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Purpose: Medical schools have faced various challenges in preparing their clinical students for the frontlines of a pandemic. This study investigated medical students’ satisfaction with their institutions during the coronavirus disease 2019 (COVID-19) pandemic with the intention of guiding educators in future public health crises.Methods: In this cross-sectional study surveying students in clinical rotations, the primary outcome was overall satisfaction regarding medical schools’ responses to the pandemic, and the four secondary outcomes were school communication, exposure to COVID-19, availability of personal protective equipment, and access to COVID-19 testing.Results: The survey was distributed to ten medical schools, of which 430 students responded for a response rate of 13.0%. While most students were satisfied (61.9%, n=266) with their schools’ response, more than one in five (21.9%, n=94) were dissatisfied. Among the four secondary outcomes, communication with students was most predictive of overall satisfaction.Conclusion: In future crises, schools can best improve student satisfaction by prioritizing timely communication.

Acupuncture has been known to be effective for atopic dermatitis, especially ameliorating itch; however, its mechanisms are still unclear. The aim of this study was to test the anti-itch effects of acupuncture and to investigate its possible mechanisms. Acupuncture was performed at Gok-Ji (LI11) acupoints just before the injection of pruritogens in the mouse cheek model of acute itch and of MC903-induced atopic dermatitis displaying serotonergic chronic itch. Acupuncture significantly reduced acute itch triggered by compound 48/80, chloroquine, or especially serotonin. It also markedly reduced scratching behaviors evoked by the serotonin 5-HT2 receptor agonist α-methylserotonin and selective 5-HT7 receptor agonist LP 44. In addition, acupuncture treatment at LI11 had the preventive and therapeutic effects on persistent itch as well as the robust skin inflammation with epidermal thickening in mice with MC903-induced atopic dermatitis. It also considerably reduced the increased expression of 5-HT2A, 5-HT2B and 5-HT7 receptors in atopic dermatitis-like skin lesions in mice treated with MC903. Taken together, these findings highlight that acupuncture significantly ameliorates not only skin inflammation, but also acute and chronic serotonergic itch, possibly through blockade of serotonin 5-HT2 and 5-HT7 receptors.

BackgroundThe recent addition of immunotherapy as a treatment modality to surgery and radiation has vastly improved disease control for patients with keratinocyte-derived carcinomas (KCs) that are incurable with local therapies alone. With the advent of immune checkpoint inhibitors (ICPis) in non-melanoma skin cancers comes diagnostic and therapeutic challenges when considering treatment strategies for patients presenting with clinical perineural invasion (cPNI) of locally advanced KC of the head and neck.ObjectivesWe report four cases that convey the diagnostic and therapeutic complexity of managing patients with neuropathic symptoms from cutaneous neurotropic carcinomas of the head and neck. We also discuss an updated review regarding immunotherapies and perineural invasion within KC management.ConclusionPatients presenting with symptoms suspicious for cPNI warrant an expanded diagnostic evaluation to correlate neurological findings with neurotropic spread of disease. While nerve biopsies can be precarious in sensitive areas, a history of skin cancer and clinical presentation suggestive of neurotropism may be enough to pursue timely management in the form of surgery, radiation, and/or systemic therapy given each patient’s individual priorities, comorbidities, and prognosis. When adding ICPi as a treatment modality for patients with disease not amenable to local therapies, the potential for immune-related adverse events must be considered. A multi-disciplinary review and approach to the management of patients with KC and cPNI is essential for obtaining optimal patient outcomes.

Optimal surgical treatment modality for invasive melanoma (IM) and melanoma in situ (MIS) has been debated with proponents for standard wide local excision (WLE), serial disk staged excision, and Mohs micrographic surgery.The purpose of this study is to identify the recurrence rates and surgical margins of invasive stage 1 melanoma and MIS lesions using serial disk staged excision technique with rush permanent processing and "bread loafing" microscopic analysis.Recurrence rates and surgical margins of 63 IM and 207 MIS at the University of California Dermatology Surgery Unit were retrospectively reviewed.No recurrences were observed with average follow-up of 34 and 36 months for IM and MIS, respectively (range, 10-92 months). Average surgical margins were 19.8 mm (SD 9.7) for IM and 12.1 mm (SD 12.2) for MIS.This SSE technique for the treatment of IM and MIS is comparable in efficacy to other SSE techniques, and it offers physicians a simple and accessible alternative to WLE and MMS.

Parkinson’s disease is the second most common neurodegenerative disease. Patients with Parkinson’s disease can be treated with a combination of acupuncture and herbal medicine, but studies on the synergistic effects of the combined treatment have not yet been conducted. Thus, we subjected an MPTP-induced Parkinson’s disease mouse model to the combined treatment. We used acupoint GB34 for acupuncture and modified Chunggantang (KD5040) as the herbal medicine, as they have been reported to be effective in Parkinson’s disease. We investigated the suboptimal dose of KD5040 and then used this dose in the combined treatment. The results showed that the combined treatment had a synergistic effect on improvements in abnormal motor function and neurodegeneration compared with the use of acupuncture or herbal medicine alone. The combined treatment also had a neuroprotective effect via the PI3K/AKT and MAPK/ERK signaling pathways. These findings suggest that the combined treatment with acupuncture and KD5040 can help improve the symptoms of Parkinson’s disease.

Acupuncture has been known to be effective for atopic dermatitis, especially ameliorating itch; however, its mechanisms are still unclear. The aim of this study was to test the anti-itch effects of acupuncture and to investigate its possible mechanisms. Acupuncture was performed at Gok-Ji (LI11) acupoints just before the injection of pruritogens in the mouse cheek model of acute itch and of MC903-induced atopic dermatitis displaying serotonergic chronic itch. Acupuncture significantly reduced acute itch triggered by compound 48/80, chloroquine, or especially serotonin. It also markedly reduced scratching behaviors evoked by the serotonin 5-HT

Growing evidences show that gut microbiota is associated with the pathogenesis of Parkinson's disease (PD) and the gut-brain axis can be promising target for the development of the therapeutic strategies for PD. Acupuncture has been used to improve brain functions and inflammation in neurological disorders such as PD, and to recover the gastrointestinal dysfunctions in various gastrointestinal disorders. Thus, we investigated whether acupuncture could improve Parkinsonism and gut microbial dysbiosis induced by 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine. First, we observed that acupuncture treatment at acupoints GB34 and ST36 could improve motor functions and comorbid anxiety in PD mice. Next, we found that acupuncture increased the levels of dopaminergic fibers and neurons in the striatum and the substantia nigra, respectively. Acupuncture also restored the overexpression of microglia and astrocyte as well as conversion of Bax and Bcl-2 expression in both the striatum and the substantia nigra, indicating that inflammatory responses and apoptosis were blocked by acupuncture. Additionally, via 16S rRNA sequence analysis, we observed that the relative abundance of 18 genera were changed in acupuncture-treated mice compared to the PD mice. Of them, Butyricimonas, Holdemania, Frisingicoccus, Gracilibacter, Phocea, and Aestuariispira showed significant correlations with anxiety as well as motor functions. Furthermore, the predicted functional analyses showed that acupuncture restored the physiology functions such as glutathione metabolism, methane metabolism, and PD pathway. In conclusion, we suggest that the effects of acupuncture on the enhanced motor function and the protection of the dopaminergic neurons may be associated with the regulation of the gut microbial dysbiosis and thus the inhibition of the neuroinflammation in the PD mice.

