can offer (collective, iterative, nonlinear, robust, and fault-tolerant approach to information processing) and the inherent capabilities of optics (parallelism and massive interconnectivity) was first pointed out and the first optical associative memory demonstrated in 1985, work and interest in neuromorphic optical signal processing has been growing steadily. For example, work in optical associative memories is currently being conducted at several academic institutions (e.g., California Institute of Technology, University of Colorado, University of California-San Diego, Stanford University, University of Rochester, and the author's own institution the University of Pennsylvania) and at several industrial and governmental laboratories (e.g., Hughes Research Laboratories - Malibu, the Naval Research Laboratory, and the Jet Propulsion Laboratory). In these efforts, in addition to the vector matrix multiplication with thresholding and feedback scheme utilized in early implementations, an arsenal of sophisticated optical tools such as holographic storage, phase conjugate optics, and wavefront modulation and mixing are being drawn on to realize associative memory functions.

  18. Mathematical modelling of the enteric nervous network. 1: Cholinergic neuron.

    PubMed

    Miftakhov, R N; Wingate, D L

    1994-01-01

    A mathematical model is proposed to describe the coupled electrochemical mechanisms of nerve-pulse transmission via cholinergic synapse. Based on pharmacological and morphophysiological data, the model describes the dynamics of the propagation of the electric signal along the unmyelinated geometrically non-uniform axon of the neuron and the chemical mechanisms of the transformation of the electrical signal in the synaptic zone into the postsynaptic output. The combined nonlinear system of partial and ordinary differential equations has been obtained and solved numerically. The results of numerical simulation of the function of the cholinergic neuron quantitatively and qualitatively describe the dynamics of Ca2+ ions influx into the terminal, acetylcholine release from the vesicles, accumulation of its free fraction, diffusion into the synaptic cleft, and binding with the receptors on the postsynaptic structures with the generation of the fast excitatory postsynaptic potential. They are in good agreement with the observed experimental findings.

  19. Stochastic resonance in neuron models: Endogenous stimulation revisited

    NASA Astrophysics Data System (ADS)

    Plesser, Hans E.; Geisel, Theo

    2001-03-01

    The paradigm of stochastic resonance (SR)-the idea that signal detection and transmission may benefit from noise-has met with great interest in both physics and the neurosciences. We investigate here the consequences of reducing the dynamics of a periodically driven neuron to a renewal process (stimulation with reset or endogenous stimulation). This greatly simplifies the mathematical analysis, but we show that stochastic resonance as reported earlier occurs in this model only as a consequence of the reduced dynamics.

  20. Analysis of Chaotic Resonance in Izhikevich Neuron Model

    PubMed Central

    Nobukawa, Sou; Nishimura, Haruhiko; Yamanishi, Teruya; Liu, Jian-Qin

    2015-01-01

    In stochastic resonance (SR), the presence of noise helps a nonlinear system amplify a weak (sub-threshold) signal. Chaotic resonance (CR) is a phenomenon similar to SR but without stochastic noise, which has been observed in neural systems. However, no study to date has investigated and compared the characteristics and performance of the signal responses of a spiking neural system in some chaotic states in CR. In this paper, we focus on the Izhikevich neuron model, which can reproduce major spike patterns that have been experimentally observed. We examine and classify the chaotic characteristics of this model by using Lyapunov exponents with a saltation matrix and Poincaré section methods in order to address the measurement challenge posed by the state-dependent jump in the resetting process. We found the existence of two distinctive states, a chaotic state involving primarily turbulent movement and an intermittent chaotic state. In order to assess the signal responses of CR in these classified states, we introduced an extended Izhikevich neuron model by considering weak periodic signals, and defined the cycle histogram of neuron spikes as well as the corresponding mutual correlation and information. Through computer simulations, we confirmed that both chaotic states in CR can sensitively respond to weak signals. Moreover, we found that the intermittent chaotic state exhibited a prompter response than the chaotic state with primarily turbulent movement. PMID:26422140

  1. Analysis of Chaotic Resonance in Izhikevich Neuron Model.

    PubMed

    Nobukawa, Sou; Nishimura, Haruhiko; Yamanishi, Teruya; Liu, Jian-Qin

    2015-01-01

    In stochastic resonance (SR), the presence of noise helps a nonlinear system amplify a weak (sub-threshold) signal. Chaotic resonance (CR) is a phenomenon similar to SR but without stochastic noise, which has been observed in neural systems. However, no study to date has investigated and compared the characteristics and performance of the signal responses of a spiking neural system in some chaotic states in CR. In this paper, we focus on the Izhikevich neuron model, which can reproduce major spike patterns that have been experimentally observed. We examine and classify the chaotic characteristics of this model by using Lyapunov exponents with a saltation matrix and Poincaré section methods in order to address the measurement challenge posed by the state-dependent jump in the resetting process. We found the existence of two distinctive states, a chaotic state involving primarily turbulent movement and an intermittent chaotic state. In order to assess the signal responses of CR in these classified states, we introduced an extended Izhikevich neuron model by considering weak periodic signals, and defined the cycle histogram of neuron spikes as well as the corresponding mutual correlation and information. Through computer simulations, we confirmed that both chaotic states in CR can sensitively respond to weak signals. Moreover, we found that the intermittent chaotic state exhibited a prompter response than the chaotic state with primarily turbulent movement.

  2. Estimation of the input parameters in the Feller neuronal model

    NASA Astrophysics Data System (ADS)

    Ditlevsen, Susanne; Lansky, Petr

    2006-06-01

    The stochastic Feller neuronal model is studied, and estimators of the model input parameters, depending on the firing regime of the process, are derived. Closed expressions for the first two moments of functionals of the first-passage time (FTP) through a constant boundary in the suprathreshold regime are derived, which are used to calculate moment estimators. In the subthreshold regime, the exponentiality of the FTP is utilized to characterize the input parameters. The methods are illustrated on simulated data. Finally, approximations of the first-passage-time moments are suggested, and biological interpretations and comparisons of the parameters in the Feller and the Ornstein-Uhlenbeck models are discussed.

  3. Stress exacerbates neuron loss and microglia proliferation in a rat model of excitotoxic lower motor neuron injury

    PubMed Central

    Puga, Denise A.; Tovar, C. Amy; Guan, Zhen; C.Gensel, John; Lyman, Matthew S.; McTigue, Dana M.; Popovich, Phillip G.

    2015-01-01

    All individuals experience stress and hormones (e.g., glucocorticoids/GCs) released during stressful events can affect the structure and function of neurons. These effects of stress are best characterized for brain neurons; however, the mechanisms controlling the expression and binding affinity of glucocorticoid receptors in the spinal cord are different than those in the brain. Accordingly, whether stress exerts unique effects on spinal cord neurons, especially in the context of pathology, is unknown. Using a controlled model of focal excitotoxic lower motor neuron injury in rats, we examined the effects of acute or chronic variable stress on spinal cord motor neuron survival and glial activation. New data indicate that stress exacerbates excitotoxic spinal cord motor neuron loss and associated activation of microglia. In contrast, hypertrophy and hyperplasia of astrocytes and NG2+ glia were unaffected or were modestly suppressed by stress. Although excitotoxic lesions cause significant motor neuron loss and stress exacerbates this pathology, overt functional impairment did not develop in the relevant forelimb up to one week post-lesion. These data indicate that stress is a disease-modifying factor capable of altering neuron and glial responses to pathological challenges in the spinal cord. PMID:26100488

  4. Stress exacerbates neuron loss and microglia proliferation in a rat model of excitotoxic lower motor neuron injury.

    PubMed

    Puga, Denise A; Tovar, C Amy; Guan, Zhen; Gensel, John C; Lyman, Matthew S; McTigue, Dana M; Popovich, Phillip G

    2015-10-01

    All individuals experience stress and hormones (e.g., glucocorticoids/GCs) released during stressful events can affect the structure and function of neurons. These effects of stress are best characterized for brain neurons; however, the mechanisms controlling the expression and binding affinity of glucocorticoid receptors in the spinal cord are different than those in the brain. Accordingly, whether stress exerts unique effects on spinal cord neurons, especially in the context of pathology, is unknown. Using a controlled model of focal excitotoxic lower motor neuron injury in rats, we examined the effects of acute or chronic variable stress on spinal cord motor neuron survival and glial activation. New data indicate that stress exacerbates excitotoxic spinal cord motor neuron loss and associated activation of microglia. In contrast, hypertrophy and hyperplasia of astrocytes and NG2+ glia were unaffected or were modestly suppressed by stress. Although excitotoxic lesions cause significant motor neuron loss and stress exacerbates this pathology, overt functional impairment did not develop in the relevant forelimb up to one week post-lesion. These data indicate that stress is a disease-modifying factor capable of altering neuron and glial responses to pathological challenges in the spinal cord. Copyright © 2015 Elsevier Inc. All rights reserved.

  5. Digital hardware implementation of a stochastic two-dimensional neuron model.

    PubMed

    Grassia, F; Kohno, T; Levi, T

    2017-02-22

    This study explores the feasibility of stochastic neuron simulation in digital systems (FPGA), which realizes an implementation of a two-dimensional neuron model. The stochasticity is added by a source of current noise in the silicon neuron using an Ornstein-Uhlenbeck process. This approach uses digital computation to emulate individual neuron behavior using fixed point arithmetic operation. The neuron model's computations are performed in arithmetic pipelines. It was designed in VHDL language and simulated prior to mapping in the FPGA. The experimental results confirmed the validity of the developed stochastic FPGA implementation, which makes the implementation of the silicon neuron more biologically plausible for future hybrid experiments.

  6. Statistics of a neuron model driven by asymmetric colored noise.

    PubMed

    Müller-Hansen, Finn; Droste, Felix; Lindner, Benjamin

    2015-02-01

    Irregular firing of neurons can be modeled as a stochastic process. Here we study the perfect integrate-and-fire neuron driven by dichotomous noise, a Markovian process that jumps between two states (i.e., possesses a non-Gaussian statistics) and exhibits nonvanishing temporal correlations (i.e., represents a colored noise). Specifically, we consider asymmetric dichotomous noise with two different transition rates. Using a first-passage-time formulation, we derive exact expressions for the probability density and the serial correlation coefficient of the interspike interval (time interval between two subsequent neural action potentials) and the power spectrum of the spike train. Furthermore, we extend the model by including additional Gaussian white noise, and we give approximations for the interspike interval (ISI) statistics in this case. Numerical simulations are used to validate the exact analytical results for pure dichotomous noise, and to test the approximations of the ISI statistics when Gaussian white noise is included. The results may help to understand how correlations and asymmetry of noise and signals in nerve cells shape neuronal firing statistics.

  7. Phase locking in driven integrate-and-fire neuron models

    NASA Astrophysics Data System (ADS)

    Bedell, Christopher; Engelbrecht, Jan R.

    2006-03-01

    We investigate phase locking between a particular non-linear oscillator and a periodic drive. The non-linear equation we study is a reduced version of the celebrated Hodgkin-Huxley equations, which we couple to a cosine drive representing an EEG Rhythm. This model is motivated by the growing interest in the role of the exact timing of action potentials in neurons. For instance, electro-physiology experiments indicate that the phase differences between action potential times and large-scale oscillatory neuron activity (EEG rhythms) carry reliable information. We study various thresholds for phase locking and the delicate interplay between coherence and decoherence leading to chaos near these phase-locking thresholds.

  8. Realistic modeling of neurons and networks: towards brain simulation.

    PubMed

    D'Angelo, Egidio; Solinas, Sergio; Garrido, Jesus; Casellato, Claudia; Pedrocchi, Alessandra; Mapelli, Jonathan; Gandolfi, Daniela; Prestori, Francesca

    2013-01-01

    Realistic modeling is a new advanced methodology for investigating brain functions. Realistic modeling is based on a detailed biophysical description of neurons and synapses, which can be integrated into microcircuits. The latter can, in turn, be further integrated to form large-scale brain networks and eventually to reconstruct complex brain systems. Here we provide a review of the realistic simulation strategy and use the cerebellar network as an example. This network has been carefully investigated at molecular and cellular level and has been the object of intense theoretical investigation. The cerebellum is thought to lie at the core of the forward controller operations of the brain and to implement timing and sensory prediction functions. The cerebellum is well described and provides a challenging field in which one of the most advanced realistic microcircuit models has been generated. We illustrate how these models can be elaborated and embedded into robotic control systems to gain insight into how the cellular properties of cerebellar neurons emerge in integrated behaviors. Realistic network modeling opens up new perspectives for the investigation of brain pathologies and for the neurorobotic field.

  9. Realistic modeling of neurons and networks: towards brain simulation

    PubMed Central

    D’Angelo, Egidio; Solinas, Sergio; Garrido, Jesus; Casellato, Claudia; Pedrocchi, Alessandra; Mapelli, Jonathan; Gandolfi, Daniela; Prestori, Francesca

    Summary Realistic modeling is a new advanced methodology for investigating brain functions. Realistic modeling is based on a detailed biophysical description of neurons and synapses, which can be integrated into microcircuits. The latter can, in turn, be further integrated to form large-scale brain networks and eventually to reconstruct complex brain systems. Here we provide a review of the realistic simulation strategy and use the cerebellar network as an example. This network has been carefully investigated at molecular and cellular level and has been the object of intense theoretical investigation. The cerebellum is thought to lie at the core of the forward controller operations of the brain and to implement timing and sensory prediction functions. The cerebellum is well described and provides a challenging field in which one of the most advanced realistic microcircuit models has been generated. We illustrate how these models can be elaborated and embedded into robotic control systems to gain insight into how the cellular properties of cerebellar neurons emerge in integrated behaviors. Realistic network modeling opens up new perspectives for the investigation of brain pathologies and for the neurorobotic field. PMID:24139652

  10. Micropatterned bioimplant with guided neuronal cells to promote tissue reconstruction and improve functional recovery after primary motor cortex insult.

    PubMed

    Vaysse, L; Beduer, A; Sol, J C; Vieu, C; Loubinoux, I

    2015-07-01

    With the ever increasing incidence of brain injury, developing new tissue engineering strategies to promote neural tissue regeneration is an enormous challenge. The goal of this study was to design and evaluate an implantable scaffold capable of directing neurite and axonal growth for neuronal brain tissue regeneration. We have previously shown in cell culture conditions that engineered micropatterned PDMS surface with straight microchannels allow directed neurite growth without perturbing cell differentiation and neurite outgrowth. In this study, the micropatterned PDMS device pre-seeded with hNT2 neuronal cells were implanted in rat model of primary motor cortex lesion which induced a strong motor deficit. Functional recovery was assessed by the forelimb grip strength test during 3 months post implantation. Results show a more rapid and efficient motor recovery with the hNT2 neuroimplants associated with an increase of neuronal tissue reconstruction and cell survival. This improvement is also hastened when compared to a direct cell graft of ten times more cells. Histological analyses showed that the implant remained structurally intact and we did not see any evidence of inflammatory reaction. In conclusion, PDMS bioimplants with guided neuronal cells seem to be a promising approach for supporting neural tissue reconstruction after central brain injury.

  11. A human pluripotent carcinoma stem cell-based model for in vitro developmental neurotoxicity testing: effects of methylmercury, lead and aluminum evaluated by gene expression studies.

    PubMed

    Laurenza, Incoronata; Pallocca, Giorgia; Mennecozzi, Milena; Scelfo, Bibiana; Pamies, David; Bal-Price, Anna

    2013-11-01

    The major advantage of the neuronal cell culture models derived from human stem cells is their ability to replicate the crucial stages of neurodevelopment such as the commitment of human stem cells to the neuronal lineage and their subsequent stages of differentiation into neuronal and glial-like cell. In these studies we used mixed neuronal/glial culture derived from the NTERA-2 (NT-2) cell line, which has been established from human pluripotent testicular embryonal carcinoma cells. After characterization of the different stages of cell differentiation into neuronal- and glial-like phenotype toxicity studies were performed to evaluate whether this model would be suitable for developmental neurotoxicity studies. The cells were exposed during the differentiation process to non-cytotoxic concentrations of methylmercury chloride, lead chloride and aluminum nitrate for two weeks. The toxicity was then evaluated by measuring the mRNA levels of cell specific markers (neuronal and glial). The results obtained suggest that lead chloride and aluminum nitrate at low concentrations were toxic primarily to astrocytes and at the higher concentrations it also induced neurotoxicity. In contrast, MetHgCl was toxic for both cell types, neuronal and glial, as mRNA specific for astrocytes and neuronal markers were affected. The results obtained suggest that a neuronal mixed culture derived from human NT2 precursor cells is a suitable model for developmental neurotoxicity studies and gene expression could be used as a sensitive endpoint for initial screening of potential neurotoxic compounds.

  12. Novel model for the mechanisms of glutamate-dependent excitotoxicity: Role of neuronal gap junctions

    PubMed Central

    Belousov, Andrei B.

    2012-01-01

    In the mammalian central nervous system (CNS), coupling of neurons by gap junctions (electrical synapses) increases during early postnatal development, then decreases, but increases in the mature CNS following neuronal injury, such as ischemia, traumatic brain injury and epilepsy. Glutamate-dependent neuronal death also occurs in the CNS during development and neuronal injury, i.e., at the time when neuronal gap junction coupling is increased. Here, we review our recent studies on regulation of neuronal gap junction coupling by glutamate during development and injury and on the role of gap junctions in neuronal cell death. A novel model of the mechanisms of glutamate-dependent neuronal death is discussed, which includes neuronal gap junction coupling as a critical part of these mechanisms. PMID:22771704

  13. A model for neurite growth and neuronal morphogenesis.

    PubMed

    Li, G H; Qin, C D

    1996-02-01

    A model is presented for tensile regulation of neuritic growth. It is proposed that the neurite tension can be determined by Hooke's law and determines the growth rate of neurites. The growth of a neurite is defined as the change in its unstretched length. Neuritic growth rate is assumed to increase in proportion to tension magnitude over a certain threshold [Dennerll et al., J. Cell Biol. 107: 665-674 (1988)]. The movement of branch nodes also contributes to the neuronal morphogenesis. It is supposed that the rate of a branch-node displacement is in proportion to the resultant neuritic tension exerted on this node. To deal with the growth-cone movement, it is further supposed that the environment exerts a traction force on the growth cone and the rate of growth-cone displacement is determined by the vector sum of the neuritic tension and the traction force. A group of differential equations are used to describe the model. The key point of the model is that the traction force and the neuritic tension are in opposition to generate a temporal contrast-enhancing mechanism. Results of a simulation study suggest that the model can explain some phenomena related to neuronal morphogenesis.

  14. From in silico astrocyte cell models to neuron-astrocyte network models: A review.

    PubMed

    Oschmann, Franziska; Berry, Hugues; Obermayer, Klaus; Lenk, Kerstin

    2017-02-08

    The idea that astrocytes may be active partners in synaptic information processing has recently emerged from abundant experimental reports. Because of their spatial proximity to neurons and their bidirectional communication with them, astrocytes are now considered as an important third element of the synapse. Astrocytes integrate and process synaptic information and by doing so generate cytosolic calcium signals that are believed to reflect neuronal transmitter release. Moreover, they regulate neuronal information transmission by releasing gliotransmitters into the synaptic cleft affecting both pre- and postsynaptic receptors. Concurrent with the first experimental reports of the astrocytic impact on neural network dynamics, computational models describing astrocytic functions have been developed. In this review, we give an overview over the published computational models of astrocytic functions, from single-cell dynamics to the tripartite synapse level and network models of astrocytes and neurons.

  15. Dynamics in the Parameter Space of a Neuron Model

    NASA Astrophysics Data System (ADS)

    Paulo, C. Rech

    2012-06-01

    Some two-dimensional parameter-space diagrams are numerically obtained by considering the largest Lyapunov exponent for a four-dimensional thirteen-parameter Hindmarsh—Rose neuron model. Several different parameter planes are considered, and it is shown that depending on the combination of parameters, a typical scenario can be preserved: for some choice of two parameters, the parameter plane presents a comb-shaped chaotic region embedded in a large periodic region. It is also shown that there exist regions close to these comb-shaped chaotic regions, separated by the comb teeth, organizing themselves in period-adding bifurcation cascades.

  16. Spatiotemporal testing and modeling of catfish retinal neurons.

    PubMed Central

    Krausz, H I; Naka, K

    1980-01-01

    The responses of retinal neurons depend on the interaction of both temporal and spatial aspects of a light stimulus. We developed a linear spatiotemporal model of receptor and horizontal cell layers in the catfish retina based on reciprocal interactions between both layers and coupling within each. Horizontal cell transfer properties were measured experimentally using white-noise intensity modulated light spots of different diameters and were compared with analytical predictions based on the model. Good agreement was obtained with a reasonable choice of model space-constants and feedback parameters. Furthermore, the same set of parameter values determined from spot experiments enabled accurate prediction of experimental horizontal cell responses to traveling gratings. The proposed feedback connections from horizontal cells to receptors quicken the time-course of responses in both layers and sharpen receptive fields. PMID:7260243

  17. A role for the anterior insular cortex in the global neuronal workspace model of consciousness.

    PubMed

    Michel, Matthias

    2017-03-01

    According to the global neuronal workspace model of consciousness, consciousness results from the global broadcast of information throughout the brain. The global neuronal workspace is mainly constituted by a fronto-parietal network. The anterior insular cortex is part of this global neuronal workspace, but the function of this region has not yet been defined within the global neuronal workspace model of consciousness. In this review, I hypothesize that the anterior insular cortex implements a cross-modal priority map, the function of which is to determine priorities for the processing of information and subsequent entrance in the global neuronal workspace.

  18. A Model of Binocular Motion Integration in MT Neurons.

    PubMed

    Baker, Pamela M; Bair, Wyeth

    2016-06-15

    Primate cortical area MT plays a central role in visual motion perception, but models of this area have largely overlooked the binocular integration of motion signals. Recent electrophysiological studies tested binocular integration in MT and found surprisingly that MT neurons lose their hallmark "pattern motion" selectivity when stimuli are presented dichoptically and that many neurons are selective for motion-in-depth (MID). By unifying these novel observations with insights from monocular, frontoparallel motion studies concurrently in a binocular MT motion model, we generated clear, testable predictions about the circuitry and mechanisms underlying visual motion processing. We built binocular models in which signals from left- and right-eye streams could be integrated at various stages from V1 to MT, attempting to create the simplest plausible circuits that accounted for the physiological range of pattern motion selectivity, that explained changes across this range for dichoptic stimulus presentation, and that spanned the spectrum of MID selectivity observed in MT. Our successful models predict that motion-opponent suppression is the key mechanism to account for the striking loss of pattern motion sensitivity with dichoptic plaids, that opponent suppression precedes binocular integration, and that opponent suppression will be stronger in inputs to pattern cells than to component cells. We also found an unexpected connection between circuits for pattern motion selectivity and MID selectivity, suggesting that these two separately studied phenomena could be related. These results also hold in models that include binocular disparity computations, providing a platform for future exploration of binocular response properties in MT. The neural pathways underlying our sense of visual motion are among the most studied and well-understood parts of the primate cerebral cortex. Nevertheless, our understanding is incomplete because electrophysiological research has focused

  19. Silicon Neuron.

    DTIC Science & Technology

    Many researchers have developed neural architectures based on extremely simplified models of neurons . Recently, researchers have developed an analog...electronic model of a neuron that more accurately reproduces its biological counterpart. This electronic neuron was designed to emulate the ionic...currents present in biological neurons . Based on this neural model, we designed and fabricated an eight input neuron on a 2mm by 2mm 40 pin VLSI (very

  20. Modeling the Electric Potential across Neuronal Membranes: The Effect of Fixed Charges on Spinal Ganglion Neurons and Neuroblastoma Cells

    PubMed Central

    Pinto, Thiago M.; Wedemann, Roseli S.; Cortez, Célia M.

    2014-01-01

    We present a model for the electric potential profile across the membranes of neuronal cells. We considered the resting and action potential states, and analyzed the influence of fixed charges of the membrane on its electric potential, based on experimental values of membrane properties of the spinal ganglion neuron and the neuroblastoma cell. The spinal ganglion neuron represents a healthy neuron, and the neuroblastoma cell, which is tumorous, represents a pathological neuron. We numerically solved the non-linear Poisson-Boltzmann equation for the regions of the membrane model we have adopted, by considering the densities of charges dissolved in an electrolytic solution and fixed on both glycocalyx and cytoplasmic proteins. Our model predicts that there is a difference in the behavior of the electric potential profiles of the two types of cells, in response to changes in charge concentrations in the membrane. Our results also describe an insensitivity of the neuroblastoma cell membrane, as observed in some biological experiments. This electrical property may be responsible for the low pharmacological response of the neuroblastoma to certain chemotherapeutic treatments. PMID:24801682

  1. Estimating the electrotonic structure of neurons with compartmental models.

    PubMed

    Holmes, W R; Rall, W

    1992-10-01

    1. A procedure based on compartmental modeling called the "constrained inverse computation" was developed for estimating the electrotonic structure of neurons. With the constrained inverse computation, a set of N electrotonic parameters are estimated iteratively with use of a Newton-Raphson algorithm given values of N parameters that can be measured or estimated from experimental data. 2. The constrained inverse computation is illustrated by several applications to the basic example of a neuron represented as one cylinder coupled to a soma. The number of unknown parameters estimated was different (ranging from 2 to 6) when different sets of constraints were chosen. The unknowns were chosen from the following: dendritic membrane resistivity Rmd, soma membrane resistivity Rms, intracellular resistivity Ri, membrane capacity Cm, dendritic membrane area AD, soma membrane area As, electrotonic length L, and resistivity-free length, rfl (rfl = 2l/d1/2 where l and d are length and diameter of the cylinder). The values of the unknown parameters were estimated from the values of an equal number of known parameters, which were chosen from the following: the time constants and coefficients of a voltage transient tau 0, tau 1, ..., C0, C1, ..., voltage-clamp time constants tau vc1, tau vc2, ..., and input resistance RN. Note that initially, morphological data were treated as unknown, rather than known. 3. When complete morphology was not known, parameters from voltage and current transients, combined with the input resistance were not sufficient to completely specify the electrotonic structure of the neuron. For a neuron represented as a cylinder coupled to a soma, there were an infinite number of combinations of Rmd, Rms, Ri, Cm, AS, AD, and L that could be fitted to the same voltage and current transients and input resistance. 4. One reason for the nonuniqueness when complete morphology was not specified is that the Ri estimate is intrinsically bound to the morphology. Ri

  2. The composite neuron: a realistic one-compartment Purkinje cell model suitable for large-scale neuronal network simulations.

    PubMed

    Coop, A D; Reeke, G N

    2001-01-01

    We present a simple method for the realistic description of neurons that is well suited to the development of large-scale neuronal network models where the interactions within and between neural circuits are the object of study rather than the details of dendritic signal propagation in individual cells. Referred to as the composite approach, it combines in a one-compartment model elements of both the leaky integrator cell and the conductance-based formalism of Hodgkin and Huxley (1952). Composite models treat the cell membrane as an equivalent circuit that contains ligand-gated synaptic, voltage-gated, and voltage- and concentration-dependent conductances. The time dependences of these various conductances are assumed to correlate with their spatial locations in the real cell. Thus, when viewed from the soma, ligand-gated synaptic and other dendritically located conductances can be modeled as either single alpha or double exponential functions of time, whereas, with the exception of discharge-related conductances, somatic and proximal dendritic conductances can be well approximated by simple current-voltage relationships. As an example of the composite approach to neuronal modeling we describe a composite model of a cerebellar Purkinje neuron.

  3. Efficient fitting of conductance-based model neurons from somatic current clamp.

    PubMed

    Lepora, Nathan F; Overton, Paul G; Gurney, Kevin

    2012-02-01

    Estimating biologically realistic model neurons from electrophysiological data is a key issue in neuroscience that is central to understanding neuronal function and network behavior. However, directly fitting detailed Hodgkin-Huxley type model neurons to somatic membrane potential data is a notoriously difficult optimization problem that can require hours/days of supercomputing time. Here we extend an efficient technique that indirectly matches neuronal currents derived from somatic membrane potential data to two-compartment model neurons with passive dendrites. In consequence, this approach can fit semi-realistic detailed model neurons in a few minutes. For validation, fits are obtained to model-derived data for various thalamo-cortical neuron types, including fast/regular spiking and bursting neurons. A key aspect of the validation is sensitivity testing to perturbations arising in experimental data, including sampling rates, inadequately estimated membrane dynamics/channel kinetics and intrinsic noise. We find that maximal conductance estimates and the resulting membrane potential fits diverge smoothly and monotonically from near-perfect matches when unperturbed. Curiously, some perturbations have little effect on the error because they are compensated by the fitted maximal conductances. Therefore, the extended current-based technique applies well under moderately inaccurate model assumptions, as required for application to experimental data. Furthermore, the accompanying perturbation analysis gives insights into neuronal homeostasis, whereby tuning intrinsic neuronal properties can compensate changes from development or neurodegeneration.

  4. Modeling neuron selectivity over simple midlevel features for image classification.

    PubMed

    Shu Kong; Zhuolin Jiang; Qiang Yang

    2015-08-01

    We now know that good mid-level features can greatly enhance the performance of image classification, but how to efficiently learn the image features is still an open question. In this paper, we present an efficient unsupervised midlevel feature learning approach (MidFea), which only involves simple operations, such as k-means clustering, convolution, pooling, vector quantization, and random projection. We show this simple feature can also achieve good performance in traditional classification task. To further boost the performance, we model the neuron selectivity (NS) principle by building an additional layer over the midlevel features prior to the classifier. The NS-layer learns category-specific neurons in a supervised manner with both bottom-up inference and top-down analysis, and thus supports fast inference for a query image. Through extensive experiments, we demonstrate that this higher level NS-layer notably improves the classification accuracy with our simple MidFea, achieving comparable performances for face recognition, gender classification, age estimation, and object categorization. In particular, our approach runs faster in inference by an order of magnitude than sparse coding-based feature learning methods. As a conclusion, we argue that not only do carefully learned features (MidFea) bring improved performance, but also a sophisticated mechanism (NS-layer) at higher level boosts the performance further.

  5. Neurons compute internal models of the physical laws of motion.

    PubMed

    Angelaki, Dora E; Shaikh, Aasef G; Green, Andrea M; Dickman, J David

    2004-07-29

    A critical step in self-motion perception and spatial awareness is the integration of motion cues from multiple sensory organs that individually do not provide an accurate representation of the physical world. One of the best-studied sensory ambiguities is found in visual processing, and arises because of the inherent uncertainty in detecting the motion direction of an untextured contour moving within a small aperture. A similar sensory ambiguity arises in identifying the actual motion associated with linear accelerations sensed by the otolith organs in the inner ear. These internal linear accelerometers respond identically during translational motion (for example, running forward) and gravitational accelerations experienced as we reorient the head relative to gravity (that is, head tilt). Using new stimulus combinations, we identify here cerebellar and brainstem motion-sensitive neurons that compute a solution to the inertial motion detection problem. We show that the firing rates of these populations of neurons reflect the computations necessary to construct an internal model representation of the physical equations of motion.

  6. Mapping Generative Models onto a Network of Digital Spiking Neurons.

    PubMed

    Pedroni, Bruno U; Das, Srinjoy; Arthur, John V; Merolla, Paul A; Jackson, Bryan L; Modha, Dharmendra S; Kreutz-Delgado, Kenneth; Cauwenberghs, Gert

    2016-05-18

    Stochastic neural networks such as Restricted Boltzmann Machines (RBMs) have been successfully used in applications ranging from speech recognition to image classification, and are particularly interesting because of their potential for generative tasks. Inference and learning in these algorithms use a Markov Chain Monte Carlo procedure called Gibbs sampling, where a logistic function forms the kernel of this sampler. On the other side of the spectrum, neuromorphic systems have shown great promise for low-power and parallelized cognitive computing, but lack well-suited applications and automation procedures. In this work, we propose a systematic method for bridging the RBM algorithm and digital neuromorphic systems, with a generative pattern completion task as proof of concept. For this, we first propose a method of producing the Gibbs sampler using bio-inspired digital noisy integrate-and-fire neurons. Next, we describe the process of mapping generative RBMs trained offline onto the IBM TrueNorth neurosynaptic processor-a low-power digital neuromorphic VLSI substrate. Mapping these algorithms onto neuromorphic hardware presents unique challenges in network connectivity and weight and bias quantization, which, in turn, require architectural and design strategies for the physical realization. Generative performance is analyzed to validate the neuromorphic requirements and to best select the neuron parameters for the model. Lastly, we describe a design automation procedure which achieves optimal resource usage, accounting for the novel hardware adaptations. This work represents the first implementation of generative RBM inference on a neuromorphic VLSI substrate.

  7. Mapping Generative Models onto a Network of Digital Spiking Neurons.

    PubMed

    Pedroni, Bruno U; Das, Srinjoy; Arthur, John V; Merolla, Paul A; Jackson, Bryan L; Modha, Dharmendra S; Kreutz-Delgado, Kenneth; Cauwenberghs, Gert

    2016-08-01

    Stochastic neural networks such as Restricted Boltzmann Machines (RBMs) have been successfully used in applications ranging from speech recognition to image classification, and are particularly interesting because of their potential for generative tasks. Inference and learning in these algorithms use a Markov Chain Monte Carlo procedure called Gibbs sampling, where a logistic function forms the kernel of this sampler. On the other side of the spectrum, neuromorphic systems have shown great promise for low-power and parallelized cognitive computing, but lack well-suited applications and automation procedures. In this work, we propose a systematic method for bridging the RBM algorithm and digital neuromorphic systems, with a generative pattern completion task as proof of concept. For this, we first propose a method of producing the Gibbs sampler using bio-inspired digital noisy integrate-and-fire neurons. Next, we describe the process of mapping generative RBMs trained offline onto the IBM TrueNorth neurosynaptic processor-a low-power digital neuromorphic VLSI substrate. Mapping these algorithms onto neuromorphic hardware presents unique challenges in network connectivity and weight and bias quantization, which, in turn, require architectural and design strategies for the physical realization. Generative performance is analyzed to validate the neuromorphic requirements and to best select the neuron parameters for the model. Lastly, we describe a design automation procedure which achieves optimal resource usage, accounting for the novel hardware adaptations. This work represents the first implementation of generative RBM inference on a neuromorphic VLSI substrate.

  8. Auditory information coding by modeled cochlear nucleus neurons.

    PubMed

    Wang, Huan; Isik, Michael; Borst, Alexander; Hemmert, Werner

    2011-06-01

    In this paper we use information theory to quantify the information in the output spike trains of modeled cochlear nucleus globular bushy cells (GBCs). GBCs are part of the sound localization pathway. They are known for their precise temporal processing, and they code amplitude modulations with high fidelity. Here we investigated the information transmission for a natural sound, a recorded vowel. We conclude that the maximum information transmission rate for a single neuron was close to 1,050 bits/s, which corresponds to a value of approximately 5.8 bits per spike. For quasi-periodic signals like voiced speech, the transmitted information saturated as word duration increased. In general, approximately 80% of the available information from the spike trains was transmitted within about 20 ms. Transmitted information for speech signals concentrated around formant frequency regions. The efficiency of neural coding was above 60% up to the highest temporal resolution we investigated (20 μs). The increase in transmitted information to that precision indicates that these neurons are able to code information with extremely high fidelity, which is required for sound localization. On the other hand, only 20% of the information was captured when the temporal resolution was reduced to 4 ms. As the temporal resolution of most speech recognition systems is limited to less than 10 ms, this massive information loss might be one of the reasons which are responsible for the lack of noise robustness of these systems.

  9. Dynamical analysis of Parkinsonian state emulated by hybrid Izhikevich neuron models

    NASA Astrophysics Data System (ADS)

    Liu, Chen; Wang, Jiang; Yu, Haitao; Deng, Bin; Wei, Xile; Li, Huiyan; Loparo, Kenneth A.; Fietkiewicz, Chris

    2015-11-01

    Computational models play a significant role in exploring novel theories to complement the findings of physiological experiments. Various computational models have been developed to reveal the mechanisms underlying brain functions. Particularly, in the development of therapies to modulate behavioral and pathological abnormalities, computational models provide the basic foundations to exhibit transitions between physiological and pathological conditions. Considering the significant roles of the intrinsic properties of the globus pallidus and the coupling connections between neurons in determining the firing patterns and the dynamical activities of the basal ganglia neuronal network, we propose a hypothesis that pathological behaviors under the Parkinsonian state may originate from combined effects of intrinsic properties of globus pallidus neurons and synaptic conductances in the whole neuronal network. In order to establish a computational efficient network model, hybrid Izhikevich neuron model is used due to its capacity of capturing the dynamical characteristics of the biological neuronal activities. Detailed analysis of the individual Izhikevich neuron model can assist in understanding the roles of model parameters, which then facilitates the establishment of the basal ganglia-thalamic network model, and contributes to a further exploration of the underlying mechanisms of the Parkinsonian state. Simulation results show that the hybrid Izhikevich neuron model is capable of capturing many of the dynamical properties of the basal ganglia-thalamic neuronal network, such as variations of the firing rates and emergence of synchronous oscillations under the Parkinsonian condition, despite the simplicity of the two-dimensional neuronal model. It may suggest that the computational efficient hybrid Izhikevich neuron model can be used to explore basal ganglia normal and abnormal functions. Especially it provides an efficient way of emulating the large-scale neuron network

  10. Implementation of Fixed-point Neuron Models with Threshold, Ramp and Sigmoid Activation Functions

    NASA Astrophysics Data System (ADS)

    Zhang, Lei

    2017-07-01

    This paper presents the hardware implementation of single-neuron models with three types of activation functions using fixed-point data format on Field Programmable Gate Arrays (FPGA). Activation function defines the transfer behavior of a neuron model and consequently the Artificial Neural Network (ANN) constructed using it. This paper compared single neuron models designed with bipolar ramp, threshold and sigmoid activation functions. It is also demonstrated that the FPGA hardware implementation performance can be significantly improved by using 16-bit fixed-point data format instead of 32-bit floating-point data format for the neuron model with sigmoid activation function.

  11. Parametric Anatomical Modeling: a method for modeling the anatomical layout of neurons and their projections

    PubMed Central

    Pyka, Martin; Klatt, Sebastian; Cheng, Sen

    2014-01-01

    Computational models of neural networks can be based on a variety of different parameters. These parameters include, for example, the 3d shape of neuron layers, the neurons' spatial projection patterns, spiking dynamics and neurotransmitter systems. While many well-developed approaches are available to model, for example, the spiking dynamics, there is a lack of approaches for modeling the anatomical layout of neurons and their projections. We present a new method, called Parametric Anatomical Modeling (PAM), to fill this gap. PAM can be used to derive network connectivities and conduction delays from anatomical data, such as the position and shape of the neuronal layers and the dendritic and axonal projection patterns. Within the PAM framework, several mapping techniques between layers can account for a large variety of connection properties between pre- and post-synaptic neuron layers. PAM is implemented as a Python tool and integrated in the 3d modeling software Blender. We demonstrate on a 3d model of the hippocampal formation how PAM can help reveal complex properties of the synaptic connectivity and conduction delays, properties that might be relevant to uncover the function of the hippocampus. Based on these analyses, two experimentally testable predictions arose: (i) the number of neurons and the spread of connections is heterogeneously distributed across the main anatomical axes, (ii) the distribution of connection lengths in CA3-CA1 differ qualitatively from those between DG-CA3 and CA3-CA3. Models created by PAM can also serve as an educational tool to visualize the 3d connectivity of brain regions. The low-dimensional, but yet biologically plausible, parameter space renders PAM suitable to analyse allometric and evolutionary factors in networks and to model the complexity of real networks with comparatively little effort. PMID:25309338

  12. Parametric Anatomical Modeling: a method for modeling the anatomical layout of neurons and their projections.

    PubMed

    Pyka, Martin; Klatt, Sebastian; Cheng, Sen

    2014-01-01

    Computational models of neural networks can be based on a variety of different parameters. These parameters include, for example, the 3d shape of neuron layers, the neurons' spatial projection patterns, spiking dynamics and neurotransmitter systems. While many well-developed approaches are available to model, for example, the spiking dynamics, there is a lack of approaches for modeling the anatomical layout of neurons and their projections. We present a new method, called Parametric Anatomical Modeling (PAM), to fill this gap. PAM can be used to derive network connectivities and conduction delays from anatomical data, such as the position and shape of the neuronal layers and the dendritic and axonal projection patterns. Within the PAM framework, several mapping techniques between layers can account for a large variety of connection properties between pre- and post-synaptic neuron layers. PAM is implemented as a Python tool and integrated in the 3d modeling software Blender. We demonstrate on a 3d model of the hippocampal formation how PAM can help reveal complex properties of the synaptic connectivity and conduction delays, properties that might be relevant to uncover the function of the hippocampus. Based on these analyses, two experimentally testable predictions arose: (i) the number of neurons and the spread of connections is heterogeneously distributed across the main anatomical axes, (ii) the distribution of connection lengths in CA3-CA1 differ qualitatively from those between DG-CA3 and CA3-CA3. Models created by PAM can also serve as an educational tool to visualize the 3d connectivity of brain regions. The low-dimensional, but yet biologically plausible, parameter space renders PAM suitable to analyse allometric and evolutionary factors in networks and to model the complexity of real networks with comparatively little effort.

  13. Bistable Dynamics Underlying Excitability of Ion Homeostasis in Neuron Models

    PubMed Central

    Hübel, Niklas; Schöll, Eckehard; Dahlem, Markus A.

    2014-01-01

    When neurons fire action potentials, dissipation of free energy is usually not directly considered, because the change in free energy is often negligible compared to the immense reservoir stored in neural transmembrane ion gradients and the long–term energy requirements are met through chemical energy, i.e., metabolism. However, these gradients can temporarily nearly vanish in neurological diseases, such as migraine and stroke, and in traumatic brain injury from concussions to severe injuries. We study biophysical neuron models based on the Hodgkin–Huxley (HH) formalism extended to include time–dependent ion concentrations inside and outside the cell and metabolic energy–driven pumps. We reveal the basic mechanism of a state of free energy–starvation (FES) with bifurcation analyses showing that ion dynamics is for a large range of pump rates bistable without contact to an ion bath. This is interpreted as a threshold reduction of a new fundamental mechanism of ionic excitability that causes a long–lasting but transient FES as observed in pathological states. We can in particular conclude that a coupling of extracellular ion concentrations to a large glial–vascular bath can take a role as an inhibitory mechanism crucial in ion homeostasis, while the pumps alone are insufficient to recover from FES. Our results provide the missing link between the HH formalism and activator–inhibitor models that have been successfully used for modeling migraine phenotypes, and therefore will allow us to validate the hypothesis that migraine symptoms are explained by disturbed function in ion channel subunits, pumps, and other proteins that regulate ion homeostasis. PMID:24784149

  14. Zebrafish models of human motor neuron diseases: advantages and limitations.

    PubMed

    Babin, Patrick J; Goizet, Cyril; Raldúa, Demetrio

    2014-07-01

    Motor neuron diseases (MNDs) are an etiologically heterogeneous group of disorders of neurodegenerative origin, which result in degeneration of lower (LMNs) and/or upper motor neurons (UMNs). Neurodegenerative MNDs include pure hereditary spastic paraplegia (HSP), which involves specific degeneration of UMNs, leading to progressive spasticity of the lower limbs. In contrast, spinal muscular atrophy (SMA) involves the specific degeneration of LMNs, with symmetrical muscle weakness and atrophy. Amyotrophic lateral sclerosis (ALS), the most common adult-onset MND, is characterized by the degeneration of both UMNs and LMNs, leading to progressive muscle weakness, atrophy, and spasticity. A review of the comparative neuroanatomy of the human and zebrafish motor systems showed that, while the zebrafish was a homologous model for LMN disorders, such as SMA, it was only partially relevant in the case of UMN disorders, due to the absence of corticospinal and rubrospinal tracts in its central nervous system. Even considering the limitation of this model to fully reproduce the human UMN disorders, zebrafish offer an excellent alternative vertebrate model for the molecular and genetic dissection of MND mechanisms. Its advantages include the conservation of genome and physiological processes and applicable in vivo tools, including easy imaging, loss or gain of function methods, behavioral tests to examine changes in motor activity, and the ease of simultaneous chemical/drug testing on large numbers of animals. This facilitates the assessment of the environmental origin of MNDs, alone or in combination with genetic traits and putative modifier genes. Positive hits obtained by phenotype-based small-molecule screening using zebrafish may potentially be effective drugs for treatment of human MNDs.

  15. Mathematical modelling and numerical simulation of the morphological development of neurons

    PubMed Central

    Graham, Bruce P; van Ooyen, Arjen

    2006-01-01

    Background The morphological development of neurons is a very complex process involving both genetic and environmental components. Mathematical modelling and numerical simulation are valuable tools in helping us unravel particular aspects of how individual neurons grow their characteristic morphologies and eventually form appropriate networks with each other. Methods A variety of mathematical models that consider (1) neurite initiation (2) neurite elongation (3) axon pathfinding, and (4) neurite branching and dendritic shape formation are reviewed. The different mathematical techniques employed are also described. Results Some comparison of modelling results with experimental data is made. A critique of different modelling techniques is given, leading to a proposal for a unified modelling environment for models of neuronal development. Conclusion A unified mathematical and numerical simulation framework should lead to an expansion of work on models of neuronal development, as has occurred with compartmental models of neuronal electrical activity. PMID:17118163

  16. Mathematical modelling and numerical simulation of the morphological development of neurons.

    PubMed

    Graham, Bruce P; van Ooyen, Arjen

    2006-10-30

    The morphological development of neurons is a very complex process involving both genetic and environmental components. Mathematical modelling and numerical simulation are valuable tools in helping us unravel particular aspects of how individual neurons grow their characteristic morphologies and eventually form appropriate networks with each other. A variety of mathematical models that consider (1) neurite initiation (2) neurite elongation (3) axon pathfinding, and (4) neurite branching and dendritic shape formation are reviewed. The different mathematical techniques employed are also described. Some comparison of modelling results with experimental data is made. A critique of different modelling techniques is given, leading to a proposal for a unified modelling environment for models of neuronal development. A unified mathematical and numerical simulation framework should lead to an expansion of work on models of neuronal development, as has occurred with compartmental models of neuronal electrical activity.

  17. Fractional cable model for signal conduction in spiny neuronal dendrites

    NASA Astrophysics Data System (ADS)

    Vitali, Silvia; Mainardi, Francesco

    2017-06-01

    The cable model is widely used in several fields of science to describe the propagation of signals. A relevant medical and biological example is the anomalous subdiffusion in spiny neuronal dendrites observed in several studies of the last decade. Anomalous subdiffusion can be modelled in several ways introducing some fractional component into the classical cable model. The Chauchy problem associated to these kind of models has been investigated by many authors, but up to our knowledge an explicit solution for the signalling problem has not yet been published. Here we propose how this solution can be derived applying the generalized convolution theorem (known as Efros theorem) for Laplace transforms. The fractional cable model considered in this paper is defined by replacing the first order time derivative with a fractional derivative of order α ∈ (0, 1) of Caputo type. The signalling problem is solved for any input function applied to the accessible end of a semi-infinite cable, which satisfies the requirements of the Efros theorem. The solutions corresponding to the simple cases of impulsive and step inputs are explicitly calculated in integral form containing Wright functions. Thanks to the variability of the parameter α, the corresponding solutions are expected to adapt to the qualitative behaviour of the membrane potential observed in experiments better than in the standard case α = 1.

  18. Dynamic characteristics of a simple bursting neuron model

    NASA Astrophysics Data System (ADS)

    Nakajima, Koji; Sato, Shigeo; Hayakawa, Yoshihiro

    We present a simple neuron model that shows a rich property in spite of the simple structure derived from the simplification of the Hindmarsh-Rose, the Morris-Lecar, and the Hodgkin-Huxley models. The model is a typical example whose characteristics can be discussed through the concept of potential with active areas. A potential function is able to provide a global landscape for dynamics of a model, and the dynamics is explained in connection with the disposition of the active areas on the potential, and hence we are able to discuss the global dynamic behaviors and the common properties among these realistic models. The obtained outputs are broadly classified as simple oscillations, spiking, bursting, and chaotic oscillations. The bursting outputs are classified as with spike undershoot and without spike undershoot, and the bursts without spike undershoot are classified as with tapered and without tapered. We show the parameter dependence of these outputs and discuss the connection between these outputs and the potential with active areas.

  19. Humoral Reactivity of Renal Transplant-Waitlisted Patients to Cells From GGTA1/CMAH/B4GalNT2, and SLA Class I Knockout Pigs.

