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David Angulo-Garcia
Grupo de Modelado Computacional—Dinámica y Complejidad de Sistemas, Instituto de Matemáticas Aplicadas, Universidad de Cartagena, Carrera 6 # 36-100, Cartagena de Indias 130001, Colombia

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Journal article
Published: 26 June 2021 in Applied Sciences
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The design of robust and reliable power converters is fundamental in the incorporation of novel power systems. In this paper, we perform a detailed theoretical analysis of a synchronous ZETA converter controlled via peak-current with ramp compensation. The controller is designed to guarantee a stable Period 1 orbit with low steady state error at different values of input and reference voltages. The stability of the desired Period 1 orbit of the converter is studied in terms of the Floquet multipliers of the solution. We show that the control strategy is stable over a wide range of parameters, and it only loses stability: (i) when extreme values of the duty cycle are required; and (ii) when input and reference voltages are comparable but small. We also show by means of bifurcation diagrams and Lyapunov exponents that the Period 1 orbit loses stability through a period doubling mechanism and transits to chaos when the duty cycle saturates. We finally present numerical experiments to show that the ramp compensation control is robust to a large set of perturbations.

ACS Style

David Angulo-García; Fabiola Angulo; Juan-Guillermo Muñoz. DC-DC Zeta Power Converter: Ramp Compensation Control Design and Stability Analysis. Applied Sciences 2021, 11, 5946 .

AMA Style

David Angulo-García, Fabiola Angulo, Juan-Guillermo Muñoz. DC-DC Zeta Power Converter: Ramp Compensation Control Design and Stability Analysis. Applied Sciences. 2021; 11 (13):5946.

Chicago/Turabian Style

David Angulo-García; Fabiola Angulo; Juan-Guillermo Muñoz. 2021. "DC-DC Zeta Power Converter: Ramp Compensation Control Design and Stability Analysis." Applied Sciences 11, no. 13: 5946.

Journal article
Published: 06 June 2021 in Infrastructures
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The study of patterns of urban mobility is of utter importance for city growth projection and development planning. In this paper, we analyze the topological aspects of the street network of the coastal city of Cartagena de Indias employing graph theory and spatial syntax tools. We find that the resulting network can be understood on the basis of 400 years of the city’s history and its peripheral location that strongly influenced and shaped the growth of the city, and that the statistical properties of the network resemble those of self-organized cities. Moreover, we study the mobility through the network using a simple agent-based model that allows us to study the level of street congestion depending on the agents’ knowledge of the traffic while they travel through the network. We found that a purely shortest-path travel scheme is not an optimal strategy and that assigning small weights to traffic avoidance schemes increases the overall performance of the agents in terms of arrival success, occupancy of the streets, and traffic accumulation. Finally, we argue that localized congestion can be only partially ascribed to topological properties of the network and that it is important to consider the decision-making capability of the agents while moving through the network to explain the emergence of traffic congestion in the system.

ACS Style

Julio Amézquita-López; Jorge Valdés-Atencio; David Angulo-García. Understanding Traffic Congestion via Network Analysis, Agent Modeling, and the Trajectory of Urban Expansion: A Coastal City Case. Infrastructures 2021, 6, 85 .

AMA Style

Julio Amézquita-López, Jorge Valdés-Atencio, David Angulo-García. Understanding Traffic Congestion via Network Analysis, Agent Modeling, and the Trajectory of Urban Expansion: A Coastal City Case. Infrastructures. 2021; 6 (6):85.

Chicago/Turabian Style

Julio Amézquita-López; Jorge Valdés-Atencio; David Angulo-García. 2021. "Understanding Traffic Congestion via Network Analysis, Agent Modeling, and the Trajectory of Urban Expansion: A Coastal City Case." Infrastructures 6, no. 6: 85.

Journal article
Published: 24 December 2020 in Energies
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The boost-flyback converter is a DC-DC step-up power converter with a wide range of technological applications. In this paper, we analyze the boost-flyback dynamics when controlled via a modified Zero-Average-Dynamics control technique, hereby named Zero-Average-Surface (ZAS). While using the ZAS strategy, it is possible to calculate the duty cycle at each PWM cycle that guarantees a desired stable period-1 solution, by forcing the system to evolve in such way that a function that is constructed with strategical combination of the states over the PWM period has a zero average. We show, by means of bifurcation diagrams, that the period-1 orbit coexists with a stable period-2 orbit with a saturated duty cycle. While using linear stability analysis, we demonstrate that the period-1 orbit is stable over a wide range of parameters and it loses stability at high gains and low loads via a period doubling bifurcation. Finally, we show that, under the right choice of parameters, the period-1 orbit controller with ZAS strategy satisfactorily rejects a wide range of disturbances.

ACS Style

Juan-Guillermo Muñoz; Fabiola Angulo; David Angulo-Garcia. Zero Average Surface Controlled Boost-Flyback Converter. Energies 2020, 14, 57 .

AMA Style

Juan-Guillermo Muñoz, Fabiola Angulo, David Angulo-Garcia. Zero Average Surface Controlled Boost-Flyback Converter. Energies. 2020; 14 (1):57.

Chicago/Turabian Style

Juan-Guillermo Muñoz; Fabiola Angulo; David Angulo-Garcia. 2020. "Zero Average Surface Controlled Boost-Flyback Converter." Energies 14, no. 1: 57.

Journal article
Published: 29 September 2020 in The Journal of Neuroscience
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The nucleus reuniens (NR) is an important anatomical and functional relay between the medial prefrontal cortex (mPFC) and the hippocampus (HPC). Whether the NR controls neuronal assemblies, a hallmark of information exchange between the HPC and mPFC for memory transfer/consolidation, is not known. Using simultaneous LFP and unit recordings in NR, HPC and mPFC in male rats during slow oscillations under anesthesia, we identified a reliable sequential activation of NR neurons at the beginning of UP states, which preceded mPFC ones. NR sequences were spatially organized, from dorsal to ventral NR. Chemical inactivation of the NR disrupted mPFC sequences at the onset of UP states as well as HPC sequences present during sharp-wave ripples. We conclude that the NR contributes to the coordination and stabilization of mPFC and HPC neuronal sequences during slow oscillations, possibly via the early activation of its own sequences. SIGNIFICANCE STATEMENT Neuronal assemblies are believed to be instrumental to code/encode/store information. They can be recorded in different brain regions, suggesting that widely distributed networks of networks are involved in such information processing. The prefrontal cortex, the hippocampus and the thalamic nucleus reuniens constitute a typical example of a complex network involved in memory consolidation. In this study, we show that spatially organized cells assemblies are recruited in the nucleus reuniens at the UP state onset during slow oscillations. Nucleus reuniens activity appears to be necessary to the stability of prefrontal cortex and hippocampal cell assembly formation during slow oscillations. This result further highlights the role of the nucleus reuniens as a functional hub for exchanging and processing memories.

ACS Style

David Angulo-Garcia; Maëva Ferraris; Antoine Ghestem; Lauriane Nallet-Khosrofian; Christophe Bernard; Pascale P. Quilichini. Cell Assemblies in the Cortico-Hippocampal-Reuniens Network during Slow Oscillations. The Journal of Neuroscience 2020, 40, 8343 -8354.

AMA Style

David Angulo-Garcia, Maëva Ferraris, Antoine Ghestem, Lauriane Nallet-Khosrofian, Christophe Bernard, Pascale P. Quilichini. Cell Assemblies in the Cortico-Hippocampal-Reuniens Network during Slow Oscillations. The Journal of Neuroscience. 2020; 40 (43):8343-8354.

Chicago/Turabian Style

David Angulo-Garcia; Maëva Ferraris; Antoine Ghestem; Lauriane Nallet-Khosrofian; Christophe Bernard; Pascale P. Quilichini. 2020. "Cell Assemblies in the Cortico-Hippocampal-Reuniens Network during Slow Oscillations." The Journal of Neuroscience 40, no. 43: 8343-8354.