Supplemental Digital Content is Available in the Text. Acupuncture treatment for 6-month period altered DNA methylation patterns, particularly in the prefrontal cortex, in a mouse model of chronic neuropathic pain., Chronic pain reduces life quality and is an important clinical problem associated with emotional and cognitive dysfunction. Epigenetic regulation of DNA methylation is involved in the induction of abnormal behaviors and pathological gene expression. We examined whether acupuncture can restore epigenetic changes caused by chronic pain, and identified the underlying mechanisms in neuropathic pain mice. Acupuncture treatment for 6 months (3 days/week) improved mechanical/cold allodynia and the emotional/cognitive dysfunction caused by left partial sciatic nerve ligation (PSNL)-induced neuropathic pain. The effects of acupuncture were associated with global DNA methylation recovery in the prefrontal cortex (PFC). Analysis of DNA methylation patterns in PFC indicated that 1364 overlapping genes among 4442 and 4416 methylated genes in the PSNL vs sham and PSNL vs acupuncture points groups, respectively, were highly associated with the DNA methylation process. Acupuncture restored the reduced expression of 5-methylcytosine, methyl-cytosine-phospho-guanine binding protein 2, and DNA methyltransferase family enzymes induced by PSNL in PFC. Methylation levels of Nr4a1 and Chkb associated with mitochondrial dysfunction were decreased in PFC of the PSNL mice, and increased by acupuncture. By contrast, high expression of Nr4a1 and Chkb mRNA in PSNL mice decreased after acupuncture. We also found that acupuncture inhibited the expression of Ras pathway-related genes such as Rasgrp1 and Rassf1. Finally, the expression of Nr4a1, Rasgrp1, Rassf1, and Chkb mRNA increased in the neuronal cells treated with Mecp2 small interfering RNA. These results suggest that acupuncture can relieve chronic pain-induced comorbid conditions by altering DNA methylation of Nr4a1, Rasgrp1, Rassf1, and Chkb in the PFC.

In this paper, we studied the mechanisms underlying the suppression of seizure-like events (SLEs) by electrical stimulation. We conducted an in-vitro experiment using entorhinal cortex combined hippocampal slices and two convulsant drugs, bicuculline and 4-aminopyridine, to induce spontaneous SLEs. We used a microelectrode array to observe network dynamics over the entire hippocampal area simultaneously, including regions far from the stimulation site. We stimulated the entorhinal cortex region, which has been determined to be a focus of SLEs by Granger causality analysis of multichannel time series data, by an external electrode. In bicuculline application, electrical stimulation showed a marked suppression effect, even though the sizes of the effective region differed. In 4-aminopyridine application, however, stimulation under the same conditions did not suppress the activities in ∼80% of SLEs. The suppression effect was more remarkable in the areas surrounding the stimulation site in both cases. Our experimental results could support the neuronal depolarization blockade mechanism by accumulation of extracellular potassium ions, which is one of the most convincing mechanisms to understand seizure suppression phenomena because of electrical stimulation. Computer simulation using a neuronal network model also confirmed the mechanism.

Clinically, it has been reported that atopic dermatitis (AD) has been linked with negative emotional problems such as depression and anxiety, thereby reducing the quality of life, but little is known about the molecular mechanism that underlies AD-associated emotional impairments. We sought to determine whether AD could induce anxiety- and depressive-like symptoms in mice and to identify pertinent signaling changes in brain reward circuitry. AD-like lesions were induced by the repeated intradermal application of MC903 into the cheek of the mouse. We assessed dermatitis severity with scratching behavior, histopathological changes, anxiety- and depressive-like behaviors using the elevated plus maze, open field and tail suspension tests, and serum corticosterone levels. In the nucleus accumbens (NAc), dorsal striatum (DS) and ventral tegmental area (VTA), protein levels of dopamine- and plasticity-related signaling molecules were determined by Western immunoblotting assay. Intradermal administration of MC903 into mouse cheek provoked a strong hind limb scratching behavior as well as the robust skin inflammation with epidermal thickening. MC903-treated mice also displayed markedly increased anxiety- and depressive-like behaviors, along with elevated serum corticosterone levels. Under these conditions, enhanced cAMP response element binding protein (CREB) and dopamine and cAMP-regulated phosphoprotein, 32 kDa (DARPP32) phosphorylation, significantly higher brain-derived neurotrophic factor (BDNF) and ΔFosB, but reduced tyrosine hydroxylase (TH) and dopamine D1 receptor (D1R) protein expression were found in the NAc, DS and VTA. Striatal BDNF, phospho-DARPP32 and phospho-CREB levels were significantly associated with the levels of depressive-like behavior in these mice. Taken together, these findings demonstrate that AD-like skin lesion elicits anxiety- and depressive-like phenotypes that are associated with neuroplasticity-related changes in reward circuitry, providing a better understanding of AD-associated emotional impairments.

Although l-3,4-dihydroxyphenylalanine (L-DOPA) is currently the most effective medication for treating Parkinson’s disease (PD) motor symptoms, its prolonged administration causes several adverse effects, including dyskinesia. To identify the mechanisms underlying the effects of acupuncture on L-DOPA-induced dyskinesia (LID), antidyskinetic effects of acupuncture were investigated in two mouse models of PD. Acupuncture stimulation at GB34 alleviated abnormal involuntary movements (AIMs) in Pitx3-deficient aphakia mice (ak/ak) following L-DOPA administration and these effects were reproduced in 6-hydroxydopamine (6-OHDA)-lesioned mice with LID. A transcriptome analysis of the hypothalamus revealed pro-melanin-concentrating hormone (Pmch) gene was highly expressed in acupuncture-treated mouse from ak/ak model of LID as well as 6-OHDA model of LID. Acupuncture combined with the administration of MCH receptor antagonist did not have any beneficial effects on dyskinesia in L-DOPA-injected ak/ak mice, but the intranasal administration of MCH attenuated LID to the same degree as acupuncture in both ak/ak and 6-OHDA mice with LID. A gene expression profile with a hierarchical clustering analysis of the dyskinesia-induced ak/ak mouse brain revealed an association between the mechanisms underlying acupuncture and MCH. Additionally, altered striatal responses to L-DOPA injection were observed after prolonged acupuncture and MCH treatments, which suggests that these treatment modalities influenced the compensatory mechanisms of LID. In summary, present study demonstrated that acupuncture decreased LID via hypothalamic MCH using L-DOPA-administered ak/ak and 6-OHDA mouse models and that MCH administration resulted in novel antidyskinetic effects in these models. Thus, acupuncture and MCH might be valuable therapeutic candidates for PD patients suffering from LID.