    PubMed

    Martens, Gregory R; Reyes, Luz M; Butler, James R; Ladowski, Joseph M; Estrada, Jose L; Sidner, Richard A; Eckhoff, Devin E; Tector, Matt; Tector, A Joseph

    2017-04-01

    Antipig antibodies are a barrier to clinical xenotransplantation. We evaluated antibody binding of waitlisted renal transplant patients to 3 glycan knockout (KO) pig cells and class I swine leukocyte antigens (SLA). Peripheral blood mononuclear cells from SLA identical wild type (WT), α1, 3-galactosyltransferase (GGTA1) KO, GGTA1/ cytidine monophosphate-N-acetylneuraminic acid hydroxylase (CMAH) KO, and GGTA1/ CMAH /b1,4 N-acetylgalactosaminyl transferase (B4GalNT2) KO pigs were screened for human antibody binding using flow cytometric crossmatch (FCXM). Sera from 820 patients were screened on GGTA1/CMAH/B4GalNT2 KO cells and a subset with elevated binding was evaluated further. FCXM was performed on SLA intact cells and GGTA1/SLA class I KO cells after depletion with WT pig RBCs to remove cell surface reactive antibodies, but leave SLA antibodies. Lastly, human and pig reactive antibodies were eluted and tested for cross-species binding and reactivity to single-antigen HLA beads. Sequential glycan KO modifications significantly reduce antibody binding of waitlisted patients. Sera exhibiting elevated binding without reduction after depletion with WT RBCs demonstrate reduced binding to SLA class I KO cells. Human IgG, eluted from human and pig peripheral blood mononuclear cells, interacted across species and bound single-antigen HLA beads in common epitope-restricted patterns. Many waitlisted patients have minimal xenoreactive antibody binding to the triple KO pig, but some HLA antibodies in sensitized patients cross-react with class I SLA. SLA class I is a target for genome editing in xenotransplantation.

  20. Electrophysiological properties of inferior olive neurons: A compartmental model.

    PubMed

    Schweighofer, N; Doya, K; Kawato, M

    1999-08-01

    As a step in exploring the functions of the inferior olive, we constructed a biophysical model of the olivary neurons to examine their unique electrophysiological properties. The model consists of two compartments to represent the known distribution of ionic currents across the cell membrane, as well as the dendritic location of the gap junctions and synaptic inputs. The somatic compartment includes a low-threshold calcium current (I(Ca_l)), an anomalous inward rectifier current (I(h)), a sodium current (I(Na)), and a delayed rectifier potassium current (I(K_dr)). The dendritic compartment contains a high-threshold calcium current (I(Ca_h)), a calcium-dependent potassium current (I(K_Ca)), and a current flowing into other cells through electrical coupling (I(c)). First, kinetic parameters for these currents were set according to previously reported experimental data. Next, the remaining free parameters were determined to account for both static and spiking properties of single olivary neurons in vitro. We then performed a series of simulated pharmacological experiments using bifurcation analysis and extensive two-parameter searches. Consistent with previous studies, we quantitatively demonstrated the major role of I(Ca_l) in spiking excitability. In addition, I(h) had an important modulatory role in the spike generation and period of oscillations, as previously suggested by Bal and McCormick. Finally, we investigated the role of electrical coupling in two coupled spiking cells. Depending on the coupling strength, the hyperpolarization level, and the I(Ca_l) and I(h) modulation, the coupled cells had four different synchronization modes: the cells could be in-phase, phase-shifted, or anti-phase or could exhibit a complex desynchronized spiking mode. Hence these simulation results support the counterintuitive hypothesis that electrical coupling can desynchronize coupled inferior olive cells.

  1. Slow motor neuron stimulation of locust skeletal muscle: model and measurement.

    PubMed

    Wilson, Emma; Rustighi, Emiliano; Newland, Philip L; Mace, Brian R

    2013-06-01

    The isometric force response of the locust hind leg extensor tibia muscle to stimulation of a slow extensor tibia motor neuron is experimentally investigated, and a mathematical model describing the response presented. The measured force response was modelled by considering the ability of an existing model, developed to describe the response to the stimulation of a fast extensor tibia motor neuron and to also model the response to slow motor neuron stimulation. It is found that despite large differences in the force response to slow and fast motor neuron stimulation, which could be accounted for by the differing physiology of the fibres they innervate, the model is able to describe the response to both fast and slow motor neuron stimulation. Thus, the presented model provides a potentially generally applicable, robust, simple model to describe the isometric force response of a range of muscles.

  2. Linear and Nonlinear Electrical Models of Neurons for Hopfield Neural Network

    NASA Astrophysics Data System (ADS)

    Sarwar, Farah; Iqbal, Shaukat; Hussain, Muhammad Waqar

    2016-11-01

    A novel electrical model of neuron is proposed in this presentation. The suggested neural network model has linear/nonlinear input-output characteristics. This new deterministic model has joint biological properties in excellent agreement with the earlier deterministic neuron model of Hopfield and Tank and to the stochastic neuron model of McCulloch and Pitts. It is an accurate portrayal of differential equation presented by Hopfield and Tank to mimic neurons. Operational amplifiers, resistances, capacitor, and diodes are used to design this system. The presented biological model of neurons remains to be advantageous for simulations. Impulse response is studied and conferred to certify the stability and strength of this innovative model. A simple illustration is mapped to demonstrate the exactness of the intended system. Precisely mapped illustration exhibits 100 % accurate results.

  3. On learning time delays between the spikes from different input neurons in a biophysical model of a pyramidal neuron.

    PubMed

    Koutsou, Achilleas; Bugmann, Guido; Christodoulou, Chris

    2015-10-01

    Biological systems are able to recognise temporal sequences of stimuli or compute in the temporal domain. In this paper we are exploring whether a biophysical model of a pyramidal neuron can detect and learn systematic time delays between the spikes from different input neurons. In particular, we investigate whether it is possible to reinforce pairs of synapses separated by a dendritic propagation time delay corresponding to the arrival time difference of two spikes from two different input neurons. We examine two subthreshold learning approaches where the first relies on the backpropagation of EPSPs (excitatory postsynaptic potentials) and the second on the backpropagation of a somatic action potential, whose production is supported by a learning-enabling background current. The first approach does not provide a learning signal that sufficiently differentiates between synapses at different locations, while in the second approach, somatic spikes do not provide a reliable signal distinguishing arrival time differences of the order of the dendritic propagation time. It appears that the firing of pyramidal neurons shows little sensitivity to heterosynaptic spike arrival time differences of several milliseconds. This neuron is therefore unlikely to be able to learn to detect such differences.

  4. Temporal structure of neuronal population oscillations with empirical model decomposition

    NASA Astrophysics Data System (ADS)

    Li, Xiaoli

    2006-08-01

    Frequency analysis of neuronal oscillation is very important for understanding the neural information processing and mechanism of disorder in the brain. This Letter addresses a new method to analyze the neuronal population oscillations with empirical mode decomposition (EMD). Following EMD of neuronal oscillation, a series of intrinsic mode functions (IMFs) are obtained, then Hilbert transform of IMFs can be used to extract the instantaneous time frequency structure of neuronal oscillation. The method is applied to analyze the neuronal oscillation in the hippocampus of epileptic rats in vivo, the results show the neuronal oscillations have different descriptions during the pre-ictal, seizure onset and ictal periods of the epileptic EEG at the different frequency band. This new method is very helpful to provide a view for the temporal structure of neural oscillation.

  5. Simple Cortical and Thalamic Neuron Models for Digital Arithmetic Circuit Implementation

    PubMed Central

    Nanami, Takuya; Kohno, Takashi

    2016-01-01

    Trade-off between reproducibility of neuronal activities and computational efficiency is one of crucial subjects in computational neuroscience and neuromorphic engineering. A wide variety of neuronal models have been studied from different viewpoints. The digital spiking silicon neuron (DSSN) model is a qualitative model that focuses on efficient implementation by digital arithmetic circuits. We expanded the DSSN model and found appropriate parameter sets with which it reproduces the dynamical behaviors of the ionic-conductance models of four classes of cortical and thalamic neurons. We first developed a four-variable model by reducing the number of variables in the ionic-conductance models and elucidated its mathematical structures using bifurcation analysis. Then, expanded DSSN models were constructed that reproduce these mathematical structures and capture the characteristic behavior of each neuron class. We confirmed that statistics of the neuronal spike sequences are similar in the DSSN and the ionic-conductance models. Computational cost of the DSSN model is larger than that of the recent sophisticated Integrate-and-Fire-based models, but smaller than the ionic-conductance models. This model is intended to provide another meeting point for above trade-off that satisfies the demand for large-scale neuronal network simulation with closer-to-biology models. PMID:27242397

  6. Simple Cortical and Thalamic Neuron Models for Digital Arithmetic Circuit Implementation.

    PubMed

    Nanami, Takuya; Kohno, Takashi

    2016-01-01

    Trade-off between reproducibility of neuronal activities and computational efficiency is one of crucial subjects in computational neuroscience and neuromorphic engineering. A wide variety of neuronal models have been studied from different viewpoints. The digital spiking silicon neuron (DSSN) model is a qualitative model that focuses on efficient implementation by digital arithmetic circuits. We expanded the DSSN model and found appropriate parameter sets with which it reproduces the dynamical behaviors of the ionic-conductance models of four classes of cortical and thalamic neurons. We first developed a four-variable model by reducing the number of variables in the ionic-conductance models and elucidated its mathematical structures using bifurcation analysis. Then, expanded DSSN models were constructed that reproduce these mathematical structures and capture the characteristic behavior of each neuron class. We confirmed that statistics of the neuronal spike sequences are similar in the DSSN and the ionic-conductance models. Computational cost of the DSSN model is larger than that of the recent sophisticated Integrate-and-Fire-based models, but smaller than the ionic-conductance models. This model is intended to provide another meeting point for above trade-off that satisfies the demand for large-scale neuronal network simulation with closer-to-biology models.

  7. A computational model for the loss of neuronal organization in microcolumns.

    PubMed

    Henderson, Maxwell; Urbanc, Brigita; Cruz, Luis

    2014-05-20

    A population of neurons in the cerebral cortex of humans and other mammals organize themselves into vertical microcolumns perpendicular to the pial surface. Anatomical changes to these microcolumns have been correlated with neurological diseases and normal aging; in particular, in area 46 of the rhesus monkey brain, the strength of microcolumns was shown to decrease with age. These changes can be caused by alterations in the spatial distribution of the neurons in microcolumns and/or neuronal loss. Using a three-dimensional computational model of neuronal arrangements derived from thin tissue sections and validated in brain tissue from rhesus monkeys, we show that neuronal loss is inconsistent with the findings in aged individuals. In contrast, a model of simple random neuronal displacements, constrained in magnitude by restorative harmonic forces, is consistent with observed changes and provides mechanistic insights into the age-induced loss of microcolumnar structure. Connection of the model to normal aging and disease are discussed.

  8. Hypoxanthine deregulates genes involved in early neuronal development. Implications in Lesch-Nyhan disease pathogenesis.

    PubMed

    Torres, R J; Puig, J G

    2015-11-01

    Neurological manifestations in Lesch-Nyhan disease (LND) are attributed to the effect of hypoxanthine-guanine phosphoribosyltransferase (HPRT) deficiency on the nervous system development. HPRT deficiency causes the excretion of increased amounts of hypoxanthine into the extracellular medium and we hypothesized that HPRT deficiency related to hypoxanthine excess may then lead, directly or indirectly, to transcriptional aberrations in a variety of genes essential for the function and development of striatal progenitor cells. We have examined the effect of hypoxanthine excess on the differentiation of neurons in the well-established human NTERA-2 cl.D1 (NT2/D1) embryonic carcinoma neurogenesis model. NT2/D1 cells differentiate along neuroectodermal lineages after exposure to retinoic acid (RA). Hypoxanthine effects on RA-differentiation were examined by the changes on the expression of various transcription factor genes essential to neuronal differentiation and by the changes in tyrosine hydroxylase (TH), dopamine, adenosine and serotonin receptors (DRD, ADORA, HTR). We report that hypoxanthine excess deregulate WNT4, from Wnt/β-catenin pathway, and engrailed homeobox 1 gene and increased TH and dopamine DRD1, adenosine ADORA2A and serotonin HTR7 receptors, whose over expression characterize early neuro-developmental processes.

  9. Continuum modeling of neuronal cell under blast loading

    PubMed Central

    Jérusalem, Antoine; Dao, Ming

    2012-01-01

    Traumatic brain injuries have recently been put under the spotlight as one of the most important causes of accidental brain dysfunctions. Significant experimental and modeling efforts are thus ongoing to study the associated biological, mechanical and physical mechanisms. In the field of cell mechanics, progresses are also being made at the experimental and modeling levels to better characterize many of the cell functions such as differentiation, growth, migration and death, among others. The work presented here aims at bridging both efforts by proposing a continuum model of neuronal cell submitted to blast loading. In this approach, cytoplasm, nucleus and membrane (plus cortex) are differentiated in a representative cell geometry, and different material constitutive models are adequately chosen for each one. The material parameters are calibrated against published experimental work of cell nanoindentation at multiple rates. The final cell model is ultimately subjected to blast loading within a complete fluid-structure interaction computational framework. The results are compared to the nanoindentation simulation and the specific effects of the blast wave on the pressure and shear levels at the interfaces are identified. As a conclusion, the presented model successfully captures some of the intrinsic intracellular phenomena occurring during its deformation under blast loading and potentially leading to cell damage. It suggests more particularly the localization of damage at the nucleus membrane similarly to what has already been observed at the overall cell membrane. This degree of damage is additionally predicted to be worsened by a longer blast positive phase duration. As a conclusion, the proposed model ultimately provides a new three dimensional computational tool to evaluate intracellular damage during blast loading. PMID:22562014

  10. On the simulation of nonlinear bidimensional spiking neuron models.

    PubMed

    Touboul, Jonathan

    2011-07-01

    Bidimensional spiking models are garnering a lot of attention for their simplicity and their ability to reproduce various spiking patterns of cortical neurons and are used particularly for large network simulations. These models describe the dynamics of the membrane potential by a nonlinear differential equation that blows up in finite time, coupled to a second equation for adaptation. Spikes are emitted when the membrane potential blows up or reaches a cutoff θ. The precise simulation of the spike times and of the adaptation variable is critical, for it governs the spike pattern produced and is hard to compute accurately because of the exploding nature of the system at the spike times. We thoroughly study the precision of fixed time-step integration schemes for this type of model and demonstrate that these methods produce systematic errors that are unbounded, as the cutoff value is increased, in the evaluation of the two crucial quantities: the spike time and the value of the adaptation variable at this time. Precise evaluation of these quantities therefore involves very small time steps and long simulation times. In order to achieve a fixed absolute precision in a reasonable computational time, we propose here a new algorithm to simulate these systems based on a variable integration step method that either integrates the original ordinary differential equation or the equation of the orbits in the phase plane, and compare this algorithm with fixed time-step Euler scheme and other more accurate simulation algorithms.

  11. On a stochastic leaky integrate-and-fire neuronal model.

    PubMed

    Buonocore, A; Caputo, L; Pirozzi, E; Ricciardi, L M

    2010-10-01

    The leaky integrate-and-fire neuronal model proposed in Stevens and Zador (1998), in which time constant and resting potential are postulated to be time dependent, is revisited within a stochastic framework in which the membrane potential is mathematically described as a gauss-diffusion process. The first-passage-time probability density, miming in such a context the firing probability density, is evaluated by either the Volterra integral equation of Buonocore, Nobile, and Ricciardi ( 1987 ) or, when possible, by the asymptotics of Giorno, Nobile, and Ricciardi (1990). The model examined here represents an extension of the classic leaky integrate-and-fire one based on the Ornstein-Uhlenbeck process in that it is in principle compatible with the inclusion of some other physiological characteristics such as relative refractoriness. It also allows finer tuning possibilities in view of its accounting for certain qualitative as well as quantitative features, such as the behavior of the time course of the membrane potential prior to firings and the computation of experimentally measurable statistical descriptors of the firing time: mean, median, coefficient of variation, and skewness. Finally, implementations of this model are provided in connection with certain experimental evidence discussed in the literature.

  12. Dynamical responses in a new neuron model subjected to electromagnetic induction and phase noise

    NASA Astrophysics Data System (ADS)

    Wu, Fuqiang; Wang, Chunni; Jin, Wuyin; Ma, Jun

    2017-03-01

    Complex electrical activities in neuron can induce time-varying electromagnetic field and the effect of various electromagnetic inductions should be considered in dealing with electrical activities of neuron. Based on an improved neuron model, the effect of electromagnetic induction is described by using magnetic flux, and the modulation of magnetic flux on membrane potential is realized by using memristor coupling. Furthermore, additive phase noise is imposed on the neuron to detect the dynamical response of neuron and phase transition in modes. The dynamical properties of electrical activities are detected and discussed, and double coherence resonance behavior is observed, respectively. Furthermore, multiple modes of electrical activities can be observed in the sampled time series for membrane potential of the neuron model.

  13. Basic Neuron Model Electrical Equivalent Circuit: An Undergraduate Laboratory Exercise

    PubMed Central

    Dabrowski, Katie M.; Castaño, Diego J.; Tartar, Jaime L.

    2013-01-01

    We developed a hands-on laboratory exercise for undergraduate students in which they can build and manipulate a neuron equivalent circuit. This exercise uses electrical circuit components that resemble neuron components and are easy to construct. We describe the methods for creating the equivalent circuit and how to observe different neuron properties through altering the structure of the equivalent circuit. We explain how this hands-on laboratory activity allows for the better understanding of this fundamental neuroscience concept. At the conclusion of this laboratory exercise, undergraduate students will be able to apply the principles of Ohm’s law, cable theory with regards to neurons, and understand the functions of resistance and capacitance in a neuron. PMID:24319391

  14. Basic neuron model electrical equivalent circuit: an undergraduate laboratory exercise.

    PubMed

    Dabrowski, Katie M; Castaño, Diego J; Tartar, Jaime L

    2013-01-01

    We developed a hands-on laboratory exercise for undergraduate students in which they can build and manipulate a neuron equivalent circuit. This exercise uses electrical circuit components that resemble neuron components and are easy to construct. We describe the methods for creating the equivalent circuit and how to observe different neuron properties through altering the structure of the equivalent circuit. We explain how this hands-on laboratory activity allows for the better understanding of this fundamental neuroscience concept. At the conclusion of this laboratory exercise, undergraduate students will be able to apply the principles of Ohm's law, cable theory with regards to neurons, and understand the functions of resistance and capacitance in a neuron.

  15. Characterization of Kiss1 neurons using transgenic mouse models

    PubMed Central

    Cravo, Roberta M.; Margatho, Lisandra O.; Osborne-Lawrence, Sherri; Donato, José; Atkin, Stan; Bookout, Angie L.; Rovinsky, Sherry; Frazão, Renata; Lee, Charlotte E.; Gautron, Laurent; Zigman, Jeffrey M.; Elias, Carol F.

    2010-01-01

    Humans and mice with loss-of-function mutations of the genes encoding kisspeptins (Kiss1) or kisspeptin receptors (Kiss1r) are infertile due to hypogonadotropic hypogonadism. Within the hypothalamus, Kiss1 mRNA is expressed in the anteroventral periventricular nucleus (AVPV) and the arcuate nucleus (Arc). In order to better study the different populations of kisspeptin cells we generated Kiss1-Cre transgenic mice. We obtained one line with Cre activity specifically within Kiss1 neurons (line J2-4), as assessed by generating mice with Cre-dependent expression of green fluorescent protein or β-galactosidase. Also, we demonstrated Kiss1 expression in the cerebral cortex and confirmed previous data showing Kiss1 mRNA in the medial nucleus of amygdala and anterodorsal preoptic nucleus. Kiss1 neurons were more concentrated towards the caudal levels of the Arc and higher leptin-responsivity was observed in the most caudal population of Arc Kiss1 neurons. No evidence for direct action of leptin in AVPV Kiss1 neurons was observed. Melanocortin fibers innervated subsets of Kiss1 neurons of the preoptic area and Arc, and both populations expressed MC4R. Specifically in the preoptic area, 18–28% of Kiss1 neurons expressed MC4R. In the Arc, 90% of Kiss1 neurons were glutamatergic, 50% of which also were GABAergic. In the AVPV, 20% of Kiss1 neurons were glutamatergic whereas 75% were GABAergic. The differences observed between the Kiss1 neurons in the preoptic area and the Arc likely represent neuronal evidence for their differential roles in metabolism and reproduction. PMID:21093546

  16. A modeling approach on why simple central pattern generators are built of irregular neurons.

    PubMed

    Reyes, Marcelo Bussotti; Carelli, Pedro Valadão; Sartorelli, José Carlos; Pinto, Reynaldo Daniel

    2015-01-01

    The crustacean pyloric Central Pattern Generator (CPG) is a nervous circuit that endogenously provides periodic motor patterns. Even after about 40 years of intensive studies, the rhythm genesis is still not rigorously understood in this CPG, mainly because it is made of neurons with irregular intrinsic activity. Using mathematical models we addressed the question of using a network of irregularly behaving elements to generate periodic oscillations, and we show some advantages of using non-periodic neurons with intrinsic behavior in the transition from bursting to tonic spiking (as found in biological pyloric CPGs) as building components. We studied two- and three-neuron model CPGs built either with Hindmarsh-Rose or with conductance-based Hodgkin-Huxley-like model neurons. By changing a model's parameter we could span the neuron's intrinsic dynamical behavior from slow periodic bursting to fast tonic spiking, passing through a transition where irregular bursting was observed. Two-neuron CPG, half center oscillator (HCO), was obtained for each intrinsic behavior of the neurons by coupling them with mutual symmetric synaptic inhibition. Most of these HCOs presented regular antiphasic bursting activity and the changes of the bursting frequencies was studied as a function of the inhibitory synaptic strength. Among all HCOs, those made of intrinsic irregular neurons presented a wider burst frequency range while keeping a reliable regular oscillatory (bursting) behavior. HCOs of periodic neurons tended to be either hard to change their behavior with synaptic strength variations (slow periodic burster neurons) or unable to perform a physiologically meaningful rhythm (fast tonic spiking neurons). Moreover, 3-neuron CPGs with connectivity and output similar to those of the pyloric CPG presented the same results.

  17. Hybrid Scheme for Modeling Local Field Potentials from Point-Neuron Networks.

    PubMed

    Hagen, Espen; Dahmen, David; Stavrinou, Maria L; Lindén, Henrik; Tetzlaff, Tom; van Albada, Sacha J; Grün, Sonja; Diesmann, Markus; Einevoll, Gaute T

    2016-12-01

    With rapidly advancing multi-electrode recording technology, the local field potential (LFP) has again become a popular measure of neuronal activity in both research and clinical applications. Proper understanding of the LFP requires detailed mathematical modeling incorporating the anatomical and electrophysiological features of neurons near the recording electrode, as well as synaptic inputs from the entire network. Here we propose a hybrid modeling scheme combining efficient point-neuron network models with biophysical principles underlying LFP generation by real neurons. The LFP predictions rely on populations of network-equivalent multicompartment neuron models with layer-specific synaptic connectivity, can be used with an arbitrary number of point-neuron network populations, and allows for a full separation of simulated network dynamics and LFPs. We apply the scheme to a full-scale cortical network model for a ∼1 mm(2) patch of primary visual cortex, predict laminar LFPs for different network states, assess the relative LFP contribution from different laminar populations, and investigate effects of input correlations and neuron density on the LFP. The generic nature of the hybrid scheme and its public implementation in hybridLFPy form the basis for LFP predictions from other and larger point-neuron network models, as well as extensions of the current application with additional biological detail.

  18. Use of human induced pluripotent stem cell-derived neurons as a model for Cerebral Toxoplasmosis.

    PubMed

    Tanaka, Naomi; Ashour, Danah; Dratz, Edward; Halonen, Sandra

    2016-01-01

    Toxoplasma gondii is a ubiquitous protozoan parasite with approximately one-third of the worlds' population chronically infected. In chronically infected individuals, the parasite resides primarily in cysts within neurons in the central nervous system. The chronic infection in immunocompetent individuals has been considered to be asymptomatic but increasing evidence indicates the chronic infection can lead to neuropsychiatric disorders such as Schizophrenia, prenatal depression and suicidal thoughts. A better understanding of the mechanism(s) by which the parasite exerts effects on human behavior is limited due to lack of suitable human neuronal models. In this paper, we report the use of human neurons derived from normal cord blood CD34+ cells generated via genetic reprogramming, as an in vitro model for the study T. gondii in neurons. This culture method resulted in a relatively pure monolayer of induced human neuronal-like cells that stained positive for neuronal markers, MAP2, NFL, NFH and NeuN. These induced human neuronal-like cells (iHNs) were efficiently infected by the Prugniad strain of the parasite and supported replication of the tachyzoite stage and development of the cyst stage. Infected iHNs could be maintained through 5 days of infection, allowing for formation of large cysts. This induced human neuronal model represents a novel culture method to study both tachyzoite and bradyzoite stages of T. gondii in human neurons. Copyright © 2016 Institut Pasteur. Published by Elsevier Masson SAS. All rights reserved.

  19. Modeling neuron-glia interactions: from parametric model to neuromorphic hardware.

    PubMed

    Ghaderi, Viviane S; Allam, Sushmita L; Ambert, N; Bouteiller, J-M C; Choma, J; Berger, T W

    2011-01-01

    Recent experimental evidence suggests that glial cells are more than just supporting cells to neurons - they play an active role in signal transmission in the brain. We herein propose to investigate the importance of these mechanisms and model neuron-glia interactions at synapses using three approaches: A parametric model that takes into account the underlying mechanisms of the physiological system, a non-parametric model that extracts its input-output properties, and an ultra-low power, fast processing, neuromorphic hardware model. We use the EONS (Elementary Objects of the Nervous System) platform, a highly elaborate synaptic modeling platform to investigate the influence of astrocytic glutamate transporters on postsynaptic responses in the detailed micro-environment of a tri-partite synapse. The simulation results obtained using EONS are then used to build a non-parametric model that captures the essential features of glutamate dynamics. The structure of the non-parametric model we use is specifically designed for efficient hardware implementation using ultra-low power subthreshold CMOS building blocks. The utilization of the approach described allows us to build large-scale models of neuron/glial interaction and consequently provide useful insights on glial modulation during normal and pathological neural function.

  20. A distance constrained synaptic plasticity model of C. elegans neuronal network

    NASA Astrophysics Data System (ADS)

    Badhwar, Rahul; Bagler, Ganesh

    2017-03-01

    Brain research has been driven by enquiry for principles of brain structure organization and its control mechanisms. The neuronal wiring map of C. elegans, the only complete connectome available till date, presents an incredible opportunity to learn basic governing principles that drive structure and function of its neuronal architecture. Despite its apparently simple nervous system, C. elegans is known to possess complex functions. The nervous system forms an important underlying framework which specifies phenotypic features associated to sensation, movement, conditioning and memory. In this study, with the help of graph theoretical models, we investigated the C. elegans neuronal network to identify network features that are critical for its control. The 'driver neurons' are associated with important biological functions such as reproduction, signalling processes and anatomical structural development. We created 1D and 2D network models of C. elegans neuronal system to probe the role of features that confer controllability and small world nature. The simple 1D ring model is critically poised for the number of feed forward motifs, neuronal clustering and characteristic path-length in response to synaptic rewiring, indicating optimal rewiring. Using empirically observed distance constraint in the neuronal network as a guiding principle, we created a distance constrained synaptic plasticity model that simultaneously explains small world nature, saturation of feed forward motifs as well as observed number of driver neurons. The distance constrained model suggests optimum long distance synaptic connections as a key feature specifying control of the network.

  1. Model of intersegmental coordination in the leech heartbeat neuronal network.

    PubMed

    Hill, Andrew A V; Masino, Mark A; Calabrese, Ronald L

    2002-03-01

    We have created a computational model of the timing network that paces the heartbeat of the medicinal leech, Hirudo medicinalis. The rhythmic activity of this network originates from two segmental oscillators located in the third and fourth midbody ganglia. In the intact nerve cord, these segmental oscillators are mutually entrained to the same cycle period. Although experiments have shown that the segmental oscillators are coupled by inhibitory coordinating interneurons, the underlying mechanisms of intersegmental coordination have not yet been elucidated. To help understand this coordination, we have created a simple computational model with two variants: symmetric and asymmetric. In the symmetric model, neurons within each segmental oscillator called oscillator interneurons, inhibit the coordinating interneurons. In contrast, in the asymmetric model only the oscillator interneurons of one segmental oscillator inhibit the coordinating interneurons. In the symmetric model, when two segmental oscillators with different inherent periods are coupled, the faster one leads in phase, and the period of the coupled system is equal to the period of the faster oscillator. This behavior arises because, during each oscillation cycle, the oscillator interneurons of the faster segmental oscillator begin to burst before those of the slower oscillator, thereby terminating spike activity in the coordinating interneurons. Thus there is a brief period of time in each cycle when the oscillator interneurons of the slower segmental oscillator are relieved of inhibition from the coordinating interneurons. This "removal of synaptic inhibition" allows, within certain limits, the slower segmental oscillator to be sped to the period of the faster one. Thus the symmetric model demonstrates a plausible biophysical mechanism by which one segmental oscillator can entrain the other. In general the asymmetric model, in which only one segmental oscillator has the ability to inhibit the

  2. Automatic parameter estimation of multicompartmental neuron models via minimization of trace error with control adjustment

    PubMed Central

    Goeritz, Marie L.; Marder, Eve

    2014-01-01

    We describe a new technique to fit conductance-based neuron models to intracellular voltage traces from isolated biological neurons. The biological neurons are recorded in current-clamp with pink (1/f) noise injected to perturb the activity of the neuron. The new algorithm finds a set of parameters that allows a multicompartmental model neuron to match the recorded voltage trace. Attempting to match a recorded voltage trace directly has a well-known problem: mismatch in the timing of action potentials between biological and model neuron is inevitable and results in poor phenomenological match between the model and data. Our approach avoids this by applying a weak control adjustment to the model to promote alignment during the fitting procedure. This approach is closely related to the control theoretic concept of a Luenberger observer. We tested this approach on synthetic data and on data recorded from an anterior gastric receptor neuron from the stomatogastric ganglion of the crab Cancer borealis. To test the flexibility of this approach, the synthetic data were constructed with conductance models that were different from the ones used in the fitting model. For both synthetic and biological data, the resultant models had good spike-timing accuracy. PMID:25008414

  3. A neuron model of stochastic resonance using rectangular pulse trains.

    PubMed

    Danziger, Zachary; Grill, Warren M

    2015-02-01

    Stochastic resonance (SR) is the enhanced representation of a weak input signal by the addition of an optimal level of broadband noise to a nonlinear (threshold) system. Since its discovery in the 1980s the domain of input signals shown to be applicable to SR has greatly expanded, from strictly periodic inputs to now nearly any aperiodic forcing function. The perturbations (noise) used to generate SR have also expanded, from white noise to now colored noise or vibrational forcing. This study demonstrates that a new class of perturbations can achieve SR, namely, series of stochastically generated biphasic pulse trains. Using these pulse trains as 'noise' we show that a Hodgkin Huxley model neuron exhibits SR behavior when detecting weak input signals. This result is of particular interest to neuroscience because nearly all artificial neural stimulation is implemented with square current or voltage pulses rather than broadband noise, and this new method may facilitate the translation of the performance gains achievable through SR to neural prosthetics.

  4. NeuroGPS: automated localization of neurons for brain circuits using L1 minimization model

    NASA Astrophysics Data System (ADS)

    Quan, Tingwei; Zheng, Ting; Yang, Zhongqing; Ding, Wenxiang; Li, Shiwei; Li, Jing; Zhou, Hang; Luo, Qingming; Gong, Hui; Zeng, Shaoqun

    2013-04-01

    Drawing the map of neuronal circuits at microscopic resolution is important to explain how brain works. Recent progresses in fluorescence labeling and imaging techniques have enabled measuring the whole brain of a rodent like a mouse at submicron-resolution. Considering the huge volume of such datasets, automatic tracing and reconstruct the neuronal connections from the image stacks is essential to form the large scale circuits. However, the first step among which, automated location the soma across different brain areas remains a challenge. Here, we addressed this problem by introducing L1 minimization model. We developed a fully automated system, NeuronGlobalPositionSystem (NeuroGPS) that is robust to the broad diversity of shape, size and density of the neurons in a mouse brain. This method allows locating the neurons across different brain areas without human intervention. We believe this method would facilitate the analysis of the neuronal circuits for brain function and disease studies.

  5. NeuroGPS: automated localization of neurons for brain circuits using L1 minimization model.

    PubMed

    Quan, Tingwei; Zheng, Ting; Yang, Zhongqing; Ding, Wenxiang; Li, Shiwei; Li, Jing; Zhou, Hang; Luo, Qingming; Gong, Hui; Zeng, Shaoqun

    2013-01-01

    Drawing the map of neuronal circuits at microscopic resolution is important to explain how brain works. Recent progresses in fluorescence labeling and imaging techniques have enabled measuring the whole brain of a rodent like a mouse at submicron-resolution. Considering the huge volume of such datasets, automatic tracing and reconstruct the neuronal connections from the image stacks is essential to form the large scale circuits. However, the first step among which, automated location the soma across different brain areas remains a challenge. Here, we addressed this problem by introducing L1 minimization model. We developed a fully automated system, NeuronGlobalPositionSystem (NeuroGPS) that is robust to the broad diversity of shape, size and density of the neurons in a mouse brain. This method allows locating the neurons across different brain areas without human intervention. We believe this method would facilitate the analysis of the neuronal circuits for brain function and disease studies.

  6. A generalized analog implementation of piecewise linear neuron models using CCII building blocks.

    PubMed

    Soleimani, Hamid; Ahmadi, Arash; Bavandpour, Mohammad; Sharifipoor, Ozra

    2014-03-01

    This paper presents a set of reconfigurable analog implementations of piecewise linear spiking neuron models using second generation current conveyor (CCII) building blocks. With the same topology and circuit elements, without W/L modification which is impossible after circuit fabrication, these circuits can produce different behaviors, similar to the biological neurons, both for a single neuron as well as a network of neurons just by tuning reference current and voltage sources. The models are investigated, in terms of analog implementation feasibility and costs, targeting large scale hardware implementations. Results show that, in order to gain the best performance, area and accuracy; these models can be compromised. Simulation results are presented for different neuron behaviors with CMOS 350 nm technology. Copyright © 2013 Elsevier Ltd. All rights reserved.

  7. The impact of neuronal Notch-1/JNK pathway on intracerebral hemorrhage-induced neuronal injury of rat model.

    PubMed

    Chen, Maohua; Sun, Jun; Lu, Chuan; Chen, Xiandong; Ba, Huajun; Lin, Qun; Cai, Jianyong; Dai, Junxia

    2016-11-08

    Notch signaling is a highly conserved pathway that regulates cell fate decisions during embryonic development. Notch activation endangers neurons by modulating NF-κB and HIF-1α pathways, however, the role of Notch signaling in activating JNK/c-Jun following intracerebral hemorrhage (ICH) has not been investigated. In this study, we used rat ICH models and thrombin-induced cell models to investigate the potential role of Notch-1/JNK signals. Our findings revealed that Notch-1 and JNK increased in hematoma-surrounding neurons tissues following ICH during ischemic conditions (all p<0.05). Besides, the expression of active caspase-3 protein was also up-regulated after ICH. According to in-vitro assays, the expression of Notch-1, p-JNK, and active caspase-3 were all up-regulated in cell viability-decreasing ICH cell models (all p<0.05). However, blocking of either Notch-1 or JNK suppressed the phosphorylation of JNK and the expression of active caspase-3, and cell viability was obviously ameliorated. In conclusion, this work suggested Notch-1 activates JNK pathway to induce the active caspase-3, leading to neuronal injury when intracerebral hemorrhage or ischemia occurred. Thus the Notch-1/JNK signal pathway has an important role in ICH process, and may be a therapeutic target to prevent brain injury.

  8. The impact of neuronal Notch-1/JNK pathway on intracerebral hemorrhage-induced neuronal injury of rat model

    PubMed Central

    Chen, Maohua; Sun, Jun; Lu, Chuan; Chen, Xiandong; Ba, Huajun; Lin, Qun; Cai, Jianyong; Dai, Junxia

    2016-01-01

    Notch signaling is a highly conserved pathway that regulates cell fate decisions during embryonic development. Notch activation endangers neurons by modulating NF-κB and HIF-1α pathways, however, the role of Notch signaling in activating JNK/c-Jun following intracerebral hemorrhage (ICH) has not been investigated. In this study, we used rat ICH models and thrombin-induced cell models to investigate the potential role of Notch-1/JNK signals. Our findings revealed that Notch-1 and JNK increased in hematoma-surrounding neurons tissues following ICH during ischemic conditions (all p<0.05). Besides, the expression of active caspase-3 protein was also up-regulated after ICH. According to in-vitro assays, the expression of Notch-1, p-JNK, and active caspase-3 were all up-regulated in cell viability-decreasing ICH cell models (all p<0.05). However, blocking of either Notch-1 or JNK suppressed the phosphorylation of JNK and the expression of active caspase-3, and cell viability was obviously ameliorated. In conclusion, this work suggested Notch-1 activates JNK pathway to induce the active caspase-3, leading to neuronal injury when intracerebral hemorrhage or ischemia occurred. Thus the Notch-1/JNK signal pathway has an important role in ICH process, and may be a therapeutic target to prevent brain injury. PMID:27655677

  9. Multiscale modeling of brain dynamics: from single neurons and networks to mathematical tools.

    PubMed

    Siettos, Constantinos; Starke, Jens

    2016-09-01

    The extreme complexity of the brain naturally requires mathematical modeling approaches on a large variety of scales; the spectrum ranges from single neuron dynamics over the behavior of groups of neurons to neuronal network activity. Thus, the connection between the microscopic scale (single neuron activity) to macroscopic behavior (emergent behavior of the collective dynamics) and vice versa is a key to understand the brain in its complexity. In this work, we attempt a review of a wide range of approaches, ranging from the modeling of single neuron dynamics to machine learning. The models include biophysical as well as data-driven phenomenological models. The discussed models include Hodgkin-Huxley, FitzHugh-Nagumo, coupled oscillators (Kuramoto oscillators, Rössler oscillators, and the Hindmarsh-Rose neuron), Integrate and Fire, networks of neurons, and neural field equations. In addition to the mathematical models, important mathematical methods in multiscale modeling and reconstruction of the causal connectivity are sketched. The methods include linear and nonlinear tools from statistics, data analysis, and time series analysis up to differential equations, dynamical systems, and bifurcation theory, including Granger causal connectivity analysis, phase synchronization connectivity analysis, principal component analysis (PCA), independent component analysis (ICA), and manifold learning algorithms such as ISOMAP, and diffusion maps and equation-free techniques. WIREs Syst Biol Med 2016, 8:438-458. doi: 10.1002/wsbm.1348 For further resources related to this article, please visit the WIREs website. © 2016 Wiley Periodicals, Inc.

  10. On dependency properties of the ISIs generated by a two-compartmental neuronal model.

    PubMed

    Benedetto, Elisa; Sacerdote, Laura

    2013-02-01

    One-dimensional leaky integrate and fire neuronal models describe interspike intervals (ISIs) of a neuron as a renewal process and disregarding the neuron geometry. Many multi-compartment models account for the geometrical features of the neuron but are too complex for their mathematical tractability. Leaky integrate and fire two-compartment models seem a good compromise between mathematical tractability and an improved realism. They indeed allow to relax the renewal hypothesis, typical of one-dimensional models, without introducing too strong mathematical difficulties. Here, we pursue the analysis of the two-compartment model studied by Lansky and Rodriguez (Phys D 132:267-286, 1999), aiming of introducing some specific mathematical results used together with simulation techniques. With the aid of these methods, we investigate dependency properties of ISIs for different values of the model parameters. We show that an increase of the input increases the strength of the dependence between successive ISIs.

  11. Noisy threshold in neuronal models: connections with the noisy leaky integrate-and-fire model.

    PubMed

    Dumont, G; Henry, J; Tarniceriu, C O

    2016-12-01

    Providing an analytical treatment to the stochastic feature of neurons' dynamics is one of the current biggest challenges in mathematical biology. The noisy leaky integrate-and-fire model and its associated Fokker-Planck equation are probably the most popular way to deal with neural variability. Another well-known formalism is the escape-rate model: a model giving the probability that a neuron fires at a certain time knowing the time elapsed since its last action potential. This model leads to a so-called age-structured system, a partial differential equation with non-local boundary condition famous in the field of population dynamics, where the age of a neuron is the amount of time passed by since its previous spike. In this theoretical paper, we investigate the mathematical connection between the two formalisms. We shall derive an integral transform of the solution to the age-structured model into the solution of the Fokker-Planck equation. This integral transform highlights the link between the two stochastic processes. As far as we know, an explicit mathematical correspondence between the two solutions has not been introduced until now.

  12. Effects of high-rate electrical stimulation upon firing in modelled and real neurons.

    PubMed

    Krauthamer, V; Crosheck, T

    2002-05-01

    Many medical devices use high-rate, low-amplitude currents to affect neural function. This study examined the effect of stimulation rate upon action potential threshold and sustained firing rate for two model neurons, the rabbit myelinated fibre and the unmyelinated leech touch sensory cell. These model neurons were constructed with the NEURON simulator from electrophysiological data. Alternating-phase current pulses (0-1250 Hz), of fixed phase duration (0.2 ms), were used to stimulate the neurons, and propagation success or failure was measured. One effect of the high pulse rates was to cause a net depolarisation, and this was verified by the relief of action potential conduction block by 500 Hz extracellular stimulation in leech neurons. The models also predicted that the neurons would maintain maximum sustained firing at a number of different stimulation rates. For example, at twice threshold, the myelinated model followed the stimulus up to 500 Hz stimulation, half the stimulus rate up to 850 Hz stimulation, and it did not fire at 1250 Hz stimulation. By contrast, the unmyelinated neuron model had a lower maximum firing rate of 190 Hz, and this rate was obtained at a number of stimulation rates, up to 1250 Hz. The myelinated model also predicted sustained firing with 1240 Hz stimulation at threshold current, but no firing when the current level was doubled. Most of these effects are explained by the interaction of stimulus pulses with the cell's refractory period.

  13. Serial correlation in neural spike trains: Experimental evidence, stochastic modeling, and single neuron variability

    NASA Astrophysics Data System (ADS)

    Farkhooi, Farzad; Strube-Bloss, Martin F.; Nawrot, Martin P.

    2009-02-01

    The activity of spiking neurons is frequently described by renewal point process models that assume the statistical independence and identical distribution of the intervals between action potentials. However, the assumption of independent intervals must be questioned for many different types of neurons. We review experimental studies that reported the feature of a negative serial correlation of neighboring intervals, commonly observed in neurons in the sensory periphery as well as in central neurons, notably in the mammalian cortex. In our experiments we observed the same short-lived negative serial dependence of intervals in the spontaneous activity of mushroom body extrinsic neurons in the honeybee. To model serial interval correlations of arbitrary lags, we suggest a family of autoregressive point processes. Its marginal interval distribution is described by the generalized gamma model, which includes as special cases the log-normal and gamma distributions, which have been widely used to characterize regular spiking neurons. In numeric simulations we investigated how serial correlation affects the variance of the neural spike count. We show that the experimentally confirmed negative correlation reduces single-neuron variability, as quantified by the Fano factor, by up to 50%, which favors the transmission of a rate code. We argue that the feature of a negative serial correlation is likely to be common to the class of spike-frequency-adapting neurons and that it might have been largely overlooked in extracellular single-unit recordings due to spike sorting errors.

  14. TRPA1 activation in a human sensory neuronal model: relevance to cough hypersensitivity?

    PubMed

    Clarke, Rebecca; Monaghan, Kevin; About, Imad; Griffin, Caoimhin S; Sergeant, Gerard P; El Karim, Ikhlas; McGeown, J Graham; Cosby, S Louise; Curtis, Timothy M; McGarvey, Lorcan P; Lundy, Fionnuala T

    2017-09-01

    The cough reflex becomes hyperresponsive in acute and chronic respiratory diseases, but understanding the underlying mechanism is hampered by difficulty accessing human tissue containing both nerve endings and neuronal cell bodies. We refined an adult stem cell sensory neuronal model to overcome the limited availability of human neurones and applied the model to study transient receptor potential ankyrin 1 (TRPA1) channel expression and activation.Human dental pulp stem cells (hDPSCs) were differentiated towards a neuronal phenotype, termed peripheral neuronal equivalents (PNEs). Using molecular and immunohistochemical techniques, together with Ca(2+) microfluorimetry and whole cell patch clamping, we investigated roles for nerve growth factor (NGF) and the viral mimic poly I:C in TRPA1 activation.PNEs exhibited morphological, molecular and functional characteristics of sensory neurons and expressed functional TRPA1 channels. PNE treatment with NGF for 20 min generated significantly larger inward and outward currents compared to untreated PNEs in response to the TRPA1 agonist cinnamaldehyde (p<0.05). PNE treatment with poly I:C caused similar transient heightened responses to TRPA1 activation compared to untreated cells.Using the PNE neuronal model we observed both NGF and poly I:C mediated sensory neuronal hyperresponsiveness, representing potential neuro-inflammatory mechanisms associated with heightened nociceptive responses recognised in cough hypersensitivity syndrome. Copyright ©ERS 2017.

  15. Generating a model of the Three-dimensional Spatial Distribution of Neurons using Density Maps

    PubMed Central

    Cruz, Luis; Urbanc, Brigita; Inglis, Andrew; Rosene, Douglas L.; Stanley, H. E.

    2009-01-01

    Microcolumns are a vertical arrangement of neocortical neurons that may constitute a fundamental computational ensemble but have been difficult to study morphologically because of the challenges of determining the three-dimensional (3D) spatial arrangements of individual neurons in the ensemble. Previously, a statistical density map method was developed to characterize microcolumns using two-dimensional (2D) coordinates of neurons from thin tissue sections. Here we extend this approach to derive the relationship between these 2D density maps and the actual 3D properties of microcolumns by creating a theoretical 3D model of cortical neurons. In seven steps we transform a 3D initial arrangement of neurons from a crystalline lattice, with distances and neuron numbers approximating the idealized cortical microcolumn as assayed by our 2D density map analysis, into a model whose neuronal locations represent a plausible 3D arrangement of neurons in the brain. Because we constrain the transformations on the 3D model by the 2D density map properties, the transformed 3D model will exhibit properties that are consistent with experimental findings regarding microcolumnar anatomy in the brain. Moreover, because our methodology only requires the x,y locations of neurons from thin sections, it is readily accessible to any set of input data regardless of preparation or staining, from human or animals. By generating 3D model neuronal arrangements and comparing between control, aged, and diseased brain, our method can be used to test hypotheses about the effects of neurological diseases as well as normal aging on the 3D structure of microcolumns in the brain. PMID:18291677

  16. Modeling ALS with motor neurons derived from human induced pluripotent stem cells.

    PubMed

    Sances, Samuel; Bruijn, Lucie I; Chandran, Siddharthan; Eggan, Kevin; Ho, Ritchie; Klim, Joseph R; Livesey, Matt R; Lowry, Emily; Macklis, Jeffrey D; Rushton, David; Sadegh, Cameron; Sareen, Dhruv; Wichterle, Hynek; Zhang, Su-Chun; Svendsen, Clive N

    2016-04-01

    Directing the differentiation of induced pluripotent stem cells into motor neurons has allowed investigators to develop new models of amyotrophic lateral sclerosis (ALS). However, techniques vary between laboratories and the cells do not appear to mature into fully functional adult motor neurons. Here we discuss common developmental principles of both lower and upper motor neuron development that have led to specific derivation techniques. We then suggest how these motor neurons may be matured further either through direct expression or administration of specific factors or coculture approaches with other tissues. Ultimately, through a greater understanding of motor neuron biology, it will be possible to establish more reliable models of ALS. These in turn will have a greater chance of validating new drugs that may be effective for the disease.

  17. Probabilistic inference in general graphical models through sampling in stochastic networks of spiking neurons.

    PubMed

    Pecevski, Dejan; Buesing, Lars; Maass, Wolfgang

    2011-12-01

    An important open problem of computational neuroscience is the generic organization of computations in networks of neurons in the brain. We show here through rigorous theoretical analysis that inherent stochastic features of spiking neurons, in combination with simple nonlinear computational operations in specific network motifs and dendritic arbors, enable networks of spiking neurons to carry out probabilistic inference through sampling in general graphical models. In particular, it enables them to carry out probabilistic inference in Bayesian networks with converging arrows ("explaining away") and with undirected loops, that occur in many real-world tasks. Ubiquitous stochastic features of networks of spiking neurons, such as trial-to-trial variability and spontaneous activity, are necessary ingredients of the underlying computational organization. We demonstrate through computer simulations that this approach can be scaled up to neural emulations of probabilistic inference in fairly large graphical models, yielding some of the most complex computations that have been carried out so far in networks of spiking neurons.

