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  • 1. Brette, Romain
    et al.
    Rudolph, Michelle
    Carnevale, Ted
    Hines, Michael
    Beeman, David
    Bower, James M.
    Diesmann, Markus
    Morrison, Abigail
    Goodman, Philip H.
    Harris, Frederick C., Jr.
    Zirpe, Milind
    Natschlaeger, Thomas
    Pecevski, Dejan
    Ermentrout, Bard
    Djurfeldt, Mikael
    KTH, School of Computer Science and Communication (CSC), Computational Biology, CB.
    Lansner, Anders
    KTH, School of Computer Science and Communication (CSC), Computational Biology, CB.
    Rochel, Olivier
    Vieville, Thierry
    Muller, Eilif
    Davison, Andrew P.
    El Boustani, Sami
    Destexhe, Alain
    Simulation of networks of spiking neurons: A review of tools and strategies2007In: Journal of Computational Neuroscience, ISSN 0929-5313, E-ISSN 1573-6873, Vol. 23, no 3, p. 349-398Article, review/survey (Refereed)
    Abstract [en]

    We review different aspects of the simulation of spiking neural networks. We start by reviewing the different types of simulation strategies and algorithms that are currently implemented. We next review the precision of those simulation strategies, in particular in cases where plasticity depends on the exact timing of the spikes. We overview different simulators and simulation environments presently available (restricted to those freely available, open source and documented). For each simulation tool, its advantages and pitfalls are reviewed, with an aim to allow the reader to identify which simulator is appropriate for a given task. Finally, we provide a series of benchmark simulations of different types of networks of spiking neurons, including Hodgkin-Huxley type, integrate-and-fire models, interacting with current-based or conductance-based synapses, using clock-driven or event-driven integration strategies. The same set of models are implemented on the different simulators, and the codes are made available. The ultimate goal of this review is to provide a resource to facilitate identifying the appropriate integration strategy and simulation tool to use for a given modeling problem related to spiking neural networks.

  • 2.
    Brocke, Ekaterina
    et al.
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Djurfeldt, M.
    Bhalla, U. S.
    Hellgren Kotaleski, Jeanette
    KTH, School of Computer Science and Communication (CSC), Computational Science and Technology (CST).
    Hanke, Michael
    KTH, School of Engineering Sciences (SCI), Mathematics (Dept.), Numerical Analysis, NA.
    Multirate method for co-simulation of electrical-chemical systems in multiscale modeling2017In: Journal of Computational Neuroscience, ISSN 0929-5313, E-ISSN 1573-6873, Vol. 42, no 3, p. 245-256Article in journal (Refereed)
    Abstract [en]

    Multiscale modeling by means of co-simulation is a powerful tool to address many vital questions in neuroscience. It can for example be applied in the study of the process of learning and memory formation in the brain. At the same time the co-simulation technique makes it possible to take advantage of interoperability between existing tools and multi-physics models as well as distributed computing. However, the theoretical basis for multiscale modeling is not sufficiently understood. There is, for example, a need of efficient and accurate numerical methods for time integration. When time constants of model components are different by several orders of magnitude, individual dynamics and mathematical definitions of each component all together impose stability, accuracy and efficiency challenges for the time integrator. Following our numerical investigations in Brocke et al. (Frontiers in Computational Neuroscience, 10, 97, 2016), we present a new multirate algorithm that allows us to handle each component of a large system with a step size appropriate to its time scale. We take care of error estimates in a recursive manner allowing individual components to follow their discretization time course while keeping numerical error within acceptable bounds. The method is developed with an ultimate goal of minimizing the communication between the components. Thus it is especially suitable for co-simulations. Our preliminary results support our confidence that the multirate approach can be used in the class of problems we are interested in. We show that the dynamics ofa communication signal as well as an appropriate choice of the discretization order between system components may have a significant impact on the accuracy of the coupled simulation. Although, the ideas presented in the paper have only been tested on a single model, it is likely that they can be applied to other problems without loss of generality. We believe that this work may significantly contribute to the establishment of a firm theoretical basis and to the development of an efficient computational framework for multiscale modeling and simulations.