Although the dopamine precursor L-3, 4-dihydroxyphenylalanine ( l -dopa) remains the gold standard pharmacological therapy for patients with Parkinson’s disease (PD), long-term treatment with this drug has been known to result in several adverse effects, including l -dopa-induced dyskinesia (LID). Recently, our group reported that KD5040, a modified herbal remedy, had neuroprotective effects in both in vitro and in vivo models of PD. Thus, the present study investigated whether KD5040 would have synergistic effects with l -dopa and antidyskinetic effects caused by l -dopa as well. The effects of KD5040 and l -dopa on motor function, expression levels of substance P (SP) and enkephalin (ENK) in the basal ganglia, and glutamate content in the motor cortex were assessed using behavioral assays, immunohistochemistry, Western blot analyses, and liquid chromatography tandem mass spectrometry in a mouse model of PD induced by 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP). In addition, the antidyskinetic effects of KD5040 on pathological movements triggered by l -dopa were investigated by testing abnormal involuntary movements (AIMs) and measuring the activations of FosB, cAMP-dependent phosphor protein of 32 kDa (DARPP-32), extracellular signal-regulated kinases (ERK), and cAMP response element-binding (CREB) protein in the striatum. KD5040 synergistically improved the motor function when low-dose l -dopa (LL) was co-administered. In addition, it significantly reversed MPTP-induced lowering of SP, improved ENK levels in the basal ganglia, and ameliorated abnormal reduction in glutamate content in the motor cortex. Furthermore, KD5040 significantly lowered AIMs and controlled abnormal levels of striatal FosB, pDARPP-32, pERK, and pCREB induced by high-dose l -dopa. KD5040 lowered the effective dose of l -dopa and alleviated LID. These findings suggest that KD5040 may be used as an adjunct therapy to enhance the efficacy of l -dopa and alleviate its adverse effects in patients with PD.

In this paper, we identified factors that can affect seizure suppression via electrical stimulation by an integrative study based on experimental and computational approach. Preferentially, we analyzed the characteristics of seizure-like events (SLEs) using our previous in vitro experimental data. The results were analyzed in two groups classified according to the size of the effective region, in which the SLE was able to be completely suppressed by local stimulation. However, no significant differences were found between these two groups in terms of signal features or propagation characteristics (i.e., propagation delays, frequency spectrum, and phase synchrony). Thus, we further investigated important factors using a computational model that was capable of evaluating specific influences on effective region size. In the proposed model, signal transmission between neurons was based on two different mechanisms: synaptic transmission and the electrical field effect. We were able to induce SLEs having similar characteristics with differentially weighted adjustments for the two transmission methods in various noise environments. Although the SLEs had similar characteristics, their suppression effects differed. First of all, the suppression effect occurred only locally where directly received the stimulation effect in the high noise environment, but it occurred in the entire network in the low noise environment. Interestingly, in the same noise environment, the suppression effect was different depending on SLE propagation mechanism; only a local suppression effect was observed when the influence of the electrical field transmission was very weak, whereas a global effect was observed with a stronger electrical field effect. These results indicate that neuronal activities synchronized by a strong electrical field effect respond more sensitively to partial changes in the entire network. In addition, the proposed model was able to predict that stimulation of a seizure focus region is more effective for suppression. In conclusion, we confirmed the possibility of a computational model as a simulation tool to analyze the efficacy of deep brain stimulation (DBS) and investigated the key factors that determine the size of an effective region in seizure suppression via electrical stimulation.

Interest in spin-torque oscillators (STOs) has been increasing due to their potential use in communication devices. In particular the magnetic tunnel junction-based STO (MTJ-STO) with high perpendicular anisotropy is gaining attention since it can generate high output power. In this paper, a circuit-level model for an in-plane magnetized MTJSTO with partial perpendicular anisotropy is proposed. The model includes the perpendicular torque and the shift field for more accurate modeling. The bias voltage dependence of perpendicular torque is represented as quadratic. The model is written in Verilog-A, and simulated using HSPICE simulator with a current-mirror circuit and a multi-stage wideband amplifier. The simulation results show the proposed model can accurately replicate the experimental data such that the power increases and the frequency decreases as the value of the perpendicular anisotropy gets close to the value of the demagnetizing field.

Spin-torque oscillators (STO) is a new device that can be used as a tunable microwave source in various wireless devices. Spin-transfer torque effect in magnetic multilayered nanostructure can induce precession of magnetization when bias current and external magnetic field are properly applied, and a microwave signal is generated from that precession. We proposed a semi-empirical circuit-level model of an STO in previous work. In this paper, we present a refined STO model which gives more accuracy by considering physical phenomena in the calculation of effective field. Characteristics of the STO are expressed as functions of external magnetic field and bias current in Verilog-A HDL such that they can be simulated with circuit-level simulators such as Hspice. The simulation results are in good agreement with the experimental data.

Background Although the dopamine precursor L-3, 4-dihydroxyphenylalanine (l-dopa) remains the gold standard pharmacological therapy for patients with Parkinson’s disease (PD), long-term treatment with this drug has been known to result in several adverse effects, including l-dopa-induced dyskinesia (LID). Recently, our group reported that KD5040, a modified herbal remedy, had neuroprotective effects in both in vitro and in vivo models of PD. Thus, the present study investigated whether KD5040 would have synergistic effects with l-dopa and antidyskinetic effects caused by l-dopa as well. Methods The effects of KD5040 and l-dopa on motor function, expression levels of substance P (SP) and enkephalin (ENK) in the basal ganglia, and glutamate content in the motor cortex were assessed using behavioral assays, immunohistochemistry, Western blot analyses, and liquid chromatography tandem mass spectrometry in a mouse model of PD induced by 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP). In addition, the antidyskinetic effects of KD5040 on pathological movements triggered by l-dopa were investigated by testing abnormal involuntary movements (AIMs) and measuring the activations of FosB, cAMP-dependent phosphor protein of 32 kDa (DARPP-32), extracellular signal-regulated kinases (ERK), and cAMP response element-binding (CREB) protein in the striatum. Results KD5040 synergistically improved the motor function when low-dose l-dopa (LL) was co-administered. In addition, it significantly reversed MPTP-induced lowering of SP, improved ENK levels in the basal ganglia, and ameliorated abnormal reduction in glutamate content in the motor cortex. Furthermore, KD5040 significantly lowered AIMs and controlled abnormal levels of striatal FosB, pDARPP-32, pERK, and pCREB induced by high-dose l-dopa. Conclusions KD5040 lowered the effective dose of l-dopa and alleviated LID. These findings suggest that KD5040 may be used as an adjunct therapy to enhance the efficacy of l-dopa and alleviate its adverse effects in patients with PD.

In this paper, we propose a comprehensive computational model that is able to reproduce three epileptiform activities. The model targets a hippocampal formation that is known to be an important lesion in medial temporal lobe epilepsy. It consists of four sub-networks consisting of excitatory and inhibitory neurons and well-known signal pathways, with consideration of propagation delay. The three epileptiform activities involve fast and slow interictal discharge and ictal discharge, and those activities can be induced in vitro by application of 4-Aminopyridine in entorhinal cortex combined hippocampal slices. We model the three epileptiform activities upon previously reported biological mechanisms and verify the simulation results by comparing them with in vitro experimental data obtained using a microelectrode array. We use the results of Granger causality analysis of recorded data to set input gains of signal pathways in the model, so that the compatibility between the computational and experimental models can be improved. The proposed model can be expanded to evaluate the suppression effect of epileptiform activities due to new treatment methods.