  18. Optimizing neuronal differentiation from induced pluripotent stem cells to model ASD

    PubMed Central

    Kim, Dae-Sung; Ross, P. Joel; Zaslavsky, Kirill; Ellis, James

    2014-01-01

    Autism spectrum disorder (ASD) is an early-onset neurodevelopmental disorder characterized by deficits in social communication, and restricted and repetitive patterns of behavior. Despite its high prevalence, discovery of pathophysiological mechanisms underlying ASD has lagged due to a lack of appropriate model systems. Recent advances in induced pluripotent stem cell (iPSC) technology and neural differentiation techniques allow for detailed functional analyses of neurons generated from living individuals with ASD. Refinement of cortical neuron differentiation methods from iPSCs will enable mechanistic studies of specific neuronal subpopulations that may be preferentially impaired in ASD. In this review, we summarize recent accomplishments in differentiation of cortical neurons from human pluripotent stems cells and efforts to establish in vitro model systems to study ASD using personalized neurons. PMID:24782713

  19. Dynamic Behavior of Artificial Hodgkin-Huxley Neuron Model Subject to Additive Noise.

    PubMed

    Kang, Qi; Huang, BingYao; Zhou, MengChu

    2016-09-01

    Motivated by neuroscience discoveries during the last few years, many studies consider pulse-coupled neural networks with spike-timing as an essential component in information processing by the brain. There also exists some technical challenges while simulating the networks of artificial spiking neurons. The existing studies use a Hodgkin-Huxley (H-H) model to describe spiking dynamics and neuro-computational properties of each neuron. But they fail to address the effect of specific non-Gaussian noise on an artificial H-H neuron system. This paper aims to analyze how an artificial H-H neuron responds to add different types of noise using an electrical current and subunit noise model. The spiking and bursting behavior of this neuron is also investigated through numerical simulations. In addition, through statistic analysis, the intensity of different kinds of noise distributions is discussed to obtain their relationship with the mean firing rate, interspike intervals, and stochastic resonance.

  20. Modeling ALS using motor neurons derived from human induced pluripotent stem cells

    PubMed Central

    Sances, S; Bruijn, LI; Chandran, S; Eggan, K; Ho, R; Klim, J; Livesey, MR; Lowry, E; Macklis, JD; Rushton, D; Sadegh, C; Sareen, D; Wichterle, H; Zhang, SC; Svendsen, CN

    2016-01-01

    Directing the differentiation of induced pluripotent stem cells into motor neurons has allowed investigators to develop novel models of ALS. However, techniques vary between laboratories and the cells do not appear to mature into fully functional adult motor neurons. Here we discuss common developmental principles of both lower and upper motor neuron development that have led to specific derivation techniques. We then suggest how these motor neurons may be matured further either through direct expression or administration of specific factors or co-culture approaches with other tissues. Ultimately, through a greater understanding of motor neuron biology, it will be possible to establish more reliable models of ALS. These in turn will have a greater chance of validating new drugs that may be effective for the disease. PMID:27021939

  1. Qualitative validation of the reduction from two reciprocally coupled neurons to one self-coupled neuron in a respiratory network model.

    PubMed

    Dunmyre, Justin R

    2011-06-01

    The pre-Bötzinger complex of the mammalian brainstem is a heterogeneous neuronal network, and individual neurons within the network have varying strengths of the persistent sodium and calcium-activated nonspecific cationic currents. Individually, these currents have been the focus of modeling efforts. Previously, Dunmyre et al. (J Comput Neurosci 1-24, 2011) proposed a model and studied the interactions of these currents within one self-coupled neuron. In this work, I consider two identical, reciprocally coupled model neurons and validate the reduction to the self-coupled case. I find that all of the dynamics of the two model neuron network and the regions of parameter space where these distinct dynamics are found are qualitatively preserved in the reduction to the self-coupled case.

  2. 3D axonal network coupled to Microelectrode Arrays: A simulation model to study neuronal dynamics.

    PubMed

    Appali, Revathi; Sriperumbudur, Kiran K; van Rienen, Ursula

    2015-01-01

    Action Potentials in a neuron are generated and propagated by exchange of ions through the membrane. The model of Hodgkin and Huxley (HH) describes these time-dependent complex ion dynamics. We have implemented the FitzHugh-Nagumo model, one of the simplified versions of HH model on a pyramidal neuron with branches of axons to study the spontaneous activity of neurons. The network is then coupled to Micro-Electrode Arrays to record the extracellular potential in a neurochip environment. This in silico model is used to study the coupling of AP with the electrodes. Such a model is also a first step to investigate the morphological influence of neurons on their signaling properties.

  3. Probing the Dynamics of Identified Neurons with a Data-Driven Modeling Approach

    PubMed Central

    Nowotny, Thomas; Levi, Rafael; Selverston, Allen I.

    2008-01-01

    In controlling animal behavior the nervous system has to perform within the operational limits set by the requirements of each specific behavior. The implications for the corresponding range of suitable network, single neuron, and ion channel properties have remained elusive. In this article we approach the question of how well-constrained properties of neuronal systems may be on the neuronal level. We used large data sets of the activity of isolated invertebrate identified cells and built an accurate conductance-based model for this cell type using customized automated parameter estimation techniques. By direct inspection of the data we found that the variability of the neurons is larger when they are isolated from the circuit than when in the intact system. Furthermore, the responses of the neurons to perturbations appear to be more consistent than their autonomous behavior under stationary conditions. In the developed model, the constraints on different parameters that enforce appropriate model dynamics vary widely from some very tightly controlled parameters to others that are almost arbitrary. The model also allows predictions for the effect of blocking selected ionic currents and to prove that the origin of irregular dynamics in the neuron model is proper chaoticity and that this chaoticity is typical in an appropriate sense. Our results indicate that data driven models are useful tools for the in-depth analysis of neuronal dynamics. The better consistency of responses to perturbations, in the real neurons as well as in the model, suggests a paradigm shift away from measuring autonomous dynamics alone towards protocols of controlled perturbations. Our predictions for the impact of channel blockers on the neuronal dynamics and the proof of chaoticity underscore the wide scope of our approach. PMID:18612435

  4. Competition model for aperiodic stochastic resonance in a Fitzhugh-Nagumo model of cardiac sensory neurons.

    PubMed

    Kember, G C; Fenton, G A; Armour, J A; Kalyaniwalla, N

    2001-04-01

    Regional cardiac control depends upon feedback of the status of the heart from afferent neurons responding to chemical and mechanical stimuli as transduced by an array of sensory neurites. Emerging experimental evidence shows that neural control in the heart may be partially exerted using subthreshold inputs that are amplified by noisy mechanical fluctuations. This amplification is known as aperiodic stochastic resonance (ASR). Neural control in the noisy, subthreshold regime is difficult to see since there is a near absence of any correlation between input and the output, the latter being the average firing (spiking) rate of the neuron. This lack of correlation is unresolved by traditional energy models of ASR since these models are unsuitable for identifying "cause and effect" between such inputs and outputs. In this paper, the "competition between averages" model is used to determine what portion of a noisy, subthreshold input is responsible, on average, for the output of sensory neurons as represented by the Fitzhugh-Nagumo equations. A physiologically relevant conclusion of this analysis is that a nearly constant amount of input is responsible for a spike, on average, and this amount is approximately independent of the firing rate. Hence, correlation measures are generally reduced as the firing rate is lowered even though neural control under this model is actually unaffected.

  5. Molecular model of cannabis sensitivity in developing neuronal circuits.

    PubMed

    Keimpema, Erik; Mackie, Ken; Harkany, Tibor

    2011-09-01

    Prenatal cannabis exposure can complicate in utero development of the nervous system. Cannabis impacts the formation and functions of neuronal circuitries by targeting cannabinoid receptors. Endocannabinoid signaling emerges as a signaling cassette that orchestrates neuronal differentiation programs through the precisely timed interaction of endocannabinoid ligands with their cognate cannabinoid receptors. By indiscriminately prolonging the 'switched-on' period of cannabinoid receptors, cannabis can hijack endocannabinoid signals to evoke molecular rearrangements, leading to the erroneous wiring of neuronal networks. Here, we formulate a hierarchical network design necessary and sufficient to describe the molecular underpinnings of cannabis-induced neural growth defects. We integrate signalosome components, deduced from genome- and proteome-wide arrays and candidate analyses, to propose a mechanistic hypothesis of how cannabis-induced ectopic cannabinoid receptor activity overrides physiological neurodevelopmental endocannabinoid signals, affecting the timely formation of synapses.

  6. Quantitative 3D investigation of Neuronal network in mouse spinal cord model

    NASA Astrophysics Data System (ADS)

    Bukreeva, I.; Campi, G.; Fratini, M.; Spanò, R.; Bucci, D.; Battaglia, G.; Giove, F.; Bravin, A.; Uccelli, A.; Venturi, C.; Mastrogiacomo, M.; Cedola, A.

    2017-01-01

    The investigation of the neuronal network in mouse spinal cord models represents the basis for the research on neurodegenerative diseases. In this framework, the quantitative analysis of the single elements in different districts is a crucial task. However, conventional 3D imaging techniques do not have enough spatial resolution and contrast to allow for a quantitative investigation of the neuronal network. Exploiting the high coherence and the high flux of synchrotron sources, X-ray Phase-Contrast multiscale-Tomography allows for the 3D investigation of the neuronal microanatomy without any aggressive sample preparation or sectioning. We investigated healthy-mouse neuronal architecture by imaging the 3D distribution of the neuronal-network with a spatial resolution of 640 nm. The high quality of the obtained images enables a quantitative study of the neuronal structure on a subject-by-subject basis. We developed and applied a spatial statistical analysis on the motor neurons to obtain quantitative information on their 3D arrangement in the healthy-mice spinal cord. Then, we compared the obtained results with a mouse model of multiple sclerosis. Our approach paves the way to the creation of a “database” for the characterization of the neuronal network main features for a comparative investigation of neurodegenerative diseases and therapies.

  7. Quantitative 3D investigation of Neuronal network in mouse spinal cord model

    PubMed Central

    Bukreeva, I.; Campi, G.; Fratini, M.; Spanò, R.; Bucci, D.; Battaglia, G.; Giove, F.; Bravin, A.; Uccelli, A.; Venturi, C.; Mastrogiacomo, M.; Cedola, A.

    2017-01-01

    The investigation of the neuronal network in mouse spinal cord models represents the basis for the research on neurodegenerative diseases. In this framework, the quantitative analysis of the single elements in different districts is a crucial task. However, conventional 3D imaging techniques do not have enough spatial resolution and contrast to allow for a quantitative investigation of the neuronal network. Exploiting the high coherence and the high flux of synchrotron sources, X-ray Phase-Contrast multiscale-Tomography allows for the 3D investigation of the neuronal microanatomy without any aggressive sample preparation or sectioning. We investigated healthy-mouse neuronal architecture by imaging the 3D distribution of the neuronal-network with a spatial resolution of 640 nm. The high quality of the obtained images enables a quantitative study of the neuronal structure on a subject-by-subject basis. We developed and applied a spatial statistical analysis on the motor neurons to obtain quantitative information on their 3D arrangement in the healthy-mice spinal cord. Then, we compared the obtained results with a mouse model of multiple sclerosis. Our approach paves the way to the creation of a “database” for the characterization of the neuronal network main features for a comparative investigation of neurodegenerative diseases and therapies. PMID:28112212

  8. Quantitative 3D investigation of Neuronal network in mouse spinal cord model.

    PubMed

    Bukreeva, I; Campi, G; Fratini, M; Spanò, R; Bucci, D; Battaglia, G; Giove, F; Bravin, A; Uccelli, A; Venturi, C; Mastrogiacomo, M; Cedola, A

    2017-01-23

    The investigation of the neuronal network in mouse spinal cord models represents the basis for the research on neurodegenerative diseases. In this framework, the quantitative analysis of the single elements in different districts is a crucial task. However, conventional 3D imaging techniques do not have enough spatial resolution and contrast to allow for a quantitative investigation of the neuronal network. Exploiting the high coherence and the high flux of synchrotron sources, X-ray Phase-Contrast multiscale-Tomography allows for the 3D investigation of the neuronal microanatomy without any aggressive sample preparation or sectioning. We investigated healthy-mouse neuronal architecture by imaging the 3D distribution of the neuronal-network with a spatial resolution of 640 nm. The high quality of the obtained images enables a quantitative study of the neuronal structure on a subject-by-subject basis. We developed and applied a spatial statistical analysis on the motor neurons to obtain quantitative information on their 3D arrangement in the healthy-mice spinal cord. Then, we compared the obtained results with a mouse model of multiple sclerosis. Our approach paves the way to the creation of a "database" for the characterization of the neuronal network main features for a comparative investigation of neurodegenerative diseases and therapies.

  9. Nonlinear Modeling of Dynamic Interactions within Neuronal Ensembles using Principal Dynamic Modes

    PubMed Central

    Marmarelis, V. Z.; Shin, D. C.; Song, D.; Hampson, R. E.; Deadwyler, S.; Berger, T. W.

    2012-01-01

    A methodology for nonlinear modeling of multi-input multi-output (MIMO) neuronal systems is presented that utilizes the concept of Principal Dynamic Modes (PDM). The efficacy of this new methodology is demonstrated in the study of the dynamic interactions between neuronal ensembles in the Pre-Frontal Cortex (PFC) of a behaving non-human primate (NHP) performing a Delayed Match-to-Sample task. Recorded spike trains from Layer-2 and Layer-5 neurons were viewed as the “inputs” and “outputs”, respectively, of a putative MIMO system/model that quantifies the dynamic transformation of multi-unit neuronal activity between Layer-2 and Layer-5 of the PFC. Model prediction performance was evaluated by means of computed Receiver Operating Characteristic (ROC) curves. The PDM-based approach seeks to reduce the complexity of MIMO models of neuronal ensembles in order to enable the practicable modeling of large-scale neural systems incorporating hundreds or thousands of neurons, which is emerging as a preeminent issue in the study of neural function. The “scaling-up” issue has attained critical importance as multi-electrode recordings are increasingly used to probe neural systems and advance our understanding of integrated neural function. The initial results indicate that the PDM-based modeling methodology may greatly reduce the complexity of the MIMO model without significant degradation of performance. Furthermore, the PDM-based approach offers the prospect of improved biological/physiological interpretation of the obtained MIMO models. PMID:23011343

  10. A Phase-Locking Analysis of Neuronal Firing Rhythms with Transcranial Magneto-Acoustical Stimulation Based on the Hodgkin-Huxley Neuron Model

    PubMed Central

    Yuan, Yi; Pang, Na; Chen, Yudong; Wang, Yi; Li, Xiaoli

    2017-01-01

    Transcranial magneto-acoustical stimulation (TMAS) uses ultrasonic waves and a static magnetic field to generate electric current in nerve tissues for the purpose of modulating neuronal activities. It has the advantage of high spatial resolution and penetration depth. Neuronal firing rhythms carry and transmit nerve information in neural systems. In this study, we investigated the phase-locking characteristics of neuronal firing rhythms with TMAS based on the Hodgkin-Huxley neuron model. The simulation results indicate that the modulation frequency of ultrasound can affect the phase-locking behaviors. The results of this study may help us to explain the potential firing mechanism of TMAS. PMID:28163679

  11. A Phase-Locking Analysis of Neuronal Firing Rhythms with Transcranial Magneto-Acoustical Stimulation Based on the Hodgkin-Huxley Neuron Model.

    PubMed

    Yuan, Yi; Pang, Na; Chen, Yudong; Wang, Yi; Li, Xiaoli

    2017-01-01

    Transcranial magneto-acoustical stimulation (TMAS) uses ultrasonic waves and a static magnetic field to generate electric current in nerve tissues for the purpose of modulating neuronal activities. It has the advantage of high spatial resolution and penetration depth. Neuronal firing rhythms carry and transmit nerve information in neural systems. In this study, we investigated the phase-locking characteristics of neuronal firing rhythms with TMAS based on the Hodgkin-Huxley neuron model. The simulation results indicate that the modulation frequency of ultrasound can affect the phase-locking behaviors. The results of this study may help us to explain the potential firing mechanism of TMAS.

  12. Channel-noise-induced stochastic facilitation in an auditory brainstem neuron model

    NASA Astrophysics Data System (ADS)

    Schmerl, Brett A.; McDonnell, Mark D.

    2013-11-01

    Neuronal membrane potentials fluctuate stochastically due to conductance changes caused by random transitions between the open and closed states of ion channels. Although it has previously been shown that channel noise can nontrivially affect neuronal dynamics, it is unknown whether ion-channel noise is strong enough to act as a noise source for hypothesized noise-enhanced information processing in real neuronal systems, i.e., “stochastic facilitation”. Here we demonstrate that biophysical models of channel noise can give rise to two kinds of recently discovered stochastic facilitation effects in a Hodgkin-Huxley-like model of auditory brainstem neurons. The first, known as slope-based stochastic resonance (SBSR), enables phasic neurons to emit action potentials that can encode the slope of inputs that vary slowly relative to key time constants in the model. The second, known as inverse stochastic resonance (ISR), occurs in tonically firing neurons when small levels of noise inhibit tonic firing and replace it with burstlike dynamics. Consistent with previous work, we conclude that channel noise can provide significant variability in firing dynamics, even for large numbers of channels. Moreover, our results show that possible associated computational benefits may occur due to channel noise in neurons of the auditory brainstem. This holds whether the firing dynamics in the model are phasic (SBSR can occur due to channel noise) or tonic (ISR can occur due to channel noise).

  13. Zinc Deficiency Induces Apoptosis via Mitochondrial p53- and Caspase-Dependent Pathways in Human Neuronal Precursor Cells

    ERIC Educational Resources Information Center

    Seth, Rohit; Corniola, Rikki S.; Gower-Winter, Shannon D.; Morgan, Thomas J., Jr.; Bishop, Brian; Levenson, Cathy W.

    2015-01-01

    Previous studies have shown that zinc deficiency leads to apoptosis of neuronal precursor cells in vivo and in vitro. In addition to the role of p53 as a nuclear transcription factor in zinc deficient cultured human neuronal precursors (NT-2), we have now identified the translocation of phosphorylated p53 to the mitochondria and p53-dependent…

  14. The Effects of Guanfacine and Phenylephrine on a Spiking Neuron Model of Working Memory.

    PubMed

    Duggins, Peter; Stewart, Terrence C; Choo, Xuan; Eliasmith, Chris

    2017-01-01

    We use a spiking neural network model of working memory (WM) capable of performing the spatial delayed response task (DRT) to investigate two drugs that affect WM: guanfacine (GFC) and phenylephrine (PHE). In this model, the loss of information over time results from changes in the spiking neural activity through recurrent connections. We reproduce the standard forgetting curve and then show that this curve changes in the presence of GFC and PHE, whose application is simulated by manipulating functional, neural, and biophysical properties of the model. In particular, applying GFC causes increased activity in neurons that are sensitive to the information currently being remembered, while applying PHE leads to decreased activity in these same neurons. Interestingly, these differential effects emerge from network-level interactions because GFC and PHE affect all neurons equally. We compare our model to both electrophysiological data from neurons in monkey dorsolateral prefrontal cortex and to behavioral evidence from monkeys performing the DRT.

  15. Reliability of neuronal information conveyed by unreliable neuristor-based leaky integrate-and-fire neurons: a model study

    PubMed Central

    Lim, Hyungkwang; Kornijcuk, Vladimir; Seok, Jun Yeong; Kim, Seong Keun; Kim, Inho; Hwang, Cheol Seong; Jeong, Doo Seok

    2015-01-01

    We conducted simulations on the neuronal behavior of neuristor-based leaky integrate-and-fire (NLIF) neurons. The phase-plane analysis on the NLIF neuron highlights its spiking dynamics – determined by two nullclines conditional on the variables on the plane. Particular emphasis was placed on the operational noise arising from the variability of the threshold switching behavior in the neuron on each switching event. As a consequence, we found that the NLIF neuron exhibits a Poisson-like noise in spiking, delimiting the reliability of the information conveyed by individual NLIF neurons. To highlight neuronal information coding at a higher level, a population of noisy NLIF neurons was analyzed in regard to probability of successful information decoding given the Poisson-like noise of each neuron. The result demonstrates highly probable success in decoding in spite of large variability – due to the variability of the threshold switching behavior – of individual neurons. PMID:25966658

  16. Mathematical Models of Cochlear Nucleus Onset Neurons: II. Model with Dynamic Spike-Blocking State

    PubMed Central

    KALLURI, SRIDHAR; DELGUTTE, BERTRAND

    2008-01-01

    Onset (On) neurons in the cochlear nucleus (CN), characterized by their prominent response to the onset followed by little or no response to the steady-state of sustained stimuli, have a remarkable ability to entrain (firing 1 spike per cycle of a periodic stimulus) to low-frequency tones up to 1000 Hz. In this article, we present a point-neuron model with independent, excitatory auditory-nerve (AN) inputs that accounts for the ability of On neurons to both produce onset responses for high-frequency tone bursts and entrain to a wide range of low-frequency tones. With a fixed-duration spike-blocking state after a spike (an absolute refractory period), the model produces entrainment to a broad range of low-frequency tones and an On response with short interspike intervals (chopping) for high-frequency tone bursts. To produce On response patterns with no chopping, we introduce a novel, more complex, active membrane model in which the spike-blocking state is maintained until the instantaneous membrane voltage falls below a transition voltage. During the sustained depolarization for a high-frequency tone burst, the new model does not chop because it enters a spike-blocking state after the first spike and fails to leave this state until the membrane voltage returns toward rest at the end of the stimulus. The model entrains to low-frequency tones because the membrane voltage falls below the transition voltage on every cycle when the AN inputs are phase-locked. With the complex membrane model, On response patterns having moderate steady-state activity for high-frequency tone bursts (On-L) are distinguished from those having no steady-state activity (On-I) by requiring fewer AN inputs. Voltage-gated ion channels found in On-responding neurons of the CN may underlie the hypothesized dynamic spike-blocking state. These results provide a mechanistic rationale for distinguishing between the different physiological classes of CN On neurons. PMID:12435926

  17. Neuropeptide Y neuronal network dysfunction in the frontal lobe of a genetic mouse model of schizophrenia.

    PubMed

    Morosawa, Shunsuke; Iritani, Shuji; Fujishiro, Hiroshige; Sekiguchi, Hirotaka; Torii, Youta; Habuchi, Chikako; Kuroda, Keisuke; Kaibuchi, Kozo; Ozaki, Norio

    2017-04-01

    Neuropeptide Y (NPY) has been found to play a critical role in various mental functions as a neurotransmitter and is involved in the development of schizophrenia, a particularly intractable psychiatric disease whose precise etiology remains unknown. Recent molecular biological investigations have identified several candidate genes which may be associated with this disease, including disrupted-in-schizophrenia 1 (DISC1). The role of DISC1 would involve neurogenesis and neuronal migration. However, the functional consequences of this gene defect have not yet been fully clarified in neuronal systems. In the present study, to clarify the neuropathological changes associated with the function of DISC1, we explored how DISC1 dysfunction can induce abnormalities in the NPY neuronal network in the central nervous system. We performed immunohistochemical analyses (including the observation of the distribution and density) of prefrontal cortex specimens from DISC1-knockout (KO) mice, which are considered to be a novel animal model of schizophrenia. We then evaluated the number and size of NPY-immunoreactive (NPY-IR) neurons and the length of NPY-IR fibers. The number of NPY-IR neurons and the length of the fibers were decreased in the prefrontal cortex of DISC1-KO mice. The decrease was particularly prominent in the superficial regions, and the distribution of NPY-IR neurons differed between wild-type and DISC1-KO mice. However, the size of the neurons in the cortices of the DISC1-KO and wild-type mice did not differ markedly. Our findings suggest that dysfunction of DISC1 may lead to the alteration of NPY neurons and neurotransmission issues in NPY-containing neuron systems, which seem to play important roles in both the mental function and neuronal development. DISC1 dysfunction may be involved in the pathogenesis of schizophrenia through the impairment of the NPY neuronal network. Copyright © 2017 Elsevier Ltd. All rights reserved.

  18. [Hardware Implementation of Numerical Simulation Function of Hodgkin-Huxley Model Neurons Action Potential Based on Field Programmable Gate Array].

    PubMed

    Wang, Jinlong; Lu, Mai; Hu, Yanwen; Chen, Xiaoqiang; Pan, Qiangqiang

    2015-12-01

    Neuron is the basic unit of the biological neural system. The Hodgkin-Huxley (HH) model is one of the most realistic neuron models on the electrophysiological characteristic description of neuron. Hardware implementation of neuron could provide new research ideas to clinical treatment of spinal cord injury, bionics and artificial intelligence. Based on the HH model neuron and the DSP Builder technology, in the present study, a single HH model neuron hardware implementation was completed in Field Programmable Gate Array (FPGA). The neuron implemented in FPGA was stimulated by different types of current, the action potential response characteristics were analyzed, and the correlation coefficient between numerical simulation result and hardware implementation result were calculated. The results showed that neuronal action potential response of FPGA was highly consistent with numerical simulation result. This work lays the foundation for hardware implementation of neural network.

  19. Histologic assessment of neurons in rat models of cerebral ischemia.

    PubMed

    Eke, A; Conger, K A; Anderson, M; Garcia, J H

    1990-02-01

    We describe a method for typing neurons into four progressive stages of ischemic deterioration based on visual characterization of the nucleus in terms of its optical contrast, delineation along the nuclear-cytoplasmic interface, and its shape. Difficulty in assessing nuclear shape required the introduction of an angularity comparator chart to improve the investigator's accuracy. Three investigators typed neurons obtained from normal, ischemic, and ischemic-reperfused rat brains. Accuracy and reproducibility of the investigators' typing decisions with and without the angularity comparator charts were evaluated. The accuracy of subjective shape assessment was compared with objective digitizer measurements of the same. The angularity comparator charts reduced subjective shape classification error by two thirds, and group error (overall performance expressed by the coefficient of variance) decreased from 15.9% to 4.7% for Type I (normal cells), from 33.9% to 17.3% for Type II (cells with angular nuclei), from 15.5% to 14.1% for Type III (cells with smeared nuclei), and from 3.2% to 5.5% for Type IV (dead cells). Thus, Type I and IV neurons can be assessed at a higher reproducibility than the intermediate Types II and III. Our typing method can also be used to evaluate the effect of treatment regimes on ischemic neuronal damage.

  20. The primary locus of motor neuron death in an ALS–PDC mouse model

    PubMed Central

    Lee, Grace; Chu, Tony; Shaw, Christopher A.

    2010-01-01

    A mouse model of amyotrophic lateral sclerosis–parkinsonism–dementia complex based on the consumption of cycad seed flour was used to determine whether the observed pathology of motor neuron loss begins in the distal axons or the spinal cord. Assessments of neuromuscular junction integrity and motor neurons were performed at multiple time points. Mice fed cycad pellets performed worse on the wire hang than controls. Microglial activation in cycad-fed mice was observed with motor neuron degeneration at 12 weeks, but reactive astrocyte proliferation was not observed. After 33 weeks of cycad feeding, motor neuron loss had stabilized, with no evidence of neuromuscular junction endplate denervation. These data suggest that neuronal pathology begins at the soma and proceeds distally in a ‘dying forward’ pattern. PMID:19633581

  1. Modeling spiking activity of in vitro neuronal networks through non linear methods.

    PubMed

    Maffezzoli, A; Signorini, M G; Gullo, F; Wanke, E

    2008-01-01

    Neuroscience research is even more exploiting technologies developed for electronic engineering use: this is the case of Micro-Electrode Array (MEA) technology, an instrumentation which is able to acquire in vitro neuron spiking activity from a finite number of channels. In this work we present three models of synaptic neuronal network connections, called 'Full-Connected', 'Hierarchical' and 'Closed-Path'. Related to each one we implemented an index giving quantitative measures of similarity and of statistical dependence among neuron activities recorded in different MEA channels. They are based on Information Theory techniques as Mutual and Multi Information: the last one extending the pair-wise information to higher-order connections on the entire MEA neuronal network. We calculated indexes for each model in order to test the presence of self-synchronization among neurons evolving in time, in response to external stimuli such as the application of chemical neuron-inhibitors. The availability of such different models helps us to investigate also how much the synaptic connections are spatially sparse or hierarchically structured and finally how much of the information exchanged on the neuronal network is regulated by higher-order correlations.

  2. A biological plausible Generalized Leaky Integrate-and-Fire neuron model.

    PubMed

    Wang, Zhenzhong; Guo, Lilin; Adjouadi, Malek

    2014-01-01

    This study introduces a new Generalized Leaky Integrate-and-Fire (GLIF) neuron model. Unlike Normal Leaky Integrate-and-Fire (NLIF) models, the leaking resistor in the GLIF model equation is assumed to be variable, and an additional term would have the bias current added to the model equation in order to improve the accuracy. Adjusting the parameters defined for the leaking resistor and bias current, a GLIF model could be accurately matched to any Hodgkin-Huxley (HH) model and be able to reproduce plausible biological neuron behaviors.

  3. Neuronal Entropy-Rate Feature of Entopeduncular Nucleus in Rat Model of Parkinson's Disease.

    PubMed

    Darbin, Olivier; Jin, Xingxing; Von Wrangel, Christof; Schwabe, Kerstin; Nambu, Atsushi; Naritoku, Dean K; Krauss, Joachim K; Alam, Mesbah

    2016-03-01

    The function of the nigro-striatal pathway on neuronal entropy in the basal ganglia (BG) output nucleus, i.e. the entopeduncular nucleus (EPN) was investigated in the unilaterally 6-hyroxydopamine (6-OHDA)-lesioned rat model of Parkinson's disease (PD). In both control subjects and subjects with 6-OHDA lesion of dopamine (DA) the nigro-striatal pathway, a histological hallmark for parkinsonism, neuronal entropy in EPN was maximal in neurons with firing rates ranging between 15 and 25 Hz. In 6-OHDA lesioned rats, neuronal entropy in the EPN was specifically higher in neurons with firing rates above 25 Hz. Our data establishes that the nigro-striatal pathway controls neuronal entropy in motor circuitry and that the parkinsonian condition is associated with abnormal relationship between firing rate and neuronal entropy in BG output nuclei. The neuronal firing rates and entropy relationship provide putative relevant electrophysiological information to investigate the sensory-motor processing in normal condition and conditions such as movement disorders.

  4. Comparative analysis of system identification techniques for nonlinear modeling of the neuron-microelectrode junction.

    PubMed

    Khan, Saad Ahmad; Thakore, Vaibhav; Behal, Aman; Bölöni, Ladislau; Hickman, James J

    2013-03-01

    Applications of non-invasive neuroelectronic interfacing in the fields of whole-cell biosensing, biological computation and neural prosthetic devices depend critically on an efficient decoding and processing of information retrieved from a neuron-electrode junction. This necessitates development of mathematical models of the neuron-electrode interface that realistically represent the extracellular signals recorded at the neuroelectronic junction without being computationally expensive. Extracellular signals recorded using planar microelectrode or field effect transistor arrays have, until now, primarily been represented using linear equivalent circuit models that fail to reproduce the correct amplitude and shape of the signals recorded at the neuron-microelectrode interface. In this paper, to explore viable alternatives for a computationally inexpensive and efficient modeling of the neuron-electrode junction, input-output data from the neuron-electrode junction is modeled using a parametric Wiener model and a Nonlinear Auto-Regressive network with eXogenous input trained using a dynamic Neural Network model (NARX-NN model). Results corresponding to a validation dataset from these models are then employed to compare and contrast the computational complexity and efficiency of the aforementioned modeling techniques with the Lee-Schetzen technique of cross-correlation for estimating a nonlinear dynamic model of the neuroelectronic junction.

  5. A learning-enabled neuron array IC based upon transistor channel models of biological phenomena.

    PubMed

    Brink, S; Nease, S; Hasler, P; Ramakrishnan, S; Wunderlich, R; Basu, A; Degnan, B

    2013-02-01

    We present a single-chip array of 100 biologically-based electronic neuron models interconnected to each other and the outside environment through 30,000 synapses. The chip was fabricated in a standard 350 nm CMOS IC process. Our approach used dense circuit models of synaptic behavior, including biological computation and learning, as well as transistor channel models. We use Address-Event Representation (AER) spike communication for inputs and outputs to this IC. We present the IC architecture and infrastructure, including IC chip, configuration tools, and testing platform. We present measurement of small network of neurons, measurement of STDP neuron dynamics, and measurement from a compiled spiking neuron WTA topology, all compiled into this IC.

  6. Neural Dynamics as Sampling: A Model for Stochastic Computation in Recurrent Networks of Spiking Neurons

    PubMed Central

    Buesing, Lars; Bill, Johannes; Nessler, Bernhard; Maass, Wolfgang

    2011-01-01

    The organization of computations in networks of spiking neurons in the brain is still largely unknown, in particular in view of the inherently stochastic features of their firing activity and the experimentally observed trial-to-trial variability of neural systems in the brain. In principle there exists a powerful computational framework for stochastic computations, probabilistic inference by sampling, which can explain a large number of macroscopic experimental data in neuroscience and cognitive science. But it has turned out to be surprisingly difficult to create a link between these abstract models for stochastic computations and more detailed models of the dynamics of networks of spiking neurons. Here we create such a link and show that under some conditions the stochastic firing activity of networks of spiking neurons can be interpreted as probabilistic inference via Markov chain Monte Carlo (MCMC) sampling. Since common methods for MCMC sampling in distributed systems, such as Gibbs sampling, are inconsistent with the dynamics of spiking neurons, we introduce a different approach based on non-reversible Markov chains that is able to reflect inherent temporal processes of spiking neuronal activity through a suitable choice of random variables. We propose a neural network model and show by a rigorous theoretical analysis that its neural activity implements MCMC sampling of a given distribution, both for the case of discrete and continuous time. This provides a step towards closing the gap between abstract functional models of cortical computation and more detailed models of networks of spiking neurons. PMID:22096452

  7. A neuronal network model with simplified tonotopicity for tinnitus generation and its relief by sound therapy.

    PubMed

    Nagashino, Hirofumi; Kinouchi, Yohsuke; Danesh, Ali A; Pandya, Abhijit S

    2013-01-01

    Tinnitus is the perception of sound in the ears or in the head where no external source is present. Sound therapy is one of the most effective techniques for tinnitus treatment that have been proposed. In order to investigate mechanisms of tinnitus generation and the clinical effects of sound therapy, we have proposed conceptual and computational models with plasticity using a neural oscillator or a neuronal network model. In the present paper, we propose a neuronal network model with simplified tonotopicity of the auditory system as more detailed structure. In this model an integrate-and-fire neuron model is employed and homeostatic plasticity is incorporated. The computer simulation results show that the present model can show the generation of oscillation and its cessation by external input. It suggests that the present framework is promising as a modeling for the tinnitus generation and the effects of sound therapy.

  8. Is the Langevin phase equation an efficient model for oscillating neurons?

    NASA Astrophysics Data System (ADS)

    Ota, Keisuke; Tsunoda, Takamasa; Omori, Toshiaki; Watanabe, Shigeo; Miyakawa, Hiroyoshi; Okada, Masato; Aonishi, Toru

    2009-12-01

    The Langevin phase model is an important canonical model for capturing coherent oscillations of neural populations. However, little attention has been given to verifying its applicability. In this paper, we demonstrate that the Langevin phase equation is an efficient model for neural oscillators by using the machine learning method in two steps: (a) Learning of the Langevin phase model. We estimated the parameters of the Langevin phase equation, i.e., a phase response curve and the intensity of white noise from physiological data measured in the hippocampal CA1 pyramidal neurons. (b) Test of the estimated model. We verified whether a Fokker-Planck equation derived from the Langevin phase equation with the estimated parameters could capture the stochastic oscillatory behavior of the same neurons disturbed by periodic perturbations. The estimated model could predict the neural behavior, so we can say that the Langevin phase equation is an efficient model for oscillating neurons.

  9. Neuronally-directed effects of RXR activation in a mouse model of Alzheimer’s disease

    PubMed Central

    Mariani, M. M.; Malm, T.; Lamb, R.; Jay, T. R.; Neilson, L.; Casali, B.; Medarametla, L.; Landreth, G. E.

    2017-01-01

    Alzheimer’s disease (AD) is characterized by extensive neuron loss that accompanies profound impairments in memory and cognition. We examined the neuronally directed effects of the retinoid X receptor agonist bexarotene in an aggressive model of AD. We report that a two week treatment of 3.5 month old 5XFAD mice with bexarotene resulted in the clearance of intraneuronal amyloid deposits. Importantly, neuronal loss was attenuated by 44% in the subiculum in mice 4 months of age and 18% in layer V of the cortex in mice 8 months of age. Moreover, bexarotene treatment improved remote memory stabilization in fear conditioned mice and improved olfactory cross habituation. These improvements in neuron viability and function were correlated with significant increases in the levels of post-synaptic marker PSD95 and the pre-synaptic marker synaptophysin. Moreover, bexarotene pretreatment improved neuron survival in primary 5XFAD neurons in vitro in response to glutamate-induced excitotoxicity. The salutary effects of bexarotene were accompanied by reduced plaque burden, decreased astrogliosis, and suppression of inflammatory gene expression. Collectively, these data provide evidence that bexarotene treatment reduced neuron loss, elevated levels of markers of synaptic integrity that was linked to improved cognition and in an aggressive model of AD. PMID:28205585

  10. A model for the characterization of the spatial properties in vestibular neurons

    NASA Technical Reports Server (NTRS)

    Angelaki, D. E.; Bush, G. A.; Perachio, A. A.

    1992-01-01

    Quantitative study of the static and dynamic response properties of some otolith-sensitive neurons has been difficult in the past partly because their responses to different linear acceleration vectors exhibited no "null" plane and a dependence of phase on stimulus orientation. The theoretical formulation of the response ellipse provides a quantitative way to estimate the spatio-temporal properties of such neurons. Its semi-major axis gives the direction of the polarization vector (i.e., direction of maximal sensitivity) and it estimates the neuronal response for stimulation along that direction. In addition, the semi-minor axis of the ellipse provides an estimate of the neuron's maximal sensitivity in the "null" plane. In this paper, extracellular recordings from otolith-sensitive vestibular nuclei neurons in decerebrate rats were used to demonstrate the practical application of the method. The experimentally observed gain and phase dependence on the orientation angle of the acceleration vector in a head-horizontal plane was described and satisfactorily fit by the response ellipse model. In addition, the model satisfactorily fits neuronal responses in three-dimensions and unequivocally demonstrates that the response ellipse formulation is the general approach to describe quantitatively the spatial properties of vestibular neurons.

  11. Lower Motor Control Modeled by Neuron With Fuzzy Synapses

    DTIC Science & Technology

    2007-11-02

    seen in parkinsonism , chorea, cerebellar disorders, and spasticity. In most cases, muscles work in opposing pairs: one muscle opens or extends a joint...performances of predictor schemes based on neurons with fuzzy synapses of order P = 3 in tremor prediction applications. The rules of these particular...Chelaru, A. Kandel, I. Tofan, M. Irimia, “Fuzzy methods in tremor assessment, prediction, and rehabilitation”, Artificial Intelligence in Medicine

  12. Assignment of Model Amygdala Neurons to the Fear Memory Trace Depends on Competitive Synaptic Interactions

    PubMed Central

    Kim, Dongbeom; Paré, Denis

    2013-01-01

    We used biophysical modeling to examine a fundamental, yet unresolved, question regarding how particular lateral amygdala (LA) neurons are assigned to fear memory traces. This revealed that neurons with high intrinsic excitability are more likely to be integrated into the memory trace, but that competitive synaptic interactions also play a critical role. Indeed, when the ratio of intrinsically excitable cells was increased or decreased, the number of plastic cells remained relatively constant. Analysis of the connectivity of plastic and nonplastic cells revealed that subsets of principal LA neurons effectively band together by virtue of their excitatory interconnections to suppress plasticity in other principal cells via the recruitment of inhibitory interneurons. PMID:24005288

  13. The effect of dendritic voltage-gated conductances on the neuronal impedance: a quantitative model.

    PubMed

    Káli, Szabolcs; Zemankovics, Rita

    2012-10-01

    Neuronal impedance characterizes the magnitude and timing of the subthreshold response of a neuron to oscillatory input at a given frequency. It is known to be influenced by both the morphology of the neuron and the presence of voltage-gated conductances in the cell membrane. Most existing theoretical accounts of neuronal impedance considered the effects of voltage-gated conductances but neglected the spatial extent of the cell, while others examined spatially extended dendrites with a passive or spatially uniform quasi-active membrane. We derived an explicit mathematical expression for the somatic input impedance of a model neuron consisting of a somatic compartment coupled to an infinite dendritic cable which contained voltage-gated conductances, in the more general case of non-uniform dendritic membrane potential. The validity and generality of this model was verified through computer simulations of various model neurons. The analytical model was then applied to the analysis of experimental data from real CA1 pyramidal neurons. The model confirmed that the biophysical properties and predominantly dendritic localization of the hyperpolarization-activated cation current I (h) were important determinants of the impedance profile, but also predicted a significant contribution from a depolarization-activated fast inward current. Our calculations also implicated the interaction of I (h) with amplifying currents as the main factor governing the shape of the impedance-frequency profile in two types of hippocampal interneuron. Our results provide not only a theoretical advance in our understanding of the frequency-dependent behavior of nerve cells, but also a practical tool for the identification of candidate mechanisms that determine neuronal response properties.

  14. Immunohistochemical visualization of hippocampal neuron activity after spatial learning in a mouse model of neurodevelopmental disorders.

    PubMed

    Provenzano, Giovanni; Pangrazzi, Luca; Poli, Andrea; Berardi, Nicoletta; Bozzi, Yuri

    2015-05-12

    Induction of phosphorylated extracellular-regulated kinase (pERK) is a reliable molecular readout of learning-dependent neuronal activation. Here, we describe a pERK immunohistochemistry protocol to study the profile of hippocampal neuron activation following exposure to a spatial learning task in a mouse model characterized by cognitive deficits of neurodevelopmental origin. Specifically, we used pERK immunostaining to study neuronal activation following Morris water maze (MWM, a classical hippocampal-dependent learning task) in Engrailed-2 knockout (En2(-/-)) mice, a model of autism spectrum disorders (ASD). As compared to wild-type (WT) controls, En2(-/-) mice showed significant spatial learning deficits in the MWM. After MWM, significant differences in the number of pERK-positive neurons were detected in specific hippocampal subfields of En2(-/-) mice, as compared to WT animals. Thus, our protocol can robustly detect differences in pERK-positive neurons associated to hippocampal-dependent learning impairment in a mouse model of ASD. More generally, our protocol can be applied to investigate the profile of hippocampal neuron activation in both genetic or pharmacological mouse models characterized by cognitive deficits.

  15. A synaptic input portal for a mapped clock oscillator model of neuronal electrical rhythmic activity

    NASA Astrophysics Data System (ADS)

    Zariffa, José; Ebden, Mark; Bardakjian, Berj L.

    2004-09-01

    Neuronal electrical oscillations play a central role in a variety of situations, such as epilepsy and learning. The mapped clock oscillator (MCO) model is a general model of transmembrane voltage oscillations in excitable cells. In order to be able to investigate the behaviour of neuronal oscillator populations, we present a neuronal version of the model. The neuronal MCO includes an extra input portal, the synaptic portal, which can reflect the biological relationships in a chemical synapse between the frequency of the presynaptic action potentials and the postsynaptic resting level, which in turn affects the frequency of the postsynaptic potentials. We propose that the synaptic input-output relationship must include a power function in order to be able to reproduce physiological behaviour such as resting level saturation. One linear and two power functions (Butterworth and sigmoidal) are investigated, using the case of an inhibitory synapse. The linear relation was not able to produce physiologically plausible behaviour, whereas both the power function examples were appropriate. The resulting neuronal MCO model can be tailored to a variety of neuronal cell types, and can be used to investigate complex population behaviour, such as the influence of network topology and stochastic resonance.

  16. Dendritic vulnerability in neurodegenerative disease: insights from analyses of cortical pyramidal neurons in transgenic mouse models

    PubMed Central

    Weaver, Christina M.; Rocher, Anne B.; Rodriguez, Alfredo; Crimins, Johanna L.; Dickstein, Dara L.; Wearne, Susan L.; Hof, Patrick R.

    2011-01-01

    In neurodegenerative disorders, such as Alzheimer’s disease, neuronal dendrites and dendritic spines undergo significant pathological changes. Because of the determinant role of these highly dynamic structures in signaling by individual neurons and ultimately in the functionality of neuronal networks that mediate cognitive functions, a detailed understanding of these changes is of paramount importance. Mutant murine models, such as the Tg2576 APP mutant mouse and the rTg4510 tau mutant mouse have been developed to provide insight into pathogenesis involving the abnormal production and aggregation of amyloid and tau proteins, because of the key role that these proteins play in neurodegenerative disease. This review showcases the multidimensional approach taken by our collaborative group to increase understanding of pathological mechanisms in neurodegenerative disease using these mouse models. This approach includes analyses of empirical 3D morphological and electrophysiological data acquired from frontal cortical pyramidal neurons using confocal laser scanning microscopy and whole-cell patch-clamp recording techniques, combined with computational modeling methodologies. These collaborative studies are designed to shed insight on the repercussions of dystrophic changes in neocortical neurons, define the cellular phenotype of differential neuronal vulnerability in relevant models of neurodegenerative disease, and provide a basis upon which to develop meaningful therapeutic strategies aimed at preventing, reversing, or compensating for neurodegenerative changes in dementia. PMID:20177698

  17. Experimentally guided modelling of dendritic excitability in rat neocortical pyramidal neurones

    PubMed Central

    Keren, Naomi; Bar-Yehuda, Dan; Korngreen, Alon

    2009-01-01

    Constructing physiologically relevant compartmental models of neurones is critical for understanding neuronal activity and function. We recently suggested that measurements from multiple locations along the soma, dendrites and axon are necessary as a data set when using a genetic optimization algorithm to constrain the parameters of a compartmental model of an entire neurone. However, recordings from L5 pyramidal neurones can routinely be performed simultaneously from only two locations. Now we show that a data set recorded from the soma and apical dendrite combined with a parameter peeling procedure is sufficient to constrain a compartmental model for the apical dendrite of L5 pyramidal neurones. The peeling procedure was tested on several compartmental models showing that it avoids local minima in parameter space. Based on the requirements of this analysis procedure, we designed and performed simultaneous whole-cell recordings from the soma and apical dendrite of rat L5 pyramidal neurones. The data set obtained from these recordings allowed constraining a simplified compartmental model for the apical dendrite of L5 pyramidal neurones containing four voltage-gated conductances. In agreement with experimental findings, the optimized model predicts that the conductance density gradients of voltage-gated K+ conductances taper rapidly proximal to the soma, while the density gradient of the voltage-gated Na+ conductance tapers slowly along the apical dendrite. The model reproduced the back-propagation of the action potential and the modulation of the resting membrane potential along the apical dendrite. Furthermore, the optimized model provided a mechanistic explanation for the back-propagation of the action potential into the apical dendrite and the generation of dendritic Na+ spikes. PMID:19171651

  18. Dopamine neuronal loss contributes to memory and reward dysfunction in a model of Alzheimer's disease.

    PubMed

    Nobili, Annalisa; Latagliata, Emanuele Claudio; Viscomi, Maria Teresa; Cavallucci, Virve; Cutuli, Debora; Giacovazzo, Giacomo; Krashia, Paraskevi; Rizzo, Francesca Romana; Marino, Ramona; Federici, Mauro; De Bartolo, Paola; Aversa, Daniela; Dell'Acqua, Maria Concetta; Cordella, Alberto; Sancandi, Marco; Keller, Flavio; Petrosini, Laura; Puglisi-Allegra, Stefano; Mercuri, Nicola Biagio; Coccurello, Roberto; Berretta, Nicola; D'Amelio, Marcello

    2017-04-03

    Alterations of the dopaminergic (DAergic) system are frequently reported in Alzheimer's disease (AD) patients and are commonly linked to cognitive and non-cognitive symptoms. However, the cause of DAergic system dysfunction in AD remains to be elucidated. We investigated alterations of the midbrain DAergic system in the Tg2576 mouse model of AD, overexpressing a mutated human amyloid precursor protein (APPswe). Here, we found an age-dependent DAergic neuron loss in the ventral tegmental area (VTA) at pre-plaque stages, although substantia nigra pars compacta (SNpc) DAergic neurons were intact. The selective VTA DAergic neuron degeneration results in lower DA outflow in the hippocampus and nucleus accumbens (NAc) shell. The progression of DAergic cell death correlates with impairments in CA1 synaptic plasticity, memory performance and food reward processing. We conclude that in this mouse model of AD, degeneration of VTA DAergic neurons at pre-plaque stages contributes to memory deficits and dysfunction of reward processing.