Journal article
Published: 11 September 2020 in Nature Communications
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The temporal embryonic origins of cortical GABA neurons are critical for their specialization. In the neonatal hippocampus, GABA cells born the earliest (ebGABAs) operate as ‘hubs’ by orchestrating population synchrony. However, their adult fate remains largely unknown. To fill this gap, we have examined CA1 ebGABAs using a combination of electrophysiology, neurochemical analysis, optogenetic connectivity mapping as well as ex vivo and in vivo calcium imaging. We show that CA1 ebGABAs not only operate as hubs during development, but also maintain distinct morpho-physiological and connectivity profiles, including a bias for long-range targets and local excitatory inputs. In vivo, ebGABAs are activated during locomotion, correlate with CA1 cell assemblies and display high functional connectivity. Hence, ebGABAs are specified from birth to ensure unique functions throughout their lifetime. In the adult brain, this may take the form of a long-range hub role through the coordination of cell assemblies across distant regions.

ACS Style

Marco Bocchio; Claire Gouny; David Angulo-Garcia; Tom Toulat; Thomas Tressard; Eleonora Quiroli; Agnès Baude; Rosa Cossart. Hippocampal hub neurons maintain distinct connectivity throughout their lifetime. Nature Communications 2020, 11, 1 -19.

AMA Style

Marco Bocchio, Claire Gouny, David Angulo-Garcia, Tom Toulat, Thomas Tressard, Eleonora Quiroli, Agnès Baude, Rosa Cossart. Hippocampal hub neurons maintain distinct connectivity throughout their lifetime. Nature Communications. 2020; 11 (1):1-19.

Chicago/Turabian Style

Marco Bocchio; Claire Gouny; David Angulo-Garcia; Tom Toulat; Thomas Tressard; Eleonora Quiroli; Agnès Baude; Rosa Cossart. 2020. "Hippocampal hub neurons maintain distinct connectivity throughout their lifetime." Nature Communications 11, no. 1: 1-19.

Journal article
Published: 31 August 2020 in Micromachines
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In this paper, we study the time optimal control problem in a DC-DC buck converter in the underdamped oscillatory regime. In particular, we derive analytic expressions for the admissible regions in the state space, satisfying the condition that every point within the region is reachable in optimal time with a single switching action. We then make use of the general result to establish the minimum and maximum variation allowed to the load in two predefined design set-ups that fulfills the time optimal single switching criteria. Finally, we make use of numerical simulations to show the performance of the proposed control under changes in the reference voltage and load resistance.

ACS Style

Ilya Dikariev; Fabiola Angulo; David Angulo-Garcia. Single-Switching Reachable Operation Points in a DC-DC Buck Converter: An Approximation from Time Optimal Control. Micromachines 2020, 11, 834 .

AMA Style

Ilya Dikariev, Fabiola Angulo, David Angulo-Garcia. Single-Switching Reachable Operation Points in a DC-DC Buck Converter: An Approximation from Time Optimal Control. Micromachines. 2020; 11 (9):834.

Chicago/Turabian Style

Ilya Dikariev; Fabiola Angulo; David Angulo-Garcia. 2020. "Single-Switching Reachable Operation Points in a DC-DC Buck Converter: An Approximation from Time Optimal Control." Micromachines 11, no. 9: 834.

Journal article
Published: 09 July 2020 in Mechanical Systems and Signal Processing
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In this paper, we present a method to control a boost flyback converter using a hysteresis band, which is designed using information of the magnetization current and a proportional integral control action of the error. The stability of the periodic orbit is proven via the monodromy matrix, and the robustness, after the first tests of the disturbance rejection, is proven using bifurcation diagrams. When the period-1 orbit losses the stability, it disappears and gives rise to other period-1 orbit with different topological sequence. In all cases that we tested, the system always presented a good performance in a period-1 orbit with very low error.

ACS Style

Juan-Guillermo Muñoz; Fabiola Angulo; David Angulo-Garcia. Designing a hysteresis band in a boost flyback converter. Mechanical Systems and Signal Processing 2020, 147, 107080 .

AMA Style

Juan-Guillermo Muñoz, Fabiola Angulo, David Angulo-Garcia. Designing a hysteresis band in a boost flyback converter. Mechanical Systems and Signal Processing. 2020; 147 ():107080.

Chicago/Turabian Style

Juan-Guillermo Muñoz; Fabiola Angulo; David Angulo-Garcia. 2020. "Designing a hysteresis band in a boost flyback converter." Mechanical Systems and Signal Processing 147, no. : 107080.

Research article
Published: 11 May 2020 in Chaos: An Interdisciplinary Journal of Nonlinear Science
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Coupling among neural rhythms is one of the most important mechanisms at the basis of cognitive processes in the brain. In this study, we consider a neural mass model, rigorously obtained from the microscopic dynamics of an inhibitory spiking network with exponential synapses, able to autonomously generate collective oscillations (COs). These oscillations emerge via a super-critical Hopf bifurcation, and their frequencies are controlled by the synaptic time scale, the synaptic coupling, and the excitability of the neural population. Furthermore, we show that two inhibitory populations in a master–slave configuration with different synaptic time scales can display various collective dynamical regimes: damped oscillations toward a stable focus, periodic and quasi-periodic oscillations, and chaos. Finally, when bidirectionally coupled, the two inhibitory populations can exhibit different types of θ– γ cross-frequency couplings (CFCs): phase-phase and phase-amplitude CFC. The coupling between θ and γ COs is enhanced in the presence of an external θ forcing, reminiscent of the type of modulation induced in hippocampal and cortex circuits via optogenetic drive.

ACS Style

Andrea Ceni; Simona Olmi; Alessandro Torcini; David Angulo-Garcia. Cross frequency coupling in next generation inhibitory neural mass models. Chaos: An Interdisciplinary Journal of Nonlinear Science 2020, 30, 053121 .

AMA Style

Andrea Ceni, Simona Olmi, Alessandro Torcini, David Angulo-Garcia. Cross frequency coupling in next generation inhibitory neural mass models. Chaos: An Interdisciplinary Journal of Nonlinear Science. 2020; 30 (5):053121.

Chicago/Turabian Style

Andrea Ceni; Simona Olmi; Alessandro Torcini; David Angulo-Garcia. 2020. "Cross frequency coupling in next generation inhibitory neural mass models." Chaos: An Interdisciplinary Journal of Nonlinear Science 30, no. 5: 053121.

Preprint content
Published: 24 August 2019
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Coupling among neural rhythms is one of the most important mechanisms at the basis of cognitive processes in the brain. In this study we consider a neural mass model, rigorously obtained from the microscopic dynamics of an inhibitory spiking network with exponential synapses, able to autonomously generate collective oscillations (COs). These oscillations emerge via a super-critical Hopf bifurcation, and their frequencies are controlled by the synaptic time scale, the synaptic coupling and the excitability of the neural population. Furthermore, we show that two inhibitory populations in a master-slave configuration with different synaptic time scales can display various collective dynamical regimes: namely, damped oscillations towards a stable focus, periodic and quasi-periodic oscillations, and chaos. Finally, when bidirectionally coupled the two inhibitory populations can exhibit different types of θ-γ cross-frequency couplings (CFCs): namely, phase-phase and phase-amplitude CFC. The coupling between θ and γ COs is enhanced in presence of a external θ forcing, reminiscent of the type of modulation induced in Hippocampal and Cortex circuits via optogenetic drive.In healthy conditions, the brain’s activity reveals a series of intermingled oscillations, generated by large ensembles of neurons, which provide a functional substrate for information processing. How single neuron properties influence neuronal population dynamics is an unsolved question, whose solution could help in the understanding of the emergent collective behaviors arising during cognitive processes. Here we consider a neural mass model, which reproduces exactly the macroscopic activity of a network of spiking neurons. This mean-field model is employed to shade some light on an important and ubiquitous neural mechanism underlying information processing in the brain: the θ-γ cross-frequency coupling. In particular, we will explore in detail the conditions under which two coupled inhibitory neural populations can generate these functionally relevant coupled rhythms.

ACS Style

Andrea Ceni; Simona Olmi; Alessandro Torcini; David Angulo-Garcia. Cross frequency coupling in next generation inhibitory neural mass models. 2019, 745828 .

AMA Style

Andrea Ceni, Simona Olmi, Alessandro Torcini, David Angulo-Garcia. Cross frequency coupling in next generation inhibitory neural mass models. . 2019; ():745828.