As they have been designed to undergo colorimetric changes that are dependent on the polarity of solvents, the majority of conventional solvatochromic molecule based sensor systems inevitably display broad overlaps in their absorption and emission bands. As a result, colorimetric differentiation of solvents of similar polarity has been extremely difficult. Here we present a tailor-made colorimetric and fluorescence turn-on type solvatochromic sensor that enables facile identification of a specific solvent. The sensor system displays a colorimetric transition only when a thin protective layer, which protects the solvatochromic materials, is destroyed or disrupted by a specific solvent. The versatility of the strategy is demonstrated by designing a sensor that differentiates chloroform and dichloromethane colorimetrically and one that performs sequence selective colorimetric sensing. In addition, the approach is employed to construct a solvatochromic molecular AND logic gate. The new strategy could open new avenues for the development of novel solvatochromic sensors., Colorimetric differentiation of solvents is difficult as many solvatochromic molecules exhibit broad overlaps in their absorption and emission bands. Here, the authors present a ‘turn-on’ type sensor in which the colorimetric sensor responds only when its protective layer is disrupted by a specific solvent.

Spin-torque oscillator (STO) is a promising new technology for the future RF oscillators, which is based on the spin-transfer torque (STT) effect in magnetic multilayered nanostructure. It is expected to provide a larger tunability, smaller size, lower power consumption, and higher level of integration than the semiconductor-based oscillators. In our previous work, a circuit-level model of the giant magnetoresistance (GMR) STO was proposed. In this paper, we present a physics-based circuit-level model of the magnetic tunnel junction (MTJ)-based STO. MTJ-STO model includes the effect of perpendicular torque that has been ignored in the GMR-STO model. The variations of three major characteristics, generation frequency, mean oscillation power, and generation linewidth of an MTJ-STO with respect to the amount of perpendicular torque, are investigated, and the results are applied to our model. The operation of the model was verified by HSPICE simulation, and the results show an excellent agreement with the experimental data. The results also prove that a full circuit-level simulation with MJT-STO devices can be made with our proposed model.

Spin-torque oscillators (STOs) are new oscillating devices based on spintronics technology with many advantageous features, i.e., nanoscale size, high tunability, and compatibility with standard silicon processing. Recent research has shown that two electrically connected STOs may operate as a single device when specific conditions are met. To overcome the limitation of the small output power of STOs, the phase-locking behavior of multiple STOs is hereby extensively investigated. In this paper, we present a circuit-level model of two coupled STOs considering the interaction between them such that it can represent the phase-locking behavior of multiple STOs. In our model, the characteristics of each STO are defined first as functions of applied DC current and external magnetic field. Then, the phase-locking condition is examined to determine the properties of the two coupled STOs on the basis of a theoretical model. The analytic model of two coupled STOs is written in Verilog-A hardware description language. The behavior of the proposed model is verified by circuit-level simulation using HSPICE with CMOS circuits including a current-mirror circuit and differential amplifiers. Simulation results with various CMOS circuits have confirmed the effectiveness of our model. # 2013 The Japan Society of Applied Physics

The spin-torque oscillator (STO) is a new compact device operating as a tunable RF oscillator in the tens of gigahertz range whose characteristics are determined by the applied current and magnetic field. In this paper, we present a physics-based empirical circuit-level model of an STO that is compatible with circuit-level simulators such as SPICE. The characteristics of an STO are modeled as physics-based analytic functions of the applied current and external magnetic field. The validity of our model was verified by the HSPICE simulation of a current mirror circuit that contains an STO element. The simulation results are in good agreement with the experimental data in the normal operation range. High-order nonlinear effects at large currents are not included in our model because there is no theoretical equation available yet that can precisely explain these effects.

Spin-torque oscillators (STOs) are new oscillating devices based on spintronics technology with many advantageous features, i.e., nanoscale size, high tunability, and compatibility with standard silicon processing. Recent research has shown that two electrically connected STOs may operate as a single device when specific conditions are met. To overcome the limitation of the small output power of STOs, the phase-locking behavior of multiple STOs is hereby extensively investigated. In this paper, we present a circuit-level model of two coupled STOs considering the interaction between them such that it can represent the phase-locking behavior of multiple STOs. In our model, the characteristics of each STO are defined first as functions of applied DC current and external magnetic field. Then, the phase-locking condition is examined to determine the properties of the two coupled STOs on the basis of a theoretical model. The analytic model of two coupled STOs is written in Verilog-A hardware description language. The behavior of the proposed model is verified by circuit-level simulation using HSPICE with CMOS circuits including a current-mirror circuit and differential amplifiers. Simulation results with various CMOS circuits have confirmed the effectiveness of our model.

The spin-torque oscillator (STO) is a new compact device operating as a tunable RF oscillator in the tens of gigahertz range whose characteristics are determined by the applied current and magnetic field. In this paper, we present a physics-based empirical circuit-level model of an STO that is compatible with circuit-level simulators such as SPICE. The characteristics of an STO are modeled as physics-based analytic functions of the applied current and external magnetic field. The validity of our model was verified by the HSPICE simulation of a current mirror circuit that contains an STO element. The simulation results are in good agreement with the experimental data in the normal operation range. High-order nonlinear effects at large currents are not included in our model because there is no theoretical equation available yet that can precisely explain these effects.