  19. Dopamine neuronal loss contributes to memory and reward dysfunction in a model of Alzheimer's disease

    PubMed Central

    Nobili, Annalisa; Latagliata, Emanuele Claudio; Viscomi, Maria Teresa; Cavallucci, Virve; Cutuli, Debora; Giacovazzo, Giacomo; Krashia, Paraskevi; Rizzo, Francesca Romana; Marino, Ramona; Federici, Mauro; De Bartolo, Paola; Aversa, Daniela; Dell'Acqua, Maria Concetta; Cordella, Alberto; Sancandi, Marco; Keller, Flavio; Petrosini, Laura; Puglisi-Allegra, Stefano; Mercuri, Nicola Biagio; Coccurello, Roberto; Berretta, Nicola; D'Amelio, Marcello

    2017-01-01

    Alterations of the dopaminergic (DAergic) system are frequently reported in Alzheimer's disease (AD) patients and are commonly linked to cognitive and non-cognitive symptoms. However, the cause of DAergic system dysfunction in AD remains to be elucidated. We investigated alterations of the midbrain DAergic system in the Tg2576 mouse model of AD, overexpressing a mutated human amyloid precursor protein (APPswe). Here, we found an age-dependent DAergic neuron loss in the ventral tegmental area (VTA) at pre-plaque stages, although substantia nigra pars compacta (SNpc) DAergic neurons were intact. The selective VTA DAergic neuron degeneration results in lower DA outflow in the hippocampus and nucleus accumbens (NAc) shell. The progression of DAergic cell death correlates with impairments in CA1 synaptic plasticity, memory performance and food reward processing. We conclude that in this mouse model of AD, degeneration of VTA DAergic neurons at pre-plaque stages contributes to memory deficits and dysfunction of reward processing. PMID:28367951

  20. From “Directed Differentiation” to “Neuronal Induction”: Modeling Neuropsychiatric Disease

    PubMed Central

    Ho, Seok-Man; Topol, Aaron; Brennand, Kristen J

    2015-01-01

    Aberrant behavior and function of neurons are believed to be the primary causes of most neurological diseases and psychiatric disorders. Human postmortem samples have limited availability and, while they provide clues to the state of the brain after a prolonged illness, they offer limited insight into the factors contributing to disease onset. Conversely, animal models cannot recapitulate the polygenic origins of neuropsychiatric disease. Novel methods, such as somatic cell reprogramming, deliver nearly limitless numbers of pathogenic human neurons for the study of the mechanism of neuropsychiatric disease initiation and progression. First, this article reviews the advent of human induced pluripotent stem cell (hiPSC) technology and introduces two major methods, “directed differentiation” and “neuronal induction,” by which it is now possible to generate neurons for modeling neuropsychiatric disease. Second, it discusses the recent applications, and the limitations, of these technologies to in vitro studies of psychiatric disorders. PMID:26045654

  1. An analytical study of relay neuron's reliability: dependence on input and model parameters.

    PubMed

    Agarwal, Rahul; Sarma, Sridevi V

    2011-01-01

    Relay neurons are widely found in our nervous system, including the Thalamus, spinal cord and lateral geniculate body. They receive a modulating input (background activity) and a reference input. The modulating input modulates relay of the reference input. This modulation is critical for correct functioning of relay neurons, but is poorly understood. In this paper, we use a biophysical-based model and systems theoretic tools to calculate how well a single relay neuron relays a reference input signal as a function of the neuron's electro physiological properties (i.e. model parameters), the modulating signal, and the reference signal parameters. Our analysis is more rigorous than previous related works and is generalizable to all relay cells in the body. Our analytical expression matches relay performance obtained in simulation and suggest that increasing the frequency of a sinusoidal modulating input or decreasing its DC offset increases the relay cell reliability.

  2. Extending the mirror neuron system model, II: what did I just do? A new role for mirror neurons.

    PubMed

    Bonaiuto, James; Arbib, Michael A

    2010-04-01

    A mirror system is active both when an animal executes a class of actions (self-actions) and when it sees another execute an action of that class. Much attention has been given to the possible roles of mirror systems in responding to the actions of others but there has been little attention paid to their role in self-actions. In the companion article (Bonaiuto et al. Biol Cybern 96:9-38, 2007) we presented MNS2, an extension of the Mirror Neuron System model of the monkey mirror system trained to recognize the external appearance of its own actions as a basis for recognizing the actions of other animals when they perform similar actions. Here we further extend the study of the mirror system by introducing the novel hypotheses that a mirror system may additionally help in monitoring the success of a self-action and may also be activated by recognition of one's own apparent actions as well as efference copy from one's intended actions. The framework for this computational demonstration is a model of action sequencing, called augmented competitive queuing, in which action choice is based on the desirability of executable actions. We show how this "what did I just do?" function of mirror neurons can contribute to the learning of both executability and desirability which in certain cases supports rapid reorganization of motor programs in the face of disruptions.

  3. Kilohertz Frequency Deep Brain Stimulation Is Ineffective at Regularizing the Firing of Model Thalamic Neurons.

    PubMed

    Couto, João; Grill, Warren M

    2016-01-01

    Deep brain stimulation (DBS) is an established therapy for movement disorders, including tremor, dystonia, and Parkinson's disease, but the mechanisms of action are not well understood. Symptom suppression by DBS typically requires stimulation frequencies ≥100 Hz, but when the frequency is increased above ~2 kHz, the effectiveness in tremor suppression declines (Benabid et al., 1991). We sought to test the hypothesis that the decline in efficacy at high frequencies is associated with desynchronization of the activity generated within a population of stimulated neurons. Regularization of neuronal firing is strongly correlated with tremor suppression by DBS, and desynchronization would disrupt the regularization of neuronal activity. We implemented computational models of CNS axons with either deterministic or stochastic membrane dynamics, and quantified the response of populations of model nerve fibers to extracellular stimulation at different frequencies and amplitudes. As stimulation frequency was increased from 2 to 80 Hz the regularity of neuronal firing increased (as assessed with direct estimates of entropy), in accord with the clinical effects on tremor of increasing stimulation frequency (Kuncel et al., 2006). Further, at frequencies between 80 and 500 Hz, increasing the stimulation amplitude (i.e., the proportion of neurons activated by the stimulus) increased the regularity of neuronal activity across the population, in accord with the clinical effects on tremor of stimulation amplitude (Kuncel et al., 2007). However, at stimulation frequencies above 1 kHz the regularity of neuronal firing declined due to irregular patterns of action potential generation and conduction block. The reductions in neuronal regularity that occurred at high frequencies paralleled the previously reported decline in tremor reduction and may be responsible for the loss of efficacy of DBS at very high frequencies. This analysis provides further support for the hypothesis that

  4. Kilohertz Frequency Deep Brain Stimulation Is Ineffective at Regularizing the Firing of Model Thalamic Neurons

    PubMed Central

    Couto, João; Grill, Warren M.

    2016-01-01

    Deep brain stimulation (DBS) is an established therapy for movement disorders, including tremor, dystonia, and Parkinson's disease, but the mechanisms of action are not well understood. Symptom suppression by DBS typically requires stimulation frequencies ≥100 Hz, but when the frequency is increased above ~2 kHz, the effectiveness in tremor suppression declines (Benabid et al., 1991). We sought to test the hypothesis that the decline in efficacy at high frequencies is associated with desynchronization of the activity generated within a population of stimulated neurons. Regularization of neuronal firing is strongly correlated with tremor suppression by DBS, and desynchronization would disrupt the regularization of neuronal activity. We implemented computational models of CNS axons with either deterministic or stochastic membrane dynamics, and quantified the response of populations of model nerve fibers to extracellular stimulation at different frequencies and amplitudes. As stimulation frequency was increased from 2 to 80 Hz the regularity of neuronal firing increased (as assessed with direct estimates of entropy), in accord with the clinical effects on tremor of increasing stimulation frequency (Kuncel et al., 2006). Further, at frequencies between 80 and 500 Hz, increasing the stimulation amplitude (i.e., the proportion of neurons activated by the stimulus) increased the regularity of neuronal activity across the population, in accord with the clinical effects on tremor of stimulation amplitude (Kuncel et al., 2007). However, at stimulation frequencies above 1 kHz the regularity of neuronal firing declined due to irregular patterns of action potential generation and conduction block. The reductions in neuronal regularity that occurred at high frequencies paralleled the previously reported decline in tremor reduction and may be responsible for the loss of efficacy of DBS at very high frequencies. This analysis provides further support for the hypothesis that

  5. A flexible, interactive software tool for fitting the parameters of neuronal models

    PubMed Central

    Friedrich, Péter; Vella, Michael; Gulyás, Attila I.; Freund, Tamás F.; Káli, Szabolcs

    2014-01-01

    The construction of biologically relevant neuronal models as well as model-based analysis of experimental data often requires the simultaneous fitting of multiple model parameters, so that the behavior of the model in a certain paradigm matches (as closely as possible) the corresponding output of a real neuron according to some predefined criterion. Although the task of model optimization is often computationally hard, and the quality of the results depends heavily on technical issues such as the appropriate choice (and implementation) of cost functions and optimization algorithms, no existing program provides access to the best available methods while also guiding the user through the process effectively. Our software, called Optimizer, implements a modular and extensible framework for the optimization of neuronal models, and also features a graphical interface which makes it easy for even non-expert users to handle many commonly occurring scenarios. Meanwhile, educated users can extend the capabilities of the program and customize it according to their needs with relatively little effort. Optimizer has been developed in Python, takes advantage of open-source Python modules for nonlinear optimization, and interfaces directly with the NEURON simulator to run the models. Other simulators are supported through an external interface. We have tested the program on several different types of problems of varying complexity, using different model classes. As targets, we used simulated traces from the same or a more complex model class, as well as experimental data. We successfully used Optimizer to determine passive parameters and conductance densities in compartmental models, and to fit simple (adaptive exponential integrate-and-fire) neuronal models to complex biological data. Our detailed comparisons show that Optimizer can handle a wider range of problems, and delivers equally good or better performance than any other existing neuronal model fitting tool. PMID

  6. A flexible, interactive software tool for fitting the parameters of neuronal models.

    PubMed

    Friedrich, Péter; Vella, Michael; Gulyás, Attila I; Freund, Tamás F; Káli, Szabolcs

    2014-01-01

    The construction of biologically relevant neuronal models as well as model-based analysis of experimental data often requires the simultaneous fitting of multiple model parameters, so that the behavior of the model in a certain paradigm matches (as closely as possible) the corresponding output of a real neuron according to some predefined criterion. Although the task of model optimization is often computationally hard, and the quality of the results depends heavily on technical issues such as the appropriate choice (and implementation) of cost functions and optimization algorithms, no existing program provides access to the best available methods while also guiding the user through the process effectively. Our software, called Optimizer, implements a modular and extensible framework for the optimization of neuronal models, and also features a graphical interface which makes it easy for even non-expert users to handle many commonly occurring scenarios. Meanwhile, educated users can extend the capabilities of the program and customize it according to their needs with relatively little effort. Optimizer has been developed in Python, takes advantage of open-source Python modules for nonlinear optimization, and interfaces directly with the NEURON simulator to run the models. Other simulators are supported through an external interface. We have tested the program on several different types of problems of varying complexity, using different model classes. As targets, we used simulated traces from the same or a more complex model class, as well as experimental data. We successfully used Optimizer to determine passive parameters and conductance densities in compartmental models, and to fit simple (adaptive exponential integrate-and-fire) neuronal models to complex biological data. Our detailed comparisons show that Optimizer can handle a wider range of problems, and delivers equally good or better performance than any other existing neuronal model fitting tool.

  7. Two-population model for medial temporal lobe neurons: The vast majority are almost silent

    NASA Astrophysics Data System (ADS)

    Magyar, Andrew; Collins, John

    2015-07-01

    Recordings in the human medial temporal lobe have found many neurons that respond to pictures (and related stimuli) of just one particular person of those presented. It has been proposed that these are concept cells, responding to just a single concept. However, a direct experimental test of the concept cell idea appears impossible, because it would need the measurement of the response of each cell to enormous numbers of other stimuli. Here we propose a new statistical method for analysis of the data that gives a more powerful way to analyze how close data are to the concept-cell idea. Central to the model is the neuronal sparsity, defined as the total fraction of stimuli that elicit an above-threshold response in the neuron. The model exploits the large number of sampled neurons to give sensitivity to situations where the average response sparsity is much less than one response for the number of presented stimuli. We show that a conventional model where a single sparsity is postulated for all neurons gives an extremely poor fit to the data. In contrast, a model with two dramatically different populations gives an excellent fit to data from the hippocampus and entorhinal cortex. In the hippocampus, one population has 7% of the cells with a 2.6% sparsity. But a much larger fraction (93%) respond to only 0.1% of the stimuli. This can result in an extreme bias in the responsiveness of reported neurons compared with a typical neuron. Finally, we show how to allow for the fact that some identified units correspond to multiple neurons and find that our conclusions at the neural level are quantitatively changed but strengthened, with an even stronger difference between the two populations.

  8. Synchronization of the minimal models of bursting neurons coupled by delayed chemical or electrical synapses

    NASA Astrophysics Data System (ADS)

    Nebojša, Vasović; Nikola, Burić; Kristina, Todorović; Ines, Grozdanović

    2012-01-01

    The minimal two-dimensional model of bursting neuronal dynamics is used to study the influence of time-delay on the properties of synchronization of bursting neurons. Generic properties of bursting and dependence of the stability of synchronization on the time-lag and the strength of coupling are described, and compared with the two common types of synaptical coupling, i.e., time-delayed chemical and electrical synapses.

  9. Random Walk Models for the Spike Activity of a Single Neuron

    PubMed Central

    Gerstein, George L.; Mandelbrot, Benoit

    1964-01-01

    Quantitative methods for the study of the statistical properties of spontaneously occurring spike trains from single neurons have recently been presented. Such measurements suggest a number of descriptive mathematical models. One of these, based on a random walk towards an absorbing barrier, can describe a wide range of neuronal activity in terms of two parameters. These parameters are readily associated with known physiological mechanisms. ImagesFigure 3 PMID:14104072

  10. A- and C-type rat nodose sensory neurons: model interpretations of dynamic discharge characteristics.

    PubMed

    Schild, J H; Clark, J W; Hay, M; Mendelowitz, D; Andresen, M C; Kunze, D L

    1994-06-01

    1. Neurons of the nodose ganglia provide the sole connection between many types of visceral sensory inputs and the central nervous system. Electrophysiological studies of isolated nodose neurons provide a practical means of measuring individual cell membrane currents and assessing their putative contributions to the overall response properties of the neuron and its terminations. Here, we present a comprehensive mathematical model of an isolated nodose sensory neuron that is based upon numerical fits to quantitative voltage- and current-clamp data recorded in our laboratory. Model development was accomplished using an iterative process of electrophysiological recordings, nonlinear parameter estimation, and computer simulation. This work is part of an integrative effort aimed at identifying and characterizing the fundamental ionic mechanisms participating in the afferent neuronal limb of the baroreceptor reflex. 2. The neuronal model consists of two parts: a Hodgkin-Huxley-type membrane model coupled to a lumped fluid compartment model that describes Ca2+ ion concentration dynamics within the intracellular and external perineuronal media. Calcium buffering via a calmodulin-type buffer is provided within the intracellular compartment. 3. The complete model accurately reproduces whole-cell voltage-clamp recordings of the major ion channel currents observed in enzymatically dispersed nodose sensory neurons. Specifically, two Na+ currents exhibiting fast (INaf) and slow tetrodotoxin (TTX)-insensitive (INas) kinetics; low- and high-threshold Ca2+ currents exhibiting transient (ICa,t) and long-lasting (ICa,n) dynamics, respectively; and outward K+ currents consisting of a delayed-rectifier current (IK), a transient outward current (I(t)) and a Ca(2+)-activated K+ current (IK,Ca). 4. Whole-cell current-clamp recordings of somatic action-potential dynamics were performed on enzymatically dispersed nodose neurons using the perforated patch-clamp technique. Stimulus protocols

  11. Thalamic neuron models encode stimulus information by burst-size modulation

    PubMed Central

    Elijah, Daniel H.; Samengo, Inés; Montemurro, Marcelo A.

    2015-01-01

    Thalamic neurons have been long assumed to fire in tonic mode during perceptive states, and in burst mode during sleep and unconsciousness. However, recent evidence suggests that bursts may also be relevant in the encoding of sensory information. Here, we explore the neural code of such thalamic bursts. In order to assess whether the burst code is generic or whether it depends on the detailed properties of each bursting neuron, we analyzed two neuron models incorporating different levels of biological detail. One of the models contained no information of the biophysical processes entailed in spike generation, and described neuron activity at a phenomenological level. The second model represented the evolution of the individual ionic conductances involved in spiking and bursting, and required a large number of parameters. We analyzed the models' input selectivity using reverse correlation methods and information theory. We found that n-spike bursts from both models transmit information by modulating their spike count in response to changes to instantaneous input features, such as slope, phase, amplitude, etc. The stimulus feature that is most efficiently encoded by bursts, however, need not coincide with one of such classical features. We therefore searched for the optimal feature among all those that could be expressed as a linear transformation of the time-dependent input current. We found that bursting neurons transmitted 6 times more information about such more general features. The relevant events in the stimulus were located in a time window spanning ~100 ms before and ~20 ms after burst onset. Most importantly, the neural code employed by the simple and the biologically realistic models was largely the same, implying that the simple thalamic neuron model contains the essential ingredients that account for the computational properties of the thalamic burst code. Thus, our results suggest the n-spike burst code is a general property of thalamic neurons. PMID

  12. A generalized Leaky Integrate-and-Fire neuron model with fast implementation method.

    PubMed

    Wang, Zhenzhong; Guo, Lilin; Adjouadi, Malek

    2014-08-01

    This study introduces a new Generalized Leaky Integrate-and-Fire (GLIF) neuron model with variable leaking resistor and bias current in order to reproduce accurately the membrane voltage dynamics of a biological neuron. The accuracy of this model is ensured by adjusting its parameters to the statistical properties of the Hodgkin-Huxley model outputs; while the speed is enhanced by introducing a Generalized Exponential Moving Average method that converts the parameterized kernel functions into pre-calculated lookup tables based on an analytic solution of the dynamic equations of the GLIF model.

  13. Modelling the firing pattern of bullfrog vestibular neurons responding to naturalistic stimuli

    NASA Technical Reports Server (NTRS)

    Paulin, M. G.; Hoffman, L. F.

    1999-01-01

    We have developed a neural system identification method for fitting models to stimulus-response data, where the response is a spike train. The method involves using a general nonlinear optimisation procedure to fit models in the time domain. We have applied the method to model bullfrog semicircular canal afferent neuron responses during naturalistic, broad-band head rotations. These neurons respond in diverse ways, but a simple four parameter class of models elegantly accounts for the various types of responses observed. c1999 Elsevier Science B.V. All rights reserved.

  14. Reduced motor neuron excitability is an important contributor to weakness in a rat model of sepsis.

    PubMed

    Nardelli, Paul; Vincent, Jacob A; Powers, Randall; Cope, Tim C; Rich, Mark M

    2016-08-01

    The mechanisms by which sepsis triggers intensive care unit acquired weakness (ICUAW) remain unclear. We previously identified difficulty with motor unit recruitment in patients as a novel contributor to ICUAW. To study the mechanism underlying poor recruitment of motor units we used the rat cecal ligation and puncture model of sepsis. We identified striking dysfunction of alpha motor neurons during repetitive firing. Firing was more erratic, and often intermittent. Our data raised the possibility that reduced excitability of motor neurons was a significant contributor to weakness induced by sepsis. In this study we quantified the contribution of reduced motor neuron excitability and compared its magnitude to the contributions of myopathy, neuropathy and failure of neuromuscular transmission. We injected constant depolarizing current pulses (5s) into the soma of alpha motor neurons in the lumbosacral spinal cord of anesthetized rats to trigger repetitive firing. In response to constant depolarization, motor neurons in untreated control rats fired at steady and continuous firing rates and generated smooth and sustained tetanic motor unit force as expected. In contrast, following induction of sepsis, motor neurons were often unable to sustain firing throughout the 5s current injection such that force production was reduced. Even when firing, motor neurons from septic rats fired erratically and discontinuously, leading to irregular production of motor unit force. Both fast and slow type motor neurons had similar disruption of excitability. We followed rats after recovery from sepsis to determine the time course of resolution of the defect in motor neuron excitability. By one week, rats appeared to have recovered from sepsis as they had no piloerection and appeared to be in no distress. The defects in motor neuron repetitive firing were still striking at 2weeks and, although improved, were present at one month. We infer that rats suffered from weakness due to reduced

  15. Reduced motor neuron excitability is an important contributor to weakness in a rat model of sepsis

    PubMed Central

    Nardelli, Paul; Vincent, Jacob A.; Powers, Randall; Cope, Tim C.; Rich, Mark M.

    2016-01-01

    The mechanisms by which sepsis triggers intensive care unit acquired weakness (ICUAW) remain unclear. We previously identified difficulty with motor unit recruitment in patients as a novel contributor to ICUAW. To study the mechanism underlying poor recruitment of motor units we used the rat cecal ligation and puncture model of sepsis. We identified striking dysfunction of alpha motor neurons during repetitive firing. Firing was more erratic, and often intermittent. Our data raised the possibility that reduced excitability of motor neurons was a significant contributor to weakness induced by sepsis. In this study we quantified the contribution of reduced motor neuron excitability and compared its magnitude to the contributions of myopathy, neuropathy and failure of neuromuscular transmission. We injected constant depolarizing current pulses (5 sec) into the soma of alpha motor neurons in the lumbosacral spinal cord of anesthetized rats to trigger repetitive firing. In response to constant depolarization, motor neurons in untreated control rats fired at steady and continuous firing rates and generated smooth and sustained tetanic motor unit force as expected. In contrast, following induction of sepsis, motor neurons were often unable to sustain firing throughout the 5s current injection such that force production was reduced. Even when firing, motor neurons from septic rats fired erratically and discontinuously, leading to irregular production of motor unit force. Both fast and slow type motor neurons had similar disruption of excitability. We followed rats after recovery from sepsis to determine the time course of resolution of the defect in motor neuron excitability. By one week, rats appeared to have recovered from sepsis as they had no piloerection and appeared to be in no distress. The defects in motor neuron repetitive firing were still striking at 2 weeks and, although improved, were present at one month. We infer that rats suffered from weakness due to

  16. Sensory neurons do not induce motor neuron loss in a human stem cell model of spinal muscular atrophy.

    PubMed

    Schwab, Andrew J; Ebert, Allison D

    2014-01-01

    Spinal muscular atrophy (SMA) is an autosomal recessive disorder leading to paralysis and early death due to reduced SMN protein. It is unclear why there is such a profound motor neuron loss, but recent evidence from fly and mouse studies indicate that cells comprising the whole sensory-motor circuit may contribute to motor neuron dysfunction and loss. Here, we used induced pluripotent stem cells derived from SMA patients to test whether sensory neurons directly contribute to motor neuron loss. We generated sensory neurons from SMA induced pluripotent stem cells and found no difference in neuron generation or survival, although there was a reduced calcium response to depolarizing stimuli. Using co-culture of SMA induced pluripotent stem cell derived sensory neurons with control induced pluripotent stem cell derived motor neurons, we found no significant reduction in motor neuron number or glutamate transporter boutons on motor neuron cell bodies or neurites. We conclude that SMA sensory neurons do not overtly contribute to motor neuron loss in this human stem cell system.

  17. A model explaining synchronization of neuron bioelectric frequency under weak alternating low frequency magnetic field

    NASA Astrophysics Data System (ADS)

    del Moral, A.; Azanza, María J.

    2015-03-01

    A biomagnetic-electrical model is presented that explains rather well the experimentally observed synchronization of the bioelectric potential firing rate ("frequency"), f, of single unit neurons of Helix aspersa mollusc under the application of extremely low frequency (ELF) weak alternating (AC) magnetic fields (MF). The proposed model incorporates to our widely experimentally tested model of superdiamagnetism (SD) and Ca2+ Coulomb explosion (CE) from lipid (LP) bilayer membrane (SD-CE model), the electrical quadrupolar long range interaction between the bilayer LP membranes of synchronized neuron pairs, not considered before. The quadrupolar interaction is capable of explaining well the observed synchronization. Actual extension of our SD-CE-model shows that the neuron firing frequency field, B, dependence becomes not modified, but the bioelectric frequency is decreased and its spontaneous temperature, T, dependence is modified. A comparison of the model with synchronization experimental results of pair of neurons under weak (B0 ≅0.2-15 mT) AC-MF of frequency fM=50 Hz is reported. From the deduced size of synchronized LP clusters under B, is suggested the formation of small neuron networks via the membrane lipid correlation.

  18. Lateral information processing by spiking neurons: a theoretical model of the neural correlate of consciousness.

    PubMed

    Ebner, Marc; Hameroff, Stuart

    2011-01-01

    Cognitive brain functions, for example, sensory perception, motor control and learning, are understood as computation by axonal-dendritic chemical synapses in networks of integrate-and-fire neurons. Cognitive brain functions may occur either consciously or nonconsciously (on "autopilot"). Conscious cognition is marked by gamma synchrony EEG, mediated largely by dendritic-dendritic gap junctions, sideways connections in input/integration layers. Gap-junction-connected neurons define a sub-network within a larger neural network. A theoretical model (the "conscious pilot") suggests that as gap junctions open and close, a gamma-synchronized subnetwork, or zone moves through the brain as an executive agent, converting nonconscious "auto-pilot" cognition to consciousness, and enhancing computation by coherent processing and collective integration. In this study we implemented sideways "gap junctions" in a single-layer artificial neural network to perform figure/ground separation. The set of neurons connected through gap junctions form a reconfigurable resistive grid or sub-network zone. In the model, outgoing spikes are temporally integrated and spatially averaged using the fixed resistive grid set up by neurons of similar function which are connected through gap-junctions. This spatial average, essentially a feedback signal from the neuron's output, determines whether particular gap junctions between neurons will open or close. Neurons connected through open gap junctions synchronize their output spikes. We have tested our gap-junction-defined sub-network in a one-layer neural network on artificial retinal inputs using real-world images. Our system is able to perform figure/ground separation where the laterally connected sub-network of neurons represents a perceived object. Even though we only show results for visual stimuli, our approach should generalize to other modalities. The system demonstrates a moving sub-network zone of synchrony, within which the contents of

  19. Neural Plasticity: Single Neuron Models for Discrimination and Generalization and AN Experimental Ensemble Approach.

    NASA Astrophysics Data System (ADS)

    Munro, Paul Wesley

    A special form for modification of neuronal response properties is described in which the change in the synaptic state vector is parallel to the vector of afferent activity. This process is termed "parallel modification" and its theoretical and experimental implications are examined. A theoretical framework has been devised to describe the complementary functions of generalization and discrimination by single neurons. This constitutes a basis for three models each describing processes for the development of maximum selectivity (discrimination) and minimum selectivity (generalization) by neurons. Strengthening and weakening of synapses is expressed as a product of the presynaptic activity and a nonlinear modulatory function of two postsynaptic variables--namely a measure of the spatially integrated activity of the cell and a temporal integration (time-average) of that activity. Some theorems are given for low-dimensional systems and computer simulation results from more complex systems are discussed. Model neurons that achieve high selectivity mimic the development of cat visual cortex neurons in a wide variety of rearing conditions. A role for low-selectivity neurons is proposed in which they provide inhibitory input to neurons of the opposite type, thereby suppressing the common component of a pattern class and enhancing their selective properties. Such contrast-enhancing circuits are analyzed and supported by computer simulation. To enable maximum selectivity, the net inhibition to a cell must become strong enough to offset whatever excitation is produced by the non-preferred patterns. Ramifications of parallel models for certain experimental paradigms are analyzed. A methodology is outlined for testing synaptic modification hypotheses in the laboratory. A plastic projection from one neuronal population to another will attain stable equilibrium under periodic electrical stimulation of constant intensity. The perturbative effect of shifting this intensity level

  20. Microglia activation and interaction with neuronal cells in a biochemical model of mevalonate kinase deficiency.

    PubMed

    Tricarico, Paola Maura; Piscianz, Elisa; Monasta, Lorenzo; Kleiner, Giulio; Crovella, Sergio; Marcuzzi, Annalisa

    2015-08-01

    Mevalonate kinase deficiency is a rare disease whose worst manifestation, characterised by severe neurologic impairment, is called mevalonic aciduria. The progressive neuronal loss associated to cell death can be studied in vitro with a simplified model based on a biochemical block of the mevalonate pathway and a subsequent inflammatory trigger. The aim of this study was to evaluate the effect of the mevalonate blocking on glial cells (BV-2) and the following effects on neuronal cells (SH-SY5Y) when the two populations were cultured together. To better understand the cross-talk between glial and neuronal cells, as it happens in vivo, BV-2 and SH-SY5Y were co-cultured in different experimental settings (alone, transwell, direct contact); the effect of mevalonate pathway biochemical block by Lovastatin, followed by LPS inflammatory trigger, were evaluated by analysing programmed cell death and mitochondrial membrane potential, cytokines' release and cells' morphology modifications. In this experimental condition, glial cells underwent an evident activation, confirmed by elevated pro-inflammatory cytokines release, typical of these disorders, and a modification in morphology. Moreover, the activation induced an increase in apoptosis. When glial cells were co-cultured with neurons, their activation caused an increase of programmed cell death also in neuronal cells, but only if the two populations were cultured in direct contact. Our findings, being aware of the limitations related to the cell models used, represent a preliminary step towards understanding the pathological and neuroinflammatory mechanisms occurring in mevalonate kinase diseases. Contact co-culture between neuronal and microglial cells seems to be a good model to study mevalonic aciduria in vitro, and to contribute to the identification of potential drugs able to block microglial activation for this orphan disease. In fact, in such a pathological condition, we demonstrated that microglial cells are

  1. Effect of antioxidant treatment on spinal GABA neurons in a neuropathic pain model in the mouse.

    PubMed

    Yowtak, June; Wang, Jigong; Kim, Hee Young; Lu, Ying; Chung, Kyungsoon; Chung, Jin Mo

    2013-11-01

    One feature of neuropathic pain is a reduced spinal gamma-aminobutyric acid (GABA)-ergic inhibitory function. However, the mechanisms behind this attenuation remain to be elucidated. This study investigated the involvement of reactive oxygen species in the spinal GABA neuron loss and reduced GABA neuron excitability in spinal nerve ligation (SNL) model of neuropathic pain in mice. The importance of spinal GABAergic inhibition in neuropathic pain was tested by examining the effects of intrathecally administered GABA receptor agonists and antagonists in SNL and naïve mice, respectively. The effects of SNL and antioxidant treatment on GABA neuron loss and functional changes were examined in transgenic GAD67-enhanced green fluorescent protein positive (EGFP+) mice. GABA receptor agonists transiently reversed mechanical hypersensitivity of the hind paw in SNL mice. On the other hand, GABA receptor antagonists made naïve mice mechanically hypersensitive. Stereological analysis showed that the numbers of enhanced green fluorescent protein positive (EGFP+) GABA neurons were significantly decreased in the lateral superficial laminae (I-II) on the ipsilateral L5 spinal cord after SNL. Repeated antioxidant treatments significantly reduced the pain behaviors and prevented the reduction in EGFP+ GABA neurons. The response rate of the tonic firing GABA neurons recorded from SNL mice increased with antioxidant treatment, whereas no change was seen in those recorded from naïve mice, which suggested that oxidative stress impaired some spinal GABA neuron activity in the neuropathic pain condition. Together the data suggest that neuropathic pain, at least partially, is attributed to oxidative stress, which induces both a GABA neuron loss and dysfunction of surviving GABA neurons. Copyright © 2013 International Association for the Study of Pain. Published by Elsevier B.V. All rights reserved.

  2. Aberrant neuronal activity-induced signaling and gene expression in a mouse model of RASopathy

    PubMed Central

    Nakhaei-Rad, Saeideh; Montenegro-Venegas, Carolina; Pina-Fernández, Eneko; Marini, Claudia; Santos, Monica; Ahmadian, Mohammad R.; Stork, Oliver; Zenker, Martin

    2017-01-01

    Noonan syndrome (NS) is characterized by reduced growth, craniofacial abnormalities, congenital heart defects, and variable cognitive deficits. NS belongs to the RASopathies, genetic conditions linked to mutations in components and regulators of the Ras signaling pathway. Approximately 50% of NS cases are caused by mutations in PTPN11. However, the molecular mechanisms underlying cognitive impairments in NS patients are still poorly understood. Here, we report the generation and characterization of a new conditional mouse strain that expresses the overactive Ptpn11D61Y allele only in the forebrain. Unlike mice with a global expression of this mutation, this strain is viable and without severe systemic phenotype, but shows lower exploratory activity and reduced memory specificity, which is in line with a causal role of disturbed neuronal Ptpn11 signaling in the development of NS-linked cognitive deficits. To explore the underlying mechanisms we investigated the neuronal activity-regulated Ras signaling in brains and neuronal cultures derived from this model. We observed an altered surface expression and trafficking of synaptic glutamate receptors, which are crucial for hippocampal neuronal plasticity. Furthermore, we show that the neuronal activity-induced ERK signaling, as well as the consecutive regulation of gene expression are strongly perturbed. Microarray-based hippocampal gene expression profiling revealed profound differences in the basal state and upon stimulation of neuronal activity. The neuronal activity-dependent gene regulation was strongly attenuated in Ptpn11D61Y neurons. In silico analysis of functional networks revealed changes in the cellular signaling beyond the dysregulation of Ras/MAPK signaling that is nearly exclusively discussed in the context of NS at present. Importantly, changes in PI3K/AKT/mTOR and JAK/STAT signaling were experimentally confirmed. In summary, this study uncovers aberrant neuronal activity-induced signaling and regulation

  3. Neuroanatomical and functional characterization of CRF neurons of the amygdala using a novel transgenic mouse model.

    PubMed

    De Francesco, P N; Valdivia, S; Cabral, A; Reynaldo, M; Raingo, J; Sakata, I; Osborne-Lawrence, S; Zigman, J M; Perelló, M

    2015-03-19

    The corticotropin-releasing factor (CRF)-producing neurons of the amygdala have been implicated in behavioral and physiological responses associated with fear, anxiety, stress, food intake and reward. To overcome the difficulties in identifying CRF neurons within the amygdala, a novel transgenic mouse line, in which the humanized recombinant Renilla reniformis green fluorescent protein (hrGFP) is under the control of the CRF promoter (CRF-hrGFP mice), was developed. First, the CRF-hrGFP mouse model was validated and the localization of CRF neurons within the amygdala was systematically mapped. Amygdalar hrGFP-expressing neurons were located primarily in the interstitial nucleus of the posterior limb of the anterior commissure, but also present in the central amygdala. Secondly, the marker of neuronal activation c-Fos was used to explore the response of amygdalar CRF neurons in CRF-hrGFP mice under different experimental paradigms. C-Fos induction was observed in CRF neurons of CRF-hrGFP mice exposed to an acute social defeat stress event, a fasting/refeeding paradigm or lipopolysaccharide (LPS) administration. In contrast, no c-Fos induction was detected in CRF neurons of CRF-hrGFP mice exposed to restraint stress, forced swimming test, 48-h fasting, acute high-fat diet (HFD) consumption, intermittent HFD consumption, ad libitum HFD consumption, HFD withdrawal, conditioned HFD aversion, ghrelin administration or melanocortin 4 receptor agonist administration. Thus, this study fully characterizes the distribution of amygdala CRF neurons in mice and suggests that they are involved in some, but not all, stress or food intake-related behaviors recruiting the amygdala.

  4. Altering neuronal excitability to preserve network connectivity in a computational model of Alzheimer's disease.

    PubMed

    de Haan, Willem; van Straaten, Elisabeth C W; Gouw, Alida A; Stam, Cornelis J

    2017-09-01

    Neuronal hyperactivity and hyperexcitability of the cerebral cortex and hippocampal region is an increasingly observed phenomenon in preclinical Alzheimer's disease (AD). In later stages, oscillatory slowing and loss of functional connectivity are ubiquitous. Recent evidence suggests that neuronal dynamics have a prominent role in AD pathophysiology, making it a potentially interesting therapeutic target. However, although neuronal activity can be manipulated by various (non-)pharmacological means, intervening in a highly integrated system that depends on complex dynamics can produce counterintuitive and adverse effects. Computational dynamic network modeling may serve as a virtual test ground for developing effective interventions. To explore this approach, a previously introduced large-scale neural mass network with human brain topology was used to simulate the temporal evolution of AD-like, activity-dependent network degeneration. In addition, six defense strategies that either enhanced or diminished neuronal excitability were tested against the degeneration process, targeting excitatory and inhibitory neurons combined or separately. Outcome measures described oscillatory, connectivity and topological features of the damaged networks. Over time, the various interventions produced diverse large-scale network effects. Contrary to our hypothesis, the most successful strategy was a selective stimulation of all excitatory neurons in the network; it substantially prolonged the preservation of network integrity. The results of this study imply that functional network damage due to pathological neuronal activity can be opposed by targeted adjustment of neuronal excitability levels. The present approach may help to explore therapeutic effects aimed at preserving or restoring neuronal network integrity and contribute to better-informed intervention choices in future clinical trials in AD.

  5. Functional astrocyte-neuron lactate shuttle in a human stem cell-derived neuronal network.

    PubMed

    Tarczyluk, Marta A; Nagel, David A; O'Neil, John D; Parri, H Rheinallt; Tse, Erin H Y; Coleman, Michael D; Hill, Eric J

    2013-09-01

    The NT2.D1 cell line is one of the most well-documented embryocarcinoma cell lines, and can be differentiated into neurons and astrocytes. Great focus has also been placed on defining the electrophysiological properties of the neuronal cells, and more recently we have investigated the functional properties of their associated astrocytes. We now show for the first time that human stem cell-derived astrocytes produce glycogen and that co-cultures of these cells demonstrate a functional astrocyte-neuron lactate shuttle (ANLS). The ANLS hypothesis proposes that during neuronal activity, glutamate released into the synaptic cleft is taken up by astrocytes and triggers glucose uptake, which is converted into lactate and released via monocarboxylate transporters for neuronal use. Using mixed cultures of NT2-derived neurons and astrocytes, we have shown that these cells modulate their glucose uptake in response to glutamate. Additionally, we demonstrate that in response to increased neuronal activity and under hypoglycaemic conditions, co-cultures modulate glycogen turnover and increase lactate production. Similar results were also shown after treatment with glutamate, potassium, isoproterenol, and dbcAMP. Together, these results demonstrate for the first time a functional ANLS in a human stem cell-derived co-culture.

  6. Modeling the network dynamics of pulse-coupled neurons

    NASA Astrophysics Data System (ADS)

    Chandra, Sarthak; Hathcock, David; Crain, Kimberly; Antonsen, Thomas M.; Girvan, Michelle; Ott, Edward

    2017-03-01

    We derive a mean-field approximation for the macroscopic dynamics of large networks of pulse-coupled theta neurons in order to study the effects of different network degree distributions and degree correlations (assortativity). Using the ansatz of Ott and Antonsen [Chaos 18, 037113 (2008)], we obtain a reduced system of ordinary differential equations describing the mean-field dynamics, with significantly lower dimensionality compared with the complete set of dynamical equations for the system. We find that, for sufficiently large networks and degrees, the dynamical behavior of the reduced system agrees well with that of the full network. This dimensional reduction allows for an efficient characterization of system phase transitions and attractors. For networks with tightly peaked degree distributions, the macroscopic behavior closely resembles that of fully connected networks previously studied by others. In contrast, networks with highly skewed degree distributions exhibit different macroscopic dynamics due to the emergence of degree dependent behavior of different oscillators. For nonassortative networks (i.e., networks without degree correlations), we observe the presence of a synchronously firing phase that can be suppressed by the presence of either assortativity or disassortativity in the network. We show that the results derived here can be used to analyze the effects of network topology on macroscopic behavior in neuronal networks in a computationally efficient fashion.

  7. Modelling the Effects of Electrical Coupling between Unmyelinated Axons of Brainstem Neurons Controlling Rhythmic Activity

    PubMed Central

    Hull, Michael J.; Soffe, Stephen R.; Willshaw, David J.; Roberts, Alan

    2015-01-01

    Gap junctions between fine unmyelinated axons can electrically couple groups of brain neurons to synchronise firing and contribute to rhythmic activity. To explore the distribution and significance of electrical coupling, we modelled a well analysed, small population of brainstem neurons which drive swimming in young frog tadpoles. A passive network of 30 multicompartmental neurons with unmyelinated axons was used to infer that: axon-axon gap junctions close to the soma gave the best match to experimentally measured coupling coefficients; axon diameter had a strong influence on coupling; most neurons were coupled indirectly via the axons of other neurons. When active channels were added, gap junctions could make action potential propagation along the thin axons unreliable. Increased sodium and decreased potassium channel densities in the initial axon segment improved action potential propagation. Modelling suggested that the single spike firing to step current injection observed in whole-cell recordings is not a cellular property but a dynamic consequence of shunting resulting from electrical coupling. Without electrical coupling, firing of the population during depolarising current was unsynchronised; with coupling, the population showed synchronous recruitment and rhythmic firing. When activated instead by increasing levels of modelled sensory pathway input, the population without electrical coupling was recruited incrementally to unpatterned activity. However, when coupled, the population was recruited all-or-none at threshold into a rhythmic swimming pattern: the tadpole “decided” to swim. Modelling emphasises uncertainties about fine unmyelinated axon physiology but, when informed by biological data, makes general predictions about gap junctions: locations close to the soma; relatively small numbers; many indirect connections between neurons; cause of action potential propagation failure in fine axons; misleading alteration of intrinsic firing

  8. Restoring the encoding properties of a stochastic neuron model by an exogenous noise.

    PubMed

    Paffi, Alessandra; Camera, Francesca; Apollonio, Francesca; d'Inzeo, Guglielmo; Liberti, Micaela

    2015-01-01

    Here we evaluate the possibility of improving the encoding properties of an impaired neuronal system by superimposing an exogenous noise to an external electric stimulation signal. The approach is based on the use of mathematical neuron models consisting of stochastic HH-like circuit, where the impairment of the endogenous presynaptic inputs is described as a subthreshold injected current and the exogenous stimulation signal is a sinusoidal voltage perturbation across the membrane. Our results indicate that a correlated Gaussian noise, added to the sinusoidal signal can significantly increase the encoding properties of the impaired system, through the Stochastic Resonance (SR) phenomenon. These results suggest that an exogenous noise, suitably tailored, could improve the efficacy of those stimulation techniques used in neuronal systems, where the presynaptic sensory neurons are impaired and have to be artificially bypassed.

  9. Restoring the encoding properties of a stochastic neuron model by an exogenous noise

    PubMed Central

    Paffi, Alessandra; Camera, Francesca; Apollonio, Francesca; d'Inzeo, Guglielmo; Liberti, Micaela

    2015-01-01

    Here we evaluate the possibility of improving the encoding properties of an impaired neuronal system by superimposing an exogenous noise to an external electric stimulation signal. The approach is based on the use of mathematical neuron models consisting of stochastic HH-like circuit, where the impairment of the endogenous presynaptic inputs is described as a subthreshold injected current and the exogenous stimulation signal is a sinusoidal voltage perturbation across the membrane. Our results indicate that a correlated Gaussian noise, added to the sinusoidal signal can significantly increase the encoding properties of the impaired system, through the Stochastic Resonance (SR) phenomenon. These results suggest that an exogenous noise, suitably tailored, could improve the efficacy of those stimulation techniques used in neuronal systems, where the presynaptic sensory neurons are impaired and have to be artificially bypassed. PMID:25999845

  10. Effects of nanosecond pulsed electric fields on the activity of a Hodgkin and Huxley neuron model.

    PubMed

    Camera, F; Paffi, A; Merla, C; Denzi, A; Apollonio, F; Marracino, P; d'Inzeo, G; Liberti, M

    2012-01-01

    The cell membrane poration is one of the main assessed biological effects of nanosecond pulsed electric fields (nsPEF). This structural change of the cell membrane appears soon after the pulse delivery and lasts for a time period long enough to modify the electrical activity of excitable membranes in neurons. Inserting such a phenomenon in a Hodgkin and Huxley neuron model by means of an enhanced time varying conductance resulted in the temporary inhibition of the action potential generation. The inhibition time is a function of the level of poration, the pore resealing time and the background stimulation level of the neuron. Such results suggest that the neuronal activity may be efficiently modulated by the delivery of repeated pulses. This opens the way to the use of nsPEFs as a stimulation technique alternative to the conventional direct electric stimulation for medical applications such as chronic pain treatment.

  11. Minocycline reduces neuroinflammation but does not ameliorate neuron loss in a mouse model of neurodegeneration.

    PubMed

    Cheng, Shanshan; Hou, Jinxing; Zhang, Chen; Xu, Congyu; Wang, Long; Zou, Xiaoxia; Yu, Huahong; Shi, Yun; Yin, Zhenyu; Chen, Guiquan

    2015-05-22

    Minocycline is a broad-spectrum tetracycline antibiotic. A number of preclinical studies have shown that minocycline exhibits neuroprotective effects in various animal models of neurological diseases. However, it remained unknown whether minocycline is effective to prevent neuron loss. To systematically evaluate its effects, minocycline was used to treat Dicer conditional knockout (cKO) mice which display age-related neuron loss. The drug was given to mutant mice prior to the occurrence of neuroinflammation and neurodegeneration, and the treatment had lasted 2 months. Levels of inflammation markers, including glial fibrillary acidic protein (GFAP), ionized calcium-binding adapter molecule1 (Iba1) and interleukin6 (IL6), were significantly reduced in minocycline-treated Dicer cKO mice. In contrast, levels of neuronal markers and the total number of apoptotic cells in Dicer cKO mice were not affected by the drug. In summary, inhibition of neuroinflammation by minocycline is insufficient to prevent neuron loss and apoptosis.

  12. A model for experience-dependent changes in the responses of inferotemporal neurons.

    PubMed

    Sohal, V S; Hasselmo, M E

    2000-08-01

    Neurons in inferior temporal (IT) cortex exhibit selectivity for complex visual stimuli and can maintain activity during the delay following the presentation of a stimulus in delayed match to sample tasks. Experimental work in awake monkeys has shown that the responses of IT neurons decline during presentation of stimuli which have been seen recently (within the past few seconds). In addition, experiments have found that the responses of IT neurons to visual stimuli also decline as the stimuli become familiar, independent of recency. Here a biologically based neural network simulation is used to model these effects primarily through two processes. The recency effects are caused by adaptation due to a calcium-dependent potassium current, and the familiarity effects are caused by competitive self-organization of modifiable feedforward synapses terminating on IT cortex neurons.

  13. Automatic Construction of Predictive Neuron Models through Large Scale Assimilation of Electrophysiological Data

    NASA Astrophysics Data System (ADS)

    Nogaret, Alain; Meliza, C. Daniel; Margoliash, Daniel; Abarbanel, Henry D. I.

    2016-09-01

    We report on the construction of neuron models by assimilating electrophysiological data with large-scale constrained nonlinear optimization. The method implements interior point line parameter search to determine parameters from the responses to intracellular current injections of zebra finch HVC neurons. We incorporated these parameters into a nine ionic channel conductance model to obtain completed models which we then use to predict the state of the neuron under arbitrary current stimulation. Each model was validated by successfully predicting the dynamics of the membrane potential induced by 20–50 different current protocols. The dispersion of parameters extracted from different assimilation windows was studied. Differences in constraints from current protocols, stochastic variability in neuron output, and noise behave as a residual temperature which broadens the global minimum of the objective function to an ellipsoid domain whose principal axes follow an exponentially decaying distribution. The maximum likelihood expectation of extracted parameters was found to provide an excellent approximation of the global minimum and yields highly consistent kinetics for both neurons studied. Large scale assimilation absorbs the intrinsic variability of electrophysiological data over wide assimilation windows. It builds models in an automatic manner treating all data as equal quantities and requiring minimal additional insight.

  14. Automatic Construction of Predictive Neuron Models through Large Scale Assimilation of Electrophysiological Data

    PubMed Central

    Nogaret, Alain; Meliza, C. Daniel; Margoliash, Daniel; Abarbanel, Henry D. I.

    2016-01-01

    We report on the construction of neuron models by assimilating electrophysiological data with large-scale constrained nonlinear optimization. The method implements interior point line parameter search to determine parameters from the responses to intracellular current injections of zebra finch HVC neurons. We incorporated these parameters into a nine ionic channel conductance model to obtain completed models which we then use to predict the state of the neuron under arbitrary current stimulation. Each model was validated by successfully predicting the dynamics of the membrane potential induced by 20–50 different current protocols. The dispersion of parameters extracted from different assimilation windows was studied. Differences in constraints from current protocols, stochastic variability in neuron output, and noise behave as a residual temperature which broadens the global minimum of the objective function to an ellipsoid domain whose principal axes follow an exponentially decaying distribution. The maximum likelihood expectation of extracted parameters was found to provide an excellent approximation of the global minimum and yields highly consistent kinetics for both neurons studied. Large scale assimilation absorbs the intrinsic variability of electrophysiological data over wide assimilation windows. It builds models in an automatic manner treating all data as equal quantities and requiring minimal additional insight. PMID:27605157

  15. A Coupled Phase-Temperature Model for Dynamics of Transient Neuronal Signal in Mammals Cold Receptor.

    PubMed

    Kirana, Firman Ahmad; Alatas, Husin; Husein, Irzaman Sulaiman

    2016-01-01

    We propose a theoretical model consisting of coupled differential equation of membrane potential phase and temperature for describing the neuronal signal in mammals cold receptor. Based on the results from previous work by Roper et al., we modified a nonstochastic phase model for cold receptor neuronal signaling dynamics in mammals. We introduce a new set of temperature adjusted functional parameters which allow saturation characteristic at high and low steady temperatures. The modified model also accommodates the transient neuronal signaling process from high to low temperature by introducing a nonlinear differential equation for the "effective temperature" changes which is coupled to the phase differential equation. This simple model can be considered as a candidate for describing qualitatively the physical mechanism of the corresponding transient process.