Chicago/Turabian Style

Andrea Ceni; Simona Olmi; Alessandro Torcini; David Angulo-Garcia. 2019. "Cross frequency coupling in next generation inhibitory neural mass models." , no. : 745828.

Article
Published: 23 May 2019 in Physical Review E
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We study a network of spiking neurons with heterogeneous excitabilities connected via inhibitory delayed pulses. For globally coupled systems the increase of the inhibitory coupling reduces the number of firing neurons by following a winner-takes-all mechanism. For sufficiently large transmission delay we observe the emergence of collective oscillations in the system beyond a critical coupling value. Heterogeneity promotes neural inactivation and asynchronous dynamics and its effect can be counteracted by considering longer time delays. In sparse networks, inhibition has the counterintuitive effect of promoting neural reactivation of silent neurons for sufficiently large coupling. In this regime, current fluctuations are on one side responsible for neural firing of subthreshold neurons and on the other side for their desynchronization. Therefore, collective oscillations are present only in a limited range of coupling values, which remains finite in the thermodynamic limit. Out of this range the dynamics is asynchronous and for very large inhibition neurons display a bursting behavior alternating periods of silence with periods where they fire freely in absence of any inhibition.

ACS Style

Stefano Luccioli; David Angulo-Garcia; Alessandro Torcini. Neural activity of heterogeneous inhibitory spiking networks with delay. Physical Review E 2019, 99, 052412 .

AMA Style

Stefano Luccioli, David Angulo-Garcia, Alessandro Torcini. Neural activity of heterogeneous inhibitory spiking networks with delay. Physical Review E. 2019; 99 (5):052412.

Chicago/Turabian Style

Stefano Luccioli; David Angulo-Garcia; Alessandro Torcini. 2019. "Neural activity of heterogeneous inhibitory spiking networks with delay." Physical Review E 99, no. 5: 052412.

Journal article
Published: 25 March 2019 in Proceedings of the National Academy of Sciences
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The hippocampus plays a critical role in episodic memory: the sequential representation of visited places and experienced events. This function is mirrored by hippocampal activity that self organizes into sequences of neuronal activation that integrate spatiotemporal information. What are the underlying mechanisms of such integration is still unknown. Single cell activity was recently shown to combine time and distance information; however, it remains unknown whether a degree of tuning between space and time can be defined at the network level. Here, combining daily calcium imaging of CA1 sequence dynamics in running head-fixed mice and network modeling, we show that CA1 network activity tends to represent a specific combination of space and time at any given moment, and that the degree of tuning can shift within a continuum from 1 day to the next. Our computational model shows that this shift in tuning can happen under the control of the external drive power. We propose that extrinsic global inputs shape the nature of spatiotemporal integration in the hippocampus at the population level depending on the task at hand, a hypothesis which may guide future experimental studies.

ACS Style

Caroline Haimerl; David Angulo Garcia; Vincent Villette; Susanne Reichinnek; Alessandro Torcini; Rosa Cossart; Arnaud Malvache. Internal representation of hippocampal neuronal population spans a time-distance continuum. Proceedings of the National Academy of Sciences 2019, 116, 7477 -7482.

AMA Style

Caroline Haimerl, David Angulo Garcia, Vincent Villette, Susanne Reichinnek, Alessandro Torcini, Rosa Cossart, Arnaud Malvache. Internal representation of hippocampal neuronal population spans a time-distance continuum. Proceedings of the National Academy of Sciences. 2019; 116 (15):7477-7482.

Chicago/Turabian Style

Caroline Haimerl; David Angulo Garcia; Vincent Villette; Susanne Reichinnek; Alessandro Torcini; Rosa Cossart; Arnaud Malvache. 2019. "Internal representation of hippocampal neuronal population spans a time-distance continuum." Proceedings of the National Academy of Sciences 116, no. 15: 7477-7482.

Preprint
Published: 29 November 2018
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The hippocampus plays a critical role in episodic memory: the sequential representation of visited places and experienced events. This function is mirrored by hippocampal activity that self organizes into sequences of neuronal activation that integrate spatio-temporal information. What are the underlying mechanisms of such integration is still unknown. Single cell activity was recently shown to combine time and distance information; however, it remains unknown whether a degree of tuning between space and time can be defined at the network level. Here, combining daily calcium imaging of CA1 sequence dynamics in running head-fixed mice and network modeling, we show that CA1 network activity tends to represent a specific combination of space and time at any given moment, and that the degree of tuning can shift within a continuum from one day to the next. Our computational model shows that this shift in tuning can happen under the control of the external drive power. We propose that extrinsic global inputs shape the nature of spatio-temporal integration in the hippocampus at the population level depending on the task at hand, a hypothesis which may guide future experimental studies.Significance StatementThe hippocampus organizes experience in sequences of events that form episodic memory. How are time and space internally computed in the hippocampus in the absence of sequential external inputs? Here we show that time and space are integrated together within the hippocampal network with different degrees of tuning across days. This was found by recording the activity of hundreds of pyramidal cells for several days. We also propose a mechanism supporting such spatio-temporal integration based on a ring attractor network model: the degree of tuning between space and time can be adjusted by modulating the power of a non-sequential external excitatory drive. In this way, the hippocampus is able to generate a spatio-temporal representation tuned to the task at hand.

ACS Style

Caroline Haimerl; David Angulo-Garcia; Vincent Villette; Susanne. Reichinnek; Alessandro Torcini; Rosa Cossart; Arnaud Malvache. Internal representation of hippocampal neuronal population span a time-distance continuum. 2018, 475095 .

AMA Style

Caroline Haimerl, David Angulo-Garcia, Vincent Villette, Susanne. Reichinnek, Alessandro Torcini, Rosa Cossart, Arnaud Malvache. Internal representation of hippocampal neuronal population span a time-distance continuum. . 2018; ():475095.

Chicago/Turabian Style

Caroline Haimerl; David Angulo-Garcia; Vincent Villette; Susanne. Reichinnek; Alessandro Torcini; Rosa Cossart; Arnaud Malvache. 2018. "Internal representation of hippocampal neuronal population span a time-distance continuum." , no. : 475095.

Preprint content
Published: 26 November 2018
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The nucleus reuniens (NR) is an important anatomical and functional relay between the medial prefrontal cortex (mPFC) and the hippocampus (HPC). Whether the NR controls neuronal assemblies, a hallmark of information exchange between the HPC and mPFC for memory transfer/consolidation, is not known. Using simultaneous LFP and unit recordings in NR, HPC and mPFC in rats during slow oscillations under anesthesia, we identified a reliable sequential activation of NR neurons at the beginning of UP states, which preceded mPFC ones. NR sequences were spatially organized, from dorsal to ventral NR. Chemical inactivation of the NR disrupted mPFC sequences at the onset of UP states as well as HPC sequences present during sharp-wave ripples. We conclude that the NR contributes to the coordination and stabilization of mPFC and HPC neuronal sequences during slow oscillations, possibly via the early activation of its own sequences. Significance Statement Neuronal assemblies are believed to be instrumental to code/encode/store information. They can be recorded in different brain regions, suggesting that widely distributed networks of networks are involved in such information processing. The prefrontal cortex, the hippocampus and the thalamic nucleus reuniens constitute a typical example of a complex network involved in memory consolidation. In this study, we show that spatially organized cells assemblies are recruited in the nucleus reuniens at the UP state onset during slow oscillations. Nucleus reuniens activity appears to be necessary to the stability of prefrontal cortex and hippocampal cell assembly formation during slow oscillations. This result further highlights the role of the Nucleus Reuniens as a functional hub for exchanging and processing memories.

ACS Style

David Angulo-Garcia; Maëva Ferraris; Antoine Ghestem; Lauriane Nallet-Khosrofian; Christophe Bernard; Pascale P Quilichini. Cell assemblies in the cortico-hippocampal-reuniens network during slow oscillations. 2018, 474973 .

AMA Style

David Angulo-Garcia, Maëva Ferraris, Antoine Ghestem, Lauriane Nallet-Khosrofian, Christophe Bernard, Pascale P Quilichini. Cell assemblies in the cortico-hippocampal-reuniens network during slow oscillations. . 2018; ():474973.