  • 3.
    Halnes, Geir
    et al.
    KTH, School of Computer Science and Communication (CSC), Computational Biology, CB.
    Ulfhielm, Erik
    KTH, School of Computer Science and Communication (CSC), Computational Biology, CB.
    Ljunggren, Emma Eklöf
    KTH, School of Computer Science and Communication (CSC), Computational Biology, CB.
    Hellgren Kotaleski, Jeanette
    KTH, School of Computer Science and Communication (CSC), Computational Biology, CB.
    Rospars, Jean-Pierre
    Modelling and sensitivity analysis of the reactions involving receptor, G-protein and effector in vertebrate olfactory receptor neurons2009In: Journal of Computational Neuroscience, ISSN 0929-5313, E-ISSN 1573-6873, Vol. 27, no 3, p. 471-491Article in journal (Refereed)
    Abstract [en]

    A biochemical model of the receptor, G-protein and effector (RGE) interactions during transduction in the cilia of vertebrate olfactory receptor neurons (ORNs) was developed and calibrated to experimental recordings of cAMP levels and the receptor current (RC). The model describes the steps from odorant binding to activation of the effector enzyme which catalyzes the conversion of ATP to cAMP, and shows how odorant stimulation is amplified and delayed by the RGE transduction cascade. A time-dependent sensitivity analysis was performed on the model. The model output-the cAMP production rate-is particularly sensitive to a few, dominant parameters. During odorant stimulation it depends mainly on the initial density of G-proteins and the catalytic constant for cAMP production.

  • 4.
    Hammarlund, Per
    et al.
    KTH, Superseded Departments, Numerical Analysis and Computer Science, NADA.
    Ekeberg, Örjan
    KTH, Superseded Departments, Numerical Analysis and Computer Science, NADA.
    Large neural network simulations on multiple hardware platforms1998In: Journal of Computational Neuroscience, ISSN 0929-5313, E-ISSN 1573-6873, Vol. 5, no 4, p. 443-459Article in journal (Refereed)
    Abstract [en]

    To efficiently simulate very large networks of interconnected neurons, particular consideration has to be given to the computer architecture being used. This article presents techniques for implementing simulators for large neural networks on a number of different computer architectures. The neuronal simulation task and the computer architectures of interest are first characterized, and the potential bottlenecks are highlighted. Then we describe the experience gained from adapting an existing simulator, sWIM, to two very different architectures-vector computers and multiprocessor workstations. This work lead to the implementation of a new simulation library, SPLIT, designed to allow efficient simulation of large networks on several architectures. Different computer architectures put different demands on the organization of both data structures and computations. Strict separation of such architecture considerations from the neuronal models and other simulation aspects makes it possible to construct both portable and extendible code.

  • 5.
    Huss, Mikael
    et al.
    KTH, School of Computer Science and Communication (CSC), Numerical Analysis and Computer Science, NADA.
    Wang, Di
    Department of Neuroscience, Nobel Institute for Neurophysiology, Karolinska Institutet.
    Trané, Camilla
    KTH, School of Electrical Engineering (EES).
    Wikström, Martin
    Department of Neuroscience, Nobel Institute for Neurophysiology, Karolinska Institutet.
    Hellgren Kotaleski, Jeanette
    KTH, School of Electrical Engineering (EES). KTH, School of Computer Science and Communication (CSC), Numerical Analysis and Computer Science, NADA.
    An experimentally constrained computational model of NMDA oscillations in lamprey CPG neurons2008In: Journal of Computational Neuroscience, ISSN 0929-5313, E-ISSN 1573-6873, Vol. 15, no 1, p. 108-121Article in journal (Other academic)
    Abstract [en]