  16. A Coupled Phase-Temperature Model for Dynamics of Transient Neuronal Signal in Mammals Cold Receptor

    PubMed Central

    Kirana, Firman Ahmad; Husein, Irzaman Sulaiman

    2016-01-01

    We propose a theoretical model consisting of coupled differential equation of membrane potential phase and temperature for describing the neuronal signal in mammals cold receptor. Based on the results from previous work by Roper et al., we modified a nonstochastic phase model for cold receptor neuronal signaling dynamics in mammals. We introduce a new set of temperature adjusted functional parameters which allow saturation characteristic at high and low steady temperatures. The modified model also accommodates the transient neuronal signaling process from high to low temperature by introducing a nonlinear differential equation for the “effective temperature” changes which is coupled to the phase differential equation. This simple model can be considered as a candidate for describing qualitatively the physical mechanism of the corresponding transient process. PMID:27774102

  17. Modeling of multisensory convergence with a network of spiking neurons: a reverse engineering approach.

    PubMed

    Lim, Hun Ki; Keniston, Leslie P; Cios, Krzysztof J

    2011-07-01

    Multisensory processing in the brain underlies a wide variety of perceptual phenomena, but little is known about the underlying mechanisms of how multisensory neurons are formed. This lack of knowledge is due to the difficulty for biological experiments to manipulate and test the parameters of multisensory convergence, the first and definitive step in the multisensory process. Therefore, by using a computational model of multisensory convergence, this study seeks to provide insight into the mechanisms of multisensory convergence. To reverse-engineer multisensory convergence, we used a biologically realistic neuron model and a biology-inspired plasticity rule, but did not make any a priori assumptions about multisensory properties of neurons in the network. The network consisted of two separate projection areas that converged upon neurons in a third area, and stimulation involved activation of one of the projection areas (or the other) or their combination. Experiments consisted of two parts: network training and multisensory simulation. Analyses were performed, first, to find multisensory properties in the simulated networks; second, to reveal properties of the network using graph theoretical approach; and third, to generate hypothesis related to the multisensory convergence. The results showed that the generation of multisensory neurons related to the topological properties of the network, in particular, the strengths of connections after training, was found to play an important role in forming and thus distinguishing multisensory neuron types.

  18. Ionic channels and conductance-based models for hypothalamic neuronal thermosensitivity.

    PubMed

    Wechselberger, Martin; Wright, Chadwick L; Bishop, Georgia A; Boulant, Jack A

    2006-09-01

    Thermoregulatory responses are partially controlled by the preoptic area and anterior hypothalamus (PO/AH), which contains a mixed population of temperature-sensitive and insensitive neurons. Immunohistochemical procedures identified the extent of various ionic channels in rat PO/AH neurons. These included pacemaker current channels [i.e., hyperpolarization-activated cyclic nucleotide-gated channels (HCN)], background potassium leak channels (TASK-1 and TRAAK), and transient receptor potential channel (TRP) TRPV4. PO/AH neurons showed dense TASK-1 and HCN-2 immunoreactivity and moderate TRAAK and HCN-4 immunoreactivity. In contrast, the neuronal cell bodies did not label for TRPV4, but instead, punctate labeling was observed in traversing axons or their terminal endings. On the basis of these results and previous electrophysiological studies, Hodgkin-Huxley-like models were constructed. These models suggest that most PO/AH neurons have the same types of ionic channels, but different levels of channel expression can explain the inherent properties of the various types of temperature-sensitive and insensitive neurons.

  19. Neural networks with multiple general neuron models: a hybrid computational intelligence approach using Genetic Programming.

    PubMed

    Barton, Alan J; Valdés, Julio J; Orchard, Robert

    2009-01-01

    Classical neural networks are composed of neurons whose nature is determined by a certain function (the neuron model), usually pre-specified. In this paper, a type of neural network (NN-GP) is presented in which: (i) each neuron may have its own neuron model in the form of a general function, (ii) any layout (i.e network interconnection) is possible, and (iii) no bias nodes or weights are associated to the connections, neurons or layers. The general functions associated to a neuron are learned by searching a function space. They are not provided a priori, but are rather built as part of an Evolutionary Computation process based on Genetic Programming. The resulting network solutions are evaluated based on a fitness measure, which may, for example, be based on classification or regression errors. Two real-world examples are presented to illustrate the promising behaviour on classification problems via construction of a low-dimensional representation of a high-dimensional parameter space associated to the set of all network solutions.

  20. Phase diagrams and dynamics of a computationally efficient map-based neuron model

    PubMed Central

    Gonsalves, Jheniffer J.; Tragtenberg, Marcelo H. R.

    2017-01-01

    We introduce a new map-based neuron model derived from the dynamical perceptron family that has the best compromise between computational efficiency, analytical tractability, reduced parameter space and many dynamical behaviors. We calculate bifurcation and phase diagrams analytically and computationally that underpins a rich repertoire of autonomous and excitable dynamical behaviors. We report the existence of a new regime of cardiac spikes corresponding to nonchaotic aperiodic behavior. We compare the features of our model to standard neuron models currently available in the literature. PMID:28358843

  1. Cofilin inhibition restores neuronal cell death in oxygen glucose deprivation model of ischemia

    PubMed Central

    Madineni, Anusha; Alhadidi, Qasim; Shah, Zahoor A.

    2014-01-01

    Ischemia is a condition associated with decreased blood supply to the brain, eventually leading to death of neurons. It is associated with a diverse cascade of responses involving both degenerative and regenerative mechanisms. At the cellular level, the changes are initiated prominently in the neuronal cytoskeleton. Cofilin, a cytoskeletal actin severing protein, is known to be involved in the early stages of apoptotic cell death. Evidence supports its intervention in the progression of disease states like Alzheimer's and ischemic kidney disease. In the present study, we have hypothesized the possible involvement of cofilin in ischemia. Using PC12 cells and mouse primary cultures of cortical neurons, we investigated the potential role of cofilin in ischemia in two different in vitro ischemic models: chemical induced oxidative stress and oxygen-glucose deprivation/reperfusion (OGD/R). The expression profile studies demonstrated a decrease in phosphocofilin levels in all models of ischemia, implying stress-induced cofilin activation. Furthermore, calcineurin and slingshot 1L (SSH) phosphatases were found to be the signaling mediators of the cofilin activation. In primary cultures of cortical neurons, cofilin was found to be significantly activated after 1 h of OGD. To delineate the role of activated cofilin in ischemia, we knocked down cofilin by siRNA technique and tested the impact of cofilin silencing on neuronal viability. Cofilin siRNA-treated neurons showed a significant reduction of cofilin levels in all treatment groups (control, OGD and OGD/R). Additionally, cofilin siRNA reduced cofilin mitochondrial translocation and caspase 3 cleavage, with a concomitant increase in neuronal viability. These results strongly support the active role of cofilin in ischemia-induced neuronal degeneration and apoptosis. We believe that targeting this protein mediator has a potential for therapeutic intervention in ischemic brain injury and stroke. PMID:25526862

  2. Cofilin Inhibition Restores Neuronal Cell Death in Oxygen-Glucose Deprivation Model of Ischemia.

    PubMed

    Madineni, Anusha; Alhadidi, Qasim; Shah, Zahoor A

    2016-03-01

    Ischemia is a condition associated with decreased blood supply to the brain, eventually leading to death of neurons. It is associated with a diverse cascade of responses involving both degenerative and regenerative mechanisms. At the cellular level, the changes are initiated prominently in the neuronal cytoskeleton. Cofilin, a cytoskeletal actin severing protein, is known to be involved in the early stages of apoptotic cell death. Evidence supports its intervention in the progression of disease states like Alzheimer's and ischemic kidney disease. In the present study, we have hypothesized the possible involvement of cofilin in ischemia. Using PC12 cells and mouse primary cultures of cortical neurons, we investigated the potential role of cofilin in ischemia in two different in vitro ischemic models: chemical induced oxidative stress and oxygen-glucose deprivation/reperfusion (OGD/R). The expression profile studies demonstrated a decrease in phosphocofilin levels in all models of ischemia, implying stress-induced cofilin activation. Furthermore, calcineurin and slingshot 1L (SSH) phosphatases were found to be the signaling mediators of the cofilin activation. In primary cultures of cortical neurons, cofilin was found to be significantly activated after 1 h of OGD. To delineate the role of activated cofilin in ischemia, we knocked down cofilin by small interfering RNA (siRNA) technique and tested the impact of cofilin silencing on neuronal viability. Cofilin siRNA-treated neurons showed a significant reduction of cofilin levels in all treatment groups (control, OGD, and OGD/R). Additionally, cofilin siRNA-reduced cofilin mitochondrial translocation and caspase 3 cleavage, with a concomitant increase in neuronal viability. These results strongly support the active role of cofilin in ischemia-induced neuronal degeneration and apoptosis. We believe that targeting this protein mediator has a potential for therapeutic intervention in ischemic brain injury and stroke.

  3. Changes in the Excitability of Neocortical Neurons in a Mouse Model of Amyotrophic Lateral Sclerosis Are Not Specific to Corticospinal Neurons and Are Modulated by Advancing Disease.

    PubMed

    Kim, Juhyun; Hughes, Ethan G; Shetty, Ashwin S; Arlotta, Paola; Goff, Loyal A; Bergles, Dwight E; Brown, Solange P

    2017-09-13

    Cell type-specific changes in neuronal excitability have been proposed to contribute to the selective degeneration of corticospinal neurons in amyotrophic lateral sclerosis (ALS) and to neocortical hyperexcitability, a prominent feature of both inherited and sporadic variants of the disease, but the mechanisms underlying selective loss of specific cell types in ALS are not known. We analyzed the physiological properties of distinct classes of cortical neurons in the motor cortex of hSOD1(G93A) mice of both sexes and found that they all exhibit increases in intrinsic excitability that depend on disease stage. Targeted recordings and in vivo calcium imaging further revealed that neurons adapt their functional properties to normalize cortical excitability as the disease progresses. Although different neuron classes all exhibited increases in intrinsic excitability, transcriptional profiling indicated that the molecular mechanisms underlying these changes are cell type specific. The increases in excitability in both excitatory and inhibitory cortical neurons show that selective dysfunction of neuronal cell types cannot account for the specific vulnerability of corticospinal motor neurons in ALS. Furthermore, the stage-dependent alterations in neuronal function highlight the ability of cortical circuits to adapt as disease progresses. These findings show that both disease stage and cell type must be considered when developing therapeutic strategies for treating ALS.SIGNIFICANCE STATEMENT It is not known why certain classes of neurons preferentially die in different neurodegenerative diseases. It has been proposed that the enhanced excitability of affected neurons is a major contributor to their selective loss. We show using a mouse model of amyotrophic lateral sclerosis (ALS), a disease in which corticospinal neurons exhibit selective vulnerability, that changes in excitability are not restricted to this neuronal class and that excitability does not increase

  4. Irradiation of Neurons with High-Energy Charged Particles: An In Silico Modeling Approach

    PubMed Central

    Alp, Murat; Parihar, Vipan K.; Limoli, Charles L.; Cucinotta, Francis A.

    2015-01-01

    In this work, a stochastic computational model of microscopic energy deposition events is used to study for the first time damage to irradiated neuronal cells of the mouse hippocampus. An extensive library of radiation tracks for different particle types is created to score energy deposition in small voxels and volume segments describing a neuron’s morphology that later are sampled for given particle fluence or dose. Methods included the construction of in silico mouse hippocampal granule cells from neuromorpho.org with spine and filopodia segments stochastically distributed along the dendritic branches. The model is tested with high-energy 56Fe, 12C, and 1H particles and electrons. Results indicate that the tree-like structure of the neuronal morphology and the microscopic dose deposition of distinct particles may lead to different outcomes when cellular injury is assessed, leading to differences in structural damage for the same absorbed dose. The significance of the microscopic dose in neuron components is to introduce specific local and global modes of cellular injury that likely contribute to spine, filopodia, and dendrite pruning, impacting cognition and possibly the collapse of the neuron. Results show that the heterogeneity of heavy particle tracks at low doses, compared to the more uniform dose distribution of electrons, juxtaposed with neuron morphology make it necessary to model the spatial dose painting for specific neuronal components. Going forward, this work can directly support the development of biophysical models of the modifications of spine and dendritic morphology observed after low dose charged particle irradiation by providing accurate descriptions of the underlying physical insults to complex neuron structures at the nano-meter scale. PMID:26252394

  5. Conductance-Based Neuron Models and the Slow Dynamics of Excitability

    PubMed Central

    Soudry, Daniel; Meir, Ron

    2012-01-01

    In recent experiments, synaptically isolated neurons from rat cortical culture, were stimulated with periodic extracellular fixed-amplitude current pulses for extended durations of days. The neuron’s response depended on its own history, as well as on the history of the input, and was classified into several modes. Interestingly, in one of the modes the neuron behaved intermittently, exhibiting irregular firing patterns changing in a complex and variable manner over the entire range of experimental timescales, from seconds to days. With the aim of developing a minimal biophysical explanation for these results, we propose a general scheme, that, given a few assumptions (mainly, a timescale separation in kinetics) closely describes the response of deterministic conductance-based neuron models under pulse stimulation, using a discrete time piecewise linear mapping, which is amenable to detailed mathematical analysis. Using this method we reproduce the basic modes exhibited by the neuron experimentally, as well as the mean response in each mode. Specifically, we derive precise closed-form input-output expressions for the transient timescale and firing rates, which are expressed in terms of experimentally measurable variables, and conform with the experimental results. However, the mathematical analysis shows that the resulting firing patterns in these deterministic models are always regular and repeatable (i.e., no chaos), in contrast to the irregular and variable behavior displayed by the neuron in certain regimes. This fact, and the sensitive near-threshold dynamics of the model, indicate that intrinsic ion channel noise has a significant impact on the neuronal response, and may help reproduce the experimentally observed variability, as we also demonstrate numerically. In a companion paper, we extend our analysis to stochastic conductance-based models, and show how these can be used to reproduce the details of the observed irregular and variable neuronal response

  6. A Possible Role for End-Stopped V1 Neurons in the Perception of Motion: A Computational Model

    PubMed Central

    Zarei Eskikand, Parvin; Kameneva, Tatiana; Ibbotson, Michael R.; Burkitt, Anthony N.; Grayden, David B.

    2016-01-01

    We present a model of the early stages of processing in the visual cortex, in particular V1 and MT, to investigate the potential role of end-stopped V1 neurons in solving the aperture problem. A hierarchical network is used in which the incoming motion signals provided by complex V1 neurons and end-stopped V1 neurons proceed to MT neurons at the next stage. MT neurons are categorized into two types based on their function: integration and segmentation. The role of integration neurons is to propagate unambiguous motion signals arriving from those V1 neurons that emphasize object terminators (e.g. corners). Segmentation neurons detect the discontinuities in the input stimulus to control the activity of integration neurons. Although the activity of the complex V1 neurons at the terminators of the object accurately represents the direction of the motion, their level of activity is less than the activity of the neurons along the edges. Therefore, a model incorporating end-stopped neurons is essential to suppress ambiguous motion signals along the edges of the stimulus. It is shown that the unambiguous motion signals at terminators propagate over the rest of the object to achieve an accurate representation of motion. PMID:27741307

  7. Integrative properties and transfer function of cortical neurons initiating absence seizures in a rat genetic model.

    PubMed

    Williams, Mark S; Altwegg-Boussac, Tristan; Chavez, Mario; Lecas, Sarah; Mahon, Séverine; Charpier, Stéphane

    2016-11-15

    Absence seizures are accompanied by spike-and-wave discharges in cortical electroencephalograms. These complex paroxysmal activities, affecting the thalamocortical networks, profoundly alter cognitive performances and preclude conscious perception. Here, using a well-recognized genetic model of absence epilepsy, we investigated in vivo how information processing was impaired in the ictogenic neurons, i.e. the population of cortical neurons responsible for seizure initiation. In between seizures, ictogenic neurons were more prone to generate bursting activity and their firing response to weak depolarizing events was considerably facilitated compared to control neurons. In the course of seizures, information processing became unstable in ictogenic cells, alternating between an increased and a decreased responsiveness to excitatory inputs, depending on the spike and wave patterns. The state-dependent modulation in the excitability of ictogenic neurons affects their inter-seizure transfer function and their time-to-time responsiveness to incoming inputs during absences. Epileptic seizures result from aberrant cellular and/or synaptic properties that can alter the capacity of neurons to integrate and relay information. During absence seizures, spike-and-wave discharges (SWDs) interfere with incoming sensory inputs and preclude conscious experience. The Genetic Absence Epilepsy Rats from Strasbourg (GAERS), a well-established animal model of absence epilepsy, allows exploration of the cellular basis of this impaired information processing. Here, by combining in vivo electrocorticographic and intracellular recordings from GAERS and control animals, we investigated how the pro-ictogenic properties of seizure-initiating cortical neurons modify their integrative properties and input-output operation during inter-ictal periods and during the spike (S-) and wave (W-) cortical patterns alternating during seizures. In addition to a sustained depolarization and an excessive

  8. Corresponding decrease in neuronal markers signals progressive parvalbumin neuron loss in MAM schizophrenia model.

    PubMed

    Gill, Kathryn M; Grace, Anthony A

    2014-10-01

    Alteration in normal hippocampal (HPC) function attributed to reduced parvalbumin (PV) expression has been consistently reported in schizophrenia patients and in animal models of schizophrenia. However, it is unclear whether there is an overall loss of interneurons as opposed to a reduction in activity-dependent PV content. Co-expression of PV and the constitutively expressed substance P (SP)-receptor protein has been utilized in other models to ascertain the degree of cell survival, as opposed to reduction in activity-dependent PV content, in the HPC. The present study measured the co-expression of PV and SP-receptors in the dentate and dorsal and ventral CA3 subregions of the HPC in the methylazoymethanol acetate (MAM) rat neurodevelopmental model of schizophrenia. In addition, these changes were compared at the post-natal day 27 (PND27) and post-natal day 240 (PND > 240) time points. Brains from PND27 and PND > 240 MAM (n = 8) and saline (SAL, n = 8) treated offspring were immunohistochemically processed for the co-expression of PV and SP-receptors. The dorsal dentate, dorsal CA3 and ventral CA3 subregions of PND27 and PND > 240 MAM rats demonstrated significant reductions in PV but not SP-receptor expression, signifying a loss of PV-content. In contrast, in the ventral dentate the co-expression of PV and SP-receptors was significantly reduced only in PND > 240 MAM animals, suggesting a reduction in cell number. While MAM-induced reduction of PV content occurs in CA3 of dorsal and ventral HPC, the most substantial loss of interneuron number is localized to the ventral dentate of PND > 240 animals. The disparate loss of PV in HPC subregions likely impacts intra-HPC network activity in MAM rats.

  9. A Wiener-type neuronal model in the presence of exponential refractoriness.

    PubMed

    Albano, Giuseppina; Giorno, Virginia; Nobile, Amelia G; Ricciardi, Luigi M

    2007-04-01

    An instantaneous return process in the presence of random refractoriness for Wiener model of single neuron activity is considered. The case of exponential distributed refractoriness is analyzed and expressions for output distributions and interspike intervals density are obtained in closed form. A computational study is performed to elucidate the role played by the model parameters in affecting the firing probabilities and the interspike distribution.

  10. Statistics of a leaky integrate-and-fire model of neurons driven by dichotomous noise

    NASA Astrophysics Data System (ADS)

    Mankin, Romi; Lumi, Neeme

    2016-05-01

    The behavior of a stochastic leaky integrate-and-fire model of neurons is considered. The effect of temporally correlated random neuronal input is modeled as a colored two-level (dichotomous) Markovian noise. Relying on the Riemann method, exact expressions for the output interspike interval density and for the serial correlation coefficient are derived, and their dependence on noise parameters (such as correlation time and amplitude) is analyzed. Particularly, noise-induced sign reversal and a resonancelike amplification of the kurtosis of the interspike interval distribution are established. The features of spike statistics, analytically revealed in our study, are compared with recently obtained results for a perfect integrate-and-fire neuron model.

  11. Emergent Central Pattern Generator Behavior in Gap-Junction-Coupled Hodgkin-Huxley Style Neuron Model

    PubMed Central

    Memelli, Heraldo; Solomon, Irene C.

    2012-01-01

    Most models of central pattern generators (CPGs) involve two distinct nuclei mutually inhibiting one another via synapses. Here, we present a single-nucleus model of biologically realistic Hodgkin-Huxley neurons with random gap junction coupling. Despite no explicit division of neurons into two groups, we observe a spontaneous division of neurons into two distinct firing groups. In addition, we also demonstrate this phenomenon in a simplified version of the model, highlighting the importance of afterhyperpolarization currents (IAHP) to CPGs utilizing gap junction coupling. The properties of these CPGs also appear sensitive to gap junction conductance, probability of gap junction coupling between cells, topology of gap junction coupling, and, to a lesser extent, input current into our simulated nucleus. PMID:23365558

  12. Emergent central pattern generator behavior in gap-junction-coupled Hodgkin-Huxley style neuron model.

    PubMed

    Horn, Kyle G; Memelli, Heraldo; Solomon, Irene C

    2012-01-01

    Most models of central pattern generators (CPGs) involve two distinct nuclei mutually inhibiting one another via synapses. Here, we present a single-nucleus model of biologically realistic Hodgkin-Huxley neurons with random gap junction coupling. Despite no explicit division of neurons into two groups, we observe a spontaneous division of neurons into two distinct firing groups. In addition, we also demonstrate this phenomenon in a simplified version of the model, highlighting the importance of afterhyperpolarization currents (I(AHP)) to CPGs utilizing gap junction coupling. The properties of these CPGs also appear sensitive to gap junction conductance, probability of gap junction coupling between cells, topology of gap junction coupling, and, to a lesser extent, input current into our simulated nucleus.

  13. A psycholinguistic model of natural language parsing implemented in simulated neurons.

    PubMed

    Huyck, Christian R

    2009-12-01

    A natural language parser implemented entirely in simulated neurons is described. It produces a semantic representation based on frames. It parses solely using simulated fatiguing Leaky Integrate and Fire neurons, that are a relatively accurate biological model that is simulated efficiently. The model works on discrete cycles that simulate 10 ms of biological time, so the parser has a simple mapping to psychological parsing time. Comparisons to human parsing studies show that the parser closely approximates this data. The parser makes use of Cell Assemblies and the semantics of lexical items is represented by overlapping hierarchical Cell Assemblies so that semantically related items share neurons. This semantic encoding is used to resolve prepositional phrase attachment ambiguities encountered during parsing. Consequently, the parser provides a neurally-based cognitive model of parsing.

  14. Statistics of a leaky integrate-and-fire model of neurons driven by dichotomous noise.

    PubMed

    Mankin, Romi; Lumi, Neeme

    2016-05-01

    The behavior of a stochastic leaky integrate-and-fire model of neurons is considered. The effect of temporally correlated random neuronal input is modeled as a colored two-level (dichotomous) Markovian noise. Relying on the Riemann method, exact expressions for the output interspike interval density and for the serial correlation coefficient are derived, and their dependence on noise parameters (such as correlation time and amplitude) is analyzed. Particularly, noise-induced sign reversal and a resonancelike amplification of the kurtosis of the interspike interval distribution are established. The features of spike statistics, analytically revealed in our study, are compared with recently obtained results for a perfect integrate-and-fire neuron model.

  15. Theoretical Analysis of Transcranial Magneto-Acoustical Stimulation with Hodgkin-Huxley Neuron Model.

    PubMed

    Yuan, Yi; Chen, Yudong; Li, Xiaoli

    2016-01-01

    Transcranial magneto-acoustical stimulation (TMAS) is a novel stimulation technology in which an ultrasonic wave within a magnetostatic field generates an electric current in an area of interest in the brain to modulate neuronal activities. As a key part of the neural network, neurons transmit information in the nervous system. However, the effect of TMAS on the neuronal firing pattern remains unknown. To address this problem, we investigated the stimulatory mechanism of TMAS on neurons, by using a Hodgkin-Huxley neuron model. The simulation results indicated that the magnetostatic field intensity and ultrasonic power affect the amplitude and interspike interval of neuronal action potential under a continuous wave ultrasound. The simulation results also showed that the ultrasonic power, duty cycle and repetition frequency can alter the firing pattern of neural action potential under pulsed wave ultrasound. This study may help to reveal and explain the biological mechanism of TMAS and to provide a theoretical basis for TMAS in the treatment or rehabilitation of neuropsychiatric disorders.

  16. Theoretical Analysis of Transcranial Magneto-Acoustical Stimulation with Hodgkin-Huxley Neuron Model

    PubMed Central

    Yuan, Yi; Chen, Yudong; Li, Xiaoli

    2016-01-01

    Transcranial magneto-acoustical stimulation (TMAS) is a novel stimulation technology in which an ultrasonic wave within a magnetostatic field generates an electric current in an area of interest in the brain to modulate neuronal activities. As a key part of the neural network, neurons transmit information in the nervous system. However, the effect of TMAS on the neuronal firing pattern remains unknown. To address this problem, we investigated the stimulatory mechanism of TMAS on neurons, by using a Hodgkin-Huxley neuron model. The simulation results indicated that the magnetostatic field intensity and ultrasonic power affect the amplitude and interspike interval of neuronal action potential under a continuous wave ultrasound. The simulation results also showed that the ultrasonic power, duty cycle and repetition frequency can alter the firing pattern of neural action potential under pulsed wave ultrasound. This study may help to reveal and explain the biological mechanism of TMAS and to provide a theoretical basis for TMAS in the treatment or rehabilitation of neuropsychiatric disorders. PMID:27148032

  17. Progranulin gene delivery protects dopaminergic neurons in a mouse model of Parkinson's disease.

    PubMed

    Van Kampen, Jackalina M; Baranowski, David; Kay, Denis G

    2014-01-01

    Parkinson's disease (PD) is a progressive neurodegenerative disorder characterized by tremor, rigidity and akinesia/bradykinesia resulting from the progressive loss of nigrostriatal dopaminergic neurons. To date, only symptomatic treatment is available for PD patients, with no effective means of slowing or stopping the progression of the disease. Progranulin (PGRN) is a 593 amino acid multifunction protein that is widely distributed throughout the CNS, localized primarily in neurons and microglia. PGRN has been demonstrated to be a potent regulator of neuroinflammation and also acts as an autocrine neurotrophic factor, important for long-term neuronal survival. Thus, enhancing PGRN expression may strengthen the cells resistance to disease. In the present study, we have used the 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP) model of PD to investigate the possible use of PGRN gene delivery as a therapy for the prevention or treatment of PD. Viral vector delivery of the PGRN gene was an effective means of elevating PGRN expression in nigrostriatal neurons. When PGRN expression was elevated in the SNC, nigrostriatal neurons were protected from MPTP toxicity in mice, along with a preservation of striatal dopamine content and turnover. Further, protection of nigrostriatal neurons by PGRN gene therapy was accompanied by reductions in markers of MPTP-induced inflammation and apoptosis as well as a complete preservation of locomotor function. We conclude that PGRN gene therapy may have beneficial effects in the treatment of PD.

  18. Neuron-specific antioxidant OXR1 extends survival of a mouse model of amyotrophic lateral sclerosis.

    PubMed

    Liu, Kevin X; Edwards, Benjamin; Lee, Sheena; Finelli, Mattéa J; Davies, Ben; Davies, Kay E; Oliver, Peter L

    2015-05-01

    Amyotrophic lateral sclerosis is a devastating neurodegenerative disorder characterized by the progressive loss of spinal motor neurons. While the aetiological mechanisms underlying the disease remain poorly understood, oxidative stress is a central component of amyotrophic lateral sclerosis and contributes to motor neuron injury. Recently, oxidation resistance 1 (OXR1) has emerged as a critical regulator of neuronal survival in response to oxidative stress, and is upregulated in the spinal cord of patients with amyotrophic lateral sclerosis. Here, we tested the hypothesis that OXR1 is a key neuroprotective factor during amyotrophic lateral sclerosis pathogenesis by crossing a new transgenic mouse line that overexpresses OXR1 in neurons with the SOD1(G93A) mouse model of amyotrophic lateral sclerosis. Interestingly, we report that overexpression of OXR1 significantly extends survival, improves motor deficits, and delays pathology in the spinal cord and in muscles of SOD1(G93A) mice. Furthermore, we find that overexpression of OXR1 in neurons significantly delays non-cell-autonomous neuroinflammatory response, classic complement system activation, and STAT3 activation through transcriptomic analysis of spinal cords of SOD1(G93A) mice. Taken together, these data identify OXR1 as the first neuron-specific antioxidant modulator of pathogenesis and disease progression in SOD1-mediated amyotrophic lateral sclerosis, and suggest that OXR1 may serve as a novel target for future therapeutic strategies. © The Author (2015). Published by Oxford University Press on behalf of the Guarantors of Brain.

  19. Neuron-specific antioxidant OXR1 extends survival of a mouse model of amyotrophic lateral sclerosis

    PubMed Central

    Liu, Kevin X.; Edwards, Benjamin; Lee, Sheena; Finelli, Mattéa J.; Davies, Ben

    2015-01-01

    Amyotrophic lateral sclerosis is a devastating neurodegenerative disorder characterized by the progressive loss of spinal motor neurons. While the aetiological mechanisms underlying the disease remain poorly understood, oxidative stress is a central component of amyotrophic lateral sclerosis and contributes to motor neuron injury. Recently, oxidation resistance 1 (OXR1) has emerged as a critical regulator of neuronal survival in response to oxidative stress, and is upregulated in the spinal cord of patients with amyotrophic lateral sclerosis. Here, we tested the hypothesis that OXR1 is a key neuroprotective factor during amyotrophic lateral sclerosis pathogenesis by crossing a new transgenic mouse line that overexpresses OXR1 in neurons with the SOD1G93A mouse model of amyotrophic lateral sclerosis. Interestingly, we report that overexpression of OXR1 significantly extends survival, improves motor deficits, and delays pathology in the spinal cord and in muscles of SOD1G93A mice. Furthermore, we find that overexpression of OXR1 in neurons significantly delays non-cell-autonomous neuroinflammatory response, classic complement system activation, and STAT3 activation through transcriptomic analysis of spinal cords of SOD1G93A mice. Taken together, these data identify OXR1 as the first neuron-specific antioxidant modulator of pathogenesis and disease progression in SOD1-mediated amyotrophic lateral sclerosis, and suggest that OXR1 may serve as a novel target for future therapeutic strategies. PMID:25753484

  20. High Prevalence of Multistability of Rest States and Bursting in a Database of a Model Neuron

    PubMed Central

    Marin, Bóris; Barnett, William H.; Doloc-Mihu, Anca; Calabrese, Ronald L.; Cymbalyuk, Gennady S.

    2013-01-01

    Flexibility in neuronal circuits has its roots in the dynamical richness of their neurons. Depending on their membrane properties single neurons can produce a plethora of activity regimes including silence, spiking and bursting. What is less appreciated is that these regimes can coexist with each other so that a transient stimulus can cause persistent change in the activity of a given neuron. Such multistability of the neuronal dynamics has been shown in a variety of neurons under different modulatory conditions. It can play either a functional role or present a substrate for dynamical diseases. We considered a database of an isolated leech heart interneuron model that can display silent, tonic spiking and bursting regimes. We analyzed only the cases of endogenous bursters producing functional half-center oscillators (HCOs). Using a one parameter (the leak conductance ()) bifurcation analysis, we extended the database to include silent regimes (stationary states) and systematically classified cases for the coexistence of silent and bursting regimes. We showed that different cases could exhibit two stable depolarized stationary states and two hyperpolarized stationary states in addition to various spiking and bursting regimes. We analyzed all cases of endogenous bursters and found that 18% of the cases were multistable, exhibiting coexistences of stationary states and bursting. Moreover, 91% of the cases exhibited multistability in some range of . We also explored HCOs built of multistable neuron cases with coexisting stationary states and a bursting regime. In 96% of cases analyzed, the HCOs resumed normal alternating bursting after one of the neurons was reset to a stationary state, proving themselves robust against this perturbation. PMID:23505348

  1. Estrous Cycle Induces Peripheral Sensitization in Trigeminal Ganglion Neurons: An Animal Model of Menstrual Migraine.

    PubMed

    Saleeon, Wachirapong; Jansri, Ukkrit; Srikiatkhachorn, Anan; Bongsebandhu-phubhakdi, Saknan

    2016-02-01

    Many women experience menstrual migraines that develop into recurrent migraine attacks during menstruation. In the human menstrual cycle, the estrogen level fluctuates according to changes in the follicular and luteal phases. The rat estrous cycle is used as an animal model to study the effects of estrogen fluctuation. To investigate whether the estrous cycle is involved in migraine development by comparing the neuronal excitability of trigeminal ganglion (TG) neurons in each stage of the estrous cycle. Female rats were divided into four experimental groups based on examinations of the cytologies of vaginal smears, and serum analyses of estrogen levels following each stage of the estrous cycle. The rats in each stage of the estrous cycle were anesthetized and their trigeminal ganglia were removed The collections of trigeminal ganglia were cultured for two to three hours, after which whole-cell patch clamp experiments were recorded to estimate the electrophysiological properties of the TG neurons. There were many vaginal epithelial cells and high estrogen levels in the proestrus and estrus stages of the estrous cycle. Electrophysiological studies revealed that the TG neurons in the proestrus and estrus stages exhibited significantly lower thresholds of stimulation, and significant increase in total spikes compared to the TG neurons that were collected in the diestrus stage. Our results revealed that high estrogen levels in the proestrus and estrus stages altered the thresholds, rheobases, and total spikes of the TG neurons. High estrogen levels in the estrous cycle induced an increase in neuronal excitability and the peripheral sensitization of TG neurons. These findings may provide an explanation for the correlation of estrogen fluctuations during the menstrual cycle with the pathogenesis of menstrual migraines.

  2. Mapping the function of neuronal ion channels in model and experiment

    PubMed Central

    Podlaski, William F; Seeholzer, Alexander; Groschner, Lukas N; Miesenböck, Gero; Ranjan, Rajnish; Vogels, Tim P

    2017-01-01

    Ion channel models are the building blocks of computational neuron models. Their biological fidelity is therefore crucial for the interpretation of simulations. However, the number of published models, and the lack of standardization, make the comparison of ion channel models with one another and with experimental data difficult. Here, we present a framework for the automated large-scale classification of ion channel models. Using annotated metadata and responses to a set of voltage-clamp protocols, we assigned 2378 models of voltage- and calcium-gated ion channels coded in NEURON to 211 clusters. The IonChannelGenealogy (ICGenealogy) web interface provides an interactive resource for the categorization of new and existing models and experimental recordings. It enables quantitative comparisons of simulated and/or measured ion channel kinetics, and facilitates field-wide standardization of experimentally-constrained modeling. DOI: http://dx.doi.org/10.7554/eLife.22152.001 PMID:28267430

  3. Mapping the function of neuronal ion channels in model and experiment.

    PubMed

    Podlaski, William F; Seeholzer, Alexander; Groschner, Lukas N; Miesenböck, Gero; Ranjan, Rajnish; Vogels, Tim P

    2017-03-06

    Ion channel models are the building blocks of computational neuron models. Their biological fidelity is therefore crucial for the interpretation of simulations. However, the number of published models, and the lack of standardization, make the comparison of ion channel models with one another and with experimental data difficult. Here, we present a framework for the automated large-scale classification of ion channel models. Using annotated metadata and responses to a set of voltage-clamp protocols, we assigned 2378 models of voltage- and calcium-gated ion channels coded in NEURON to 211 clusters. The IonChannelGenealogy (ICGenealogy) web interface provides an interactive resource for the categorization of new and existing models and experimental recordings. It enables quantitative comparisons of simulated and/or measured ion channel kinetics, and facilitates field-wide standardization of experimentally-constrained modeling.

  4. Successive neuron loss in the thalamus and cortex in a mouse model of infantile neuronal ceroid lipofuscinosis

    PubMed Central

    Kielar, Catherine; Maddox, Lucy; Bible, Ellen; Pontikis, Charlie C; Macauley, Shannon L; Griffey, Megan A; Wong, Michael; Sands, Mark S; Cooper, Jonathan D

    2007-01-01

    Infantile neuronal ceroid lipofuscinosis (INCL) is caused by deficiency of the lysosomal enzyme, palmitoyl protein thioesterase 1 (PPT1). We have investigated the onset and progression of pathological changes in Ppt1-deficient mice (Ppt1−/−) and the development of their seizure phenotype. Surprisingly, cortical atrophy and neuron loss occurred only late in disease progression, but were preceded by localized astrocytosis within individual thalamic nuclei and the progressive loss of thalamic neurons that relay different sensory modalities to the cortex. This thalamic neuron loss occurred first within the visual system and only subsequently in auditory and somatosensory relay nuclei or the inhibitory reticular thalamic nucleus. The loss of granule neurons and GABAergic interneurons followed in each corresponding cortical region, before the onset of seizure activity. These findings provide novel evidence for successive neuron loss within the thalamus and cortex in Ppt1−/− mice, revealing the thalamus as an important early focus of INCL pathogenesis. PMID:17046272

  5. Models and simulation of 3D neuronal dendritic trees using Bayesian networks.

    PubMed

    López-Cruz, Pedro L; Bielza, Concha; Larrañaga, Pedro; Benavides-Piccione, Ruth; DeFelipe, Javier

    2011-12-01

    Neuron morphology is crucial for neuronal connectivity and brain information processing. Computational models are important tools for studying dendritic morphology and its role in brain function. We applied a class of probabilistic graphical models called Bayesian networks to generate virtual dendrites from layer III pyramidal neurons from three different regions of the neocortex of the mouse. A set of 41 morphological variables were measured from the 3D reconstructions of real dendrites and their probability distributions used in a machine learning algorithm to induce the model from the data. A simulation algorithm is also proposed to obtain new dendrites by sampling values from Bayesian networks. The main advantage of this approach is that it takes into account and automatically locates the relationships between variables in the data instead of using predefined dependencies. Therefore, the methodology can be applied to any neuronal class while at the same time exploiting class-specific properties. Also, a Bayesian network was defined for each part of the dendrite, allowing the relationships to change in the different sections and to model heterogeneous developmental factors or spatial influences. Several univariate statistical tests and a novel multivariate test based on Kullback-Leibler divergence estimation confirmed that virtual dendrites were similar to real ones. The analyses of the models showed relationships that conform to current neuroanatomical knowledge and support model correctness. At the same time, studying the relationships in the models can help to identify new interactions between variables related to dendritic morphology.

  6. Stability and Hopf Bifurcation Analysis in Hindmarsh-Rose Neuron Model with Multiple Time Delays

    NASA Astrophysics Data System (ADS)

    Hu, Dongpo; Cao, Hongjun

    In this paper, the dynamical behaviors of a single Hindmarsh-Rose neuron model with multiple time delays are investigated. By linearizing the system at equilibria and analyzing the associated characteristic equation, the conditions for local stability and the existence of local Hopf bifurcation are obtained. To discuss the properties of Hopf bifurcation, we derive explicit formulas to determine the direction of Hopf bifurcation and the stability of bifurcated periodic solutions occurring through Hopf bifurcation. The qualitative analyses have demonstrated that the values of multiple time delays can affect the stability of equilibrium and play an important role in determining the properties of Hopf bifurcation. Some numerical simulations are given for confirming the qualitative results. Numerical simulations on the effect of delays show that the delays have different scales when the two delay values are not equal. The physiological basis is most likely that Hindmarsh-Rose neuron model has two different time scales. Finally, the bifurcation diagrams of inter-spike intervals of the single Hindmarsh-Rose neuron model are presented. These bifurcation diagrams show the existence of complex bifurcation structures and further indicate that the multiple time delays are very important parameters in determining the dynamical behaviors of the single neuron. Therefore, these results in this paper could be helpful for further understanding the role of multiple time delays in the information transmission and processing of a single neuron.

  7. Phase-model analysis of coupled neuronal oscillators with multiple connections

    NASA Astrophysics Data System (ADS)

    Hwang, Dong-Uk; Lee, Sang-Gui; Han, Seung Kee; Kook, Hyungtae

    2006-09-01

    Synchronization of the coupled neuronal oscillators with multiple connections of different coupling nature is analyzed using the phase-model reduction method. Each coupling connection contributes to the dynamic behavior of the system in a complex nonlinear fashion. In the phase-model scheme, the contribution of the individual connections can be separated in terms of the effective coupling functions associated with each connection and a linear superposition of them provides the total effective coupling of the coupled system. The case of multiple connections with various conduction time delays is also examined, which is shown to be capable of promoting synchronization over an ensemble of spatially distributed neuronal oscillators in an efficient way.

  8. Nonlinear Maps for Design of Discrete Time Models of Neuronal Network Dynamics

    DTIC Science & Technology

    2016-02-29

    Performance/Technic~ 02-01-2016- 02-29-2016 4. TITLE AND SUBTITLE Sa. CONTRACT NUMBER Nonlinear Maps for Design of Discrete-Time Models of Neuronal...Network Dynamics Sb. GRANT NUMBER N00014-16-1-2252 Sc. PROGRAM ELEMENT NUMBER 6. AUTHOR(S) Sd. PROJECT NUMBER Nikolai Rulkov Se. TASK NUMBER Sf...N00014-16-1-2252 Report #1 Performance/Technical Monthly Report Nonlinear Maps for Design of Discrete-Time Models of Neuronal Network Dynamics

  9. Emulating the Visual Receptive Field Properties of MST Neurons with a Template Model of Heading Estimation

    NASA Technical Reports Server (NTRS)

    Perrone, John A.; Stone, Leland S.

    1997-01-01

    We have previously proposed a computational neural-network model by which the complex patterns of retinal image motion generated during locomotion (optic flow) can be processed by specialized detectors acting as templates for specific instances of self-motion. The detectors in this template model respond to global optic flow by sampling image motion over a large portion of the visual field through networks of local motion sensors with properties similar to neurons found in the middle temporal (MT) area of primate extrastriate visual cortex. The model detectors were designed to extract self-translation (heading), self-rotation, as well as the scene layout (relative distances) ahead of a moving observer, and are arranged in cortical-like heading maps to perform this function. Heading estimation from optic flow has been postulated by some to be implemented within the medial superior temporal (MST) area. Others have questioned whether MST neurons can fulfill this role because some of their receptive-field properties appear inconsistent with a role in heading estimation. To resolve this issue, we systematically compared MST single-unit responses with the outputs of model detectors under matched stimulus conditions. We found that the basic physiological properties of MST neurons can be explained by the template model. We conclude that MST neurons are well suited to support heading estimation and that the template model provides an explicit set of testable hypotheses which can guide future exploration of MST and adjacent areas within the primate superior temporal sulcus.

  10. Computational modeling of direct neuronal recruitment during intracortical microstimulation in somatosensory cortex

    NASA Astrophysics Data System (ADS)

    Overstreet, C. K.; Klein, J. D.; Helms Tillery, S. I.

    2013-12-01

    Objective. Electrical stimulation of cortical tissue could be used to deliver sensory information as part of a neuroprosthetic device, but current control of the location, resolution, quality, and intensity of sensations elicited by intracortical microstimulation (ICMS) remains inadequate for this purpose. One major obstacle to resolving this problem is the poor understanding of the neural activity induced by ICMS. Even with new imaging methods, quantifying the activity of many individual neurons within cortex is difficult. Approach. We used computational modeling to examine the response of somatosensory cortex to ICMS. We modeled the axonal arbors of eight distinct morphologies of interneurons and seven types of pyramidal neurons found in somatosensory cortex and identified their responses to extracellular stimulation. We then combined these axonal elements to form a multi-layered slab of simulated cortex and investigated the patterns of neural activity directly induced by ICMS. Specifically we estimated the number, location, and variety of neurons directly recruited by stimulation on a single penetrating microelectrode. Main results. The population of neurons activated by ICMS was dependent on both stimulation strength and the depth of the electrode within cortex. Strikingly, stimulation recruited interneurons and pyramidal neurons in very different patterns. Interneurons are primarily recruited within a dense, continuous region around the electrode, while pyramidal neurons were recruited in a sparse fashion both near the electrode and up to several millimeters away. Thus ICMS can lead to an unexpectedly complex spatial distribution of firing neurons. Significance. These results lend new insights to the complexity and range of neural activity that can be induced by ICMS. This work also suggests mechanisms potentially responsible for the inconsistency and unnatural quality of sensations initiated by ICMS. Understanding these mechanisms will aid in the design of

  11. Rescue of neuronal migration deficits in a mouse model of fetal Minamata disease by increasing neuronal Ca2+ spike frequency.

    PubMed

    Fahrion, Jennifer K; Komuro, Yutaro; Li, Ying; Ohno, Nobuhiko; Littner, Yoav; Raoult, Emilie; Galas, Ludovic; Vaudry, David; Komuro, Hitoshi

    2012-03-27

    In the brains of patients with fetal Minamata disease (FMD), which is caused by exposure to methylmercury (MeHg) during development, many neurons are hypoplastic, ectopic, and disoriented, indicating disrupted migration, maturation, and growth. MeHg affects a myriad of signaling molecules, but little is known about which signals are primary targets for MeHg-induced deficits in neuronal development. In this study, using a mouse model of FMD, we examined how MeHg affects the migration of cerebellar granule cells during early postnatal development. The cerebellum is one of the most susceptible brain regions to MeHg exposure, and profound loss of cerebellar granule cells is detected in the brains of patients with FMD. We show that MeHg inhibits granule cell migration by reducing the frequency of somal Ca(2+) spikes through alterations in Ca(2+), cAMP, and insulin-like growth factor 1 (IGF1) signaling. First, MeHg slows the speed of granule cell migration in a dose-dependent manner, independent of the mode of migration. Second, MeHg reduces the frequency of spontaneous Ca(2+) spikes in granule cell somata in a dose-dependent manner. Third, a unique in vivo live-imaging system for cell migration reveals that reducing the inhibitory effects of MeHg on somal Ca(2+) spike frequency by stimulating internal Ca(2+) release and Ca(2+) influxes, inhibiting cAMP activity, or activating IGF1 receptors ameliorates the inhibitory effects of MeHg on granule cell migration. These results suggest that alteration of Ca(2+) spike frequency and Ca(2+), cAMP, and IGF1 signaling could be potential therapeutic targets for infants with MeHg intoxication.

  12. Synaptic potentiation onto habenula neurons in the learned helplessness model of depression

    SciTech Connect

    Li, B.; Schulz, D.; Li, B; Piriz, J.; Mirrione, M.; Chung, C.H.; Proulx, C.D.; Schulz, D.; Henn, F.; Malinow, R.

    2011-02-24

    The cellular basis of depressive disorders is poorly understood. Recent studies in monkeys indicate that neurons in the lateral habenula (LHb), a nucleus that mediates communication between forebrain and midbrain structures, can increase their activity when an animal fails to receive an expected positive reward or receives a stimulus that predicts aversive conditions (that is, disappointment or anticipation of a negative outcome). LHb neurons project to, and modulate, dopamine-rich regions, such as the ventral tegmental area (VTA), that control reward-seeking behaviour and participate in depressive disorders. Here we show that in two learned helplessness models of depression, excitatory synapses onto LHb neurons projecting to the VTA are potentiated. Synaptic potentiation correlates with an animal's helplessness behaviour and is due to an enhanced presynaptic release probability. Depleting transmitter release by repeated electrical stimulation of LHb afferents, using a protocol that can be effective for patients who are depressed, markedly suppresses synaptic drive onto VTA-projecting LHb neurons in brain slices and can significantly reduce learned helplessness behaviour in rats. Our results indicate that increased presynaptic action onto LHb neurons contributes to the rodent learned helplessness model of depression.

  13. Linked Gauss-Diffusion processes for modeling a finite-size neuronal network.

    PubMed

    Carfora, M F; Pirozzi, E

    2017-08-02

    A Leaky Integrate-and-Fire (LIF) model with stochastic current-based linkages is considered to describe the firing activity of neurons interacting in a (2×2)-size feed-forward network. In the subthreshold regime and under the assumption that no more than one spike is exchanged between coupled neurons, the stochastic evolution of the neuronal membrane voltage is subject to random jumps due to interactions in the network. Linked Gauss-Diffusion processes are proposed to describe this dynamics and to provide estimates of the firing probability density of each neuron. To this end, an iterated integral equation-based approach is applied to evaluate numerically the first passage time density of such processes through the firing threshold. Asymptotic approximations of the firing densities of surrounding neurons are used to obtain closed-form expressions for the mean of the involved processes and to simplify the numerical procedure. An extension of the model to an (N×N)-size network is also given. Histograms of firing times obtained by simulations of the LIF dynamics and numerical firings estimates are compared. Copyright © 2017 Elsevier B.V. All rights reserved.