Chicago/Turabian Style

David Angulo-Garcia; Maëva Ferraris; Antoine Ghestem; Lauriane Nallet-Khosrofian; Christophe Bernard; Pascale P Quilichini. 2018. "Cell assemblies in the cortico-hippocampal-reuniens network during slow oscillations." , no. : 474973.

Journal article
Published: 08 November 2018 in Energies
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Reliable and robust control of power converters is a key issue in the performance of numerous technological devices. In this paper we show a design technique for the control of a DC-DC buck converter with a switching technique that guarantees both good performance and global stability. We show that making use of the contraction theorem in the Jordan canonical form of the buck converter, it is possible to find a switching surface that guarantees stability but it is incapable of rejecting load perturbations. To overcome this, we expand the system to include the dynamics of the voltage error and we demonstrate that the same design procedure is not only able to stabilize the system to the desired operation point but also to reject load, input voltage, and reference voltage perturbations.

ACS Style

David Angulo-Garcia; Fabiola Angulo; Gustavo Osorio; Gerard Olivar. Control of a DC-DC Buck Converter through Contraction Techniques. Energies 2018, 11, 3086 .

AMA Style

David Angulo-Garcia, Fabiola Angulo, Gustavo Osorio, Gerard Olivar. Control of a DC-DC Buck Converter through Contraction Techniques. Energies. 2018; 11 (11):3086.

Chicago/Turabian Style

David Angulo-Garcia; Fabiola Angulo; Gustavo Osorio; Gerard Olivar. 2018. "Control of a DC-DC Buck Converter through Contraction Techniques." Energies 11, no. 11: 3086.

Research article
Published: 02 November 2018 in PLOS Computational Biology
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Spontaneous emergence of synchronized population activity is a characteristic feature of developing brain circuits. Recent experiments in the developing neo-cortex showed the existence of driver cells able to impact the synchronization dynamics when single-handedly stimulated. We have developed a spiking network model capable to reproduce the experimental results, thus identifying two classes of driver cells: functional hubs and low functionally connected (LC) neurons. The functional hubs arranged in a clique orchestrated the synchronization build-up, while the LC drivers were lately or not at all recruited in the synchronization process. Notwithstanding, they were able to alter the network state when stimulated by modifying the temporal activation of the functional clique or even its composition. LC drivers can lead either to higher population synchrony or even to the arrest of population dynamics, upon stimulation. Noticeably, some LC driver can display both effects depending on the received stimulus. We show that in the model the presence of inhibitory neurons together with the assumption that younger cells are more excitable and less connected is crucial for the emergence of LC drivers. These results provide a further understanding of the structural-functional mechanisms underlying synchronized firings in developing circuits possibly related to the coordinated activity of cell assemblies in the adult brain. There is timely interest on the impact of peculiar neurons (driver cells) and of small neuronal sub-networks (cliques) on operational brain dynamics. We first provide experimental data concerning the effect of stimulated driver cells on the bursting activity observable in the developing entorhinal cortex. Secondly, we develop a network model able to fully reproduce the experimental observations. Analogously to the experiments two types of driver cells can be identified: functional hubs and low functionally connected (LC) cells. We explain the role of hub neurons, arranged in a clique, for the orchestration of the bursting activity in control conditions. Furthermore, we report a new mechanism which can explain why and how LC drivers emerge in the structural-functional organization of the entorhinal cortex.

ACS Style

Stefano Luccioli; David Angulo-Garcia; Rosa Cossart; Arnaud Malvache; Laura Módol; Vitor Hugo Sousa; Paolo Bonifazi; Alessandro Torcini. Modeling driver cells in developing neuronal networks. PLOS Computational Biology 2018, 14, e1006551 .

AMA Style

Stefano Luccioli, David Angulo-Garcia, Rosa Cossart, Arnaud Malvache, Laura Módol, Vitor Hugo Sousa, Paolo Bonifazi, Alessandro Torcini. Modeling driver cells in developing neuronal networks. PLOS Computational Biology. 2018; 14 (11):e1006551.

Chicago/Turabian Style

Stefano Luccioli; David Angulo-Garcia; Rosa Cossart; Arnaud Malvache; Laura Módol; Vitor Hugo Sousa; Paolo Bonifazi; Alessandro Torcini. 2018. "Modeling driver cells in developing neuronal networks." PLOS Computational Biology 14, no. 11: e1006551.

Preprint
Published: 05 February 2018
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Spontaneous emergence of synchronized population activity is a characteristic feature of developing brain circuits. Recent experiments in the developing neo-cortex showed the existence of driver cells able to impact the synchronization dynamics when single-handedly stimulated. We have developed a spiking network model capable to reproduce the experimental results, thus identifying two classes of driver cells: functional hubs and low functionally connected (LC) neurons. The functional hubs arranged in a clique orchestrated the synchronization build-up, while the LC drivers were lately or not at all recruited in the synchronization process. Notwithstanding, they were able to alter the network state when stimulated by modifying the temporal activation of the functional clique or even its composition. LC drivers can lead either to higher population synchrony or even to the arrest of population dynamics, upon stimulation. Noticeably, some LC driver can display both effects depending on the received stimulus. We show that in the model the presence of inhibitory neurons together with the assumption that younger cells are more excitable and less connected is crucial for the emergence of LC drivers. These results provide a further understanding of the structural-functional mechanisms underlying synchronized firings in developing circuits possibly related to the coordinated activity of cell assemblies in the adult brain.Author SummaryThere is timely interest on the impact of peculiar neurons (driver cells) and of small neuronal sub-networks (cliques) on operational brain dynamics. We first provide experimental data concerning the effect of stimulated driver cells on the bursting activity observable in the developing entorhinal cortex. Secondly, we develop a network model able to fully reproduce the experimental observations. Analogously to the experiments two types of driver cells can be identified: functional hubs and low functionally connected (LC) drivers. We explain the role of hub neurons, arranged in a clique, for the orchestration of the bursting activity in control conditions. Furthermore, we report a new mechanism, which can explain why and how LC drivers emerge in the structural-functional organization of the enthorinal cortex.

ACS Style

Stefano Luccioli; David Angulo-Garcia; Rosa Cossart; Arnaud Malvache; Laura Mòdol; Vitor Hugo Sousa; Paolo Bonifazi; Alessandro Torcini. Modeling driver cells in developing neuronal networks. 2018, 260422 .

AMA Style

Stefano Luccioli, David Angulo-Garcia, Rosa Cossart, Arnaud Malvache, Laura Mòdol, Vitor Hugo Sousa, Paolo Bonifazi, Alessandro Torcini. Modeling driver cells in developing neuronal networks. . 2018; ():260422.

Chicago/Turabian Style

Stefano Luccioli; David Angulo-Garcia; Rosa Cossart; Arnaud Malvache; Laura Mòdol; Vitor Hugo Sousa; Paolo Bonifazi; Alessandro Torcini. 2018. "Modeling driver cells in developing neuronal networks." , no. : 260422.