    Rhythmicity is a characteristic of neural networks responsible for locomotion. In many organisms, activation of N-methyl-D-aspartate (NMDA) receptors leads to generation of rhythmic locomotor patterns. In addition, single neurons can display intrinsic, NMDA-dependent membrane potential oscillations when pharmacologically isolated from each other by tetrodotoxin (TTX) application. Such NMDA-TTX oscillations have been characterized, for instance, in lamprey locomotor network neurons. Conceptual and computational models have been put forward to explain the appearance and characteristics of these oscillations. Here, we seek to refine the understanding of NMDA-TTX oscillations by combining new experimental evidence with computational modelling. We find that, in contrast to previous computational predictions, the oscillation frequency tends to increase when the NMDA concentration is increased. We develop a new, minimal computational model which can incorporate this new information. This model is further constrained by another new piece of experimental evidence: that regular-looking NMDA-TTX oscillations can be obtained even after voltage-dependent potassium and high-voltage-activated calcium channels have been pharmacologically blocked. Our model conforms to several experimentally derived criteria that we have set up and is robust to parameter changes, as evaluated through sensitivity analysis. We use the model to re-analyze an old NMDA-TTX oscillation model, and suggest an explanation of why it failed to reproduce the new experimental data that we present here.

  • 6.
    Kozlov, Alexander
    et al.
    KTH, School of Computer Science and Communication (CSC), Computational Biology, CB.
    Hellgren Kotaleski, Jeanette
    KTH, School of Computer Science and Communication (CSC), Computational Biology, CB.
    Aurell, Erik
    KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre. KTH, School of Computer Science and Communication (CSC), Computational Biology, CB.
    Grillner, S
    Lansner, A
    Modeling of substance P and 5-HT induced synaptic plasticity in the lamprey spinal CPG - consequences for network pattern generation2001In: Journal of Computational Neuroscience, ISSN 0929-5313, E-ISSN 1573-6873, Vol. 11, no 2, p. 183-200Article in journal (Refereed)
    Abstract [en]

    Consequences of synaptic plasticity in the lamprey spinal CPG are analyzed by means of simulations. This is motivated by the effects substance P (a tachykinin) and serotonin (5-hydroxytryptamin; 5-HT) have on synaptic transmission in the locomotor network. Activity-dependent synaptic depression and potentiation have recently been shown experimentally using paired intracellular recordings. Although normally activity-dependent plasticity presumably does not contribute to the patterning of network activity, this changes in the presence of the neuromodulators substance P and 5-HT, which evoke significant plasticity. Substance P can induce a faster and larger depression of inhibitory connections but potentiation of excitatory inputs, whereas 5-HT induces facilitation of both inhibitory and excitatory inputs. Changes in the amplitude of the first postsynaptic potential are also seen. These changes could thus be a potential mechanism underlying the modulatory role these substances have on the rhythmic network activity. The aim of the present study has been to implement the activity dependent synaptic depression and facilitation induced by substance P and 5-HT into two alternative models of the lamprey spinal locomotor network, one relying on reciprocal inhibition for bursting and one in which each hemicord is capable of oscillations. The consequences of the plasticity of inhibitory and excitatory connections are then explored on the network level. In the intact spinal cord, tachykinins and 5-HT, which can be endogenously released, increase and decrease the frequency of the alternating left-right burst pattern, respectively. The frequency decreasing effect of 5-HT has previously been explained based on its conductance decreasing effect on K(Ca) underlying the postspike afterhyperpolarization (AHP). The present simulations show that short-term synaptic plasticity may have strong effects on frequency regulation in the lamprey spinal CPG. In the network model relying on reciprocal inhibition, the observed effects substance P and 5-HT have on network behavior (i.e., a frequency increase and decrease respectively) can to a substantial part be explained by their effects on the total extent and time dynamics of synaptic depression and facilitation. The cellular effects of these substances will in the 5-HT case further contribute to its network effect.