Table of contents A1 Functional advantages of cell-type heterogeneity in neural circuits Tatyana O. Sharpee A2 Mesoscopic modeling of propagating waves in visual cortex Alain Destexhe A3 Dynamics and biomarkers of mental disorders Mitsuo Kawato F1 Precise recruitment of spiking output at theta frequencies requires dendritic h-channels in multi-compartment models of oriens-lacunosum/moleculare hippocampal interneurons Vladislav Sekulić, Frances K. Skinner F2 Kernel methods in reconstruction of current sources from extracellular potentials for single cells and the whole brains Daniel K. Wójcik, Chaitanya Chintaluri, Dorottya Cserpán, Zoltán Somogyvári F3 The synchronized periods depend on intracellular transcriptional repression mechanisms in circadian clocks. Jae Kyoung Kim, Zachary P. Kilpatrick, Matthew R. Bennett, Kresimir Josić O1 Assessing irregularity and coordination of spiking-bursting rhythms in central pattern generators Irene Elices, David Arroyo, Rafael Levi, Francisco B. Rodriguez, Pablo Varona O2 Regulation of top-down processing by cortically-projecting parvalbumin positive neurons in basal forebrain Eunjin Hwang, Bowon Kim, Hio-Been Han, Tae Kim, James T. McKenna, Ritchie E. Brown, Robert W. McCarley, Jee Hyun Choi O3 Modeling auditory stream segregation, build-up and bistability James Rankin, Pamela Osborn Popp, John Rinzel O4 Strong competition between tonotopic neural ensembles explains pitch-related dynamics of auditory cortex evoked fields Alejandro Tabas, André Rupp, Emili Balaguer-Ballester O5 A simple model of retinal response to multi-electrode stimulation Matias I. Maturana, David B. Grayden, Shaun L. Cloherty, Tatiana Kameneva, Michael R. Ibbotson, Hamish Meffin O6 Noise correlations in V4 area correlate with behavioral performance in visual discrimination task Veronika Koren, Timm Lochmann, Valentin Dragoi, Klaus Obermayer O7 Input-location dependent gain modulation in cerebellar nucleus neurons Maria Psarrou, Maria Schilstra, Neil Davey, Benjamin Torben-Nielsen, Volker Steuber O8 Analytic solution of cable energy function for cortical axons and dendrites Huiwen Ju, Jiao Yu, Michael L. Hines, Liang Chen, Yuguo Yu O9 C. elegans interactome: interactive visualization of Caenorhabditis elegans worm neuronal network Jimin Kim, Will Leahy, Eli Shlizerman O10 Is the model any good? Objective criteria for computational neuroscience model selection Justas Birgiolas, Richard C. Gerkin, Sharon M. Crook O11 Cooperation and competition of gamma oscillation mechanisms Atthaphon Viriyopase, Raoul-Martin Memmesheimer, Stan Gielen O12 A discrete structure of the brain waves Yuri Dabaghian, Justin DeVito, Luca Perotti O13 Direction-specific silencing of the Drosophila gaze stabilization system Anmo J. Kim, Lisa M. Fenk, Cheng Lyu, Gaby Maimon O14 What does the fruit fly think about values? A model of olfactory associative learning Chang Zhao, Yves Widmer, Simon Sprecher,Walter Senn O15 Effects of ionic diffusion on power spectra of local field potentials (LFP) Geir Halnes, Tuomo Mäki-Marttunen, Daniel Keller, Klas H. Pettersen,Ole A. Andreassen, Gaute T. Einevoll O16 Large-scale cortical models towards understanding relationship between brain structure abnormalities and cognitive deficits Yasunori Yamada O17 Spatial coarse-graining the brain: origin of minicolumns Moira L. Steyn-Ross, D. Alistair Steyn-Ross O18 Modeling large-scale cortical networks with laminar structure Jorge F. Mejias, John D. Murray, Henry Kennedy, Xiao-Jing Wang O19 Information filtering by partial synchronous spikes in a neural population Alexandra Kruscha, Jan Grewe, Jan Benda, Benjamin Lindner O20 Decoding context-dependent olfactory valence in Drosophila Laurent Badel, Kazumi Ohta, Yoshiko Tsuchimoto, Hokto Kazama P1 Neural network as a scale-free network: the role of a hub B. Kahng P2 Hemodynamic responses to emotions and decisions using near-infrared spectroscopy optical imaging Nicoladie D. Tam P3 Phase space analysis of hemodynamic responses to intentional movement directions using functional near-infrared spectroscopy (fNIRS) optical imaging technique Nicoladie D.Tam, Luca Pollonini, George Zouridakis P4 Modeling jamming avoidance of weakly electric fish Jaehyun Soh, DaeEun Kim P5 Synergy and redundancy of retinal ganglion cells in prediction Minsu Yoo, S. E. Palmer P6 A neural field model with a third dimension representing cortical depth Viviana Culmone, Ingo Bojak P7 Network analysis of a probabilistic connectivity model of the Xenopus tadpole spinal cord Andrea Ferrario, Robert Merrison-Hort, Roman Borisyuk P8 The recognition dynamics in the brain Chang Sub Kim P9 Multivariate spike train analysis using a positive definite kernel Taro Tezuka P10 Synchronization of burst periods may govern slow brain dynamics during general anesthesia Pangyu Joo P11 The ionic basis of heterogeneity affects stochastic synchrony Young-Ah Rho, Shawn D. Burton, G. Bard Ermentrout, Jaeseung Jeong, Nathaniel N. Urban P12 Circular statistics of noise in spike trains with a periodic component Petr Marsalek P14 Representations of directions in EEG-BCI using Gaussian readouts Hoon-Hee Kim, Seok-hyun Moon, Do-won Lee, Sung-beom Lee, Ji-yong Lee, Jaeseung Jeong P15 Action selection and reinforcement learning in basal ganglia during reaching movements Yaroslav I. Molkov, Khaldoun Hamade, Wondimu Teka, William H. Barnett, Taegyo Kim, Sergey Markin, Ilya A. Rybak P17 Axon guidance: modeling axonal growth in T-Junction assay Csaba Forro, Harald Dermutz, László Demkó, János Vörös P19 Transient cell assembly networks encode persistent spatial memories Yuri Dabaghian, Andrey Babichev P20 Theory of population coupling and applications to describe high order correlations in large populations of interacting neurons Haiping Huang P21 Design of biologically-realistic simulations for motor control Sergio Verduzco-Flores P22 Towards understanding the functional impact of the behavioural variability of neurons Filipa Dos Santos, Peter Andras P23 Different oscillatory dynamics underlying gamma entrainment deficits in schizophrenia Christoph Metzner, Achim Schweikard, Bartosz Zurowski P24 Memory recall and spike frequency adaptation James P. Roach, Leonard M. Sander, Michal R. Zochowski P25 Stability of neural networks and memory consolidation preferentially occur near criticality Quinton M. Skilling, Nicolette Ognjanovski, Sara J. Aton, Michal Zochowski P26 Stochastic Oscillation in Self-Organized Critical States of Small Systems: Sensitive Resting State in Neural Systems Sheng-Jun Wang, Guang Ouyang, Jing Guang, Mingsha Zhang, K. Y. Michael Wong, Changsong Zhou P27 Neurofield: a C++ library for fast simulation of 2D neural field models Peter A. Robinson, Paula Sanz-Leon, Peter M. Drysdale, Felix Fung, Romesh G. Abeysuriya, Chris J. Rennie, Xuelong Zhao P28 Action-based grounding: Beyond encoding/decoding in neural code Yoonsuck Choe, Huei-Fang Yang P29 Neural computation in a dynamical system with multiple time scales Yuanyuan Mi, Xiaohan Lin, Si Wu P30 Maximum entropy models for 3D layouts of orientation selectivity Joscha Liedtke, Manuel Schottdorf, Fred Wolf P31 A behavioral assay for probing computations underlying curiosity in rodents Yoriko Yamamura, Jeffery R. Wickens P32 Using statistical