Meeting abstracts
Published: 18 August 2017 in BMC Neuroscience
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BMC Neuroscience 2017, 18 (Suppl 1):P156 Ischemic stroke is fundamentally a multiscale phenomenon [1]. Occlusion of blood vessels in the brain triggers a cascade of changes including: 1. synaptic glutamate release, related to excitotoxicity; 2. elevated extracellular potassium, leading to spreading depression; 3. cell swelling, reducing the extracellular volume and diffusion; 4. production of reactive oxygen species, which give rise to inflammation. These cascades occur over multiple time-scales, with the initial rapid changes in cell metabolism and ionic concentrations trigging several damaging agents that may ultimately leads to cell death. Tissue affected by ischemic stroke is divided into three regions; 1. a core where cells suffer irreparable damage and death, 2. a penumbra where cells may recover with reperfusion, 3. a further region of edema where spontaneous recovery is expected. Multiscale modeling and multiphysics modeling is essential to capture this cascade. Such modeling requires coupling complex intracellular molecular alterations with electrophysiology, and consideration of network properties in the context of bulk tissue alterations mediated by extracellular diffusion. Spreading depression is a wave of depolarization that propagates through tissue and causes cells in the penumbra to expend energy by repolarization, increasing their vulnerability to cell death. We modeled the spreading depression seen in ischemic stroke by coupling a detailed biophysical model of cortical pyramidal neurons equipped with Na+/K+-ATPase pumps with reaction-diffusion of ions in the extracellular space (ECS). A macroscopic view of the ECS is characterised by its tortuosity (a reduction in the diffusion coefficient due to obstructions) and its free volume fraction (typically ~20%). The addition of reactions allows the ECS be modeled as an active medium glial buffering of K+. Ischemia impedes ATP production which results in a failure of the Na+/K+-ATPase pump and a rise in extracellular K+. Once extracellular K+ exceeds a threshold it will cause neurons to depolarize, further increasing extracellular K+. NEURON’s reaction-diffusion module NRxD [2] provides a platform where detailed neurons models can be embedded in a macroscopic model of tissue. This is demonstrated with a multiscale biophysical model of ischemic stroke where the rapid intracellular changes are coupled with the slower diffusive signaling. Acknowledgements Research supported by NIH grant 5R01MH086638 References 1. Newton, AJH, and Lytton, WW: Computer modeling of ischemic stroke. Drug Discovery Today: Disease Models. 2017. 2. McDougal RA, Hines ML, Lytton WW: Reaction-diffusion in the NEURON simulator. Frontiers in neuroinformatics. 2013, 7(28). BMC Neuroscience 2017, 18 (Suppl 1):P157 A neuron’s electrical activity is governed not just by presynaptic activity, but also by its internal state. This state is a function of history including prior synaptic input (e.g. cytosolic calcium concentration, protein expression in SCN neurons), cellular health, and routine biological processes. The NEURON simulator [1], like much of computational neuroscience, has traditionally focused on electrophysiology. NEURON has included NRxD to give standardized support for reaction-diffusion (i.e. intracellular) modeling for the past 5 years [2], facilitating studies into the role of electrical-chemical interactions. The original reaction-diffusion support was written in vectorized Python, which offered limited performance, but ongoing improvements have now significantly reduced run-times, making larger-scale studies more practical. New accelerated reaction-diffusion methods are being developed as part of a separate NEURON module, crxd. This new module will ultimately be a fully compatible replacement for the existing NRxD module (rxd). Developing it as a separate module allows us to make it available to the community before it supports the full functionality of NRxD. The interface code for crxd remains in Python, but it now transfers model structure to C code via ctypes, which performs all run-time calculations; Python is no longer invoked during simulation. Dynamic code generation allows arbitrary reaction schemes to run at full compiled speed. Thread-based parallelization accelerates extracellular reaction-diffusion simulations. Preliminary tests suggest an approximately 10x reduction in 1D run-time using crxd instead of the Python-based rxd. Like rxd, crxd uses the Hines method [3] for O(n) 1D reaction-diffusion simulations. Using 4 cores for extracellular diffusion currently reduces the runtime by a factor of 2.3. Additionally, using the crxd module simplifies setup relative to rxd-based simulations since it does not require installing scipy. Once crxd supports the entire documented NRxD interface and has been thoroughly tested, it will replace the rxd module and thus become NEURON’s default module for specifying reaction-diffusion kinetics. Acknowledgements Research supported by NIH R01 MH086638. References 1. NEURON | for empirically based simulations of neurons and networks of neurons [http://neuron.yale.edu] 2. McDougal RA, Hines ML, Lytton WW: Reaction-diffusion in the NEURON simulator. Front. Neuroinform 2013, 7:28. 3. Hines M: Efficient computation of branched nerve equations. Int. J. Bio-Medical Computing 1984, 15:69–76. BMC Neuroscience 2017, 18 (Suppl 1):P158 Background oscillations, reflecting the excitability of neurons, are ubiquitous in the brain. Some studies have conjectured that when spikes sent by one population reach the other population in the peaks of excitability, then information transmission between two oscillating neuronal groups is more effective [1]. In this context, the phase relationship between oscillating neuronal populations may have implications in neuronal communication between brain areas [2, 3]. The Phase Response Curve (PRC) of a neural oscillator measures the phase-shift resulting...

ACS Style

Adam J. H. Newton; Alexandra H. Seidenstein; Robert A. McDougal; Alberto Pérez-Cervera; Gemma Huguet; Tere M-Seara; Caroline Haimerl; David Angulo-Garcia; Alessandro Torcini; Rosa Cossart; Arnaud Malvache; Kaoutar Skiker; Mounir Maouene; Gianmarco Ragognetti; Letizia Lorusso; Andrea Viggiano; Angelo Marcelli; Rosa Senatore; Antonio Parziale; S. Stramaglia; M. Pellicoro; L. Angelini; Enrico Amico; H. Aerts; J. Cortés; Steven Laureys; D. Marinazzo; I. Bassez; L. Faes; Hannes Almgren; Adeel Razi; Frederik Van De Steen; Ruth Krebs; Lida Kanari; Pawel Dlotko; Martina Scolamiero; Ran Levi; Julian Shillcock; Christiaan P.J. De Kock; Kathryn Hess; Henry Markram; Cheng Ly; Gary Marsat; Tom Gillespie; Malin Sandström; Mathew Abrams; Jeffrey S. Grethe; Maryann Martone; Robin De Gernier; Sergio Solinas; Christian Rössert; Marc Haelterman; Serge Massar; Valentina Pasquale; Vito Paolo Pastore; Sergio Martinoia; Paolo Massobrio; Cristiano Capone; Núria Tort-Colet; Maria V. Sanchez-Vives; Maurizio Mattia; Ali Almasi; Shaun L. Cloherty; David B. Grayden; Yan T. Wong; Michael R. Ibbotson; Hamish Meffin; Luke Y. Prince; Krasimira Tsaneva-Atanasova; Jack R. Mellor; Alberto Mazzoni; Manuela Rosa; Jacopo Carpaneto; Luigi M. Romito; Alberto Priori; Silvestro Micera; Rosanna Migliore; Carmen Alina Lupascu; Francesco Franchina; Luca Leonardo Bologna; Armando Romani; Sára Saray; Werner Van Geit; Szabolcs Káli; Alex Thomson; Audrey Mercer; Sigrun Lange; Joanne Falck; Eilif Muller; Felix Schürmann; Dmitrii Todorov; Robert Capps; William Barnett; Yaroslav Molkov; Federico Devalle; Diego Pazó; Ernest Montbrió; Gabriela Mochol; Habiba Azab; Benjamin Y. Hayden; Rubén Moreno-Bote; Pragathi Priyadharsini Balasubramani; Srinivasa V. Chakravarthy; Vignayanandam R. Muddapu; Medorian D. Gheorghiu; Bartul Mimica; Jonathan Withlock; Raul C. Mureșan; Jennifer L. Zick; Kelsey Schultz; Rachael K. Blackman; Matthew V. Chafee; Theoden I. Netoff; Nicholas Roberts; Vivek Nagaraj; Andrew Lamperski; Logan L. Grado; Matthew D. Johnson; David P. Darrow; Davide Lonardoni; Hayder Amin; Stefano Di Marco; Alessandro Maccione; Luca Berdondini; Thierry Nieus; Marcel Stimberg; Dan F. M. Goodman; Thomas Nowotny; Veronika Koren; Valentin Dragoi; Klaus Obermayer; Samy Castro; Mariano Fernandez; Wael El-Deredy; Kesheng Xu; Jean Paul Maidana; Patricio Orio; Weiliang Chen; Iain Hepburn; Francesco Casalegno; Adrien Devresse; Aleksandr Ovcharenko; Fernando Pereira; Fabien Delalondre; Erik De Schutter; Peter Bratby; Andrew R. Gallimore; Guido Klingbeil; Criseida Zamora; Yunliang Zang; Patrick Crotty; Eric Palmerduca; Alberto Antonietti; Claudia Casellato; Csaba Erö; Egidio D’Angelo; Marc-Oliver Gewaltig; Alessandra Pedrocchi; Ilja Bytschok; Dominik Dold; Johannes Schemmel; Karlheinz Meier; Mihai A. Petrovici; Hui-An Shen; Simone Carlo Surace; Jean-Pascal Pfister; Baptiste Lefebvre; Olivier Marre; Pierre Yger; Athanasia Papoutsi; Jiyoung Park; Ryan Ash; Stelios Smirnakis; Panayiota Poirazi; Richard A. Felix; Alexander G. Dimitrov; Christine Portfors; Silvia Daun; Tibor I. Toth; Joanna Jędrzejewska-Szmek; Nadine Kabbani; Kim T. Blackwel; Bahar Moezzi; Natalie Schaworonkow; Lukas Plogmacher; Mitchell R. Goldsworthy; Brenton Hordacre; Mark McDonnell; Nicolangelo Iannella; Michael C. Ridding; Jochen Triesch; Reinoud Maex; Karen Safaryan; Volker Steuber; Rongxiang Tang; Yi-Yuan Tang; Darya V. Verveyko; Alexey R. Brazhe; Andrey Yu Verisokin; Dmitry E. Postnov; Cengiz Günay; Gabriella Panuccio; Michele Giugliano; Astrid A. Prinz; Pablo Varona; Mikhail I. Rabinovich; Jack Denham; Thomas Ranner; Netta Cohen; Maria Reva; Nelson Rebola; Tekla Kirizs; Zoltan Nusser; David DiGregorio; Eirini Mavritsaki; Panos Rentzelas; Nikul H. Ukani; Adam Tomkins; Chung-Heng Yeh; Wesley Bruning; Allison L. Fenichel; Yiyin Zhou; Yu-Chi Huang; Dorian Florescu; Carlos Luna Ortiz; Paul Richmond; Chung-Chuan Lo; Daniel Coca; Ann-Shyn Chiang; Aurel A. Lazar; Jennifer L. Creaser; Congping Lin; Peter Ashwin; Jonathan T. Brown; Thomas Ridler; Daniel Levenstein; Brendon O. Watson; György Buzsáki; John Rinzel; Rodica Curtu; Anh Nguyen; Sahand Assadzadeh; Peter A. Robinson; Paula Sanz-Leon; Caroline G. Forlim; Lírio O. B. De Almeida; Reynaldo D. Pinto; Francisco De Borja Rodriguez; Ángel Lareo; Aaron Montero; Thiago Mosqueiro; Ramon Huerta; Vinicio Changoluisa; Vinícius L. Cordeiro; César C. Ceballos; Nilton L. Kamiji; Antonio Roque; William W. Lytton; Andrew Knox; Joshua J. C. Rosenthal; Svitlana Popovych; Liqing Liu; Bin A. Wang; Christian Grefkes; Gereon R. Fink; Nils Rosjat; Abraham Perez-Trujillo; Andres Espinal; Marco A. Sotelo-Figueroa; Ivan Cruz-Aceves; Horacio Rostro-Gonzalez; Martin Zapotocky; Martina Hoskovcová; Jana Kopecká; Olga Ulmanová; Evžen Růžička; Matthias Gärtner; Sevil Duvarci; Jochen Roeper; Gaby Schneider; Stefan Albert; Katharina Schmack; Michiel Remme; Susanne Schreiber; Michele Migliore; Stefano M. Antonel; Jean-Denis Courcol; Sami Utku Çelikok; Eva M. Navarro-López; Neslihan Serap Şengör; Rahmi Elibol; Mustafa Yasir Özdemir; Tianyi Li; Angelo Arleo; Denis Sheynikhovich; Akihiro Nakamura; Masanori Shimono; Youngjo Song; Sol Park; Ilhwan Choi; Jaeseung Jeong; Hee-Sup Shin; Sadra Sadeh; Padraig Gleeson; R. Angus Silver; Alexandra Pierri Chatzikalymniou. 26th Annual Computational Neuroscience Meeting (CNS*2017): Part 3. BMC Neuroscience 2017, 18, 1 -82.