  14. Probabilistic Inference in General Graphical Models through Sampling in Stochastic Networks of Spiking Neurons

    PubMed Central

    Pecevski, Dejan; Buesing, Lars; Maass, Wolfgang

    2011-01-01

    An important open problem of computational neuroscience is the generic organization of computations in networks of neurons in the brain. We show here through rigorous theoretical analysis that inherent stochastic features of spiking neurons, in combination with simple nonlinear computational operations in specific network motifs and dendritic arbors, enable networks of spiking neurons to carry out probabilistic inference through sampling in general graphical models. In particular, it enables them to carry out probabilistic inference in Bayesian networks with converging arrows (“explaining away”) and with undirected loops, that occur in many real-world tasks. Ubiquitous stochastic features of networks of spiking neurons, such as trial-to-trial variability and spontaneous activity, are necessary ingredients of the underlying computational organization. We demonstrate through computer simulations that this approach can be scaled up to neural emulations of probabilistic inference in fairly large graphical models, yielding some of the most complex computations that have been carried out so far in networks of spiking neurons. PMID:22219717

  15. Computational Modeling of Single Neuron Extracellular Electric Potentials and Network Local Field Potentials using LFPsim.

    PubMed

    Parasuram, Harilal; Nair, Bipin; D'Angelo, Egidio; Hines, Michael; Naldi, Giovanni; Diwakar, Shyam

    2016-01-01

    Local Field Potentials (LFPs) are population signals generated by complex spatiotemporal interaction of current sources and dipoles. Mathematical computations of LFPs allow the study of circuit functions and dysfunctions via simulations. This paper introduces LFPsim, a NEURON-based tool for computing population LFP activity and single neuron extracellular potentials. LFPsim was developed to be used on existing cable compartmental neuron and network models. Point source, line source, and RC based filter approximations can be used to compute extracellular activity. As a demonstration of efficient implementation, we showcase LFPs from mathematical models of electrotonically compact cerebellum granule neurons and morphologically complex neurons of the neocortical column. LFPsim reproduced neocortical LFP at 8, 32, and 56 Hz via current injection, in vitro post-synaptic N2a, N2b waves and in vivo T-C waves in cerebellum granular layer. LFPsim also includes a simulation of multi-electrode array of LFPs in network populations to aid computational inference between biophysical activity in neural networks and corresponding multi-unit activity resulting in extracellular and evoked LFP signals.

  16. Arabidopsis thaliana, a plant model organism for the neuronal microtubule cytoskeleton?

    PubMed

    Gardiner, John; Marc, Jan

    2011-01-01

    The microtubule cytoskeleton is an important component of both neuronal cells and plant cells. While there are large differences in the function of microtubules between the two groups of organisms, for example plants coordinate the ordered deposition of cellulose through the microtubule cytoskeleton, there are also some notable similarities. It is suggested that Arabidopsis thaliana, with its superior availability of knockout lines, may be a suitable model organism for some aspects of the neuronal microtubule cytoskeleton. Some cellular processes that involve the neuronal microtubule cytoskeleton including neurotransmitter signalling and neurotrophic support may have homologous processes in plant cells. A number of microtubule-associated proteins (MAPs) are conserved, including katanin, EB1, CLASP, spastin, gephyrin, CRIPT, Atlastin/RHD3, and ELP3. As a demonstration of the usefulness of a plant model system for neuronal biology, an analysis of plant tubulin-binding proteins was used to show that Charcot-Marie-Tooth disease type 2D and spinal muscular atrophy may be due to microtubule dysfunction and suggest that indeed the plant microtubule cytoskeleton may be particularly similar to that of motor neurons as both are heavily reliant upon motor proteins.

  17. Neurons Self-Organize Around Salivary Epithelial Cells in Novel Co-Culture Model

    PubMed Central

    Sommakia, Salah; Baker, Olga J.

    2016-01-01

    Salivary gland bioengineering requires understanding the interaction between salivary epithelium and surrounding tissues. An important component of salivary glands is the presence of neurons. No previous studies have investigated how neurons and salivary epithelial cells interact in an in vitro co-culture model. In this study, we describe the self-organization of neurons around salivary epithelial cells in co-culture, in a similar fashion to what occurs in native tissue. We cultured primary mouse cortical neurons (m-CN) with a salivary epithelial cell line (Par-C10) on growth factor-reduced Matrigel (GFR-MG) for 4 days. After this time, co-cultures were compared with native salivary glands using confocal microscopy. Our findings indicate that m-CN were able to self-organize basolaterally to salivary epithelial cell clusters in a similar manner to what occurs in native tissue. These results indicate that this model can be developed as a potential platform for studying neuron-salivary epithelial cell interactions for bioengineering purposes. PMID:27833941

  18. Computational Modeling of Single Neuron Extracellular Electric Potentials and Network Local Field Potentials using LFPsim

    PubMed Central

    Parasuram, Harilal; Nair, Bipin; D'Angelo, Egidio; Hines, Michael; Naldi, Giovanni; Diwakar, Shyam

    2016-01-01

    Local Field Potentials (LFPs) are population signals generated by complex spatiotemporal interaction of current sources and dipoles. Mathematical computations of LFPs allow the study of circuit functions and dysfunctions via simulations. This paper introduces LFPsim, a NEURON-based tool for computing population LFP activity and single neuron extracellular potentials. LFPsim was developed to be used on existing cable compartmental neuron and network models. Point source, line source, and RC based filter approximations can be used to compute extracellular activity. As a demonstration of efficient implementation, we showcase LFPs from mathematical models of electrotonically compact cerebellum granule neurons and morphologically complex neurons of the neocortical column. LFPsim reproduced neocortical LFP at 8, 32, and 56 Hz via current injection, in vitro post-synaptic N2a, N2b waves and in vivo T-C waves in cerebellum granular layer. LFPsim also includes a simulation of multi-electrode array of LFPs in network populations to aid computational inference between biophysical activity in neural networks and corresponding multi-unit activity resulting in extracellular and evoked LFP signals. PMID:27445781

  19. Hopf bifurcation of an (n + 1) -neuron bidirectional associative memory neural network model with delays.

    PubMed

    Xiao, Min; Zheng, Wei Xing; Cao, Jinde

    2013-01-01

    Recent studies on Hopf bifurcations of neural networks with delays are confined to simplified neural network models consisting of only two, three, four, five, or six neurons. It is well known that neural networks are complex and large-scale nonlinear dynamical systems, so the dynamics of the delayed neural networks are very rich and complicated. Although discussing the dynamics of networks with a few neurons may help us to understand large-scale networks, there are inevitably some complicated problems that may be overlooked if simplified networks are carried over to large-scale networks. In this paper, a general delayed bidirectional associative memory neural network model with n + 1 neurons is considered. By analyzing the associated characteristic equation, the local stability of the trivial steady state is examined, and then the existence of the Hopf bifurcation at the trivial steady state is established. By applying the normal form theory and the center manifold reduction, explicit formulae are derived to determine the direction and stability of the bifurcating periodic solution. Furthermore, the paper highlights situations where the Hopf bifurcations are particularly critical, in the sense that the amplitude and the period of oscillations are very sensitive to errors due to tolerances in the implementation of neuron interconnections. It is shown that the sensitivity is crucially dependent on the delay and also significantly influenced by the feature of the number of neurons. Numerical simulations are carried out to illustrate the main results.

  20. Silencing neuronal mutant androgen receptor in a mouse model of spinal and bulbar muscular atrophy.

    PubMed

    Sahashi, Kentaro; Katsuno, Masahisa; Hung, Gene; Adachi, Hiroaki; Kondo, Naohide; Nakatsuji, Hideaki; Tohnai, Genki; Iida, Madoka; Bennett, C Frank; Sobue, Gen

    2015-11-01

    Spinal and bulbar muscular atrophy (SBMA), an adult-onset neurodegenerative disease that affects males, results from a CAG triplet repeat/polyglutamine expansions in the androgen receptor (AR) gene. Patients develop progressive muscular weakness and atrophy, and no effective therapy is currently available. The tissue-specific pathogenesis, especially relative pathological contributions between degenerative motor neurons and muscles, remains inconclusive. Though peripheral pathology in skeletal muscle caused by toxic AR protein has been recently reported to play a pivotal role in the pathogenesis of SBMA using mouse models, the role of motor neuron degeneration in SBMA has not been rigorously investigated. Here, we exploited synthetic antisense oligonucleotides to inhibit the RNA levels of mutant AR in the central nervous system (CNS) and explore its therapeutic effects in our SBMA mouse model that harbors a mutant AR gene with 97 CAG expansions and characteristic SBMA-like neurogenic phenotypes. A single intracerebroventricular administration of the antisense oligonucleotides in the presymptomatic phase efficiently suppressed the mutant gene expression in the CNS, and delayed the onset and progression of motor dysfunction, improved body weight gain and survival with the amelioration of neuronal histopathology in motor units such as spinal motor neurons, neuromuscular junctions and skeletal muscle. These findings highlight the importance of the neurotoxicity of mutant AR protein in motor neurons as a therapeutic target. © The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

  1. Synergy of AMPA and NMDA Receptor Currents in Dopaminergic Neurons: A Modeling Study

    PubMed Central

    Zakharov, Denis; Lapish, Christopher; Gutkin, Boris; Kuznetsov, Alexey

    2016-01-01

    Dopaminergic (DA) neurons display two modes of firing: low-frequency tonic and high-frequency bursts. The high frequency firing within the bursts is attributed to NMDA, but not AMPA receptor activation. In our models of the DA neuron, both biophysical and abstract, the NMDA receptor current can significantly increase their firing frequency, whereas the AMPA receptor current is not able to evoke high-frequency activity and usually suppresses firing. However, both currents are produced by glutamate receptors and, consequently, are often co-activated. Here we consider combined influence of AMPA and NMDA synaptic input in the models of the DA neuron. Different types of neuronal activity (resting state, low frequency, or high frequency firing) are observed depending on the conductance of the AMPAR and NMDAR currents. In two models, biophysical and reduced, we show that the firing frequency increases more effectively if both receptors are co-activated for certain parameter values. In particular, in the more quantitative biophysical model, the maximal frequency is 40% greater than that with NMDAR alone. The dynamical mechanism of such frequency growth is explained in the framework of phase space evolution using the reduced model. In short, both the AMPAR and NMDAR currents flatten the voltage nullcline, providing the frequency increase, whereas only NMDA prevents complete unfolding of the nullcline, providing robust firing. Thus, we confirm a major role of the NMDAR in generating high-frequency firing and conclude that AMPAR activation further significantly increases the frequency. PMID:27252643

  2. Computational and Electronic Analog Implementation of the Hodgkin-Huxley Model of Action Potentials in Neurons

    NASA Astrophysics Data System (ADS)

    Smith, Peter; Link, Justin

    2012-02-01

    Alan Loyd Hodgkin and Andrew Huxley's mathematical model of action potential initiation and propagation in neurons is one of the greatest hallmarks of biophysics. Two techniques for implementing the Hodgkin-Huxley model were explored: computational and electronic analog. Computational modeling was done using NEURON 7.1. NEURON is a free, robust, and relatively user friendly simulation environment that enables quantitatively accurate computational modeling of neurons and neural networks. An analog electronic circuit was built using field-effect transistors (FETs) to simulate the non-linear, voltage-dependent (sodium and potassium) conductances that are responsible for membrane excitability. While the electronic analog qualitatively reproduces many of the key features of the action potential including overall shape, inactivation period, and propagation, it was difficult to quantitatively reproduce the Hodgkin-Huxley model. In addition, while the relative cost to build circuits equivalent to small membrane patches is minimal (˜50), implementation of larger cells or networks would prove uneconomical. Still, both techniques are viable avenues toward introducing interdisciplinary research into either a computational or electronics lab setting at the undergraduate level.

  3. Modeling non-syndromic autism and the impact of TRPC6 disruption in human neurons.

    PubMed

    Griesi-Oliveira, K; Acab, A; Gupta, A R; Sunaga, D Y; Chailangkarn, T; Nicol, X; Nunez, Y; Walker, M F; Murdoch, J D; Sanders, S J; Fernandez, T V; Ji, W; Lifton, R P; Vadasz, E; Dietrich, A; Pradhan, D; Song, H; Ming, G-L; Gu, X; Haddad, G; Marchetto, M C N; Spitzer, N; Passos-Bueno, M R; State, M W; Muotri, A R

    2015-11-01

    An increasing number of genetic variants have been implicated in autism spectrum disorders (ASDs), and the functional study of such variants will be critical for the elucidation of autism pathophysiology. Here, we report a de novo balanced translocation disruption of TRPC6, a cation channel, in a non-syndromic autistic individual. Using multiple models, such as dental pulp cells, induced pluripotent stem cell (iPSC)-derived neuronal cells and mouse models, we demonstrate that TRPC6 reduction or haploinsufficiency leads to altered neuronal development, morphology and function. The observed neuronal phenotypes could then be rescued by TRPC6 complementation and by treatment with insulin-like growth factor-1 or hyperforin, a TRPC6-specific agonist, suggesting that ASD individuals with alterations in this pathway may benefit from these drugs. We also demonstrate that methyl CpG binding protein-2 (MeCP2) levels affect TRPC6 expression. Mutations in MeCP2 cause Rett syndrome, revealing common pathways among ASDs. Genetic sequencing of TRPC6 in 1041 ASD individuals and 2872 controls revealed significantly more nonsynonymous mutations in the ASD population, and identified loss-of-function mutations with incomplete penetrance in two patients. Taken together, these findings suggest that TRPC6 is a novel predisposing gene for ASD that may act in a multiple-hit model. This is the first study to use iPSC-derived human neurons to model non-syndromic ASD and illustrate the potential of modeling genetically complex sporadic diseases using such cells.

  4. Modeling non-syndromic autism and the impact of TRPC6 disruption in human neurons

    PubMed Central

    Griesi-Oliveira, Karina; Acab, Allan; Gupta, Abha R.; Sunaga, Daniele Yumi; Chailangkarn, Thanathom; Nicol, Xavier; Nunez, Yanelli; Walker, Michael F.; Murdoch, John D.; Sanders, Stephan J.; Fernandez, Thomas V.; Ji, Weizhen; Lifton, Richard P.; Vadasz, Estevão; Dietrich, Alexander; Pradhan, Dennis; Song, Hongjun; Ming, Guo-li; Guoe, Xiang; Haddad, Gabriel; Marchetto, Maria C. N.; Spitzer, Nicholas; Passos-Bueno, Maria Rita; State, Matthew W.; Muotri, Alysson R.

    2014-01-01

    An increasing number of genetic variants have been implicated in autism spectrum disorders (ASD), and the functional study of such variants will be critical for the elucidation of autism pathophysiology. Here, we report a de novo balanced translocation disruption of TRPC6, a cation channel, in a non-syndromic autistic individual. Using multiple models, such as dental pulp cells, iPSC-derived neuronal cells and mouse models, we demonstrate that TRPC6 reduction or haploinsufficiency leads to altered neuronal development, morphology, and function. The observed neuronal phenotypes could then be rescued by TRPC6 complementation and by treatment with IGF1 or hyperforin, a TRPC6-specific agonist, suggesting that ASD individuals with alterations in this pathway might benefit from these drugs. We also demonstrate that MeCP2 levels affect TRPC6 expression. Mutations in MeCP2 cause Rett syndrome, revealing common pathways among ASDs. Genetic sequencing of TRPC6 in 1041 ASD individuals and 2872 controls revealed significantly more nonsynonymous mutations in the ASD population, and identified loss-of-function mutations with incomplete penetrance in two patients. Taken together, these findings suggest that TRPC6 is a novel predisposing gene for ASD that may act in a multiple-hit model. This is the first study to use iPSC-derived human neurons to model non-syndromic ASD and illustrate the potential of modeling genetically complex sporadic diseases using such cells. PMID:25385366

  5. Transgenic Expression of Glud1 (Glutamate Dehydrogenase 1) in Neurons: In Vivo Model of Enhanced Glutamate Release, Altered Synaptic Plasticity, and Selective Neuronal Vulnerability

    PubMed Central

    Bao, Xiaodong; Pal, Ranu; Hascup, Kevin N.; Wang, Yongfu; Wang, Wen-Tung; Xu, Wenhao; Hui, Dongwei; Agbas, Abdulbaki; Wang, Xinkun; Michaelis, Mary L.; Choi, In-Young; Belousov, Andrei B.; Gerhardt, Greg A.; Michaelis, Elias K.

    2010-01-01

    The effects of lifelong, moderate excess release of glutamate (Glu) in the CNS have not been previously characterized. We created a transgenic (Tg) mouse model of lifelong excess synaptic Glu release in the CNS by introducing the gene for glutamate dehydrogenase 1 (Glud1) under the control of the neuron-specific enolase promoter. Glud1 is, potentially, an important enzyme in the pathway of Glu synthesis in nerve terminals. Increased levels of GLUD protein and activity in CNS neurons of hemizygous Tg mice were associated with increases in the in vivo release of Glu after neuronal depolarization in striatum and in the frequency and amplitude of miniature EPSCs in the CA1 region of the hippocampus. Despite overexpression of Glud1 in all neurons of the CNS, the Tg mice suffered neuronal losses in select brain regions (e.g., the CA1 but not the CA3 region). In vulnerable regions, Tg mice had decreases in MAP2A labeling of dendrites and in synaptophysin labeling of presynaptic terminals; the decreases in neuronal numbers and dendrite and presynaptic terminal labeling increased with advancing age. In addition, the Tg mice exhibited decreases in long-term potentiation of synaptic activity and in spine density in dendrites of CA1 neurons. Behaviorally, the Tg mice were significantly more resistant than wild-type mice to induction and duration of anesthesia produced by anesthetics that suppress Glu neurotransmission. The Glud1 mouse might be a useful model for the effects of lifelong excess synaptic Glu release on CNS neurons and for age-associated neurodegenerative processes. PMID:19890003

  6. Postnatal Gene Therapy Improves Spatial Learning Despite the Presence of Neuronal Ectopia in a Model of Neuronal Migration Disorder

    PubMed Central

    Hu, Huaiyu; Liu, Yu; Bampoe, Kevin; He, Yonglin; Yu, Miao

    2016-01-01

    Patients with type II lissencephaly, a neuronal migration disorder with ectopic neurons, suffer from severe mental retardation, including learning deficits. There is no effective therapy to prevent or correct the formation of neuronal ectopia, which is presumed to cause cognitive deficits. We hypothesized that learning deficits were not solely caused by neuronal ectopia and that postnatal gene therapy could improve learning without correcting the neuronal ectopia formed during fetal development. To test this hypothesis, we evaluated spatial learning of cerebral cortex-specific protein O-mannosyltransferase 2 (POMT2, an enzyme required for O-mannosyl glycosylation) knockout mice and compared to the knockout mice that were injected with an adeno-associated viral vector (AAV) encoding POMT2 into the postnatal brains with Barnes maze. The data showed that the knockout mice exhibited reduced glycosylation in the cerebral cortex, reduced dendritic spine density on CA1 neurons, and increased latency to the target hole in the Barnes maze, indicating learning deficits. Postnatal gene therapy restored functional glycosylation, rescued dendritic spine defects, and improved performance on the Barnes maze by the knockout mice even though neuronal ectopia was not corrected. These results indicate that postnatal gene therapy improves spatial learning despite the presence of neuronal ectopia. PMID:27916859

  7. Postnatal Gene Therapy Improves Spatial Learning Despite the Presence of Neuronal Ectopia in a Model of Neuronal Migration Disorder.

    PubMed

    Hu, Huaiyu; Liu, Yu; Bampoe, Kevin; He, Yonglin; Yu, Miao

    2016-11-29

    Patients with type II lissencephaly, a neuronal migration disorder with ectopic neurons, suffer from severe mental retardation, including learning deficits. There is no effective therapy to prevent or correct the formation of neuronal ectopia, which is presumed to cause cognitive deficits. We hypothesized that learning deficits were not solely caused by neuronal ectopia and that postnatal gene therapy could improve learning without correcting the neuronal ectopia formed during fetal development. To test this hypothesis, we evaluated spatial learning of cerebral cortex-specific protein O-mannosyltransferase 2 (POMT2, an enzyme required for O-mannosyl glycosylation) knockout mice and compared to the knockout mice that were injected with an adeno-associated viral vector (AAV) encoding POMT2 into the postnatal brains with Barnes maze. The data showed that the knockout mice exhibited reduced glycosylation in the cerebral cortex, reduced dendritic spine density on CA1 neurons, and increased latency to the target hole in the Barnes maze, indicating learning deficits. Postnatal gene therapy restored functional glycosylation, rescued dendritic spine defects, and improved performance on the Barnes maze by the knockout mice even though neuronal ectopia was not corrected. These results indicate that postnatal gene therapy improves spatial learning despite the presence of neuronal ectopia.

  8. Optimizing computer models of corticospinal neurons to replicate in vitro dynamics.

    PubMed

    Neymotin, Samuel A; Suter, Benjamin A; Dura-Bernal, Salvador; Shepherd, Gordon M G; Migliore, Michele; Lytton, William W

    2017-01-01

    Corticospinal neurons (SPI), thick-tufted pyramidal neurons in motor cortex layer 5B that project caudally via the medullary pyramids, display distinct class-specific electrophysiological properties in vitro: strong sag with hyperpolarization, lack of adaptation, and a nearly linear frequency-current (F-I) relationship. We used our electrophysiological data to produce a pair of large archives of SPI neuron computer models in two model classes: 1) detailed models with full reconstruction; and 2) simplified models with six compartments. We used a PRAXIS and an evolutionary multiobjective optimization (EMO) in sequence to determine ion channel conductances. EMO selected good models from each of the two model classes to form the two model archives. Archived models showed tradeoffs across fitness functions. For example, parameters that produced excellent F-I fit produced a less-optimal fit for interspike voltage trajectory. Because of these tradeoffs, there was no single best model but rather models that would be best for particular usages for either single neuron or network explorations. Further exploration of exemplar models with strong F-I fit demonstrated that both the detailed and simple models produced excellent matches to the experimental data. Although dendritic ion identities and densities cannot yet be fully determined experimentally, we explored the consequences of a demonstrated proximal to distal density gradient of Ih, demonstrating that this would lead to a gradient of resonance properties with increased resonant frequencies more distally. We suggest that this dynamical feature could serve to make the cell particularly responsive to major frequency bands that differ by cortical layer. We developed models of motor cortex corticospinal neurons that replicate in vitro dynamics, including hyperpolarization-induced sag and realistic firing patterns. Models demonstrated resonance in response to synaptic stimulation, with resonance frequency increasing in apical

  9. Efferent projections of NPY expressing neurons of the dorsomedial hypothalamus in chronic hyperphagic models

    PubMed Central

    Lee, Shin J.; Kirigiti, Melissa; Lindsley, Sarah R; Loche, Alberto; Madden, Christopher J.; Morrison, Shaun F.; Smith, M Susan; Grove, Kevin L.

    2013-01-01

    The dorsomedial hypothalamus (DMH) has long been implicated in feeding behavior and thermogenesis. The DMH contains orexigenic neuropeptide Y (NPY) neurons, but the role of these neurons in the control of energy homeostasis is not well understood. NPY expression in the DMH is low under normal conditions in adult rodents, but is significantly increased during chronic hyperphagic conditions such as lactation and diet-induced obesity (DIO). To better understand the role of DMH-NPY neurons, we characterized the efferent projections of DMH-NPY neurons using the anterograde tracer biotinylated dextran amine (BDA) in lactating rats and DIO mice. In both models, BDA and NPY co-labeled fibers were mainly limited to the hypothalamus including the paraventricular nucleus of the hypothalamus (PVH), lateral hypothalamus/perifornical area (LH/PFA), and anteroventral periventricular nucleus (AVPV). Specifically in lactating rats, BDA and NPY co-labeled axonal swellings were in close apposition to CART expressing neurons in the PVH and AVPV. Although the DMH neurons project to the rostral raphe pallidus (rRPa) these projections did not contain NPY immunoreactivity in either the lactating rat or DIO mouse. Instead, the majority of BDA-labeled fibers in the rRPa were orexin positive. Furthermore, DMH-NPY projections were not observed within the nucleus of the solitary tract (NTS), another brainstem site critical for the regulation of sympathetic outflow. The present data suggest that NPY expression in the DMH during chronic hyperphagic conditions plays important roles in feeding behavior and thermogenesis by modulating neuronal functions within the hypothalamus, but not in the brainstem. PMID:23172177

  10. Network Receptive Field Modeling Reveals Extensive Integration and Multi-feature Selectivity in Auditory Cortical Neurons.

    PubMed

    Harper, Nicol S; Schoppe, Oliver; Willmore, Ben D B; Cui, Zhanfeng; Schnupp, Jan W H; King, Andrew J

    2016-11-01

    Cortical sensory neurons are commonly characterized using the receptive field, the linear dependence of their response on the stimulus. In primary auditory cortex neurons can be characterized by their spectrotemporal receptive fields, the spectral and temporal features of a sound that linearly drive a neuron. However, receptive fields do not capture the fact that the response of a cortical neuron results from the complex nonlinear network in which it is embedded. By fitting a nonlinear feedforward network model (a network receptive field) to cortical responses to natural sounds, we reveal that primary auditory cortical neurons are sensitive over a substantially larger spectrotemporal domain than is seen in their standard spectrotemporal receptive fields. Furthermore, the network receptive field, a parsimonious network consisting of 1-7 sub-receptive fields that interact nonlinearly, consistently better predicts neural responses to auditory stimuli than the standard receptive fields. The network receptive field reveals separate excitatory and inhibitory sub-fields with different nonlinear properties, and interaction of the sub-fields gives rise to important operations such as gain control and conjunctive feature detection. The conjunctive effects, where neurons respond only if several specific features are present together, enable increased selectivity for particular complex spectrotemporal structures, and may constitute an important stage in sound recognition. In conclusion, we demonstrate that fitting auditory cortical neural responses with feedforward network models expands on simple linear receptive field models in a manner that yields substantially improved predictive power and reveals key nonlinear aspects of cortical processing, while remaining easy to interpret in a physiological context.

  11. Network Receptive Field Modeling Reveals Extensive Integration and Multi-feature Selectivity in Auditory Cortical Neurons

    PubMed Central

    Willmore, Ben D. B.; Cui, Zhanfeng; Schnupp, Jan W. H.; King, Andrew J.

    2016-01-01

    Cortical sensory neurons are commonly characterized using the receptive field, the linear dependence of their response on the stimulus. In primary auditory cortex neurons can be characterized by their spectrotemporal receptive fields, the spectral and temporal features of a sound that linearly drive a neuron. However, receptive fields do not capture the fact that the response of a cortical neuron results from the complex nonlinear network in which it is embedded. By fitting a nonlinear feedforward network model (a network receptive field) to cortical responses to natural sounds, we reveal that primary auditory cortical neurons are sensitive over a substantially larger spectrotemporal domain than is seen in their standard spectrotemporal receptive fields. Furthermore, the network receptive field, a parsimonious network consisting of 1–7 sub-receptive fields that interact nonlinearly, consistently better predicts neural responses to auditory stimuli than the standard receptive fields. The network receptive field reveals separate excitatory and inhibitory sub-fields with different nonlinear properties, and interaction of the sub-fields gives rise to important operations such as gain control and conjunctive feature detection. The conjunctive effects, where neurons respond only if several specific features are present together, enable increased selectivity for particular complex spectrotemporal structures, and may constitute an important stage in sound recognition. In conclusion, we demonstrate that fitting auditory cortical neural responses with feedforward network models expands on simple linear receptive field models in a manner that yields substantially improved predictive power and reveals key nonlinear aspects of cortical processing, while remaining easy to interpret in a physiological context. PMID:27835647

  12. ACCUMULATION OF METHYLMERCURY OR POLYCHLORINATED BIPHENYLS IN IN VITRO MODELS OF RAT NEURONAL TISSUE.

    EPA Science Inventory

    This manuscript reports data examining the accumulation of PCBs or methylmercury into the tissue of three commonly used in vitro neuronal models.

    ? The results demonstrate that these lipophilic compounds can accumulate to levels 5 to 100 fold higher than the surrounding s...

  13. A stochastic neuronal model predicts random search behaviors at multiple spatial scales in C. elegans

    PubMed Central

    Roberts, William M; Augustine, Steven B; Lawton, Kristy J; Lindsay, Theodore H; Thiele, Tod R; Izquierdo, Eduardo J; Faumont, Serge; Lindsay, Rebecca A; Britton, Matthew Cale; Pokala, Navin; Bargmann, Cornelia I; Lockery, Shawn R

    2016-01-01

    Random search is a behavioral strategy used by organisms from bacteria to humans to locate food that is randomly distributed and undetectable at a distance. We investigated this behavior in the nematode Caenorhabditis elegans, an organism with a small, well-described nervous system. Here we formulate a mathematical model of random search abstracted from the C. elegans connectome and fit to a large-scale kinematic analysis of C. elegans behavior at submicron resolution. The model predicts behavioral effects of neuronal ablations and genetic perturbations, as well as unexpected aspects of wild type behavior. The predictive success of the model indicates that random search in C. elegans can be understood in terms of a neuronal flip-flop circuit involving reciprocal inhibition between two populations of stochastic neurons. Our findings establish a unified theoretical framework for understanding C. elegans locomotion and a testable neuronal model of random search that can be applied to other organisms. DOI: http://dx.doi.org/10.7554/eLife.12572.001 PMID:26824391

  14. Stochastic Wilson-Cowan models of neuronal network dynamics with memory and delay

    NASA Astrophysics Data System (ADS)

    Goychuk, Igor; Goychuk, Andriy

    2015-04-01

    We consider a simple Markovian class of the stochastic Wilson-Cowan type models of neuronal network dynamics, which incorporates stochastic delay caused by the existence of a refractory period of neurons. From the point of view of the dynamics of the individual elements, we are dealing with a network of non-Markovian stochastic two-state oscillators with memory, which are coupled globally in a mean-field fashion. This interrelation of a higher-dimensional Markovian and lower-dimensional non-Markovian dynamics is discussed in its relevance to the general problem of the network dynamics of complex elements possessing memory. The simplest model of this class is provided by a three-state Markovian neuron with one refractory state, which causes firing delay with an exponentially decaying memory within the two-state reduced model. This basic model is used to study critical avalanche dynamics (the noise sustained criticality) in a balanced feedforward network consisting of the excitatory and inhibitory neurons. Such avalanches emerge due to the network size dependent noise (mesoscopic noise). Numerical simulations reveal an intermediate power law in the distribution of avalanche sizes with the critical exponent around -1.16. We show that this power law is robust upon a variation of the refractory time over several orders of magnitude. However, the avalanche time distribution is biexponential. It does not reflect any genuine power law dependence.

  15. Modeling the formation process of grouping stimuli sets through cortical columns and microcircuits to feature neurons.

    PubMed

    Klefenz, Frank; Williamson, Adam

    2013-01-01

    A computational model of a self-structuring neuronal net is presented in which repetitively applied pattern sets induce the formation of cortical columns and microcircuits which decode distinct patterns after a learning phase. In a case study, it is demonstrated how specific neurons in a feature classifier layer become orientation selective if they receive bar patterns of different slopes from an input layer. The input layer is mapped and intertwined by self-evolving neuronal microcircuits to the feature classifier layer. In this topical overview, several models are discussed which indicate that the net formation converges in its functionality to a mathematical transform which maps the input pattern space to a feature representing output space. The self-learning of the mathematical transform is discussed and its implications are interpreted. Model assumptions are deduced which serve as a guide to apply model derived repetitive stimuli pattern sets to in vitro cultures of neuron ensembles to condition them to learn and execute a mathematical transform.

  16. Approaches and tools for modeling signaling pathways and calcium dynamics in neurons

    PubMed Central

    Blackwell, KT

    2013-01-01

    Signaling pathways are cascades of intracellular biochemical reactions that are activated by transmembrane receptors, and ultimately lead to transcription in the nucleus. In neurons, both calcium permeable synaptic and ionic channels as well as G protein coupled receptors initiate activation of signaling pathway molecules that interact with electrical activity at multiple spatial and time scales. At small temporal and spatial scales, calcium modifies the properties of ionic channels, whereas at larger temporal and spatial scales, various kinases and phosphatases modify the properties of ionic channels, producing phenomena such as synaptic plasticity and homeostatic plasticity. The elongated structure of neuronal dendrites and the organization of multi-protein complexes by anchoring proteins implies that the spatial dimension must be explicit. Therefore, modeling signaling pathways in neurons utilizes algorithms for both diffusion and reactions. The small size of spines coupled with small concentrations of some molecules implies that some reactions occur stochastically. The need for stochastic simulation of many reaction and diffusion events coupled with the multiple temporal and spatial scales makes modeling of signaling pathways a difficult problem. Several different software programs have achieved different aspects of these capabilities. This review explains some of the mathematical formulas used for modeling reactions and diffusion. In addition, it briefly presents the simulators used for modeling reaction-diffusion systems in neurons, together with scientific problems addressed. PMID:23743449

  17. Modeling the Formation Process of Grouping Stimuli Sets through Cortical Columns and Microcircuits to Feature Neurons

    PubMed Central

    Williamson, Adam

    2013-01-01

    A computational model of a self-structuring neuronal net is presented in which repetitively applied pattern sets induce the formation of cortical columns and microcircuits which decode distinct patterns after a learning phase. In a case study, it is demonstrated how specific neurons in a feature classifier layer become orientation selective if they receive bar patterns of different slopes from an input layer. The input layer is mapped and intertwined by self-evolving neuronal microcircuits to the feature classifier layer. In this topical overview, several models are discussed which indicate that the net formation converges in its functionality to a mathematical transform which maps the input pattern space to a feature representing output space. The self-learning of the mathematical transform is discussed and its implications are interpreted. Model assumptions are deduced which serve as a guide to apply model derived repetitive stimuli pattern sets to in vitro cultures of neuron ensembles to condition them to learn and execute a mathematical transform. PMID:24369455

  18. Development of modified cable models to simulate accurate neuronal active behaviors

    PubMed Central

    2014-01-01

    In large network and single three-dimensional (3-D) neuron simulations, high computing speed dictates using reduced cable models to simulate neuronal firing behaviors. However, these models are unwarranted under active conditions and lack accurate representation of dendritic active conductances that greatly shape neuronal firing. Here, realistic 3-D (R3D) models (which contain full anatomical details of dendrites) of spinal motoneurons were systematically compared with their reduced single unbranched cable (SUC, which reduces the dendrites to a single electrically equivalent cable) counterpart under passive and active conditions. The SUC models matched the R3D model's passive properties but failed to match key active properties, especially active behaviors originating from dendrites. For instance, persistent inward currents (PIC) hysteresis, frequency-current (FI) relationship secondary range slope, firing hysteresis, plateau potential partial deactivation, staircase currents, synaptic current transfer ratio, and regional FI relationships were not accurately reproduced by the SUC models. The dendritic morphology oversimplification and lack of dendritic active conductances spatial segregation in the SUC models caused significant underestimation of those behaviors. Next, SUC models were modified by adding key branching features in an attempt to restore their active behaviors. The addition of primary dendritic branching only partially restored some active behaviors, whereas the addition of secondary dendritic branching restored most behaviors. Importantly, the proposed modified models successfully replicated the active properties without sacrificing model simplicity, making them attractive candidates for running R3D single neuron and network simulations with accurate firing behaviors. The present results indicate that using reduced models to examine PIC behaviors in spinal motoneurons is unwarranted. PMID:25277743

  19. On the applicability of STDP-based learning mechanisms to spiking neuron network models

    NASA Astrophysics Data System (ADS)

    Sboev, A.; Vlasov, D.; Serenko, A.; Rybka, R.; Moloshnikov, I.

    2016-11-01

    The ways to creating practically effective method for spiking neuron networks learning, that would be appropriate for implementing in neuromorphic hardware and at the same time based on the biologically plausible plasticity rules, namely, on STDP, are discussed. The influence of the amount of correlation between input and output spike trains on the learnability by different STDP rules is evaluated. A usability of alternative combined learning schemes, involving artificial and spiking neuron models is demonstrated on the iris benchmark task and on the practical task of gender recognition.

  20. Modeling the phenotype of spinal muscular atrophy by the direct conversion of human fibroblasts to motor neurons.

    PubMed

    Zhang, Qi-Jie; Li, Jin-Jing; Lin, Xiang; Lu, Ying-Qian; Guo, Xin-Xin; Dong, En-Lin; Zhao, Miao; He, Jin; Wang, Ning; Chen, Wan-Jin

    2017-02-14

    Spinal muscular atrophy (SMA) is a lethal autosomal recessive neurological disease characterized by selective degeneration of motor neurons in the spinal cord. In recent years, the development of cellular reprogramming technology has provided an alternative and effective method for obtaining patient-specific neurons in vitro. In the present study, we applied this technology to the field of SMA to acquire patient-specific induced motor neurons that were directly converted from fibroblasts via the forced expression of 8 defined transcription factors. The infected fibroblasts began to grow in a dipolar manner, and the nuclei gradually enlarged. Typical Tuj1-positive neurons were generated at day 23. After day 35, induced neurons with multiple neurites were observed, and these neurons also expressed the hallmarks of Tuj1, HB9, ISL1 and CHAT. The conversion efficiencies were approximately 5.8% and 5.5% in the SMA and control groups, respectively. Additionally, the SMA-induced neurons exhibited a significantly reduced neurite outgrowth rate compared with the control neurons. After day 60, the SMA-induced neurons also exhibited a liability of neuronal degeneration and remarkable fracturing of the neurites was observed. By directly reprogramming fibroblasts, we established a feeder-free conversion system to acquire SMA patient-specific induced motor neurons that partially modeled the phenotype of SMA in vitro.

  1. Speed of Neuron Conduction Is Not the Basis of the IQ-RT Correlation: Results from a Simple Neural Model.

    ERIC Educational Resources Information Center

    Anderson, Britt

    1994-01-01

    Using a simple neural model comprising between two and four neurons, it is concluded that speed of neuron conduction is not the probable basis of the intelligence quotient (IQ)-reaction time (RT) correlation. This result illustrates that neural modeling can be applied to biological theories of individual differences in intelligence. (SLD)

  2. A biophysical observation model for field potentials of networks of leaky integrate-and-fire neurons.

    PubMed

    Beim Graben, Peter; Rodrigues, Serafim

    2012-01-01

    We present a biophysical approach for the coupling of neural network activity as resulting from proper dipole currents of cortical pyramidal neurons to the electric field in extracellular fluid. Starting from a reduced three-compartment model of a single pyramidal neuron, we derive an observation model for dendritic dipole currents in extracellular space and thereby for the dendritic field potential (DFP) that contributes to the local field potential (LFP) of a neural population. This work aligns and satisfies the widespread dipole assumption that is motivated by the "open-field" configuration of the DFP around cortical pyramidal cells. Our reduced three-compartment scheme allows to derive networks of leaky integrate-and-fire (LIF) models, which facilitates comparison with existing neural network and observation models. In particular, by means of numerical simulations we compare our approach with an ad hoc model by Mazzoni et al. (2008), and conclude that our biophysically motivated approach yields substantial improvement.

  3. A biophysical observation model for field potentials of networks of leaky integrate-and-fire neurons

    PubMed Central

    beim Graben, Peter; Rodrigues, Serafim

    2013-01-01

    We present a biophysical approach for the coupling of neural network activity as resulting from proper dipole currents of cortical pyramidal neurons to the electric field in extracellular fluid. Starting from a reduced three-compartment model of a single pyramidal neuron, we derive an observation model for dendritic dipole currents in extracellular space and thereby for the dendritic field potential (DFP) that contributes to the local field potential (LFP) of a neural population. This work aligns and satisfies the widespread dipole assumption that is motivated by the “open-field” configuration of the DFP around cortical pyramidal cells. Our reduced three-compartment scheme allows to derive networks of leaky integrate-and-fire (LIF) models, which facilitates comparison with existing neural network and observation models. In particular, by means of numerical simulations we compare our approach with an ad hoc model by Mazzoni et al. (2008), and conclude that our biophysically motivated approach yields substantial improvement. PMID:23316157

  4. Emergent spindle oscillations and intermittent burst firing in a thalamic model: specific neuronal mechanisms.

    PubMed Central

    Wang, X J; Golomb, D; Rinzel, J

    1995-01-01

    The rhythmogenesis of 10-Hz sleep spindles is studied in a large-scale thalamic network model with two cell populations: the excitatory thalamocortical (TC) relay neurons and the inhibitory nucleus reticularis thalami (RE) neurons. Spindle-like bursting oscillations emerge naturally from reciprocal interactions between TC and RE neurons. We find that the network oscillations can be synchronized coherently, even though the RE-TC connections are random and sparse, and even though individual neurons fire rebound bursts intermittently in time. When the fast gamma-aminobutyrate type A synaptic inhibition is blocked, synchronous slow oscillations resembling absence seizures are observed. Near-maximal network synchrony is established with even modest convergence in the RE-to-TC projection (as few as 5-10 RE inputs per TC cell suffice). The hyperpolarization-activated cation current (Ih) is found to provide a cellular basis for the intermittency of rebound bursting that is commonly observed in TC neurons during spindles. Such synchronous oscillations with intermittency can be maintained only with a significant degree of convergence for the TC-to-RE projection. PMID:7777551

  5. Persistent neuronal Ube3a expression in the suprachiasmatic nucleus of Angelman syndrome model mice.

    PubMed

    Jones, Kelly A; Han, Ji Eun; DeBruyne, Jason P; Philpot, Benjamin D

    2016-06-16

    Mutations or deletions of the maternal allele of the UBE3A gene cause Angelman syndrome (AS), a severe neurodevelopmental disorder. The paternal UBE3A/Ube3a allele becomes epigenetically silenced in most neurons during postnatal development in humans and mice; hence, loss of the maternal allele largely eliminates neuronal expression of UBE3A protein. However, recent studies suggest that paternal Ube3a may escape silencing in certain neuron populations, allowing for persistent expression of paternal UBE3A protein. Here we extend evidence in AS model mice (Ube3a(m-/p+)) of paternal UBE3A expression within the suprachiasmatic nucleus (SCN), the master circadian pacemaker. Paternal UBE3A-positive cells in the SCN show partial colocalization with the neuropeptide arginine vasopressin (AVP) and clock proteins (PER2 and BMAL1), supporting that paternal UBE3A expression in the SCN is often of neuronal origin. Paternal UBE3A also partially colocalizes with a marker of neural progenitors, SOX2, implying that relaxed or incomplete imprinting of paternal Ube3a reflects an overall immature molecular phenotype. Our findings highlight the complexity of Ube3a imprinting in the brain and illuminate a subpopulation of SCN neurons as a focal point for future studies aimed at understanding the mechanisms of Ube3a imprinting.

  6. Persistent neuronal Ube3a expression in the suprachiasmatic nucleus of Angelman syndrome model mice

    PubMed Central

    Jones, Kelly A.; Han, Ji Eun; DeBruyne, Jason P.; Philpot, Benjamin D.

    2016-01-01

    Mutations or deletions of the maternal allele of the UBE3A gene cause Angelman syndrome (AS), a severe neurodevelopmental disorder. The paternal UBE3A/Ube3a allele becomes epigenetically silenced in most neurons during postnatal development in humans and mice; hence, loss of the maternal allele largely eliminates neuronal expression of UBE3A protein. However, recent studies suggest that paternal Ube3a may escape silencing in certain neuron populations, allowing for persistent expression of paternal UBE3A protein. Here we extend evidence in AS model mice (Ube3am–/p+) of paternal UBE3A expression within the suprachiasmatic nucleus (SCN), the master circadian pacemaker. Paternal UBE3A-positive cells in the SCN show partial colocalization with the neuropeptide arginine vasopressin (AVP) and clock proteins (PER2 and BMAL1), supporting that paternal UBE3A expression in the SCN is often of neuronal origin. Paternal UBE3A also partially colocalizes with a marker of neural progenitors, SOX2, implying that relaxed or incomplete imprinting of paternal Ube3a reflects an overall immature molecular phenotype. Our findings highlight the complexity of Ube3a imprinting in the brain and illuminate a subpopulation of SCN neurons as a focal point for future studies aimed at understanding the mechanisms of Ube3a imprinting. PMID:27306933

  7. Dopaminergic neurons generated from monkey embryonic stem cells function in a Parkinson primate model.

    PubMed

    Takagi, Yasushi; Takahashi, Jun; Saiki, Hidemoto; Morizane, Asuka; Hayashi, Takuya; Kishi, Yo; Fukuda, Hitoshi; Okamoto, Yo; Koyanagi, Masaomi; Ideguchi, Makoto; Hayashi, Hideki; Imazato, Takayuki; Kawasaki, Hiroshi; Suemori, Hirofumi; Omachi, Shigeki; Iida, Hidehiko; Itoh, Nobuyuki; Nakatsuji, Norio; Sasai, Yoshiki; Hashimoto, Nobuo

    2005-01-01

    Parkinson disease (PD) is a neurodegenerative disorder characterized by loss of midbrain dopaminergic (DA) neurons. ES cells are currently the most promising donor cell source for cell-replacement therapy in PD. We previously described a strong neuralizing activity present on the surface of stromal cells, named stromal cell-derived inducing activity (SDIA). In this study, we generated neurospheres composed of neural progenitors from monkey ES cells, which are capable of producing large numbers of DA neurons. We demonstrated that FGF20, preferentially expressed in the substantia nigra, acts synergistically with FGF2 to increase the number of DA neurons in ES cell-derived neurospheres. We also analyzed the effect of transplantation of DA neurons generated from monkey ES cells into 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine-treated (MPTP-treated) monkeys, a primate model for PD. Behavioral studies and functional imaging revealed that the transplanted cells functioned as DA neurons and attenuated MPTP-induced neurological symptoms.

  8. BGP-15 prevents the death of neurons in a mouse model of familial dysautonomia.

    PubMed

    Ohlen, Sarah B; Russell, Magdalena L; Brownstein, Michael J; Lefcort, Frances

    2017-05-09

    Hereditary sensory and autonomic neuropathy type III, or familial dysautonomia [FD; Online Mendelian Inheritance in Man (OMIM) 223900], affects the development and long-term viability of neurons in the peripheral nervous system (PNS) and retina. FD is caused by a point mutation in the gene IKBKAP/ELP1 that results in a tissue-specific reduction of the IKAP/ELP1 protein, a subunit of the Elongator complex. Hallmarks of the disease include vasomotor and cardiovascular instability and diminished pain and temperature sensation caused by reductions in sensory and autonomic neurons. It has been suggested but not demonstrated that mitochondrial function may be abnormal in FD. We previously generated an Ikbkap/Elp1 conditional-knockout mouse model that recapitulates the selective death of sensory (dorsal root ganglia) and autonomic neurons observed in FD. We now show that in these mice neuronal mitochondria have abnormal membrane potentials, produce elevated levels of reactive oxygen species, are fragmented, and do not aggregate normally at axonal branch points. The small hydroxylamine compound BGP-15 improved mitochondrial function, protecting neurons from dying in vitro and in vivo, and promoted cardiac innervation in vivo. Given that impairment of mitochondrial function is a common pathological component of neurodegenerative diseases such as amyotrophic lateral sclerosis and Alzheimer's, Parkinson's, and Huntington's diseases, our findings identify a therapeutic approach that may have efficacy in multiple degenerative conditions.

  9. Progression of Neuronal Damage in an In Vitro Model of the Ischemic Penumbra

    PubMed Central

    le Feber, Joost; Tzafi Pavlidou, Stelina; Erkamp, Niels; van Putten, Michel J. A. M.; Hofmeijer, Jeannette

    2016-01-01

    Improvement of neuronal recovery in the ischemic penumbra around a brain infarct has a large potential to advance clinical recovery of patients with acute ischemic stroke. However, pathophysiological mechanisms leading to either recovery or secondary damage in the penumbra are not completely understood. We studied neuronal dynamics in a model system of the penumbra consisting of networks of cultured cortical neurons exposed to controlled levels and durations of hypoxia. Short periods of hypoxia (pO2≈20mmHg) reduced spontaneous activity, due to impeded synaptic function. After ≈6 hours, activity and connectivity partially recovered, even during continuing hypoxia. If the oxygen supply was restored within 12 hours, changes in network connectivity were completely reversible. For longer periods of hypoxia (12–30 h), activity levels initially increased, but eventually decreased and connectivity changes became partially irreversible. After ≈30 hours, all functional connections disappeared and no activity remained. Since this complete silence seemed unrelated to hypoxic depths, but always followed an extended period of low activity, we speculate that irreversible damage (at least partly) results from insufficient neuronal activation. This opens avenues for therapies to improve recovery by neuronal activation. PMID:26871437

  10. Nanosecond laser pulse stimulation of spiral ganglion neurons and model cells

    PubMed Central

    Rettenmaier, Alexander; Lenarz, Thomas; Reuter, Günter

    2014-01-01

    Optical stimulation of the inner ear has recently attracted attention, suggesting a higher frequency resolution compared to electrical cochlear implants due to its high spatial stimulation selectivity. Although the feasibility of the effect is shown in multiple in vivo experiments, the stimulation mechanism remains open to discussion. Here we investigate in single-cell measurements the reaction of spiral ganglion neurons and model cells to irradiation with a nanosecond-pulsed laser beam over a broad wavelength range from 420 nm up to 1950 nm using the patch clamp technique. Cell reactions were wavelength- and pulse-energy-dependent but too small to elicit action potentials in the investigated spiral ganglion neurons. As the applied radiant exposure was much higher than the reported threshold for in vivo experiments in the same laser regime, we conclude that in a stimulation paradigm with nanosecond-pulses, direct neuronal stimulation is not the main cause of optical cochlea stimulation. PMID:24761285

  11. Optimal stimulus current waveshape for a Hodgkin-Huxley model neuron.

    PubMed

    Tahayori, Bahman; Dokos, Socrates

    2012-01-01

    Traditionally, rectangular Lilly-type current pulses have been employed to electrically stimulate a neuron. In this paper, we utilize a least squares optimisation approach to assess the optimality of rectangular pulses in the context of electrical current stimulation. To this end, an appropriate cost function to minimise the total charge delivered to a neuron while keeping the waveshape sufficiently smooth, is developed and applied to a Hodgkin-Huxley ionic model of the neural action potential. Cubic spline parameters were utilized to find the optimal stimulation profile for a fixed peak current. Simulation results demonstrate that the optimal stimulation profile for a specified single neuron is a non-rectangular pulse whose shape depends upon the maximum allowable current as well as the stimulus duration.