sampling to balance error function contributions to optimization of conductance-based models Timothy Rumbell, Julia Ramsey, Amy Reyes, Danel Draguljić, Patrick R. Hof, Jennifer Luebke, Christina M. Weaver P33 Exploration and implementation of a self-growing and self-organizing neuron network building algorithm Hu He, Xu Yang, Hailin Ma, Zhiheng Xu, Yuzhe Wang P34 Disrupted resting state brain network in obese subjects: a data-driven graph theory analysis Kwangyeol Baek, Laurel S. Morris, Prantik Kundu, Valerie Voon P35 Dynamics of cooperative excitatory and inhibitory plasticity Everton J. Agnes, Tim P. Vogels P36 Frequency-dependent oscillatory signal gating in feed-forward networks of integrate-and-fire neurons William F. Podlaski, Tim P. Vogels P37 Phenomenological neural model for adaptation of neurons in area IT Martin Giese, Pradeep Kuravi, Rufin Vogels P38 ICGenealogy: towards a common topology of neuronal ion channel function and genealogy in model and experiment Alexander Seeholzer, William Podlaski, Rajnish Ranjan, Tim Vogels P39 Temporal input discrimination from the interaction between dynamic synapses and neural subthreshold oscillations Joaquin J. Torres, Fabiano Baroni, Roberto Latorre, Pablo Varona P40 Different roles for transient and sustained activity during active visual processing Bart Gips, Eric Lowet, Mark J. Roberts, Peter de Weerd, Ole Jensen, Jan van der Eerden P41 Scale-free functional networks of 2D Ising model are highly robust against structural defects: neuroscience implications Abdorreza Goodarzinick, Mohammad D. Niry, Alireza Valizadeh P42 High frequency neuron can facilitate propagation of signal in neural networks Aref Pariz, Shervin S. Parsi, Alireza Valizadeh P43 Investigating the effect of Alzheimer’s disease related amyloidopathy on gamma oscillations in the CA1 region of the hippocampus Julia M. Warburton, Lucia Marucci, Francesco Tamagnini, Jon Brown, Krasimira Tsaneva-Atanasova P44 Long-tailed distributions of inhibitory and excitatory weights in a balanced network with eSTDP and iSTDP Florence I. Kleberg, Jochen Triesch P45 Simulation of EMG recording from hand muscle due to TMS of motor cortex Bahar Moezzi, Nicolangelo Iannella, Natalie Schaworonkow, Lukas Plogmacher, Mitchell R. Goldsworthy, Brenton Hordacre, Mark D. McDonnell, Michael C. 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Voronenko, Benjamin Lindner P53 Behavioral embedding suggests multiple chaotic dimensions underlie C. elegans locomotion Tosif Ahamed, Greg Stephens P54 Fast and scalable spike sorting for large and dense multi-electrodes recordings Pierre Yger, Baptiste Lefebvre, Giulia Lia Beatrice Spampinato, Elric Esposito, Marcel Stimberg et Olivier Marre P55 Sufficient sampling rates for fast hand motion tracking Hansol Choi, Min-Ho Song P56 Linear readout of object manifolds SueYeon Chung, Dan D. Lee, Haim Sompolinsky P57 Differentiating models of intrinsic bursting and rhythm generation of the respiratory pre-Bötzinger complex using phase response curves Ryan S. Phillips, Jeffrey Smith P58 The effect of inhibitory cell network interactions during theta rhythms on extracellular field potentials in CA1 hippocampus Alexandra Pierri Chatzikalymniou, Katie Ferguson, Frances K. Skinner P59 Expansion recoding through sparse sampling in the cerebellar input layer speeds learning N. Alex Cayco Gajic, Claudia Clopath, R. Angus Silver P60 A set of curated cortical models at multiple scales on Open Source Brain Padraig Gleeson, Boris Marin, Sadra Sadeh, Adrian Quintana, Matteo Cantarelli, Salvador Dura-Bernal, William W. Lytton, Andrew Davison, R. Angus Silver P61 A synaptic story of dynamical information encoding in neural adaptation Luozheng Li, Wenhao Zhang, Yuanyuan Mi, Dahui Wang, Si Wu P62 Physical modeling of rule-observant rodent behavior Youngjo Song, Sol Park, Ilhwan Choi, Jaeseung Jeong, Hee-sup Shin P64 Predictive coding in area V4 and prefrontal cortex explains dynamic discrimination of partially occluded shapes Hannah Choi, Anitha Pasupathy, Eric Shea-Brown P65 Stability of FORCE learning on spiking and rate-based networks Dongsung Huh, Terrence J. Sejnowski P66 Stabilising STDP in striatal neurons for reliable fast state recognition in noisy environments Simon M. Vogt, Arvind Kumar, Robert Schmidt P67 Electrodiffusion in one- and two-compartment neuron models for characterizing cellular effects of electrical stimulation Stephen Van Wert, Steven J. Schiff P68 STDP improves speech recognition capabilities in spiking recurrent circuits parameterized via differential evolution Markov Chain Monte Carlo Richard Veale, Matthias Scheutz P69 Bidirectional transformation between dominant cortical neural activities and phase difference distributions Sang Wan Lee P70 Maturation of sensory networks through homeostatic structural plasticity Júlia Gallinaro, Stefan Rotter P71 Corticothalamic dynamics: structure, number of solutions and stability of steady-state solutions in the space of synaptic couplings Paula Sanz-Leon, Peter A. Robinson P72 Optogenetic versus electrical stimulation of the parkinsonian basal ganglia. Computational study Leonid L. Rubchinsky, Chung Ching Cheung, Shivakeshavan Ratnadurai-Giridharan P73 Exact spike-timing distribution reveals higher-order interactions of neurons Safura Rashid Shomali, Majid Nili Ahmadabadi, Hideaki Shimazaki, S. Nader Rasuli P74 Neural mechanism of visual perceptual learning using a multi-layered neural network Xiaochen Zhao, Malte J. Rasch P75 Inferring collective spiking dynamics from mostly unobserved systems Jens Wilting, Viola Priesemann P76 How to infer distributions in the brain from subsampled observations Anna Levina, Viola Priesemann P77 Influences of embedding and estimation strategies on the inferred memory of single spiking neurons Lucas Rudelt, Joseph T. Lizier, Viola Priesemann P78 A nearest-neighbours based estimator for transfer entropy between spike trains Joseph T. Lizier, Richard E. Spinney, Mikail Rubinov, Michael Wibral, Viola Priesemann P79 Active learning of psychometric functions with multinomial logistic models Ji Hyun Bak, Jonathan Pillow P81 Inferring low-dimensional network dynamics with variational latent Gaussian process Yuan Zaho, Il Memming Park P82 Computational investigation of energy landscapes in the resting state subcortical brain network Jiyoung Kang, Hae-Jeong Park P83 Local repulsive interaction between retinal ganglion cells can generate a consistent spatial periodicity of orientation map Jaeson Jang, Se-Bum Paik P84 Phase duration of bistable perception reveals intrinsic time scale of perceptual decision under noisy condition Woochul Choi, Se-Bum Paik P85 Feedforward convergence between retina and primary visual cortex can determine the structure of orientation map Changju Lee, Jaeson Jang, Se-Bum Paik P86 Computational method classifying neural network activity patterns for imaging data Min Song, Hyeonsu Lee, Se-Bum Paik P87 Symmetry of spike-timing-dependent-plasticity kernels regulates volatility of memory Youngjin Park, Woochul Choi, Se-Bum Paik P88 Effects of time-periodic coupling strength on the first-spike latency dynamics of a scale-free network of stochastic Hodgkin-Huxley neurons Ergin Yilmaz, Veli Baysal, Mahmut Ozer P89 Spectral properties of spiking responses in V1 and V4 change within the trial and are highly relevant for behavioral performance Veronika Koren, Klaus Obermayer P90 Methods for building accurate models of individual neurons Daniel Saska, Thomas Nowotny P91 A full size mathematical model of the early olfactory system of honeybees Ho Ka Chan, Alan Diamond, Thomas Nowotny P92 Stimulation-induced tuning of ongoing oscillations in spiking neural networks Christoph S. 