  • 7.
    Kozlov, Alexander
    et al.
    KTH, Superseded Departments, Numerical Analysis and Computer Science, NADA.
    Hellgren Kotaleski, Jeanette
    KTH, Superseded Departments, Numerical Analysis and Computer Science, NADA.
    Aurell, Erik
    KTH, Superseded Departments, Numerical Analysis and Computer Science, NADA.
    Grillner, S.
    Lansner, Anders
    KTH, Superseded Departments, Numerical Analysis and Computer Science, NADA.
    Modeling of substance P and 5-HT induced synaptic plasticity in the lamprey spinal CPG: Consequences for network pattern generation2001In: Journal of Computational Neuroscience, ISSN 0929-5313, E-ISSN 1573-6873, Vol. 11, no 2, p. 183-200Article in journal (Refereed)
    Abstract [en]

    Consequences of synaptic plasticity in the lamprey spinal CPG are analyzed by means of simulations. This is motivated by the effects substance P (a tachykinin) and serotonin (5-hydroxytryptamin; 5-HT) have on synaptic transmission in the locomotor network. Activity-dependent synaptic depression and potentiation have recently been shown experimentally using paired intracellular recordings. Although normally activity-dependent plasticity presumably does not contribute to the patterning of network activity, this changes in the presence of the neuromodulators substance P and 5-HT, which evoke significant plasticity. Substance P can induce a faster and larger depression of inhibitory connections but potentiation of excitatory inputs, whereas 5-HT induces facilitation of both inhibitory and excitatory inputs. Changes in the amplitude of the first postsynaptic potential are also seen. These changes could thus be a potential mechanism underlying the modulatory role these substances have on the rhythmic network activity. The aim of the present study has been to implement the activity dependent synaptic depression and facilitation induced by substance P and 5-HT into two alternative models of the lamprey spinal locomotor network, one relying on reciprocal inhibition for bursting and one in which each hemicord is capable of oscillations. The consequences of the plasticity of inhibitory and excitatory connections are then explored on the network level. In the intact spinal cord, tachykinins and 5-HT, which can be endogenously released, increase and decrease the frequency of the alternating left-right burst pattern, respectively. The frequency decreasing effect of 5-HT has previously been explained based on its conductance decreasing effect on K underlying the postspike afterhyperpolarization (AHP). The present simulations show that short-term synaptic plasticity may have strong effects on frequency regulation in the lamprey spinal CPG. In the network model relying on reciprocal inhibition, the observed effects substance P and 5-HT have on network behavior (i.e., a frequency increase and decrease respectively) can to a substantial part be explained by their effects on the total extent and time dynamics of synaptic depression and facilitation. The cellular effects of these substances will in the 5-HT case further contribute to its network effect.

  • 8.
    Lindén, Henrik
    et al.
    Norwegian University of Life Sciences.
    Pettersen, K.H.
    Einrvoll, G.T.
    Intrinsic dendritic filtering gives low-pass power spectra of local field potentials2010In: Journal of Computational Neuroscience, ISSN 0929-5313, E-ISSN 1573-6873, Vol. 29, no 3, p. 423-444Article in journal (Refereed)
    Abstract [en]

    The local field potential (LFP) is among the most important experimental measures when probing neural population activity, but a proper understanding of the link between the underlying neural activity and the LFP signal is still missing. Here we investigate this link by mathematical modeling of contributions to the LFP from a single layer-5 pyramidal neuron and a single layer-4 stellate neuron receiving synaptic input. An intrinsic dendritic low-pass filtering effect of the LFP signal, previously demonstrated for extracellular signatures of action potentials, is seen to strongly affect the LFP power spectra, even for frequencies as low as 10 Hz for the example pyramidal neuron. Further, the LFP signal is found to depend sensitively on both the recording position and the position of the synaptic input: the LFP power spectra recorded close to the active synapse are typically found to be less low-pass filtered than spectra recorded further away. Some recording positions display striking band-pass characteristics of the LFP. The frequency dependence of the properties of the current dipole moment set up by the synaptic input current is found to qualitatively account for several salient features of the observed LFP. Two approximate schemes for calculating the LFP, the dipole approximation and the two-monopole approximation, are tested and found to be potentially useful for translating results from large-scale neural network models into predictions for results from electroencephalographic (EEG) or electrocorticographic (ECoG) recordings.