  12. Evolution of periodic states and chaos in two types of neuronal models

    NASA Astrophysics Data System (ADS)

    Chay, Teresa R.; Fan, Yinshui

    1993-11-01

    Studies on how chaos theory may be applied to neural disorders is a very challenging theoretical problem. But, to determine the applications of chaos theory cellular functions, it is best to study the genesis of chaos and its characteristics using a minimal model of cellular excitability. In this paper we present two neuronal models which gives rise to interesting types of bursting and chaos. The first model is based on the model of Chay, in which the bursting of neuronal cells is caused by voltage- and time-dependent inactivation of calcium channels. The second model is based on Chay's work in which the bursting is caused by the conformational transformation of the calcium channels that is induced by binding of Ca2+ ion to the receptor site. With these two models, we elucidate how the periodic states and chaos can be evolved when the properties of two types of inward current change. Our bifurcation diagram reveals new types of bifurcations and chaos which were not seen in the other non-linear dynamic models. The predicted chaos from the models closely resembles that observed experimentally in neuronal cells. An implication of our finding is that chaos theory may be used to understand and improve the treatment of certain irregular activities in the brain.

  13. The dynamical analysis of modified two-compartment neuron model and FPGA implementation

    NASA Astrophysics Data System (ADS)

    Lin, Qianjin; Wang, Jiang; Yang, Shuangming; Yi, Guosheng; Deng, Bin; Wei, Xile; Yu, Haitao

    2017-10-01

    The complexity of neural models is increasing with the investigation of larger biological neural network, more various ionic channels and more detailed morphologies, and the implementation of biological neural network is a task with huge computational complexity and power consumption. This paper presents an efficient digital design using piecewise linearization on field programmable gate array (FPGA), to succinctly implement the reduced two-compartment model which retains essential features of more complicated models. The design proposes an approximate neuron model which is composed of a set of piecewise linear equations, and it can reproduce different dynamical behaviors to depict the mechanisms of a single neuron model. The consistency of hardware implementation is verified in terms of dynamical behaviors and bifurcation analysis, and the simulation results including varied ion channel characteristics coincide with the biological neuron model with a high accuracy. Hardware synthesis on FPGA demonstrates that the proposed model has reliable performance and lower hardware resource compared with the original two-compartment model. These investigations are conducive to scalability of biological neural network in reconfigurable large-scale neuromorphic system.

  14. A mathematical model of communication between groups of circadian neurons in Drosophila melanogaster.

    PubMed

    Risau-Gusman, Sebastián; Gleiser, Pablo M

    2014-12-01

    In the fruit fly, circadian behavior is controlled by a small number of specialized neurons, whose molecular clocks are relatively well known. However, much less is known about how these neurons communicate among themselves. In particular, only 1 circadian neuropeptide, pigment-dispersing factor (PDF), has been identified, and most aspects of its interaction with the molecular clock remain to be elucidated. Furthermore, it is speculated that many other peptides should contribute to circadian communication. We have developed a relatively detailed model of the 2 main groups of circadian pacemaker neurons (sLNvs and LNds) to investigate these issues. We have proposed many possible mechanisms for the interaction between the synchronization factors and the molecular clock, and we have compared the outputs with the experimental results reported in the literature both for the wild-type and PDF-null mutant. We have studied how different the properties of each neuron should be to account for the observations reported for the sLNvs in the mutant. We have found that only a few mechanisms, mostly related to the slowing down of nuclear entry of a circadian protein, can synchronize neurons that present these differences. Detailed immunofluorescent recordings have suggested that, whereas in the mutant, LNd neurons are synchronized, in the wild-type, a subset of the LNds oscillate faster than the rest. With our model, we find that a more likely explanation for the same observations is that this subset is being driven outside its synchronization range and displays therefore a complex pattern of oscillation.

  15. Computational Models of Neuron-Astrocyte Interactions Lead to Improved Efficacy in the Performance of Neural Networks

    PubMed Central

    Alvarellos-González, Alberto; Pazos, Alejandro; Porto-Pazos, Ana B.

    2012-01-01

    The importance of astrocytes, one part of the glial system, for information processing in the brain has recently been demonstrated. Regarding information processing in multilayer connectionist systems, it has been shown that systems which include artificial neurons and astrocytes (Artificial Neuron-Glia Networks) have well-known advantages over identical systems including only artificial neurons. Since the actual impact of astrocytes in neural network function is unknown, we have investigated, using computational models, different astrocyte-neuron interactions for information processing; different neuron-glia algorithms have been implemented for training and validation of multilayer Artificial Neuron-Glia Networks oriented toward classification problem resolution. The results of the tests performed suggest that all the algorithms modelling astrocyte-induced synaptic potentiation improved artificial neural network performance, but their efficacy depended on the complexity of the problem. PMID:22649480

  16. Lateral Information Processing by Spiking Neurons: A Theoretical Model of the Neural Correlate of Consciousness

    PubMed Central

    Ebner, Marc; Hameroff, Stuart

    2011-01-01

    Cognitive brain functions, for example, sensory perception, motor control and learning, are understood as computation by axonal-dendritic chemical synapses in networks of integrate-and-fire neurons. Cognitive brain functions may occur either consciously or nonconsciously (on “autopilot”). Conscious cognition is marked by gamma synchrony EEG, mediated largely by dendritic-dendritic gap junctions, sideways connections in input/integration layers. Gap-junction-connected neurons define a sub-network within a larger neural network. A theoretical model (the “conscious pilot”) suggests that as gap junctions open and close, a gamma-synchronized subnetwork, or zone moves through the brain as an executive agent, converting nonconscious “auto-pilot” cognition to consciousness, and enhancing computation by coherent processing and collective integration. In this study we implemented sideways “gap junctions” in a single-layer artificial neural network to perform figure/ground separation. The set of neurons connected through gap junctions form a reconfigurable resistive grid or sub-network zone. In the model, outgoing spikes are temporally integrated and spatially averaged using the fixed resistive grid set up by neurons of similar function which are connected through gap-junctions. This spatial average, essentially a feedback signal from the neuron's output, determines whether particular gap junctions between neurons will open or close. Neurons connected through open gap junctions synchronize their output spikes. We have tested our gap-junction-defined sub-network in a one-layer neural network on artificial retinal inputs using real-world images. Our system is able to perform figure/ground separation where the laterally connected sub-network of neurons represents a perceived object. Even though we only show results for visual stimuli, our approach should generalize to other modalities. The system demonstrates a moving sub-network zone of synchrony, within which

  17. Chimera-like states in a neuronal network model of the cat brain

    NASA Astrophysics Data System (ADS)

    Santos, M. S.; Szezech, J. D.; Borges, F. S.; Iarosz, K. C.; Caldas, I. L.; Batista, A. M.; Viana, R. L.; Kurths, J.

    2017-08-01

    Neuronal systems have been modeled by complex networks in different description levels. Recently, it has been verified that networks can simultaneously exhibit one coherent and other incoherent domain, known as chimera states. In this work, we study the existence of chimera states in a network considering the connectivity matrix based on the cat cerebral cortex. The cerebral cortex of the cat can be separated in 65 cortical areas organised into the four cognitive regions: visual, auditory, somatosensory-motor and frontolimbic. We consider a network where the local dynamics is given by the Hindmarsh-Rose model. The Hindmarsh-Rose equations are a well known model of neuronal activity that has been considered to simulate membrane potential in neuron. Here, we analyse under which conditions chimera states are present, as well as the affects induced by intensity of coupling on them. We observe the existence of chimera states in that incoherent structure can be composed of desynchronised spikes or desynchronised bursts. Moreover, we find that chimera states with desynchronised bursts are more robust to neuronal noise than with desynchronised spikes.

  18. Parkin is protective for substantia nigra dopamine neurons in a tau gene transfer neurodegeneration model.

    PubMed

    Klein, Ronald L; Dayton, Robert D; Henderson, Karen M; Petrucelli, Leonard

    2006-06-19

    Parkin is a ubiquitin ligase involved in the ubiquitin-proteasome system. Elevating parkin expression in cells reduces markers of oxidative stress while blocking parkin expression increases oxidative stress. In parkin gene knock down mouse and fly models, mitochondria function is deficient. Parkin is neuroprotective against a variety of toxic insults, while it remains unclear which of the above properties of parkin may mediate the protective actions. One of the models for which parkin is protective is overexpression of alpha-synuclein, a protein that self-aggregates in Parkinson disease. The microtubule-associated protein tau is another protein that self-aggregates in specific neurodegenerative diseases that also involve loss of dopamine neurons such as frontotemporal dementia with parkinsonism linked to chromosome 17, progressive supranuclear palsy and corticobasal degeneration. We recently developed a tau-induced dopaminergic degeneration model in rats using adeno-associated virus vectors. In this study, we successfully targeted either a mixed tau/parkin vector or mixed tau/control vector to the rat substantia nigra. While there was significant loss of dopamine neurons in the tau/control group relative to uninjected substantia nigra, there was no cell loss in the tau/parkin group. We found no difference in total tau levels between tau/control and tau/parkin groups. Parkin therefore protects dopamine neurons against tau as it does against alpha-synuclein, which further supports parkin as a therapeutic target for diseases involving loss of dopamine neurons.

  19. An Augmented Two-Layer Model Captures Nonlinear Analog Spatial Integration Effects in Pyramidal Neuron Dendrites.

    PubMed

    Jadi, Monika P; Behabadi, Bardia F; Poleg-Polsky, Alon; Schiller, Jackie; Mel, Bartlett W

    2014-05-01

    In pursuit of the goal to understand and eventually reproduce the diverse functions of the brain, a key challenge lies in reverse engineering the peculiar biology-based "technology" that underlies the brain's remarkable ability to process and store information. The basic building block of the nervous system is the nerve cell, or "neuron," yet after more than 100 years of neurophysiological study and 60 years of modeling, the information processing functions of individual neurons, and the parameters that allow them to engage in so many different types of computation (sensory, motor, mnemonic, executive, etc.) remain poorly understood. In this paper, we review both historical and recent findings that have led to our current understanding of the analog spatial processing capabilities of dendrites, the major input structures of neurons, with a focus on the principal cell type of the neocortex and hippocampus, the pyramidal neuron (PN). We encapsulate our current understanding of PN dendritic integration in an abstract layered model whose spatially sensitive branch-subunits compute multidimensional sigmoidal functions. Unlike the 1-D sigmoids found in conventional neural network models, multidimensional sigmoids allow the cell to implement a rich spectrum of nonlinear modulation effects directly within their dendritic trees.

  20. Information Processing and Collective Behavior in a Model Neuronal System

    DTIC Science & Technology

    2014-03-28

    at times weekly) telecoms with them and Eli Shlizerman at the University of Washington. In summary, we were happy to work with many groups within...states cause high intracellular calcium concentrations, which could trigger transcription of clock genes . The model also predicts that circadian...phenotypes of mutations or knockdown of clock genes as well as the time courses and relative expression of clock transcripts and proteins. Using this model

  1. Using a hybrid neuron in physiologically inspired models of the basal ganglia

    PubMed Central

    Thibeault, Corey M.; Srinivasa, Narayan

    2013-01-01

    Our current understanding of the basal ganglia (BG) has facilitated the creation of computational models that have contributed novel theories, explored new functional anatomy and demonstrated results complementing physiological experiments. However, the utility of these models extends beyond these applications. Particularly in neuromorphic engineering, where the basal ganglia's role in computation is important for applications such as power efficient autonomous agents and model-based control strategies. The neurons used in existing computational models of the BG, however, are not amenable for many low-power hardware implementations. Motivated by a need for more hardware accessible networks, we replicate four published models of the BG, spanning single neuron and small networks, replacing the more computationally expensive neuron models with an Izhikevich hybrid neuron. This begins with a network modeling action-selection, where the basal activity levels and the ability to appropriately select the most salient input is reproduced. A Parkinson's disease model is then explored under normal conditions, Parkinsonian conditions and during subthalamic nucleus deep brain stimulation (DBS). The resulting network is capable of replicating the loss of thalamic relay capabilities in the Parkinsonian state and its return under DBS. This is also demonstrated using a network capable of action-selection. Finally, a study of correlation transfer under different patterns of Parkinsonian activity is presented. These networks successfully captured the significant results of the originals studies. This not only creates a foundation for neuromorphic hardware implementations but may also support the development of large-scale biophysical models. The former potentially providing a way of improving the efficacy of DBS and the latter allowing for the efficient simulation of larger more comprehensive networks. PMID:23847524

  2. Neuronal Injury, Gliosis, and Glial Proliferation in Two Models of Temporal Lobe Epilepsy.

    PubMed

    Loewen, Jaycie L; Barker-Haliski, Melissa L; Dahle, E Jill; White, H Steve; Wilcox, Karen S

    2016-04-01

    It is estimated that 30%-40% of epilepsy patients are refractory to therapy and animal models are useful for the identification of more efficacious therapeutic agents. Various well-characterized syndrome-specific models are needed to assess their relevance to human seizure disorders and their validity for testing potential therapies. The corneal kindled mouse model of temporal lobe epilepsy (TLE) allows for the rapid screening of investigational compounds, but there is a lack of information as to the specific inflammatory pathology in this model. Similarly, the Theiler murine encephalomyelitis virus (TMEV) model of TLE may prove to be useful for screening, but quantitative assessment of hippocampal pathology is also lacking. We used immunohistochemistry to characterize and quantitate acute neuronal injury and inflammatory features in dorsal CA1 and dentate gyrus regions and in the directly overlying posterior parietal cortex at 2 time points in each of these TLE models. Corneal kindled mice were observed to have astrogliosis, but not microgliosis or neuron cell death. In contrast, TMEV-injected mice had astrogliosis, microgliosis, neuron death, and astrocyte and microglial proliferation. Our results suggest that these 2 animal models might be appropriate for evaluation of distinct therapies for TLE.

  3. Numerical treatment of a mathematical model arising from a model of neuronal variability

    NASA Astrophysics Data System (ADS)

    Kadalbajoo, M. K.; Sharma, K. K.

    2005-07-01

    In this paper, we describe a numerical approach based on finite difference method to solve a mathematical model arising from a model of neuronal variability. The mathematical modelling of the determination of the expected time for generation of action potentials in nerve cells by random synaptic inputs in dendrites includes a general boundary-value problem for singularly perturbed differential-difference equation with small shifts. In the numerical treatment for such type of boundary-value problems, first we use Taylor approximation to tackle the terms containing small shifts which converts it to a boundary-value problem for singularly perturbed differential equation. A rigorous analysis is carried out to obtain priori estimates on the solution of the problem and its derivatives up to third order. Then a parameter uniform difference scheme is constructed to solve the boundary-value problem so obtained. A parameter uniform error estimate for the numerical scheme so constructed is established. Though the convergence of the difference scheme is almost linear but its beauty is that it converges independently of the singular perturbation parameter, i.e., the numerical scheme converges for each value of the singular perturbation parameter (however small it may be but remains positive). Several test examples are solved to demonstrate the efficiency of the numerical scheme presented in the paper and to show the effect of the small shift on the solution behavior.

  4. A minimal actomyosin-based model predicts the dynamics of filopodia on neuronal dendrites

    PubMed Central

    Marchenko, Olena O.; Das, Sulagna; Yu, Ji; Novak, Igor L.; Rodionov, Vladimir I.; Efimova, Nadia; Svitkina, Tatyana; Wolgemuth, Charles W.; Loew, Leslie M.

    2017-01-01

    Dendritic filopodia are actin-filled dynamic subcellular structures that sprout on neuronal dendrites during neurogenesis. The exploratory motion of the filopodia is crucial for synaptogenesis, but the underlying mechanisms are poorly understood. To study filopodial motility, we collected and analyzed image data on filopodia in cultured rat hippocampal neurons. We hypothesized that mechanical feedback among the actin retrograde flow, myosin activity, and substrate adhesion gives rise to various filopodial behaviors. We formulated a minimal one-dimensional partial differential equation model that reproduced the range of observed motility. To validate our model, we systematically manipulated experimental correlates of parameters in the model: substrate adhesion strength, actin polymerization rate, myosin contractility, and the integrity of the putative microtubule-based barrier at the filopodium base. The model predicts the response of the system to each of these experimental perturbations, supporting the hypothesis that our actomyosin-driven mechanism controls dendritic filopodia dynamics. PMID:28228546

  5. Hodgkin-Huxley type modelling and parameter estimation of GnRH neurons.

    PubMed

    Csercsik, Dávid; Farkas, Imre; Szederkényi, Gábor; Hrabovszky, Erik; Liposits, Zsolt; Hangos, Katalin M

    2010-06-01

    In this paper a simple one compartment Hodgkin-Huxley type electrophysiological model of GnRH neurons is presented, that is able to reasonably reproduce the most important qualitative features of the firing pattern, such as baseline potential, depolarization amplitudes, sub-baseline hyperpolarization phenomenon and average firing frequency in response to excitatory current. In addition, the same model provides an acceptable numerical fit of voltage clamp (VC) measurement results. The parameters of the model have been estimated using averaged VC traces, and characteristic values of measured current clamp traces originating from GnRH neurons in hypothalamic slices. The resulting parameter values show a good agreement with literature data in most of the cases. Applying parametric changes, which lead to the increase of baseline potential and enhance cell excitability, the model becomes capable of bursting. The effects of various parameters to burst length have been analyzed by simulation.

  6. Models of the Neuronal Mechanisms of Target Localization of the Barn Owl

    DTIC Science & Technology

    1991-11-01

    laminaris cell employing the Hodgkin - Huxley model of action potential generation to which parameter modifications are applied. Modelina the laminaris...spike output mechanisms, a standard model of action potential generation ( Hodgkin and Huxley 1952) was employed (see Appendix I). Using the synaptic... action potentials that are phase-locked to the stimulus (up to 10 kHz) and nearly independent of intensity. Laminaris neurons are tuned to a particular

  7. Recapitulation of spinal motor neuron-specific disease phenotypes in a human cell model of spinal muscular atrophy

    PubMed Central

    Wang, Zhi-Bo; Zhang, Xiaoqing; Li, Xue-Jun

    2013-01-01

    Establishing human cell models of spinal muscular atrophy (SMA) to mimic motor neuron-specific phenotypes holds the key to understanding the pathogenesis of this devastating disease. Here, we developed a closely representative cell model of SMA by knocking down the disease-determining gene, survival motor neuron (SMN), in human embryonic stem cells (hESCs). Our study with this cell model demonstrated that knocking down of SMN does not interfere with neural induction or the initial specification of spinal motor neurons. Notably, the axonal outgrowth of spinal motor neurons was significantly impaired and these disease-mimicking neurons subsequently degenerated. Furthermore, these disease phenotypes were caused by SMN-full length (SMN-FL) but not SMN-Δ7 (lacking exon 7) knockdown, and were specific to spinal motor neurons. Restoring the expression of SMN-FL completely ameliorated all of the disease phenotypes, including specific axonal defects and motor neuron loss. Finally, knockdown of SMN-FL led to excessive mitochondrial oxidative stress in human motor neuron progenitors. The involvement of oxidative stress in the degeneration of spinal motor neurons in the SMA cell model was further confirmed by the administration of N-acetylcysteine, a potent antioxidant, which prevented disease-related apoptosis and subsequent motor neuron death. Thus, we report here the successful establishment of an hESC-based SMA model, which exhibits disease gene isoform specificity, cell type specificity, and phenotype reversibility. Our model provides a unique paradigm for studying how motor neurons specifically degenerate and highlights the potential importance of antioxidants for the treatment of SMA. PMID:23208423

  8. Recapitulation of spinal motor neuron-specific disease phenotypes in a human cell model of spinal muscular atrophy.

    PubMed

    Wang, Zhi-Bo; Zhang, Xiaoqing; Li, Xue-Jun

    2013-03-01

    Establishing human cell models of spinal muscular atrophy (SMA) to mimic motor neuron-specific phenotypes holds the key to understanding the pathogenesis of this devastating disease. Here, we developed a closely representative cell model of SMA by knocking down the disease-determining gene, survival motor neuron (SMN), in human embryonic stem cells (hESCs). Our study with this cell model demonstrated that knocking down of SMN does not interfere with neural induction or the initial specification of spinal motor neurons. Notably, the axonal outgrowth of spinal motor neurons was significantly impaired and these disease-mimicking neurons subsequently degenerated. Furthermore, these disease phenotypes were caused by SMN-full length (SMN-FL) but not SMN-Δ7 (lacking exon 7) knockdown, and were specific to spinal motor neurons. Restoring the expression of SMN-FL completely ameliorated all of the disease phenotypes, including specific axonal defects and motor neuron loss. Finally, knockdown of SMN-FL led to excessive mitochondrial oxidative stress in human motor neuron progenitors. The involvement of oxidative stress in the degeneration of spinal motor neurons in the SMA cell model was further confirmed by the administration of N-acetylcysteine, a potent antioxidant, which prevented disease-related apoptosis and subsequent motor neuron death. Thus, we report here the successful establishment of an hESC-based SMA model, which exhibits disease gene isoform specificity, cell type specificity, and phenotype reversibility. Our model provides a unique paradigm for studying how motor neurons specifically degenerate and highlights the potential importance of antioxidants for the treatment of SMA.

  9. Brain-derived neurotrophic factor as an indicator of chemical neurotoxicity: an animal-free CNS cell culture model.

    PubMed

    Woehrling, Elizabeth K; Hill, Eric J; Nagel, David; Coleman, Michael D

    2013-12-01

    Recent changes to the legislation on chemicals and cosmetics testing call for a change in the paradigm regarding the current 'whole animal' approach for identifying chemical hazards, including the assessment of potential neurotoxins. Accordingly, since 2004, we have worked on the development of the integrated co-culture of post-mitotic, human-derived neurons and astrocytes (NT2.N/A), for use as an in vitro functional central nervous system (CNS) model. We have used it successfully to investigate indicators of neurotoxicity. For this purpose, we used NT2.N/A cells to examine the effects of acute exposure to a range of test chemicals on the cellular release of brain-derived neurotrophic factor (BDNF). It was demonstrated that the release of this protective neurotrophin into the culture medium (above that of control levels) occurred consistently in response to sub-cytotoxic levels of known neurotoxic, but not non-neurotoxic, chemicals. These increases in BDNF release were quantifiable, statistically significant, and occurred at concentrations below those at which cell death was measureable, which potentially indicates specific neurotoxicity, as opposed to general cytotoxicity. The fact that the BDNF immunoassay is non-invasive, and that NT2.N/A cells retain their functionality for a period of months, may make this system useful for repeated-dose toxicity testing, which is of particular relevance to cosmetics testing without the use of laboratory animals. In addition, the production of NT2.N/A cells without the use of animal products, such as fetal bovine serum, is being explored, to produce a fully-humanised cellular model.

  10. Human ESC-derived dopamine neurons show similar preclinical efficacy and potency to fetal neurons when grafted in a rat model of Parkinson's disease.

    PubMed

    Grealish, Shane; Diguet, Elsa; Kirkeby, Agnete; Mattsson, Bengt; Heuer, Andreas; Bramoulle, Yann; Van Camp, Nadja; Perrier, Anselme L; Hantraye, Philippe; Björklund, Anders; Parmar, Malin

    2014-11-06

    Considerable progress has been made in generating fully functional and transplantable dopamine neurons from human embryonic stem cells (hESCs). Before these cells can be used for cell replacement therapy in Parkinson's disease (PD), it is important to verify their functional properties and efficacy in animal models. Here we provide a comprehensive preclinical assessment of hESC-derived midbrain dopamine neurons in a rat model of PD. We show long-term survival and functionality using clinically relevant MRI and PET imaging techniques and demonstrate efficacy in restoration of motor function with a potency comparable to that seen with human fetal dopamine neurons. Furthermore, we show that hESC-derived dopamine neurons can project sufficiently long distances for use in humans, fully regenerate midbrain-to-forebrain projections, and innervate correct target structures. This provides strong preclinical support for clinical translation of hESC-derived dopamine neurons using approaches similar to those established with fetal cells for the treatment of Parkinson's disease. Copyright © 2014 The Authors. Published by Elsevier Inc. All rights reserved.

  11. Human ESC-Derived Dopamine Neurons Show Similar Preclinical Efficacy and Potency to Fetal Neurons when Grafted in a Rat Model of Parkinson’s Disease

    PubMed Central

    Grealish, Shane; Diguet, Elsa; Kirkeby, Agnete; Mattsson, Bengt; Heuer, Andreas; Bramoulle, Yann; Van Camp, Nadja; Perrier, Anselme L.; Hantraye, Philippe; Björklund, Anders; Parmar, Malin

    2014-01-01

    Summary Considerable progress has been made in generating fully functional and transplantable dopamine neurons from human embryonic stem cells (hESCs). Before these cells can be used for cell replacement therapy in Parkinson’s disease (PD), it is important to verify their functional properties and efficacy in animal models. Here we provide a comprehensive preclinical assessment of hESC-derived midbrain dopamine neurons in a rat model of PD. We show long-term survival and functionality using clinically relevant MRI and PET imaging techniques and demonstrate efficacy in restoration of motor function with a potency comparable to that seen with human fetal dopamine neurons. Furthermore, we show that hESC-derived dopamine neurons can project sufficiently long distances for use in humans, fully regenerate midbrain-to-forebrain projections, and innervate correct target structures. This provides strong preclinical support for clinical translation of hESC-derived dopamine neurons using approaches similar to those established with fetal cells for the treatment of Parkinson’s disease. PMID:25517469

  12. Selective disruption of acetylcholine synthesis in subsets of motor neurons: a new model of late-onset motor neuron disease.

    PubMed

    Lecomte, Marie-José; Bertolus, Chloé; Santamaria, Julie; Bauchet, Anne-Laure; Herbin, Marc; Saurini, Françoise; Misawa, Hidemi; Maisonobe, Thierry; Pradat, Pierre-François; Nosten-Bertrand, Marika; Mallet, Jacques; Berrard, Sylvie

    2014-05-01

    Motor neuron diseases are characterized by the selective chronic dysfunction of a subset of motor neurons and the subsequent impairment of neuromuscular function. To reproduce in the mouse these hallmarks of diseases affecting motor neurons, we generated a mouse line in which ~40% of motor neurons in the spinal cord and the brainstem become unable to sustain neuromuscular transmission. These mice were obtained by conditional knockout of the gene encoding choline acetyltransferase (ChAT), the biosynthetic enzyme for acetylcholine. The mutant mice are viable and spontaneously display abnormal phenotypes that worsen with age including hunched back, reduced lifespan, weight loss, as well as striking deficits in muscle strength and motor function. This slowly progressive neuromuscular dysfunction is accompanied by muscle fiber histopathological features characteristic of neurogenic diseases. Unexpectedly, most changes appeared with a 6-month delay relative to the onset of reduction in ChAT levels, suggesting that compensatory mechanisms preserve muscular function for several months and then are overwhelmed. Deterioration of mouse phenotype after ChAT gene disruption is a specific aging process reminiscent of human pathological situations, particularly among survivors of paralytic poliomyelitis. These mutant mice may represent an invaluable tool to determine the sequence of events that follow the loss of function of a motor neuron subset as the disease progresses, and to evaluate therapeutic strategies. They also offer the opportunity to explore fundamental issues of motor neuron biology.

  13. The isotropic fractionator provides evidence for differential loss of hippocampal neurons in two mouse models of Alzheimer's disease.

    PubMed

    Brautigam, Hannah; Steele, John W; Westaway, David; Fraser, Paul E; St George-Hyslop, Peter H; Gandy, Sam; Hof, Patrick R; Dickstein, Dara L

    2012-11-22

    The accumulation of amyloid beta (Aβ) oligomers or fibrils is thought to be one of the main causes of synaptic and neuron loss, believed to underlie cognitive dysfunction in Alzheimer's disease (AD). Neuron loss has rarely been documented in amyloid precursor protein (APP) transgenic mouse models. We investigated whether two APP mouse models characterized by different folding states of amyloid showed different neuronal densities using an accurate method of cell counting. We examined total cell and neuronal populations in Swedish/Indiana APP mutant mice (TgCRND8) with severe Aβ pathology that includes fibrils, plaques, and oligomers, and Dutch APP mutant mice with only Aβ oligomer pathology. Using the isotropic fractionator, we found no differences from control mice in regional total cell populations in either TgCRND8 or Dutch mice. However, there were 31.8% fewer hippocampal neurons in TgCRND8 compared to controls, while no such changes were observed in Dutch mice. We show that the isotropic fractionator is a convenient method for estimating neuronal content in milligram quantities of brain tissue and represents a useful tool to assess cell loss efficiently in transgenic models with different types of neuropathology. Our data support the hypothesis that TgCRND8 mice with a spectrum of Aβ plaque, fibril, and oligomer pathology exhibit neuronal loss whereas Dutch mice with only oligomers, showed no evidence for neuronal loss. This suggests that the combination of plaques, fibrils, and oligomers causes more damage to mouse hippocampal neurons than Aβ oligomers alone.

  14. Modeling the Influence of Ion Channels on Neuron Dynamics in Drosophila

    PubMed Central

    Berger, Sandra D.; Crook, Sharon M.

    2015-01-01

    Voltage gated ion channels play a major role in determining a neuron's firing behavior, resulting in the specific processing of synaptic input patterns. Drosophila and other invertebrates provide valuable model systems for investigating ion channel kinetics and their impact on firing properties. Despite the increasing importance of Drosophila as a model system, few computational models of its ion channel kinetics have been developed. In this study, experimentally observed biophysical properties of voltage gated ion channels from the fruitfly Drosophila melanogaster are used to develop a minimal, conductance based neuron model. We investigate the impact of the densities of these channels on the excitability of the model neuron. Changing the channel densities reproduces different in situ observed firing patterns and induces a switch from integrator to resonator properties. Further, we analyze the preference to input frequency and how it depends on the channel densities and the resulting bifurcation type the system undergoes. An extension to a three dimensional model demonstrates that the inactivation kinetics of the sodium channels play an important role, allowing for firing patterns with a delayed first spike and subsequent high frequency firing as often observed in invertebrates, without altering the kinetics of the delayed rectifier current. PMID:26635592

  15. A mathematical model towards understanding the mechanism of neuronal regulation of wake-NREMS-REMS states.

    PubMed

    Kumar, Rupesh; Bose, Amitabha; Mallick, Birendra Nath

    2012-01-01

    In this study we have constructed a mathematical model of a recently proposed functional model known to be responsible for inducing waking, NREMS and REMS. Simulation studies using this model reproduced sleep-wake patterns as reported in normal animals. The model helps to explain neural mechanism(s) that underlie the transitions between wake, NREMS and REMS as well as how both the homeostatic sleep-drive and the circadian rhythm shape the duration of each of these episodes. In particular, this mathematical model demonstrates and confirms that an underlying mechanism for REMS generation is pre-synaptic inhibition from substantia nigra onto the REM-off terminals that project on REM-on neurons, as has been recently proposed. The importance of orexinergic neurons in stabilizing the wake-sleep cycle is demonstrated by showing how even small changes in inputs to or from those neurons can have a large impact on the ensuing dynamics. The results from this model allow us to make predictions of the neural mechanisms of regulation and patho-physiology of REMS.

  16. Modeling the Influence of Ion Channels on Neuron Dynamics in Drosophila.

    PubMed

    Berger, Sandra D; Crook, Sharon M

    2015-01-01

    Voltage gated ion channels play a major role in determining a neuron's firing behavior, resulting in the specific processing of synaptic input patterns. Drosophila and other invertebrates provide valuable model systems for investigating ion channel kinetics and their impact on firing properties. Despite the increasing importance of Drosophila as a model system, few computational models of its ion channel kinetics have been developed. In this study, experimentally observed biophysical properties of voltage gated ion channels from the fruitfly Drosophila melanogaster are used to develop a minimal, conductance based neuron model. We investigate the impact of the densities of these channels on the excitability of the model neuron. Changing the channel densities reproduces different in situ observed firing patterns and induces a switch from integrator to resonator properties. Further, we analyze the preference to input frequency and how it depends on the channel densities and the resulting bifurcation type the system undergoes. An extension to a three dimensional model demonstrates that the inactivation kinetics of the sodium channels play an important role, allowing for firing patterns with a delayed first spike and subsequent high frequency firing as often observed in invertebrates, without altering the kinetics of the delayed rectifier current.

  17. Single Neuron Optimization as a Basis for Accurate Biophysical Modeling: The Case of Cerebellar Granule Cells.

    PubMed

    Masoli, Stefano; Rizza, Martina F; Sgritta, Martina; Van Geit, Werner; Schürmann, Felix; D'Angelo, Egidio

    2017-01-01

    In realistic neuronal modeling, once the ionic channel complement has been defined, the maximum ionic conductance (Gi-max) values need to be tuned in order to match the firing pattern revealed by electrophysiological recordings. Recently, selection/mutation genetic algorithms have been proposed to efficiently and automatically tune these parameters. Nonetheless, since similar firing patterns can be achieved through different combinations of Gi-max values, it is not clear how well these algorithms approximate the corresponding properties of real cells. Here we have evaluated the issue by exploiting a unique opportunity offered by the cerebellar granule cell (GrC), which is electrotonically compact and has therefore allowed the direct experimental measurement of ionic currents. Previous models were constructed using empirical tuning of Gi-max values to match the original data set. Here, by using repetitive discharge patterns as a template, the optimization procedure yielded models that closely approximated the experimental Gi-max values. These models, in addition to repetitive firing, captured additional features, including inward rectification, near-threshold oscillations, and resonance, which were not used as features. Thus, parameter optimization using genetic algorithms provided an efficient modeling strategy for reconstructing the biophysical properties of neurons and for the subsequent reconstruction of large-scale neuronal network models.

  18. Single Neuron Optimization as a Basis for Accurate Biophysical Modeling: The Case of Cerebellar Granule Cells

    PubMed Central

    Masoli, Stefano; Rizza, Martina F.; Sgritta, Martina; Van Geit, Werner; Schürmann, Felix; D'Angelo, Egidio

    2017-01-01

    In realistic neuronal modeling, once the ionic channel complement has been defined, the maximum ionic conductance (Gi-max) values need to be tuned in order to match the firing pattern revealed by electrophysiological recordings. Recently, selection/mutation genetic algorithms have been proposed to efficiently and automatically tune these parameters. Nonetheless, since similar firing patterns can be achieved through different combinations of Gi-max values, it is not clear how well these algorithms approximate the corresponding properties of real cells. Here we have evaluated the issue by exploiting a unique opportunity offered by the cerebellar granule cell (GrC), which is electrotonically compact and has therefore allowed the direct experimental measurement of ionic currents. Previous models were constructed using empirical tuning of Gi-max values to match the original data set. Here, by using repetitive discharge patterns as a template, the optimization procedure yielded models that closely approximated the experimental Gi-max values. These models, in addition to repetitive firing, captured additional features, including inward rectification, near-threshold oscillations, and resonance, which were not used as features. Thus, parameter optimization using genetic algorithms provided an efficient modeling strategy for reconstructing the biophysical properties of neurons and for the subsequent reconstruction of large-scale neuronal network models. PMID:28360841

  19. Seizure-related activity of intralaminar thalamic neurons in a genetic model of absence epilepsy.

    PubMed

    Gorji, Ali; Mittag, Christoph; Shahabi, Parviz; Seidenbecher, Thomas; Pape, Hans-Christian

    2011-07-01

    Absence seizures are characterized by bilateral spike-and-wave discharges (SWDs) in thalamo-cortical circuits. In view of clinical studies indicating a critical involvement of intralaminar thalamic nuclei, we thought it timely to characterize the specific role and activity patterns of the respective neurons. Electrocorticographic (ECoG), intracellular, and unit activity recordings were performed in vivo from intralaminar thalamic neurons of the centrolateral (CL) and the paracentral (PC) thalamic nucleus in an established genetic rat model of absence epilepsy (WAG/Rij). Neurons in PC are depolarized to produce tonic series of action potentials at seizure-free episodes, and are rhythmically silenced concomitant with SWDs in a spike-locked manner. Rebound from spike-locked inhibition is associated with a transient increase in action potential activity. Neurons in CL possess a relatively negative membrane potential with overall low electrogenic activity at seizure-free episodes and generate burst-like discharges during SWDs that are locked to the decaying phase of the spike component on the ECoG. The SWD-locked membrane responses reverse close to the presumed chloride equilibrium potential, indicating GABA(A) receptor-mediated inhibitory postsynaptic potentials (IPSPs), with cell-type specific differences in polarity. In PC neurons, hyperpolarizing IPSPs result in spike-locked silencing of tonic firing and rebound burst discharges, while in CL neurons, IPSPs are depolarizing and trigger low-threshold burst firing likely mediated by a t-type Ca(2+) conductance. These data show a unique pattern of rhythmic SWD-locked IPSPs in PC and CL associated with paroxysms apt to impose a transient dysfunctional state to thalamo-striato-prefrontocortical networks during absence seizures. Copyright © 2011 Elsevier Inc. All rights reserved.

  20. Increased acid responsiveness in vagal sensory neurons in a guinea pig model of eosinophilic esophagitis

    PubMed Central

    Hu, Youtian; Liu, Zhenyu; Yu, Xiaoyun; Pasricha, Pankaj J.; Undem, Bradley J.

    2014-01-01

    Eosinophilic esophagitis (EoE) is characterized with eosinophils and mast cells predominated allergic inflammation in the esophagus and present with esophageal dysfunctions such as dysphagia, food impaction, and heartburn. However, the underlying mechanism of esophageal dysfunctions is unclear. This study aims to determine whether neurons in the vagal sensory ganglia are modulated in a guinea pig model of EoE. Animals were actively sensitized by ovalbumin (OVA) and then challenged with aerosol OVA inhalation for 2 wk. This results in a mild esophagitis with increases in mast cells and eosinophils in the esophageal wall. Vagal nodose and jugular neurons were disassociated, and their responses to acid, capsaicin, and transient receptor potential vanilloid type 1 (TRPV1) antagonist AMG-9810 were studied by calcium imaging and whole cell patch-clamp recording. Compared with naïve animals, antigen challenge significantly increased acid responsiveness in both nodose and jugular neurons. Their responses to capsaicin were also increased after antigen challenge. AMG-9810, at a concentration that blocked capsaicin-evoked calcium influx, abolished the increase in acid-induced activation in both nodose and jugular neurons. Vagotomy strongly attenuated those increased responses of nodose and jugular neurons to both acid and capsaicin induced by antigen challenge. These data for the first time demonstrated that prolonged antigen challenge significantly increases acid responsiveness in vagal nodose and jugular ganglia neurons. This sensitization effect is mediated largely through TRPV1 and initiated at sensory nerve endings in the peripheral tissues. Allergen-induced enhancement of responsiveness to noxious stimulation by acid in sensory nerve may contribute to the development of esophageal dysfunctions such as heartburn in EoE. PMID:24875100

  1. Early Deficits in Glycolysis Are Specific to Striatal Neurons from a Rat Model of Huntington Disease

    PubMed Central

    Gouarné, Caroline; Tardif, Gwenaëlle; Tracz, Jennifer; Latyszenok, Virginie; Michaud, Magali; Clemens, Laura Emily; Yu-Taeger, Libo; Nguyen, Huu Phuc; Bordet, Thierry; Pruss, Rebecca M.

    2013-01-01

    In Huntington disease (HD), there is increasing evidence for a link between mutant huntingtin expression, mitochondrial dysfunction, energetic deficits and neurodegeneration but the precise nature, causes and order of these events remain to be determined. In this work, our objective was to evaluate mitochondrial respiratory function in intact, non-permeabilized, neurons derived from a transgenic rat model for HD compared to their wild type littermates by measuring oxygen consumption rates and extracellular acidification rates. Although HD striatal neurons had similar respiratory capacity as those from their wild-type littermates when they were incubated in rich medium containing a supra-physiological glucose concentration (25 mM), pyruvate and amino acids, respiratory defects emerged when cells were incubated in media containing only a physiological cerebral level of glucose (2.5 mM). According to the concept that glucose is not the sole substrate used by the brain for neuronal energy production, we provide evidence that primary neurons can use lactate as well as pyruvate to fuel the mitochondrial respiratory chain. In contrast to glucose, we found no major deficits in HD striatal neurons’ capacity to use pyruvate as a respiratory substrate compared to wild type littermates. Additionally, we used extracellular acidification rates to confirm a reduction in anaerobic glycolysis in the same cells. Interestingly, the metabolic disturbances observed in striatal neurons were not seen in primary cortical neurons, a brain region affected in later stages of HD. In conclusion, our results argue for a dysfunction in glycolysis, which might precede any defects in the respiratory chain itself, and these are early events in the onset of disease. PMID:24303051

  2. Dendritic atrophy constricts functional maps in resonance and impedance properties of hippocampal model neurons

    PubMed Central

    Dhupia, Neha; Rathour, Rahul K.; Narayanan, Rishikesh

    2015-01-01

    A gradient in the density of hyperpolarization-activated cyclic-nucleotide gated (HCN) channels is necessary for the emergence of several functional maps within hippocampal pyramidal neurons. Here, we systematically analyzed the impact of dendritic atrophy on nine such functional maps, related to input resistance and local/transfer impedance properties, using conductance-based models of hippocampal pyramidal neurons. We introduced progressive dendritic atrophy in a CA1 pyramidal neuron reconstruction through a pruning algorithm, measured all functional maps in each pruned reconstruction, and arrived at functional forms for the dependence of underlying measurements on dendritic length. We found that, across frequencies, atrophied neurons responded with higher efficiency to incoming inputs, and the transfer of signals across the dendritic tree was more effective in an atrophied reconstruction. Importantly, despite the presence of identical HCN-channel density gradients, spatial gradients in input resistance, local/transfer resonance frequencies and impedance profiles were significantly constricted in reconstructions with dendritic atrophy, where these physiological measurements across dendritic locations converged to similar values. These results revealed that, in atrophied dendritic structures, the presence of an ion channel density gradient alone was insufficient to sustain homologous functional maps along the same neuronal topograph. We assessed the biophysical basis for these conclusions and found that this atrophy-induced constriction of functional maps was mediated by an enhanced spatial spread of the influence of an HCN-channel cluster in atrophied trees. These results demonstrated that the influence fields of ion channel conductances need to be localized for channel gradients to express themselves as homologous functional maps, suggesting that ion channel gradients are necessary but not sufficient for the emergence of functional maps within single neurons

  3. Dendritic atrophy constricts functional maps in resonance and impedance properties of hippocampal model neurons.

    PubMed

    Dhupia, Neha; Rathour, Rahul K; Narayanan, Rishikesh

    2014-01-01

    A gradient in the density of hyperpolarization-activated cyclic-nucleotide gated (HCN) channels is necessary for the emergence of several functional maps within hippocampal pyramidal neurons. Here, we systematically analyzed the impact of dendritic atrophy on nine such functional maps, related to input resistance and local/transfer impedance properties, using conductance-based models of hippocampal pyramidal neurons. We introduced progressive dendritic atrophy in a CA1 pyramidal neuron reconstruction through a pruning algorithm, measured all functional maps in each pruned reconstruction, and arrived at functional forms for the dependence of underlying measurements on dendritic length. We found that, across frequencies, atrophied neurons responded with higher efficiency to incoming inputs, and the transfer of signals across the dendritic tree was more effective in an atrophied reconstruction. Importantly, despite the presence of identical HCN-channel density gradients, spatial gradients in input resistance, local/transfer resonance frequencies and impedance profiles were significantly constricted in reconstructions with dendritic atrophy, where these physiological measurements across dendritic locations converged to similar values. These results revealed that, in atrophied dendritic structures, the presence of an ion channel density gradient alone was insufficient to sustain homologous functional maps along the same neuronal topograph. We assessed the biophysical basis for these conclusions and found that this atrophy-induced constriction of functional maps was mediated by an enhanced spatial spread of the influence of an HCN-channel cluster in atrophied trees. These results demonstrated that the influence fields of ion channel conductances need to be localized for channel gradients to express themselves as homologous functional maps, suggesting that ion channel gradients are necessary but not sufficient for the emergence of functional maps within single neurons.

  4. Neurokinin B and reproductive functions: "KNDy neuron" model in mammals and the emerging story in fish.

    PubMed

    Hu, Guangfu; Lin, Chengyuan; He, Mulan; Wong, Anderson O L

    2014-11-01

    In mammals, neurokinin B (NKB), the gene product of the tachykinin family member TAC3, is known to be a key regulator for episodic release of luteinizing hormone (LH). Its regulatory actions are mediated by a subpopulation of kisspeptin neurons within the arcuate nucleus with co-expression of NKB and dynorphin A (commonly called the "KNDy neurons"). By forming an "autosynaptic feedback loop" within the hypothalamus, the KNDy neurons can modulate gonadotropin-releasing hormone (GnRH) pulsatility and subsequent LH release in the pituitary. NKB regulation of LH secretion has been recently demonstrated in zebrafish, suggesting that the reproductive functions of NKB may be conserved from fish to mammals. Interestingly, the TAC3 genes in fish not only encode the mature peptide of NKB but also a novel tachykinin-like peptide, namely NKB-related peptide (or neurokinin F). Recent studies in zebrafish also reveal that the neuroanatomy of TAC3/kisspeptin system within the fish brain is quite different from that of mammals. In this article, the current ideas of "KNDy neuron" model for GnRH regulation and steroid feedback, other reproductive functions of NKB including its local actions in the gonad and placenta, the revised model of tachykinin evolution from invertebrates to vertebrates, as well as the emerging story of the two TAC3 gene products in fish, NKB and NKB-related peptide, will be reviewed with stress on the areas with interesting questions for future investigations. Copyright © 2014 Elsevier Inc. All rights reserved.

  5. Inhibiting sphingosine kinase 2 mitigates mutant Huntingtin-induced neurodegeneration in neuron models of Huntington disease.

    PubMed

    Moruno-Manchon, Jose F; Uzor, Ndidi-Ese; Blasco-Conesa, Maria P; Mannuru, Sishira; Putluri, Nagireddy; Furr-Stimming, Erin E; Tsvetkov, Andrey S

    2017-04-01

    Huntington disease (HD) is the most common inherited neurodegenerative disorder. It has no cure. The protein huntingtin causes HD, and mutations to it confer toxic functions to the protein that lead to neurodegeneration. Thus, identifying modifiers of mutant huntingtin-mediated neurotoxicity might be a therapeutic strategy for HD. Sphingosine kinases 1 (SK1) and 2 (SK2) synthesize sphingosine-1-phosphate (S1P), a bioactive lipid messenger critically involved in many vital cellular processes, such as cell survival. In the nucleus, SK2 binds to and inhibits histone deacetylases 1 and 2 (HDAC1/2). Inhibiting both HDACs has been suggested as a potential therapy in HD. Here, we found that SK2 is nuclear in primary neurons and, unexpectedly, overexpressed SK2 is neurotoxic in a dose-dependent manner. SK2 promotes DNA double-strand breaks in cultured primary neurons. We also found that SK2 is hyperphosphorylated in the brain samples from a model of HD, the BACHD mice. These data suggest that the SK2 pathway may be a part of a pathogenic pathway in HD. ABC294640, an inhibitor of SK2, reduces DNA damage in neurons and increases survival in two neuron models of HD. Our results identify a novel regulator of mutant huntingtin-mediated neurotoxicity and provide a new target for developing therapies for HD. © The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

  6. Dynamics of Disordered Network of Coupled Hindmarsh-Rose Neuronal Models

    NASA Astrophysics Data System (ADS)

    Dtchetgnia Djeundam, S. R.; Yamapi, R.; Filatrella, G.; Kofane, T. C.

    We investigate the effects of disorder on the synchronized state of a network of Hindmarsh-Rose neuronal models. Disorder, introduced as a perturbation of the neuronal parameters, destroys the network activity by wrecking the synchronized state. The dynamics of the synchronized state is analyzed through the Kuramoto order parameter, adapted to the neuronal Hindmarsh-Rose model. We find that the coupling deeply alters the dynamics of the single units, thus demonstrating that coupling not only affects the relative motion of the units, but also the dynamical behavior of each neuron; Thus, synchronization results in a structural change of the dynamics. The Kuramoto order parameter allows to clarify the nature of the transition from perfect phase synchronization to the disordered states, supporting the notion of an abrupt, second order-like, dynamical phase transition. We find that the system is resilient up to a certain disorder threshold, after that the network abruptly collapses to a desynchronized state. The loss of perfect synchronization seems to occur even for vanishingly small values of the disorder, but the degree of synchronization (as measured by the Kuramoto order parameter) gently decreases, and the completely disordered state is never reached.