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McDonnell P99 Limits of temporal encoding of thalamocortical inputs in a neocortical microcircuit Max Nolte, Michael W. Reimann, Eilif Muller, Henry Markram P100 On the representation of arm reaching movements: a computational model Antonio Parziale, Rosa Senatore, Angelo Marcelli P101 A computational model for investigating the role of cerebellum in acquisition and retention of motor behavior Rosa Senatore, Antonio Parziale, Angelo Marcelli P102 The emergence of semantic categories from a large-scale brain network of semantic knowledge K. Skiker, M. Maouene P103 Multiscale modeling of M1 multitarget pharmacotherapy for dystonia Samuel A. Neymotin, Salvador Dura-Bernal, Alexandra Seidenstein, Peter Lakatos, Terence D. Sanger, William W. Lytton P104 Effect of network size on computational capacity Salvador Dura-Bernal, Rosemary J. Menzies, Campbell McLauchlan, Sacha J. van Albada, David J. Kedziora, Samuel Neymotin, William W. Lytton, Cliff C. Kerr P105 NetPyNE: a Python package for NEURON to facilitate development and parallel simulation of biological neuronal networks Salvador Dura-Bernal, Benjamin A. Suter, Samuel A. Neymotin, Cliff C. Kerr, Adrian Quintana, Padraig Gleeson, Gordon M. G. Shepherd, William W. Lytton P107 Inter-areal and inter-regional inhomogeneity in co-axial anisotropy of Cortical Point Spread in human visual areas Juhyoung Ryu, Sang-Hun Lee P108 Two bayesian quanta of uncertainty explain the temporal dynamics of cortical activity in the non-sensory areas during bistable perception Joonwon Lee, Sang-Hun Lee P109 Optimal and suboptimal integration of sensory and value information in perceptual decision making Hyang Jung Lee, Sang-Hun Lee P110 A Bayesian algorithm for phoneme Perception and its neural implementation Daeseob Lim, Sang-Hun Lee P111 Complexity of EEG signals is reduced during unconsciousness induced by ketamine and propofol Jisung Wang, Heonsoo Lee P112 Self-organized criticality of neural avalanche in a neural model on complex networks Nam Jung, Le Anh Quang, Seung Eun Maeng, Tae Ho Lee, Jae Woo Lee P113 Dynamic alterations in connection topology of the hippocampal network during ictal-like epileptiform activity in an in vitro rat model Chang-hyun Park, Sora Ahn, Jangsup Moon, Yun Seo Choi, Juhee Kim, Sang Beom Jun, Seungjun Lee, Hyang Woon Lee P114 Computational model to replicate seizure suppression effect by electrical stimulation Sora Ahn, Sumin Jo, Eunji Jun, Suin Yu, Hyang Woon Lee, Sang Beom Jun, Seungjun Lee P115 Identifying excitatory and inhibitory synapses in neuronal networks from spike trains using sorted local transfer entropy Felix Goetze, Pik-Yin Lai P116 Neural network model for obstacle avoidance based on neuromorphic computational model of boundary vector cell and head direction cell Seonghyun Kim, Jeehyun Kwag P117 Dynamic gating of spike pattern propagation by Hebbian and anti-Hebbian spike timing-dependent plasticity in excitatory feedforward network model Hyun Jae Jang, Jeehyun Kwag P118 Inferring characteristics of input correlations of cells exhibiting up-down state transitions in the rat striatum Marko Filipović, Ramon Reig, Ad Aertsen, Gilad Silberberg, Arvind Kumar P119 Graph properties of the functional connected brain under the influence of Alzheimer’s disease Claudia Bachmann, Simone Buttler, Heidi Jacobs, Kim Dillen, Gereon R. Fink, Juraj Kukolja, Abigail Morrison P120 Learning sparse representations in the olfactory bulb Daniel Kepple, Hamza Giaffar, Dima Rinberg, Steven Shea, Alex Koulakov P121 Functional classification of homologous basal-ganglia networks Jyotika Bahuguna,Tom Tetzlaff, Abigail Morrison, Arvind Kumar, Jeanette Hellgren Kotaleski P122 Short term memory based on multistability Tim Kunze, Andre Peterson, Thomas Knösche P123 A physiologically plausible, computationally efficient model and simulation software for mammalian motor units Minjung Kim, Hojeong Kim P125 Decoding laser-induced somatosensory information from EEG Ji Sung Park, Ji Won Yeon, Sung-Phil Kim P126 Phase synchronization of alpha activity for EEG-based personal authentication Jae-Hwan Kang, Chungho Lee, Sung-Phil Kim P129 Investigating phase-lags in sEEG data using spatially distributed time delays in a large-scale brain network model Andreas Spiegler, Spase Petkoski, Matias J. Palva, Viktor K. Jirsa P130 Epileptic seizures in the unfolding of a codimension-3 singularity Maria L. Saggio, Silvan F. Siep, Andreas Spiegler, William C. Stacey, Christophe Bernard, Viktor K. Jirsa P131 Incremental dimensional exploratory reasoning under multi-dimensional environment Oh-hyeon Choung, Yong Jeong P132 A low-cost model of eye movements and memory in personal visual cognition Yong-il Lee, Jaeseung Jeong P133 Complex network analysis of structural connectome of autism spectrum disorder patients Su Hyun Kim, Mir Jeong, Jaeseung Jeong P134 Cognitive motives and the neural correlates underlying human social information transmission, gossip Jeungmin Lee, Jaehyung Kwon, Jerald D. Kralik, Jaeseung Jeong P135 EEG hyperscanning detects neural oscillation for the social interaction during the economic decision-making Jaehwan Jahng, Dong-Uk Hwang, Jaeseung Jeong P136 Detecting purchase decision based on hyperfrontality of the EEG Jae-Hyung Kwon, Sang-Min Park, Jaeseung Jeong P137 Vulnerability-based critical neurons, synapses, and pathways in the Caenorhabditis elegans connectome Seongkyun Kim, Hyoungkyu Kim, Jerald D. Kralik, Jaeseung Jeong P138 Motif analysis reveals functionally asymmetrical neurons in C. elegans Pyeong Soo Kim, Seongkyun Kim, Hyoungkyu Kim, Jaeseung Jeong P139 Computational approach to preference-based serial decision dynamics: do temporal discounting and working memory affect it? Sangsup Yoon, Jaehyung Kwon, Sewoong Lim, Jaeseung Jeong P141 Social stress induced neural network reconfiguration affects decision making and learning in zebrafish Choongseok Park, Thomas Miller, Katie Clements, Sungwoo Ahn, Eoon Hye Ji, Fadi A. Issa P142 Descriptive, generative, and hybrid approaches for neural connectivity inference from neural activity data JeongHun Baek, Shigeyuki Oba, Junichiro Yoshimoto, Kenji Doya, Shin Ishii P145 Divergent-convergent synaptic connectivities accelerate coding in multilayered sensory systems Thiago S. Mosqueiro, Martin F. Strube-Bloss, Brian Smith, Ramon Huerta P146 Swinging networks Michal Hadrava, Jaroslav Hlinka P147 Inferring dynamically relevant motifs from oscillatory stimuli: challenges, pitfalls, and solutions Hannah Bos, Moritz Helias P148 Spatiotemporal mapping of brain network dynamics during cognitive tasks using magnetoencephalography and deep learning Charles M. Welzig, Zachary J. Harper P149 Multiscale complexity analysis for the segmentation of MRI images Won Sup Kim, In-Seob