AMA Style

Adam J. H. Newton, Alexandra H. Seidenstein, Robert A. McDougal, Alberto Pérez-Cervera, Gemma Huguet, Tere M-Seara, Caroline Haimerl, David Angulo-Garcia, Alessandro Torcini, Rosa Cossart, Arnaud Malvache, Kaoutar Skiker, Mounir Maouene, Gianmarco Ragognetti, Letizia Lorusso, Andrea Viggiano, Angelo Marcelli, Rosa Senatore, Antonio Parziale, S. Stramaglia, M. Pellicoro, L. Angelini, Enrico Amico, H. Aerts, J. Cortés, Steven Laureys, D. Marinazzo, I. Bassez, L. Faes, Hannes Almgren, Adeel Razi, Frederik Van De Steen, Ruth Krebs, Lida Kanari, Pawel Dlotko, Martina Scolamiero, Ran Levi, Julian Shillcock, Christiaan P.J. De Kock, Kathryn Hess, Henry Markram, Cheng Ly, Gary Marsat, Tom Gillespie, Malin Sandström, Mathew Abrams, Jeffrey S. Grethe, Maryann Martone, Robin De Gernier, Sergio Solinas, Christian Rössert, Marc Haelterman, Serge Massar, Valentina Pasquale, Vito Paolo Pastore, Sergio Martinoia, Paolo Massobrio, Cristiano Capone, Núria Tort-Colet, Maria V. Sanchez-Vives, Maurizio Mattia, Ali Almasi, Shaun L. Cloherty, David B. Grayden, Yan T. Wong, Michael R. Ibbotson, Hamish Meffin, Luke Y. Prince, Krasimira Tsaneva-Atanasova, Jack R. Mellor, Alberto Mazzoni, Manuela Rosa, Jacopo Carpaneto, Luigi M. Romito, Alberto Priori, Silvestro Micera, Rosanna Migliore, Carmen Alina Lupascu, Francesco Franchina, Luca Leonardo Bologna, Armando Romani, Sára Saray, Werner Van Geit, Szabolcs Káli, Alex Thomson, Audrey Mercer, Sigrun Lange, Joanne Falck, Eilif Muller, Felix Schürmann, Dmitrii Todorov, Robert Capps, William Barnett, Yaroslav Molkov, Federico Devalle, Diego Pazó, Ernest Montbrió, Gabriela Mochol, Habiba Azab, Benjamin Y. Hayden, Rubén Moreno-Bote, Pragathi Priyadharsini Balasubramani, Srinivasa V. Chakravarthy, Vignayanandam R. Muddapu, Medorian D. Gheorghiu, Bartul Mimica, Jonathan Withlock, Raul C. Mureșan, Jennifer L. Zick, Kelsey Schultz, Rachael K. Blackman, Matthew V. Chafee, Theoden I. Netoff, Nicholas Roberts, Vivek Nagaraj, Andrew Lamperski, Logan L. Grado, Matthew D. Johnson, David P. Darrow, Davide Lonardoni, Hayder Amin, Stefano Di Marco, Alessandro Maccione, Luca Berdondini, Thierry Nieus, Marcel Stimberg, Dan F. M. Goodman, Thomas Nowotny, Veronika Koren, Valentin Dragoi, Klaus Obermayer, Samy Castro, Mariano Fernandez, Wael El-Deredy, Kesheng Xu, Jean Paul Maidana, Patricio Orio, Weiliang Chen, Iain Hepburn, Francesco Casalegno, Adrien Devresse, Aleksandr Ovcharenko, Fernando Pereira, Fabien Delalondre, Erik De Schutter, Peter Bratby, Andrew R. Gallimore, Guido Klingbeil, Criseida Zamora, Yunliang Zang, Patrick Crotty, Eric Palmerduca, Alberto Antonietti, Claudia Casellato, Csaba Erö, Egidio D’Angelo, Marc-Oliver Gewaltig, Alessandra Pedrocchi, Ilja Bytschok, Dominik Dold, Johannes Schemmel, Karlheinz Meier, Mihai A. Petrovici, Hui-An Shen, Simone Carlo Surace, Jean-Pascal Pfister, Baptiste Lefebvre, Olivier Marre, Pierre Yger, Athanasia Papoutsi, Jiyoung Park, Ryan Ash, Stelios Smirnakis, Panayiota Poirazi, Richard A. Felix, Alexander G. Dimitrov, Christine Portfors, Silvia Daun, Tibor I. Toth, Joanna Jędrzejewska-Szmek, Nadine Kabbani, Kim T. Blackwel, Bahar Moezzi, Natalie Schaworonkow, Lukas Plogmacher, Mitchell R. Goldsworthy, Brenton Hordacre, Mark McDonnell, Nicolangelo Iannella, Michael C. Ridding, Jochen Triesch, Reinoud Maex, Karen Safaryan, Volker Steuber, Rongxiang Tang, Yi-Yuan Tang, Darya V. Verveyko, Alexey R. Brazhe, Andrey Yu Verisokin, Dmitry E. Postnov, Cengiz Günay, Gabriella Panuccio, Michele Giugliano, Astrid A. Prinz, Pablo Varona, Mikhail I. Rabinovich, Jack Denham, Thomas Ranner, Netta Cohen, Maria Reva, Nelson Rebola, Tekla Kirizs, Zoltan Nusser, David DiGregorio, Eirini Mavritsaki, Panos Rentzelas, Nikul H. Ukani, Adam Tomkins, Chung-Heng Yeh, Wesley Bruning, Allison L. Fenichel, Yiyin Zhou, Yu-Chi Huang, Dorian Florescu, Carlos Luna Ortiz, Paul Richmond, Chung-Chuan Lo, Daniel Coca, Ann-Shyn Chiang, Aurel A. Lazar, Jennifer L. Creaser, Congping Lin, Peter Ashwin, Jonathan T. Brown, Thomas Ridler, Daniel Levenstein, Brendon O. Watson, György Buzsáki, John Rinzel, Rodica Curtu, Anh Nguyen, Sahand Assadzadeh, Peter A. Robinson, Paula Sanz-Leon, Caroline G. Forlim, Lírio O. B. De Almeida, Reynaldo D. Pinto, Francisco De Borja Rodriguez, Ángel Lareo, Aaron Montero, Thiago Mosqueiro, Ramon Huerta, Vinicio Changoluisa, Vinícius L. Cordeiro, César C. Ceballos, Nilton L. Kamiji, Antonio Roque, William W. Lytton, Andrew Knox, Joshua J. C. Rosenthal, Svitlana Popovych, Liqing Liu, Bin A. Wang, Christian Grefkes, Gereon R. Fink, Nils Rosjat, Abraham Perez-Trujillo, Andres Espinal, Marco A. Sotelo-Figueroa, Ivan Cruz-Aceves, Horacio Rostro-Gonzalez, Martin Zapotocky, Martina Hoskovcová, Jana Kopecká, Olga Ulmanová, Evžen Růžička, Matthias Gärtner, Sevil Duvarci, Jochen Roeper, Gaby Schneider, Stefan Albert, Katharina Schmack, Michiel Remme, Susanne Schreiber, Michele Migliore, Stefano M. Antonel, Jean-Denis Courcol, Sami Utku Çelikok, Eva M. Navarro-López, Neslihan Serap Şengör, Rahmi Elibol, Mustafa Yasir Özdemir, Tianyi Li, Angelo Arleo, Denis Sheynikhovich, Akihiro Nakamura, Masanori Shimono, Youngjo Song, Sol Park, Ilhwan Choi, Jaeseung Jeong, Hee-Sup Shin, Sadra Sadeh, Padraig Gleeson, R. Angus Silver, Alexandra Pierri Chatzikalymniou. 26th Annual Computational Neuroscience Meeting (CNS*2017): Part 3. BMC Neuroscience. 2017; 18 (1):1-82.