  7. Parameters of the diffusion leaky integrate-and-fire neuronal model for a slowly fluctuating signal.

    PubMed

    Picchini, Umberto; Ditlevsen, Susanne; De Gaetano, Andrea; Lansky, Petr

    2008-11-01

    Stochastic leaky integrate-and-fire (LIF) neuronal models are common theoretical tools for studying properties of real neuronal systems. Experimental data of frequently sampled membrane potential measurements between spikes show that the assumption of constant parameter values is not realistic and that some (random) fluctuations are occurring. In this letter, we extend the stochastic LIF model, allowing a noise source determining slow fluctuations in the signal. This is achieved by adding a random variable to one of the parameters characterizing the neuronal input, considering each interspike interval (ISI) as an independent experimental unit with a different realization of this random variable. In this way, the variation of the neuronal input is split into fast (within-interval) and slow (between-intervals) components. A parameter estimation method is proposed, allowing the parameters to be estimated simultaneously over the entire data set. This increases the statistical power, and the average estimate over all ISIs will be improved in the sense of decreased variance of the estimator compared to previous approaches, where the estimation has been conducted on each individual ISI. The results obtained on real data show good agreement with classical regression methods.

  8. Neuronal driven pre-plaque inflammation in a transgenic rat model of Alzheimer's disease.

    PubMed

    Hanzel, Cecilia E; Pichet-Binette, Alexa; Pimentel, Luisa S B; Iulita, M Florencia; Allard, Simon; Ducatenzeiler, Adriana; Do Carmo, Sonia; Cuello, A Claudio

    2014-10-01

    Chronic brain inflammation is associated with Alzheimer's disease (AD) and is classically attributed to amyloid plaque deposition. However, whether the amyloid pathology can trigger early inflammatory processes before plaque deposition remains a matter of debate. To address the possibility that a pre-plaque inflammatory process occurs, we investigated the status of neuronal, astrocytic, and microglial markers in pre- and post-amyloid plaque stages in a novel transgenic rat model of an AD-like amyloid pathology (McGill-R-Thy1-APP). In this model, we found a marked upregulation of several classical inflammatory markers such as COX-2, IL-1β, TNF-α, and fractalkine (CX3CL1) in the cerebral cortex and hippocampus. Interestingly, many of these markers were highly expressed in amyloid beta-burdened neurons. Activated astrocytes and microglia were associated with these Aβ-burdened neurons. These findings confirm the occurrence of a proinflammatory process preceding amyloid plaque deposition and suggest that Aβ-burdened neurons play a crucial role in initiating inflammation in AD. Copyright © 2014. Published by Elsevier Inc.

  9. Oleuropein Prevents Neuronal Death, Mitigates Mitochondrial Superoxide Production and Modulates Autophagy in a Dopaminergic Cellular Model.

    PubMed

    Achour, Imène; Arel-Dubeau, Anne-Marie; Renaud, Justine; Legrand, Manon; Attard, Everaldo; Germain, Marc; Martinoli, Maria-Grazia

    2016-08-09

    Parkinson's disease (PD) is a progressive neurodegenerative disorder, primarily affecting dopaminergic neurons in the substantia nigra. There is currently no cure for PD and present medications aim to alleviate clinical symptoms, thus prevention remains the ideal strategy to reduce the prevalence of this disease. The goal of this study was to investigate whether oleuropein (OLE), the major phenolic compound in olive derivatives, may prevent neuronal degeneration in a cellular dopaminergic model of PD, differentiated PC12 cells exposed to the potent parkinsonian toxin 6-hydroxydopamine (6-OHDA). We also investigated OLE's ability to mitigate mitochondrial oxidative stress and modulate the autophagic flux. Our results obtained by measuring cytotoxicity and apoptotic events demonstrate that OLE significantly decreases neuronal death. OLE could also reduce mitochondrial production of reactive oxygen species resulting from blocking superoxide dismutase activity. Moreover, quantification of autophagic and acidic vesicles in the cytoplasm alongside expression of specific autophagic markers uncovered a regulatory role for OLE against autophagic flux impairment induced by bafilomycin A1. Altogether, our results define OLE as a neuroprotective, anti-oxidative and autophagy-regulating molecule, in a neuronal dopaminergic cellular model.

  10. Oleuropein Prevents Neuronal Death, Mitigates Mitochondrial Superoxide Production and Modulates Autophagy in a Dopaminergic Cellular Model

    PubMed Central

    Achour, Imène; Arel-Dubeau, Anne-Marie; Renaud, Justine; Legrand, Manon; Attard, Everaldo; Germain, Marc; Martinoli, Maria-Grazia

    2016-01-01

    Parkinson’s disease (PD) is a progressive neurodegenerative disorder, primarily affecting dopaminergic neurons in the substantia nigra. There is currently no cure for PD and present medications aim to alleviate clinical symptoms, thus prevention remains the ideal strategy to reduce the prevalence of this disease. The goal of this study was to investigate whether oleuropein (OLE), the major phenolic compound in olive derivatives, may prevent neuronal degeneration in a cellular dopaminergic model of PD, differentiated PC12 cells exposed to the potent parkinsonian toxin 6-hydroxydopamine (6-OHDA). We also investigated OLE’s ability to mitigate mitochondrial oxidative stress and modulate the autophagic flux. Our results obtained by measuring cytotoxicity and apoptotic events demonstrate that OLE significantly decreases neuronal death. OLE could also reduce mitochondrial production of reactive oxygen species resulting from blocking superoxide dismutase activity. Moreover, quantification of autophagic and acidic vesicles in the cytoplasm alongside expression of specific autophagic markers uncovered a regulatory role for OLE against autophagic flux impairment induced by bafilomycin A1. Altogether, our results define OLE as a neuroprotective, anti-oxidative and autophagy-regulating molecule, in a neuronal dopaminergic cellular model. PMID:27517912

  11. The Drive-Reinforcement Neuronal Model: A Real-Time Learning Mechanism For Unsupervised Learning

    NASA Astrophysics Data System (ADS)

    Klopf, A. H.

    1988-05-01

    The drive-reinforcement neuronal model is described as an example of a newly discovered class of real-time learning mechanisms that correlate earlier derivatives of inputs with later derivatives of outputs. The drive-reinforcement neuronal model has been demonstrated to predict a wide range of classical conditioning phenomena in animal learning. A variety of classes of connectionist and neural network models have been investigated in recent years (Hinton and Anderson, 1981; Levine, 1983; Barto, 1985; Feldman, 1985; Rumelhart and McClelland, 1986). After a brief review of these models, discussion will focus on the class of real-time models because they appear to be making the strongest contact with the experimental evidence of animal learning. Theoretical models in physics have inspired Boltzmann machines (Ackley, Hinton, and Sejnowski, 1985) and what are sometimes called Hopfield networks (Hopfield, 1982; Hopfield and Tank, 1986). These connectionist models utilize symmetric connections and adaptive equilibrium processes during which the networks settle into minimal energy states. Networks utilizing error-correction learning mechanisms go back to Rosenblatt's (1962) perception and Widrow's (1962) adaline and currently take the form of back propagation networks (Parker, 1985; Rumelhart, Hinton, and Williams, 1985, 1986). These networks require a "teacher" or "trainer" to provide error signals indicating the difference between desired and actual responses. Networks employing real-time learning mechanisms, in which the temporal association of signals is of fundamental importance, go back to Hebb (1949). Real-time learning mechanisms may require no teacher or trainer and thus may lend themselves to unsupervised learning. Such models have been extended by Klopf (1972, 1982), who introduced the notions of synaptic eligibility and generalized reinforcement. Sutton and Barto (1981) advanced this class of models by proposing that a derivative of the theoretical neuron's out

  12. An optimization-based study of equivalent circuit models for representing recordings at the neuron-electrode interface.

    PubMed

    Thakore, V; Molnar, P; Hickman, J J

    2012-08-01

    Extracellular neuroelectronic interfacing is an emerging field with important applications in the fields of neural prosthetics, biological computation, and biosensors. Traditionally, neuron-electrode interfaces have been modeled as linear point or area contact equivalent circuits but it is now being increasingly realized that such models cannot explain the shapes and magnitudes of the observed extracellular signals. Here, results were compared and contrasted from an unprecedented optimization-based study of the point contact models for an extracellular "on-cell" neuron-patch electrode and a planar neuron-microelectrode interface. Concurrent electrophysiological recordings from a single neuron simultaneously interfaced to three distinct electrodes (intracellular, "on-cell" patch, and planar microelectrode) allowed novel insights into the mechanism of signal transduction at the neuron-electrode interface. After a systematic isolation of the nonlinear neuronal contribution to the extracellular signal, a consistent underestimation of the simulated suprathreshold extracellular signals compared to the experimentally recorded signals was observed. This conclusively demonstrated that the dynamics of the interfacial medium contribute nonlinearly to the process of signal transduction at the neuron-electrode interface. Further, an examination of the optimized model parameters for the experimental extracellular recordings from sub- and suprathreshold stimulations of the neuron-electrode junctions revealed that ionic transport at the "on-cell" neuron-patch electrode is dominated by diffusion whereas at the neuron-microelectrode interface the electric double layer (EDL) effects dominate. Based on this study, the limitations of the equivalent circuit models in their failure to account for the nonlinear EDL and ionic electrodiffusion effects occurring during signal transduction at the neuron-electrode interfaces are discussed.

  13. Analysis of stochastic phenomena in 2D Hindmarsh-Rose neuron model

    NASA Astrophysics Data System (ADS)

    Bashkirtseva, I.; Ryashko, L.; Slepukhina, E.

    2016-10-01

    In mathematical research of neuronal activity, conceptual models play an important role. We consider 2D Hindmarsh-Rose model, which exhibits the fundamental property of neuron, the excitability. We study how random disturbances affect this property. The effects of noise are analysed in the parametric zone where the deterministic model is characterized by the coexistence of two stable equilibria. We show that under random disturbances, noise-induced transitions between the attractors occur, forming a new complex dynamic regime of stochastic bursting. It is confirmed by changes of distribution of random trajectories and interspike intervals. For the analysis of this noise-induced phenomenon, we apply the stochastic sensitivity technique and confidence domains method. We suggest a method for estimation of threshold noise intensity corresponding to the onset of noise-induced bursting. We show that the obtained values are in a good agreement with direct numerical simulations.

  14. A new simple /spl infin/OH neuron model as a biologically plausible principal component analyzer.

    PubMed

    Jankovic, M V

    2003-01-01

    A new approach to unsupervised learning in a single-layer neural network is discussed. An algorithm for unsupervised learning based upon the Hebbian learning rule is presented. A simple neuron model is analyzed. A dynamic neural model, which contains both feed-forward and feedback connections between the input and the output, has been adopted. The, proposed learning algorithm could be more correctly named self-supervised rather than unsupervised. The solution proposed here is a modified Hebbian rule, in which the modification of the synaptic strength is proportional not to pre- and postsynaptic activity, but instead to the presynaptic and averaged value of postsynaptic activity. It is shown that the model neuron tends to extract the principal component from a stationary input vector sequence. Usually accepted additional decaying terms for the stabilization of the original Hebbian rule are avoided. Implementation of the basic Hebbian scheme would not lead to unrealistic growth of the synaptic strengths, thanks to the adopted network structure.

  15. Computer Modelling of Functional Aspects of Noise in Endogenously Oscillating Neurons

    NASA Astrophysics Data System (ADS)

    Huber, M. T.; Dewald, M.; Voigt, K.; Braun, H. A.; Moss, F.

    1998-03-01

    Membrane potential oscillations are a widespread feature of neuronal activity. When such oscillations operate close to the spike-triggering threshold, noise can become an essential property of spike-generation. According to that, we developed a minimal Hodgkin-Huxley-type computer model which includes a noise term. This model accounts for experimental data from quite different cells ranging from mammalian cortical neurons to fish electroreceptors. With slight modifications of the parameters, the model's behavior can be tuned to bursting activity, which additionally allows it to mimick temperature encoding in peripheral cold receptors including transitions to apparently chaotic dynamics as indicated by methods for the detection of unstable periodic orbits. Under all conditions, cooperative effects between noise and nonlinear dynamics can be shown which, beyond stochastic resonance, might be of functional significance for stimulus encoding and neuromodulation.

  16. Neuroprotective Role of Gap Junctions in a Neuron Astrocyte Network Model.

    PubMed

    Huguet, Gemma; Joglekar, Anoushka; Messi, Leopold Matamba; Buckalew, Richard; Wong, Sarah; Terman, David

    2016-07-26

    A detailed biophysical model for a neuron/astrocyte network is developed to explore mechanisms responsible for the initiation and propagation of cortical spreading depolarizations and the role of astrocytes in maintaining ion homeostasis, thereby preventing these pathological waves. Simulations of the model illustrate how properties of spreading depolarizations, such as wave speed and duration of depolarization, depend on several factors, including the neuron and astrocyte Na(+)-K(+) ATPase pump strengths. In particular, we consider the neuroprotective role of astrocyte gap junction coupling. The model demonstrates that a syncytium of electrically coupled astrocytes can maintain a physiological membrane potential in the presence of an elevated extracellular K(+) concentration and efficiently distribute the excess K(+) across the syncytium. This provides an effective neuroprotective mechanism for delaying or preventing the initiation of spreading depolarizations.

  17. Wnt1 from cochlear schwann cells enhances neuronal differentiation of transplanted neural stem cells in a rat spiral ganglion neuron degeneration model.

    PubMed

    He, Ya; Zhang, Peng-Zhi; Sun, Dong; Mi, Wen-Juan; Zhang, Xin-Yi; Cui, Yong; Jiang, Xing-Wang; Mao, Xiao-Bo; Qiu, Jian-Hua

    2014-04-01

    Although neural stem cell (NSC) transplantation is widely expected to become a therapy for nervous system degenerative diseases and injuries, the low neuronal differentiation rate of NSCs transplanted into the inner ear is a major obstacle for the successful treatment of spiral ganglion neuron (SGN) degeneration. In this study, we validated whether the local microenvironment influences the neuronal differentiation of transplanted NSCs in the inner ear. Using a rat SGN degeneration model, we demonstrated that transplanted NSCs were more likely to differentiate into microtubule-associated protein 2 (MAP2)-positive neurons in SGN-degenerated cochleae than in control cochleae. Using real-time quantitative PCR and an immunofluorescence assay, we also proved that the expression of Wnt1 (a ligand of Wnt signaling) increases significantly in Schwann cells in the SGN-degenerated cochlea. We further verified that NSC cultures express receptors and signaling components for Wnts. Based on these expression patterns, we hypothesized that Schwann cell-derived Wnt1 and Wnt signaling might be involved in the regulation of the neuronal differentiation of transplanted NSCs. We verified our hypothesis in vitro using a coculture system. We transduced a lentiviral vector expressing Wnt1 into cochlear Schwann cell cultures and cocultured them with NSC cultures. The coculture with Wnt1-expressing Schwann cells resulted in a significant increase in the percentage of NSCs that differentiated into MAP2-positive neurons, whereas this differentiation-enhancing effect was prevented by Dkk1 (an inhibitor of the Wnt signaling pathway). These results suggested that Wnt1 derived from cochlear Schwann cells enhanced the neuronal differentiation of transplanted NSCs through Wnt signaling pathway activation. Alterations of the microenvironment deserve detailed investigation because they may help us to conceive effective strategies to overcome the barrier of the low differentiation rate of transplanted

  18. Electrodiffusive Model for Astrocytic and Neuronal Ion Concentration Dynamics

    PubMed Central

    Halnes, Geir; Østby, Ivar; Pettersen, Klas H.; Omholt, Stig W.; Einevoll, Gaute T.

    2013-01-01

    The cable equation is a proper framework for modeling electrical neural signalling that takes place at a timescale at which the ionic concentrations vary little. However, in neural tissue there are also key dynamic processes that occur at longer timescales. For example, endured periods of intense neural signaling may cause the local extracellular K+-concentration to increase by several millimolars. The clearance of this excess K+ depends partly on diffusion in the extracellular space, partly on local uptake by astrocytes, and partly on intracellular transport (spatial buffering) within astrocytes. These processes, that take place at the time scale of seconds, demand a mathematical description able to account for the spatiotemporal variations in ion concentrations as well as the subsequent effects of these variations on the membrane potential. Here, we present a general electrodiffusive formalism for modeling of ion concentration dynamics in a one-dimensional geometry, including both the intra- and extracellular domains. Based on the Nernst-Planck equations, this formalism ensures that the membrane potential and ion concentrations are in consistency, it ensures global particle/charge conservation and it accounts for diffusion and concentration dependent variations in resistivity. We apply the formalism to a model of astrocytes exchanging ions with the extracellular space. The simulations show that K+-removal from high-concentration regions is driven by a local depolarization of the astrocyte membrane, which concertedly (i) increases the local astrocytic uptake of K+, (ii) suppresses extracellular transport of K+, (iii) increases axial transport of K+ within astrocytes, and (iv) facilitates astrocytic relase of K+ in regions where the extracellular concentration is low. Together, these mechanisms seem to provide a robust regulatory scheme for shielding the extracellular space from excess K+. PMID:24367247

  19. Mathematical model of bursting in dissociated purkinje neurons.

    PubMed

    Forrest, Michael D

    2013-01-01

    In vitro, Purkinje cell behaviour is sometimes studied in a dissociated soma preparation in which the dendritic projection has been cleaved. A fraction of these dissociated somas spontaneously burst. The mechanism of this bursting is incompletely understood. We have constructed a biophysical Purkinje soma model, guided and constrained by experimental reports in the literature, that can replicate the somatically driven bursting pattern and which hypothesises Persistent Na(+) current (INaP) to be its burst initiator and SK K(+) current (ISK) to be its burst terminator.

  20. A central neuropathic pain model by DSP-4 induced lesion of noradrenergic neurons: preliminary report.

    PubMed

    Kudo, Takashi; Kushikata, Tetsuya; Kudo, Mihoko; Kudo, Tsuyoshi; Hirota, Kazuyoshi

    2010-09-06

    Neuropathic pain models are classified as central and peripheral pain models. Although various peripheral neuropathic pain models are established, central pain models are based only on spinal cord injury. DSP-4 is a competitive inhibitor of norepinephrine uptake that selectively degenerates the locus coeruleus (LC)-noradrenergic neurons projection to the cerebral cortex and hippocampus. In the present study, we have tested whether lesion of LC-noradrenergic neurons by ip DSP-4 (0, 10, 30, 50 mg/kg, n=7 each) could provide a new central neuropathic pain model in rats using a hot-plate and tail-flick tests. DSP-4 significantly reduced the hot-plate latency and norepinephrine contents especially in the coerulean regions. However, DSP-4 did not change tail-flick latency. There are significant correlations of the latency in the hot-plate test with norepinephrine contents in the cerebral cortex (r=0.432, p=0.022), the hippocampus (r=0.465, p=0.013) and the pons (r=0.400, p=0.035) but not with those in the hypothalamus and the spinal cord. As response to hot-plate and tail-flick implies supra-spinal process and spinal reflex, respectively, central neuropathic pain may be facilitated by DSP-4 depleting LC-noradrenergic neurons although the present data are preliminary.

  1. Minimal Hodgkin-Huxley type models for different classes of cortical and thalamic neurons.

    PubMed

    Pospischil, Martin; Toledo-Rodriguez, Maria; Monier, Cyril; Piwkowska, Zuzanna; Bal, Thierry; Frégnac, Yves; Markram, Henry; Destexhe, Alain

    2008-11-01

    We review here the development of Hodgkin-Huxley (HH) type models of cerebral cortex and thalamic neurons for network simulations. The intrinsic electrophysiological properties of cortical neurons were analyzed from several preparations, and we selected the four most prominent electrophysiological classes of neurons. These four classes are "fast spiking", "regular spiking", "intrinsically bursting" and "low-threshold spike" cells. For each class, we fit "minimal" HH type models to experimental data. The models contain the minimal set of voltage-dependent currents to account for the data. To obtain models as generic as possible, we used data from different preparations in vivo and in vitro, such as rat somatosensory cortex and thalamus, guinea-pig visual and frontal cortex, ferret visual cortex, cat visual cortex and cat association cortex. For two cell classes, we used automatic fitting procedures applied to several cells, which revealed substantial cell-to-cell variability within each class. The selection of such cellular models constitutes a necessary step towards building network simulations of the thalamocortical system with realistic cellular dynamical properties.

  2. Using evolutionary algorithms for fitting high-dimensional models to neuronal data.

    PubMed

    Svensson, Carl-Magnus; Coombes, Stephen; Peirce, Jonathan Westley

    2012-04-01

    In the study of neurosciences, and of complex biological systems in general, there is frequently a need to fit mathematical models with large numbers of parameters to highly complex datasets. Here we consider algorithms of two different classes, gradient following (GF) methods and evolutionary algorithms (EA) and examine their performance in fitting a 9-parameter model of a filter-based visual neuron to real data recorded from a sample of 107 neurons in macaque primary visual cortex (V1). Although the GF method converged very rapidly on a solution, it was highly susceptible to the effects of local minima in the error surface and produced relatively poor fits unless the initial estimates of the parameters were already very good. Conversely, although the EA required many more iterations of evaluating the model neuron's response to a series of stimuli, it ultimately found better solutions in nearly all cases and its performance was independent of the starting parameters of the model. Thus, although the fitting process was lengthy in terms of processing time, the relative lack of human intervention in the evolutionary algorithm, and its ability ultimately to generate model fits that could be trusted as being close to optimal, made it far superior in this particular application than the gradient following methods. This is likely to be the case in many further complex systems, as are often found in neuroscience.

  3. GSK-3 Mouse Models to Study Neuronal Apoptosis and Neurodegeneration

    PubMed Central

    Gómez-Sintes, Raquel; Hernández, Félix; Lucas, José J.; Avila, Jesús

    2011-01-01

    Increased GSK-3 activity is believed to contribute to the etiology of chronic disorders like Alzheimer’s disease (AD), schizophrenia, diabetes, and some types of cancer, thus supporting therapeutic potential of GSK-3 inhibitors. Numerous mouse models with modified GSK-3 have been generated in order to study the physiology of GSK-3, its implication in diverse pathologies and the potential effect of GSK-3 inhibitors. In this review we have focused on the relevance of these mouse models for the study of the role of GSK-3 in apoptosis. GSK-3 is involved in two apoptotic pathways, intrinsic and extrinsic pathways, and plays opposite roles depending on the apoptotic signaling process that is activated. It promotes cell death when acting through intrinsic pathway and plays an anti-apoptotic role if the extrinsic pathway is occurring. It is important to dissect this duality since, among the diseases in which GSK-3 is involved, excessive cell death is crucial in some illnesses like neurodegenerative diseases, while a deficient apoptosis is occurring in others such as cancer or autoimmune diseases. The clinical application of a classical GSK-3 inhibitor, lithium, is limited by its toxic consequences, including motor side effects. Recently, the mechanism leading to activation of apoptosis following chronic lithium administration has been described. Understanding this mechanism could help to minimize side effects and to improve application of GSK-3 inhibitors to the treatment of AD and to extend the application to other diseases. PMID:22110426

  4. The Gamma renewal process as an output of the diffusion leaky integrate-and-fire neuronal model.

    PubMed

    Lansky, Petr; Sacerdote, Laura; Zucca, Cristina

    2016-06-01

    Statistical properties of spike trains as well as other neurophysiological data suggest a number of mathematical models of neurons. These models range from entirely descriptive ones to those deduced from the properties of the real neurons. One of them, the diffusion leaky integrate-and-fire neuronal model, which is based on the Ornstein-Uhlenbeck (OU) stochastic process that is restricted by an absorbing barrier, can describe a wide range of neuronal activity in terms of its parameters. These parameters are readily associated with known physiological mechanisms. The other model is descriptive, Gamma renewal process, and its parameters only reflect the observed experimental data or assumed theoretical properties. Both of these commonly used models are related here. We show under which conditions the Gamma model is an output from the diffusion OU model. In some cases, we can see that the Gamma distribution is unrealistic to be achieved for the employed parameters of the OU process.

  5. A computational model of the respiratory network challenged and optimized by data from optogenetic manipulation of glycinergic neurons.

    PubMed

    Oku, Yoshitaka; Hülsmann, Swen

    2017-04-07

    The topology of the respiratory network in the brainstem has been addressed using different computational models, which help to understand the functional properties of the system. We tested a neural mass model by comparing the result of activation and inhibition of inhibitory neurons in silico with recently published results of optogenetic manipulation of glycinergic neurons [Sherman, et al. (2015) Nat Neurosci 18:408]. The comparison revealed that a five-cell type model consisting of three classes of inhibitory neurons [I-DEC, E-AUG, E-DEC (PI)] and two excitatory populations (pre-I/I) and (I-AUG) neurons can be applied to explain experimental observations made by stimulating or inhibiting inhibitory neurons by light sensitive ion channels.

  6. Reduction of stochastic conductance-based neuron models with time-scales separation.

    PubMed

    Wainrib, Gilles; Thieullen, Michèle; Pakdaman, Khashayar

    2012-04-01

    We introduce a method for systematically reducing the dimension of biophysically realistic neuron models with stochastic ion channels exploiting time-scales separation. Based on a combination of singular perturbation methods for kinetic Markov schemes with some recent mathematical developments of the averaging method, the techniques are general and applicable to a large class of models. As an example, we derive and analyze reductions of different stochastic versions of the Hodgkin Huxley (HH) model, leading to distinct reduced models. The bifurcation analysis of one of the reduced models with the number of channels as a parameter provides new insights into some features of noisy discharge patterns, such as the bimodality of interspike intervals distribution. Our analysis of the stochastic HH model shows that, besides being a method to reduce the number of variables of neuronal models, our reduction scheme is a powerful method for gaining understanding on the impact of fluctuations due to finite size effects on the dynamics of slow fast systems. Our analysis of the reduced model reveals that decreasing the number of sodium channels in the HH model leads to a transition in the dynamics reminiscent of the Hopf bifurcation and that this transition accounts for changes in characteristics of the spike train generated by the model. Finally, we also examine the impact of these results on neuronal coding, notably, reliability of discharge times and spike latency, showing that reducing the number of channels can enhance discharge time reliability in response to weak inputs and that this phenomenon can be accounted for through the analysis of the reduced model.

  7. The influences of somatic and dendritic inhibition on bursting patterns in a neuronal circuit model.

    PubMed

    Yang, Keun-Hang; Franaszczuk, Piotr J; Bergey, Gregory K

    2003-10-01

    The balance between inhibition and excitation plays a crucial role in the generation of synchronous bursting activity in neuronal circuits. In human and animal models of epilepsy, changes in both excitatory and inhibitory synaptic inputs are known to occur. Locations and distribution of these excitatory and inhibitory synaptic inputs on pyramidal cells play a role in the integrative properties of neuronal activity, e.g., epileptiform activity. Thus the location and distribution of the inputs onto pyramidal cells are important parameters that influence neuronal activity in epilepsy. However, the location and distribution of inhibitory synapses converging onto pyramidal cells have not been fully studied. The objectives of this study are to investigate the roles of the relative location of inhibitory synapses on the dendritic tree and soma in the generation of bursting activity. We investigate influences of somatic and dendritic inhibition on bursting activity patterns in several paradigms of potential connections using a simplified multicompartmental model. We also investigate the effects of distribution of fast and slow components of GABAergic inhibition in pyramidal cells. Interspike interval (ISI) analysis is used for examination of bursting patterns. Simulations show that the inhibitory interneuron regulates neuronal bursting activity. Bursting behavior patterns depend on the synaptic weight and delay of the inhibitory connection as well as the location of the synapse. When the inhibitory interneuron synapses on the pyramidal neuron, inhibitory action is stronger if the inhibitory synapse is close to the soma. Alterations of synaptic weight of the interneuron can be compensatory for changes in the location of synaptic input. The relative changes in these parameters exert a considerable influence on whether synchronous bursting activity is facilitated or reduced. Additional simulations show that the slow GABAergic inhibitory component is more effective than the

  8. Tocilizumab inhibits neuronal cell apoptosis and activates STAT3 in cerebral infarction rat model.

    PubMed

    Wang, Shaojun; Zhou, Jun; Kang, Weijie; Dong, Zhaoni; Wang, Hezuo

    2016-01-15

    Cerebral infarction is a severe hypoxic ischemic necrosis with accelerated neuronal cell apoptosis in the brain. As a monoclonal antibody against interleukin 6, tocilizumab (TCZ) is widely used in immune diseases, whose function in cerebral infarction has not been studied. This study aims to reveal the role of TCZ in regulating neuronal cell apoptosis in cerebral infarction. The cerebral infarction rat model was constructed by middle cerebral artery occlusion and treated with TCZ. Cell apoptosis in hippocampus and cortex of the brain was examined with TUNEL method. Rat neuronal cells cultured in oxygen-glucose deprivation (OGD) conditions and treated with TCZ were used to compare cell viability and apoptosis. Apoptosis-related factors including B-cell lymphoma extra large (Bcl-xL) and Caspase 3, as well as the phosphorylated signal transducer and activator of transcription 3 (p-STAT3) in brain cortex were analyzed from the protein level. Results indicated that TCZ treatment could significantly prevent the promoted cell apoptosis caused by cerebral infarction or OGD (P < 0.05 or P < 0.01). In brain cortex of the rat model, TCZ up-regulated Bcl-xL and down-regulated Caspase 3, consistent with the inhibited cell apoptosis. It also promoted tyrosine 705 phosphorylation of STAT3, which might be the potential regulatory mechanism of TCZ in neuronal cells. This study provided evidence for the protective role of TCZ against neuronal cell apoptosis in cerebral infarction. Based on these fundamental data, TCZ is a promising option for treating cerebral infarction, but further investigations on related mechanisms are still necessary.

  9. Tocilizumab inhibits neuronal cell apoptosis and activates STAT3 in cerebral infarction rat model

    PubMed Central

    Wang, Shaojun; Zhou, Jun; Kang, Weijie; Dong, Zhaoni; Wang, Hezuo

    2016-01-01

    Cerebral infarction is a severe hypoxic ischemic necrosis with accelerated neuronal cell apoptosis in the brain. As a monoclonal antibody against interleukin 6, tocilizumab (TCZ) is widely used in immune diseases, whose function in cerebral infarction has not been studied. This study aims to reveal the role of TCZ in regulating neuronal cell apoptosis in cerebral infarction. The cerebral infarction rat model was constructed by middle cerebral artery occlusion and treated with TCZ. Cell apoptosis in hippocampus and cortex of the brain was examined with TUNEL method. Rat neuronal cells cultured in oxygen-glucose deprivation (OGD) conditions and treated with TCZ were used to compare cell viability and apoptosis. Apoptosis-related factors including B-cell lymphoma extra large (Bcl-xL) and Caspase 3, as well as the phosphorylated signal transducer and activator of transcription 3 (p-STAT3) in brain cortex were analyzed from the protein level. Results indicated that TCZ treatment could significantly prevent the promoted cell apoptosis caused by cerebral infarction or OGD (P < 0.05 or P < 0.01). In brain cortex of the rat model, TCZ up-regulated Bcl-xL and down-regulated Caspase 3, consistent with the inhibited cell apoptosis. It also promoted tyrosine 705 phosphorylation of STAT3, which might be the potential regulatory mechanism of TCZ in neuronal cells. This study provided evidence for the protective role of TCZ against neuronal cell apoptosis in cerebral infarction. Based on these fundamental data, TCZ is a promising option for treating cerebral infarction, but further investigations on related mechanisms are still necessary. PMID:26773188

  10. Modeling the effects of extracellular potassium on bursting properties in pre-Bötzinger complex neurons

    PubMed Central

    Segaran, Joshua; Molkov, Yaroslav I.

    2015-01-01

    There are many types of neurons that intrinsically generate rhythmic bursting activity, even when isolated, and these neurons underlie several specific motor behaviors. Rhythmic neurons that drive the inspiratory phase of respiration are located in the medullary pre-Bötzinger Complex (pre-BötC). However, it is not known if their rhythmic bursting is the result of intrinsic mechanisms or synaptic interactions. In many cases, for bursting to occur, the excitability of these neurons needs to be elevated. This excitation is provided in vitro (e.g. in slices), by increasing extracellular potassium concentration (Kout) well beyond physiologic levels. Elevated Kout shifts the reversal potentials for all potassium currents including the potassium component of leakage to higher values. However, how an increase in Kout, and the resultant changes in potassium currents, induce bursting activity, have yet to be established. Moreover, it is not known if the endogenous bursting induced in vitro is representative of neural behavior in vivo. Our modeling study examines the interplay between Kout, excitability, and selected currents, as they relate to endogenous rhythmic bursting. Starting with a Hodgkin-Huxley formalization of a pre-BötC neuron, a potassium ion component was incorporated into the leakage current, and model behaviors were investigated at varying concentrations of Kout. Our simulations show that endogenous bursting activity, evoked in vitro by elevation of Kout, is the result of a specific relationship between the leakage and voltage-dependent, delayed rectifier potassium currents, which may not be observed at physiological levels of extracellular potassium. PMID:26899961

  11. Carbon Monoxide Releasing Molecule-A1 (CORM-A1) Improves Neurogenesis: Increase of Neuronal Differentiation Yield by Preventing Cell Death.

    PubMed

    Almeida, Ana S; Soares, Nuno L; Vieira, Melissa; Gramsbergen, Jan Bert; Vieira, Helena L A

    2016-01-01

    Cerebral ischemia and neurodegenerative diseases lead to impairment or death of neurons in the central nervous system. Stem cell based therapies are promising strategies currently under investigation. Carbon monoxide (CO) is an endogenous product of heme degradation by heme oxygenase (HO) activity. Administration of CO at low concentrations produces several beneficial effects in distinct tissues, namely anti-apoptotic and anti-inflammatory. Herein the CO role on modulation of neuronal differentiation was assessed. Three different models with increasing complexity were used: human neuroblastoma SH-S5Y5 cell line, human teratocarcinoma NT2 cell line and organotypic hippocampal slice cultures (OHSC). Cell lines were differentiated into post-mitotic neurons by treatment with retinoic acid (RA) supplemented with CO-releasing molecule A1 (CORM-A1). CORM-A1 positively modulated neuronal differentiation, since it increased final neuronal production and enhanced the expression of specific neuronal genes: Nestin, Tuj1 and MAP2. Furthermore, during neuronal differentiation process, there was an increase in proliferative cell number (ki67 mRNA expressing cells) and a decrease in cell death (lower propidium iodide (PI) uptake, limitation of caspase-3 activation and higher Bcl-2 expressing cells). CO supplementation did not increase the expression of RA receptors. In the case of SH-S5Y5 model, small amounts of reactive oxygen species (ROS) generation emerges as important signaling molecules during CO-promoted neuronal differentiation. CO's improvement of neuronal differentiation yield was validated using OHSC as ex vivo model. CORM-A1 treatment of OHSC promoted higher levels of cells expressing the neuronal marker Tuj1. Still, CORM-A1 increased cell proliferation assessed by ki67 expression and also prevented cell death, which was followed by increased Bcl-2 expression, decreased levels of active caspase-3 and PI uptake. Likewise, ROS signaling emerged as key factors in CO

  12. Inferring Neuronal Dynamics from Calcium Imaging Data Using Biophysical Models and Bayesian Inference.

    PubMed

    Rahmati, Vahid; Kirmse, Knut; Marković, Dimitrije; Holthoff, Knut; Kiebel, Stefan J

    2016-02-01

    Calcium imaging has been used as a promising technique to monitor the dynamic activity of neuronal populations. However, the calcium trace is temporally smeared which restricts the extraction of quantities of interest such as spike trains of individual neurons. To address this issue, spike reconstruction algorithms have been introduced. One limitation of such reconstructions is that the underlying models are not informed about the biophysics of spike and burst generations. Such existing prior knowledge might be useful for constraining the possible solutions of spikes. Here we describe, in a novel Bayesian approach, how principled knowledge about neuronal dynamics can be employed to infer biophysical variables and parameters from fluorescence traces. By using both synthetic and in vitro recorded fluorescence traces, we demonstrate that the new approach is able to reconstruct different repetitive spiking and/or bursting patterns with accurate single spike resolution. Furthermore, we show that the high inference precision of the new approach is preserved even if the fluorescence trace is rather noisy or if the fluorescence transients show slow rise kinetics lasting several hundred milliseconds, and inhomogeneous rise and decay times. In addition, we discuss the use of the new approach for inferring parameter changes, e.g. due to a pharmacological intervention, as well as for inferring complex characteristics of immature neuronal circuits.

  13. V642I APP-inducible neuronal cells: a model system for investigating Alzheimer's disorders.

    PubMed

    Niikura, T; Murayama, N; Hashimoto, Y; Ito, Y; Yamagishi, Y; Matsuoka, M; Takeuchi, Y; Aiso, S; Nishimoto, I

    2000-08-02

    APP is a precursor of beta amyloid deposited in Alzheimer's disease (AD). Although genetic studies established that mutations in APP cause familial AD (FAD), the mechanism for neuronal death by FAD mutants has not been well understood. We established neuronal cells (F11/EcR/V642I cells) in which V642I APP was inducibly expressed by ecdysone. Treatment with ecdysone, but not vehicle, killed most cells within a few days, with rounding, shrinkage, and detachment as well as nuclear fragmentation. Death was suppressed by Ac-DEVD-CHO and pertussis toxin. Electron microscopic analysis revealed that apoptosis occurred in ecdysone-treated cells. V642I-APP-induced death was suppressed by the anti-AD factors estrogen and apoE2. These data demonstrate not only that expression of this FAD gene causes neuronal apoptosis, but that F11/EcR/V642I cells, the first neuronal cells with inducible FAD gene expression, provide a useful model system in investigating AD disorders.

  14. Bone Marrow Mononuclear Cells Protect Neurons and Modulate Microglia in Cell Culture Models of Ischemic Stroke

    PubMed Central

    Sharma, Sushil; Yang, Bing; Strong, Roger; Xi, Xiao Pei; Brenneman, Miranda; Grotta, James C.; Aronowski, Jaroslaw; Savitz, Sean I.

    2010-01-01

    Background Although several studies have provided evidence for the therapeutic potential of bone marrow-derived mononuclear cells (MNCs) in animal models of stroke, the mechanisms underlying their benefits remain largely unknown. We have determined the neuroprotective potential of MNCs in primary neuronal cultures exposed to various injuries in vitro. Methods Cortical neurons in culture were exposed to oxygen-glucose deprivation, hypoxia, or hydrogen peroxide and cell death was assayed by MTT, caspase-3 activation or TUNEL labelling at 24 hrs. Cultures were randomized to co-treatment with MNC-derived supernatants or media before injury exposure. In separate experiments, macrophage or microglial cultures were exposed to lipopolypolysacharide (LPS) in the presence and absence of MNC-derived supernatants. Neuronal cultures were then exposed to conditioned media derived from activated macrophages or microglia. Cytokines from the supernantants of MNC cultures exposed to normoxia or hypoxia were also estimated by enzyme-linked immunosorbant assay (ELISA). Results MNC-derived supernatants attenuated neuronal death induced by OGD, hypoxia, hydrogen peroxide, and conditioned macrophage/microglial media and contain a number of trophic factors including IL-10, IGF-1, VEGF, and SDF-1. Conclusion MNCs provide broad neuroprotection against a variety of injuries relevant to stroke. PMID:20629187

  15. Nonnative SOD1 trimer is toxic to motor neurons in a model of amyotrophic lateral sclerosis

    PubMed Central

    Fee, Lanette; Tao, Yazhong; Redler, Rachel L.; Fay, James M.; Zhang, Yuliang; Lv, Zhengjian; Mercer, Ian P.; Deshmukh, Mohanish; Lyubchenko, Yuri L.; Dokholyan, Nikolay V.

    2016-01-01

    Since the linking of mutations in the Cu,Zn superoxide dismutase gene (sod1) to amyotrophic lateral sclerosis (ALS) in 1993, researchers have sought the connection between SOD1 and motor neuron death. Disease-linked mutations tend to destabilize the native dimeric structure of SOD1, and plaques containing misfolded and aggregated SOD1 have been found in the motor neurons of patients with ALS. Despite advances in understanding of ALS disease progression and SOD1 folding and stability, cytotoxic species and mechanisms remain unknown, greatly impeding the search for and design of therapeutic interventions. Here, we definitively link cytotoxicity associated with SOD1 aggregation in ALS to a nonnative trimeric SOD1 species. We develop methodology for the incorporation of low-resolution experimental data into simulations toward the structural modeling of metastable, multidomain aggregation intermediates. We apply this methodology to derive the structure of a SOD1 trimer, which we validate in vitro and in hybridized motor neurons. We show that SOD1 mutants designed to promote trimerization increase cell death. Further, we demonstrate that the cytotoxicity of the designed mutants correlates with trimer stability, providing a direct link between the presence of misfolded oligomers and neuron death. Identification of cytotoxic species is the first and critical step in elucidating the molecular etiology of ALS, and the ability to manipulate formation of these species will provide an avenue for the development of future therapeutic strategies. PMID:26719414

  16. MND2: A new mouse model of inherited motor neuron disease

    SciTech Connect

    Jones, J.M.; Albin, R.L.; Feldman, E.L.; Simin, K.; Schuster, T.G.; Dunnick, W.A.; Collins, J.T.; Chrisp, C.E.; Meisler, M.H. ); Taylor, B.A. )

    1993-06-01

    The autosomal recessive mutation mnd2 results in early onset motor neuron disease with rapidly progressive paralysis, severe muscle wasting, regression of thymus and spleen, and death before 40 days of age. mnd2 has been mapped to mouse chromosome 6 with the gene order: centromere-Tcrb-Ly-2-Sftp-3-D6Mit4-mnd2-D6Mit6, D6Mit9-D6Rck132-Raf-1, D6Mit11-D6Mit12-D6Mit14. mnd2 is located within a conserved linkage group with homologs on human chromosome 2p12-p13. Spinal motor neurons of homozygous affected animals are swollen and stain weakly, and electromyography revealed spontaneous activity characteristic of muscle denervation. Myelin staining was normal throughout the neuraxis. The clinical observations are consistent with a primary abnormality of lower motor neuron function. This new animal model will be of value for identification of a genetic defect responsible for motor neuron disease and for evaluation of new therapies. 36 refs., 7 figs., 2 tabs.

  17. Altered neuronal network and rescue in a human MECP2 duplication model

    PubMed Central

    Nageshappa, Savitha; Carromeu, Cassiano; Trujillo, Cleber A.; Mesci, Pinar; Espuny-Camacho, Ira; Pasciuto, Emanuela; Vanderhaeghen, Pierre; Verfaillie, Catherine; Raitano, Susanna; Kumar, Anujith; Carvalho, Claudia M.B.; Bagni, Claudia; Ramocki, Melissa B.; Araujo, Bruno H. S.; Torres, Laila B.; Lupski, James R.; Van Esch, Hilde; Muotri, Alysson R.

    2015-01-01

    Increased dosage of MeCP2 results in a dramatic neurodevelopmental phenotype with onset at birth. We generated induced pluripotent stem cells (iPSC) from patients with the MECP2 duplication syndrome (MECP2dup), carrying different duplication sizes, to study the impact of increased MeCP2 dosage in human neurons. We show that cortical neurons derived from these different MECP2dup iPSC lines have increase synaptogenesis and dendritic complexity. Additionally, using multi-electrodes arrays, we show that neuronal network synchronization was altered in MECP2dup-derived neurons. Given MeCP2 function at the epigenetic level, we tested if these alterations were reversible using a library of compounds with defined activity on epigenetic pathways. One histone deacetylase inhibitor, NCH-51, was validated as a potential clinical candidate. Interestingly, this compound has never been considered before as a therapeutic alternative for neurological disorders. Our model recapitulates early stages of the human MECP2 duplication syndrome and represents a promising cellular tool to facilitate therapeutic drug screening for severe neurodevelopmental disorders. PMID:26347316

  18. Metabolic cost of neuronal information in an empirical stimulus-response model.

    PubMed

    Kostal, Lubomir; Lansky, Petr; McDonnell, Mark D

    2013-06-01

    The limits on maximum information that can be transferred by single neurons may help us to understand how sensory and other information is being processed in the brain. According to the efficient-coding hypothesis (Barlow, Sensory Comunication, MIT press, Cambridge, 1961), neurons are adapted to the statistical properties of the signals to which they are exposed. In this paper we employ methods of information theory to calculate, both exactly (numerically) and approximately, the ultimate limits on reliable information transmission for an empirical neuronal model. We couple information transfer with the metabolic cost of neuronal activity and determine the optimal information-to-metabolic cost ratios. We find that the optimal input distribution is discrete with only six points of support, both with and without a metabolic constraint. However, we also find that many different input distributions achieve mutual information close to capacity, which implies that the precise structure of the capacity-achieving input is of lesser importance than the value of capacity.

  19. Deterministic and stochastic bifurcations in the Hindmarsh-Rose neuronal model.

    PubMed

    Dtchetgnia Djeundam, S R; Yamapi, R; Kofane, T C; Aziz-Alaoui, M A

    2013-09-01

    We analyze the bifurcations occurring in the 3D Hindmarsh-Rose neuronal model with and without random signal. When under a sufficient stimulus, the neuron activity takes place; we observe various types of bifurcations that lead to chaotic transitions. Beside the equilibrium solutions and their stability, we also investigate the deterministic bifurcation. It appears that the neuronal activity consists of chaotic transitions between two periodic phases called bursting and spiking solutions. The stochastic bifurcation, defined as a sudden change in character of a stochastic attractor when the bifurcation parameter of the system passes through a critical value, or under certain condition as the collision of a stochastic attractor with a stochastic saddle, occurs when a random Gaussian signal is added. Our study reveals two kinds of stochastic bifurcation: the phenomenological bifurcation (P-bifurcations) and the dynamical bifurcation (D-bifurcations). The asymptotical method is used to analyze phenomenological bifurcation. We find that the neuronal activity of spiking and bursting chaos remains for finite values of the noise intensity.

  20. Inferring Neuronal Dynamics from Calcium Imaging Data Using Biophysical Models and Bayesian Inference

    PubMed Central

    Rahmati, Vahid; Kirmse, Knut; Marković, Dimitrije; Holthoff, Knut; Kiebel, Stefan J.

    2016-01-01

    Calcium imaging has been used as a promising technique to monitor the dynamic activity of neuronal populations. However, the calcium trace is temporally smeared which restricts the extraction of quantities of interest such as spike trains of individual neurons. To address this issue, spike reconstruction algorithms have been introduced. One limitation of such reconstructions is that the underlying models are not informed about the biophysics of spike and burst generations. Such existing prior knowledge might be useful for constraining the possible solutions of spikes. Here we describe, in a novel Bayesian approach, how principled knowledge about neuronal dynamics can be employed to infer biophysical variables and parameters from fluorescence traces. By using both synthetic and in vitro recorded fluorescence traces, we demonstrate that the new approach is able to reconstruct different repetitive spiking and/or bursting patterns with accurate single spike resolution. Furthermore, we show that the high inference precision of the new approach is preserved even if the fluorescence trace is rather noisy or if the fluorescence transients show slow rise kinetics lasting several hundred milliseconds, and inhomogeneous rise and decay times. In addition, we discuss the use of the new approach for inferring parameter changes, e.g. due to a pharmacological intervention, as well as for inferring complex characteristics of immature neuronal circuits. PMID:26894748

  1. Phosphodiesterase 7 Inhibition Preserves Dopaminergic Neurons in Cellular and Rodent Models of Parkinson Disease

    PubMed Central

    Morales-Garcia, Jose A.; Redondo, Miriam; Alonso-Gil, Sandra; Gil, Carmen; Perez, Concepción; Martinez, Ana; Santos, Angel; Perez-Castillo, Ana

    2011-01-01

    Background Phosphodiesterase 7 plays a major role in down-regulation of protein kinase A activity by hydrolyzing cAMP in many cell types. This cyclic nucleotide plays a key role in signal transduction in a wide variety of cellular responses. In the brain, cAMP has been implicated in learning, memory processes and other brain functions. Methodology/Principal Findings Here we show a novel function of phosphodiesterase 7 inhibition on nigrostriatal dopaminergic neuronal death. We found that S14, a heterocyclic small molecule inhibitor of phosphodiesterase 7, conferred significant neuronal protection against different insults both in the human dopaminergic cell line SH-SY5Y and in primary rat mesencephalic cultures. S14 treatment also reduced microglial activation, protected dopaminergic neurons and improved motor function in the lipopolysaccharide rat model of Parkinson disease. Finally, S14 neuroprotective effects were reversed by blocking the cAMP signaling pathways that operate through cAMP-dependent protein kinase A. Conclusions/Significance Our findings demonstrate that phosphodiesterase 7 inhibition can protect dopaminergic neurons against different insults, and they provide support for the therapeutic potential of phosphodiesterase 7 inhibitors in the treatment of neurodegenerative disorders, particularly Parkinson disease. PMID:21390306

  2. Small GSK-3 Inhi