  • 9.
    Rehn, Martin
    et al.
    KTH, School of Computer Science and Communication (CSC), Computational Biology, CB.
    Sommer, Friedrich T.
    A network that uses few active neurones to code visual input predicts the diverse shapes of cortical receptive fields2007In: Journal of Computational Neuroscience, ISSN 0929-5313, E-ISSN 1573-6873, Vol. 22, no 2, p. 135-146Article in journal (Refereed)
    Abstract [en]

    Computational models of primary visual cortexhave demonstrated that principles of efficient coding andneuronal sparseness can explain the emergence of neuroneswith localised oriented receptive fields. Yet, existing modelshave failed to predict the diverse shapes of receptive fieldsthat occur in nature. The existing models used a particular“soft” form of sparseness that limits average neuronal activity.Here we study models of efficient coding in a broadercontext by comparing soft and “hard” forms of neuronalsparseness.As a result of our analyses, we propose a novel networkmodel for visual cortex. Themodel forms efficient visual representationsin which the number of active neurones, ratherthan mean neuronal activity, is limited. This form of hardsparseness also economises cortical resources like synapticmemory and metabolic energy. Furthermore, our model accuratelypredicts the distribution of receptive field shapesfound in the primary visual cortex of cat and monkey.

  • 10.
    Sandström, Malin
    et al.
    KTH, School of Computer Science and Communication (CSC), Computational Biology, CB.
    Lansner, Anders
    KTH, School of Computer Science and Communication (CSC), Computational Biology, CB.
    Hellgren Kotaleski, Jeanette
    KTH, School of Computer Science and Communication (CSC), Computational Biology, CB.
    Rospars, Jean-Pierre
    Modeling the response of a population of olfactory receptor neurons to an odorant2009In: Journal of Computational Neuroscience, ISSN 0929-5313, E-ISSN 1573-6873, Vol. 27, p. 337-355Article in journal (Refereed)
    Abstract [en]

    We modeled the firing rate of populations of olfactory receptor neurons (ORNs) responding to an odorant at different concentrations. Two cases were considered: a population of ORNs that all express the same olfactory receptor (OR), and a population that expresses many different ORs. To take into account ORN variability, we replaced single parameter values in a biophysical ORN model with values drawn from statistical distributions, chosen to correspond to experimental data. For ORNs expressing the same OR, we found that the distributions of firing frequencies are Gaussian at all concentrations, with larger mean and standard deviation at higher concentrations. For a population expressing different ORs, the distribution of firing frequencies can be described as the superposition of a Gaussian distribution and a lognormal distribution. Distributions of maximum value and dynamic range of spiking frequencies in the simulated ORN population were similar to experimental results.

  • 11.
    Yim, Man Yi
    et al.
    University of Hong Kong, Hong Kong.
    Kumar, Arvind
    Bernstein Center Freiburg, University of Freiburg, Germany .
    Aertsen, Ad
    Rotter, Stefan
    Impact of correlated inputs to neurons: modeling observations from in vivo intracellular recordings2014In: Journal of Computational Neuroscience, ISSN 0929-5313, E-ISSN 1573-6873, Vol. 37, no 2, p. 293-304Article in journal (Refereed)
    Abstract [en]

    In vivo recordings in rat somatosensory cortex suggest that excitatory and inhibitory inputs are often correlated during spontaneous and sensory-evoked activity. Using a computational approach, we study how the interplay of input correlations and timing observed in experiments controls the spiking probability of single neurons. Several correlation-based mechanisms are identified, which can effectively switch a neuron on and off. In addition, we investigate the transfer of input correlation to output correlation in pairs of neurons, at the spike train and the membrane potential levels, by considering spike-driving and non-spike-driving inputs separately. In particular, we propose a plausible explanation for the in vivo finding that membrane potentials in neighboring neurons are correlated, but the spike-triggered averages of membrane potentials preceding a spike are not: Neighboring neurons possibly receive an ongoing bombardment of correlated subthreshold background inputs, and occasionally uncorrelated spike-driving inputs.

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