Chicago/Turabian Style

Adam J. H. Newton; Alexandra H. Seidenstein; Robert A. McDougal; Alberto Pérez-Cervera; Gemma Huguet; Tere M-Seara; Caroline Haimerl; David Angulo-Garcia; Alessandro Torcini; Rosa Cossart; Arnaud Malvache; Kaoutar Skiker; Mounir Maouene; Gianmarco Ragognetti; Letizia Lorusso; Andrea Viggiano; Angelo Marcelli; Rosa Senatore; Antonio Parziale; S. Stramaglia; M. Pellicoro; L. Angelini; Enrico Amico; H. Aerts; J. Cortés; Steven Laureys; D. Marinazzo; I. Bassez; L. Faes; Hannes Almgren; Adeel Razi; Frederik Van De Steen; Ruth Krebs; Lida Kanari; Pawel Dlotko; Martina Scolamiero; Ran Levi; Julian Shillcock; Christiaan P.J. De Kock; Kathryn Hess; Henry Markram; Cheng Ly; Gary Marsat; Tom Gillespie; Malin Sandström; Mathew Abrams; Jeffrey S. Grethe; Maryann Martone; Robin De Gernier; Sergio Solinas; Christian Rössert; Marc Haelterman; Serge Massar; Valentina Pasquale; Vito Paolo Pastore; Sergio Martinoia; Paolo Massobrio; Cristiano Capone; Núria Tort-Colet; Maria V. Sanchez-Vives; Maurizio Mattia; Ali Almasi; Shaun L. Cloherty; David B. Grayden; Yan T. Wong; Michael R. Ibbotson; Hamish Meffin; Luke Y. Prince; Krasimira Tsaneva-Atanasova; Jack R. Mellor; Alberto Mazzoni; Manuela Rosa; Jacopo Carpaneto; Luigi M. Romito; Alberto Priori; Silvestro Micera; Rosanna Migliore; Carmen Alina Lupascu; Francesco Franchina; Luca Leonardo Bologna; Armando Romani; Sára Saray; Werner Van Geit; Szabolcs Káli; Alex Thomson; Audrey Mercer; Sigrun Lange; Joanne Falck; Eilif Muller; Felix Schürmann; Dmitrii Todorov; Robert Capps; William Barnett; Yaroslav Molkov; Federico Devalle; Diego Pazó; Ernest Montbrió; Gabriela Mochol; Habiba Azab; Benjamin Y. Hayden; Rubén Moreno-Bote; Pragathi Priyadharsini Balasubramani; Srinivasa V. Chakravarthy; Vignayanandam R. Muddapu; Medorian D. Gheorghiu; Bartul Mimica; Jonathan Withlock; Raul C. Mureșan; Jennifer L. Zick; Kelsey Schultz; Rachael K. Blackman; Matthew V. Chafee; Theoden I. Netoff; Nicholas Roberts; Vivek Nagaraj; Andrew Lamperski; Logan L. Grado; Matthew D. Johnson; David P. Darrow; Davide Lonardoni; Hayder Amin; Stefano Di Marco; Alessandro Maccione; Luca Berdondini; Thierry Nieus; Marcel Stimberg; Dan F. M. Goodman; Thomas Nowotny; Veronika Koren; Valentin Dragoi; Klaus Obermayer; Samy Castro; Mariano Fernandez; Wael El-Deredy; Kesheng Xu; Jean Paul Maidana; Patricio Orio; Weiliang Chen; Iain Hepburn; Francesco Casalegno; Adrien Devresse; Aleksandr Ovcharenko; Fernando Pereira; Fabien Delalondre; Erik De Schutter; Peter Bratby; Andrew R. Gallimore; Guido Klingbeil; Criseida Zamora; Yunliang Zang; Patrick Crotty; Eric Palmerduca; Alberto Antonietti; Claudia Casellato; Csaba Erö; Egidio D’Angelo; Marc-Oliver Gewaltig; Alessandra Pedrocchi; Ilja Bytschok; Dominik Dold; Johannes Schemmel; Karlheinz Meier; Mihai A. Petrovici; Hui-An Shen; Simone Carlo Surace; Jean-Pascal Pfister; Baptiste Lefebvre; Olivier Marre; Pierre Yger; Athanasia Papoutsi; Jiyoung Park; Ryan Ash; Stelios Smirnakis; Panayiota Poirazi; Richard A. Felix; Alexander G. Dimitrov; Christine Portfors; Silvia Daun; Tibor I. Toth; Joanna Jędrzejewska-Szmek; Nadine Kabbani; Kim T. Blackwel; Bahar Moezzi; Natalie Schaworonkow; Lukas Plogmacher; Mitchell R. Goldsworthy; Brenton Hordacre; Mark McDonnell; Nicolangelo Iannella; Michael C. Ridding; Jochen Triesch; Reinoud Maex; Karen Safaryan; Volker Steuber; Rongxiang Tang; Yi-Yuan Tang; Darya V. Verveyko; Alexey R. Brazhe; Andrey Yu Verisokin; Dmitry E. Postnov; Cengiz Günay; Gabriella Panuccio; Michele Giugliano; Astrid A. Prinz; Pablo Varona; Mikhail I. Rabinovich; Jack Denham; Thomas Ranner; Netta Cohen; Maria Reva; Nelson Rebola; Tekla Kirizs; Zoltan Nusser; David DiGregorio; Eirini Mavritsaki; Panos Rentzelas; Nikul H. Ukani; Adam Tomkins; Chung-Heng Yeh; Wesley Bruning; Allison L. Fenichel; Yiyin Zhou; Yu-Chi Huang; Dorian Florescu; Carlos Luna Ortiz; Paul Richmond; Chung-Chuan Lo; Daniel Coca; Ann-Shyn Chiang; Aurel A. Lazar; Jennifer L. Creaser; Congping Lin; Peter Ashwin; Jonathan T. Brown; Thomas Ridler; Daniel Levenstein; Brendon O. Watson; György Buzsáki; John Rinzel; Rodica Curtu; Anh Nguyen; Sahand Assadzadeh; Peter A. Robinson; Paula Sanz-Leon; Caroline G. Forlim; Lírio O. B. De Almeida; Reynaldo D. Pinto; Francisco De Borja Rodriguez; Ángel Lareo; Aaron Montero; Thiago Mosqueiro; Ramon Huerta; Vinicio Changoluisa; Vinícius L. Cordeiro; César C. Ceballos; Nilton L. Kamiji; Antonio Roque; William W. Lytton; Andrew Knox; Joshua J. C. Rosenthal; Svitlana Popovych; Liqing Liu; Bin A. Wang; Christian Grefkes; Gereon R. Fink; Nils Rosjat; Abraham Perez-Trujillo; Andres Espinal; Marco A. Sotelo-Figueroa; Ivan Cruz-Aceves; Horacio Rostro-Gonzalez; Martin Zapotocky; Martina Hoskovcová; Jana Kopecká; Olga Ulmanová; Evžen Růžička; Matthias Gärtner; Sevil Duvarci; Jochen Roeper; Gaby Schneider; Stefan Albert; Katharina Schmack; Michiel Remme; Susanne Schreiber; Michele Migliore; Stefano M. Antonel; Jean-Denis Courcol; Sami Utku Çelikok; Eva M. Navarro-López; Neslihan Serap Şengör; Rahmi Elibol; Mustafa Yasir Özdemir; Tianyi Li; Angelo Arleo; Denis Sheynikhovich; Akihiro Nakamura; Masanori Shimono; Youngjo Song; Sol Park; Ilhwan Choi; Jaeseung Jeong; Hee-Sup Shin; Sadra Sadeh; Padraig Gleeson; R. Angus Silver; Alexandra Pierri Chatzikalymniou. 2017. "26th Annual Computational Neuroscience Meeting (CNS*2017): Part 3." BMC Neuroscience 18, no. 1: 1-82.