Shin, Hyeon-Man Baek, Seung Kee Han P150 A neuro-computational model of emotional attention René Richter, Julien Vitay, Frederick Beuth, Fred H. Hamker P151 Multi-site delayed feedback stimulation in parkinsonian networks Kelly Toppin, Yixin Guo P152 Bistability in Hodgkin–Huxley-type equations Tatiana Kameneva, Hamish Meffin, Anthony N. Burkitt, David B. Grayden P153 Phase changes in postsynaptic spiking due to synaptic connectivity and short term plasticity: mathematical analysis of frequency dependency Mark D. McDonnell, Bruce P. Graham P154 Quantifying resilience patterns in brain networks: the importance of directionality Penelope J. Kale, Leonardo L. Gollo P155 Dynamics of rate-model networks with separate excitatory and inhibitory populations Merav Stern, L. F. Abbott P156 A model for multi-stable dynamics in action recognition modulated by integration of silhouette and shading cues Leonid A. Fedorov, Martin A. Giese P157 Spiking model for the interaction between action recognition and action execution Mohammad Hovaidi Ardestani, Martin Giese P158 Surprise-modulated belief update: how to learn within changing environments? Mohammad Javad Faraji, Kerstin Preuschoff, Wulfram Gerstner P159 A fast, stochastic and adaptive model of auditory nerve responses to cochlear implant stimulation Margriet J. van Gendt, Jeroen J. Briaire, Randy K. Kalkman, Johan H. M. Frijns P160 Quantitative comparison of graph theoretical measures of simulated and empirical functional brain networks Won Hee Lee, Sophia Frangou P161 Determining discriminative properties of fMRI signals in schizophrenia using highly comparative time-series analysis Ben D. Fulcher, Patricia H. P. Tran, Alex Fornito P162 Emergence of narrowband LFP oscillations from completely asynchronous activity during seizures and high-frequency oscillations Stephen V. Gliske, William C. Stacey, Eugene Lim, Katherine A. Holman, Christian G. Fink P163 Neuronal diversity in structure and function: cross-validation of anatomical and physiological classification of retinal ganglion cells in the mouse Jinseop S. Kim, Shang Mu, Kevin L. Briggman, H. Sebastian Seung, the EyeWirers P164 Analysis and modelling of transient firing rate changes in area MT in response to rapid stimulus feature changes Detlef Wegener, Lisa Bohnenkamp, Udo A. Ernst P165 Step-wise model fitting accounting for high-resolution spatial measurements: construction of a layer V pyramidal cell model with reduced morphology Tuomo Mäki-Marttunen, Geir Halnes, Anna Devor, Christoph Metzner, Anders M. Dale, Ole A. Andreassen, Gaute T. Einevoll P166 Contributions of schizophrenia-associated genes to neuron firing and cardiac pacemaking: a polygenic modeling approach Tuomo Mäki-Marttunen, Glenn T. Lines, Andy Edwards, Aslak Tveito, Anders M. Dale, Gaute T. Einevoll, Ole A. Andreassen P167 Local field potentials in a 4 × 4 mm2 multi-layered network model Espen Hagen, Johanna Senk, Sacha J. van Albada, Markus Diesmann P168 A spiking network model explains multi-scale properties of cortical dynamics Maximilian Schmidt, Rembrandt Bakker, Kelly Shen, Gleb Bezgin, Claus-Christian Hilgetag, Markus Diesmann, Sacha Jennifer van Albada P169 Using joint weight-delay spike-timing dependent plasticity to find polychronous neuronal groups Haoqi Sun, Olga Sourina, Guang-Bin Huang, Felix Klanner, Cornelia Denk P170 Tensor decomposition reveals RSNs in simulated resting state fMRI Katharina Glomb, Adrián Ponce-Alvarez, Matthieu Gilson, Petra Ritter, Gustavo Deco P171 Getting in the groove: testing a new model-based method for comparing task-evoked vs resting-state activity in fMRI data on music listening Matthieu Gilson, Maria AG Witek, Eric F. Clarke, Mads Hansen, Mikkel Wallentin, Gustavo Deco, Morten L. Kringelbach, Peter Vuust P172 STochastic engine for pathway simulation (STEPS) on massively parallel processors Guido Klingbeil, Erik De Schutter P173 Toolkit support for complex parallel spatial stochastic reaction–diffusion simulation in STEPS Weiliang Chen, Erik De Schutter P174 Modeling the generation and propagation of Purkinje cell dendritic spikes caused by parallel fiber synaptic input Yunliang Zang, Erik De Schutter P175 Dendritic morphology determines how dendrites are organized into functional subunits Sungho Hong, Akira Takashima, Erik De Schutter P176 A model of Ca2+/calmodulin-dependent protein kinase II activity in long term depression at Purkinje cells Criseida Zamora, Andrew R. Gallimore, Erik De Schutter P177 Reward-modulated learning of population-encoded vectors for insect-like navigation in embodied agents Dennis Goldschmidt, Poramate Manoonpong, Sakyasingha Dasgupta P178 Data-driven neural models part II: connectivity patterns of human seizures Philippa J. Karoly, Dean R. Freestone, Daniel Soundry, Levin Kuhlmann, Liam Paninski, Mark Cook P179 Data-driven neural models part I: state and parameter estimation Dean R. Freestone, Philippa J. Karoly, Daniel Soundry, Levin Kuhlmann, Mark Cook P180 Spectral and spatial information processing in human auditory streaming Jaejin Lee, Yonatan I. Fishman, Yale E. Cohen P181 A tuning curve for the global effects of local perturbations in neural activity: Mapping the systems-level susceptibility of the brain Leonardo L. Gollo, James A. Roberts, Luca Cocchi P182 Diverse homeostatic responses to visual deprivation mediated by neural ensembles Yann Sweeney, Claudia Clopath P183 Opto-EEG: a novel method for investigating functional connectome in mouse brain based on optogenetics and high density electroencephalography Soohyun Lee, Woo-Sung Jung, Jee Hyun Choi P184 Biphasic responses of frontal gamma network to repetitive sleep deprivation during REM sleep Bowon Kim, Youngsoo Kim, Eunjin Hwang, Jee Hyun Choi P185 Brain-state correlate and cortical connectivity for frontal gamma oscillations in top-down fashion assessed by auditory steady-state response Younginha Jung, Eunjin Hwang, Yoon-Kyu Song, Jee Hyun Choi P186 Neural field model of localized orientation selective activation in V1 James Rankin, Frédéric Chavane P187 An oscillatory network model of Head direction and Grid cells using locomotor inputs Karthik Soman, Vignesh Muralidharan, V. 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Brinkman, Tyler Kekona, Fred Rieke, Eric Shea-Brown, Michael Buice P193 Characterization of neural firing in the presence of astrocyte-synapse signaling Maurizio De Pittà, Hugues Berry, Nicolas Brunel P194 Metastability of spatiotemporal patterns in a large-scale network model of brain dynamics James A. Roberts, Leonardo L. Gollo, Michael Breakspear P195 Comparison of three methods to quantify detection and discrimination capacity estimated from neural population recordings Gary Marsat, Jordan Drew, Phillip D. Chapman, Kevin C. Daly, Samual P. Bradley P196 Quantifying the constraints for independent evoked and spontaneous NMDA receptor mediated synaptic transmission at individual synapses Sat Byul Seo, Jianzhong Su, Ege T. Kavalali, Justin Blackwell P199 Gamma oscillation via adaptive exponential integrate-and-fire neurons LieJune Shiau, Laure Buhry, Kanishka Basnayake P200 Visual face representations during memory retrieval compared to perception Sue-Hyun Lee, Brandon A. 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