Journal article
Published: 08 May 2017 in Scientific Reports
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Neurons in the intact brain receive a continuous and irregular synaptic bombardment from excitatory and inhibitory pre- synaptic neurons, which determines the firing activity of the stimulated neuron. In order to investigate the influence of inhibitory stimulation on the firing time statistics, we consider Leaky Integrate-and-Fire neurons subject to inhibitory instantaneous post- synaptic potentials. In particular, we report exact results for the firing rate, the coefficient of variation and the spike train spectrum for various synaptic weight distributions. Our results are not limited to stimulations of infinitesimal amplitude, but they apply as well to finite amplitude post-synaptic potentials, thus being able to capture the effect of rare and large spikes. The developed methods are able to reproduce also the average firing properties of heterogeneous neuronal populations.

ACS Style

Simona Olmi; David Angulo Garcia; Alberto Imparato; Alessandro Torcini. Exact firing time statistics of neurons driven by discrete inhibitory noise. Scientific Reports 2017, 7, 1577 .

AMA Style

Simona Olmi, David Angulo Garcia, Alberto Imparato, Alessandro Torcini. Exact firing time statistics of neurons driven by discrete inhibitory noise. Scientific Reports. 2017; 7 (1):1577.

Chicago/Turabian Style

Simona Olmi; David Angulo Garcia; Alberto Imparato; Alessandro Torcini. 2017. "Exact firing time statistics of neurons driven by discrete inhibitory noise." Scientific Reports 7, no. 1: 1577.

Paper
Published: 01 May 2017 in New Journal of Physics
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Inhibition is a key aspect of neural dynamics playing a fundamental role for the emergence of neural rhythms and the implementation of various information coding strategies. Inhibitory populations are present in several brain structures, and the comprehension of their dynamics is strategical for the understanding of neural processing. In this paper, we clarify the mechanisms underlying a general phenomenon present in pulse-coupled heterogeneous inhibitory networks: inhibition can induce not only suppression of neural activity, as expected, but can also promote neural re-activation. In particular, for globally coupled systems, the number of firing neurons monotonically reduces upon increasing the strength of inhibition (neuronal death). However, the random pruning of connections is able to reverse the action of inhibition, i.e. in a random sparse network a sufficiently strong synaptic strength can surprisingly promote, rather than depress, the activity of neurons (neuronal rebirth). Thus, the number of firing neurons reaches a minimum value at some intermediate synaptic strength. We show that this minimum signals a transition from a regime dominated by neurons with a higher firing activity to a phase where all neurons are effectively sub-threshold and their irregular firing is driven by current fluctuations. We explain the origin of the transition by deriving a mean field formulation of the problem able to provide the fraction of active neurons as well as the first two moments of their firing statistics. The introduction of a synaptic time scale does not modify the main aspects of the reported phenomenon. However, for sufficiently slow synapses the transition becomes dramatic, and the system passes from a perfectly regular evolution to irregular bursting dynamics. In this latter regime the model provides predictions consistent with experimental findings for a specific class of neurons, namely the medium spiny neurons in the striatum.

ACS Style

David Angulo-Garcia; Stefano Luccioli; Simona Olmi; Alessandro Torcini. Death and rebirth of neural activity in sparse inhibitory networks. New Journal of Physics 2017, 19, 053011 .

AMA Style

David Angulo-Garcia, Stefano Luccioli, Simona Olmi, Alessandro Torcini. Death and rebirth of neural activity in sparse inhibitory networks. New Journal of Physics. 2017; 19 (5):053011.

Chicago/Turabian Style

David Angulo-Garcia; Stefano Luccioli; Simona Olmi; Alessandro Torcini. 2017. "Death and rebirth of neural activity in sparse inhibitory networks." New Journal of Physics 19, no. 5: 053011.

Journal article
Published: 14 March 2017
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Neurons in the intact brain receive a continuous and irregular synaptic bombardment from excitatory and inhibitory presynaptic neurons, which determines the firing activity of the stimulated neuron. In orderto investigate the influence of inhibitory stimulation on the firing time statistics, we consider Leaky Integrate-and-Fire neurons subject to inhibitory instantaneous postsynaptic potentials. In particular, we report exact results for the firing rate, the coefficient of variation and the spike train spectrum for various synaptic weight distributions. Our results are not limited to stimulations of infinitesimal amplitude, but they apply as well to finite amplitude post-synaptic potentials, thus being able to capture the effect of rare and large spikes. The developed methods are able to reproduce also the average firing properties of heterogeneous neuronal populations.

ACS Style

Simona Olmi; David Angulo-Garcia; Alberto Imparato; Alessandro Torcini. Exact firing time statistics of neurons driven by discrete inhibitory noise. 2017, 116467 .

AMA Style

Simona Olmi, David Angulo-Garcia, Alberto Imparato, Alessandro Torcini. Exact firing time statistics of neurons driven by discrete inhibitory noise. . 2017; ():116467.

Chicago/Turabian Style

Simona Olmi; David Angulo-Garcia; Alberto Imparato; Alessandro Torcini. 2017. "Exact firing time statistics of neurons driven by discrete inhibitory noise." , no. : 116467.