| 1. |
- Brette, Romain, et al.
(författare)
-
Simulation of networks of spiking neurons : A review of tools and strategies
- 2007
-
Ingår i: Journal of Computational Neuroscience. - : Springer Science and Business Media LLC. - 0929-5313 .- 1573-6873. ; 23:3, s. 349-398
-
Forskningsöversikt (refereegranskat)abstract
- 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.
(författare)
-
Multirate method for co-simulation of electrical-chemical systems in multiscale modeling
- 2017
-
Ingår i: Journal of Computational Neuroscience. - : Springer-Verlag New York. - 0929-5313 .- 1573-6873. ; 42:3, s. 245-256
-
Tidskriftsartikel (refereegranskat)abstract
- 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. |
- Chrysanthidis, Nikolaos, et al.
(författare)
-
Introducing double bouquet cells into a modular cortical associative memory model
- 2019
-
Ingår i: Journal of Computational Neuroscience. - : Springer. - 0929-5313 .- 1573-6873. ; 47:2-3, s. 223-230
-
Tidskriftsartikel (refereegranskat)abstract
- We present an electrophysiological model of double bouquet cells and integrate them into an established cortical columnar microcircuit model that has previously been used as a spiking attractor model for memory. Learning in that model relies on a Hebbian-Bayesian learning rule to condition recurrent connectivity between pyramidal cells. We here demonstrate that the inclusion of a biophysically plausible double bouquet cell model can solve earlier concerns about learning rules that simultaneously learn excitation and inhibition and might thus violate Dale's principle. We show that learning ability and resulting effective connectivity between functional columns of previous network models is preserved when pyramidal synapses onto double bouquet cells are plastic under the same Hebbian-Bayesian learning rule. The proposed architecture draws on experimental evidence on double bouquet cells and effectively solves the problem of duplexed learning of inhibition and excitation by replacing recurrent inhibition between pyramidal cells in functional columns of different stimulus selectivity with a plastic disynaptic pathway. We thus show that the resulting change to the microcircuit architecture improves the model's biological plausibility without otherwise impacting the model's spiking activity, basic operation, and learning abilities.
|
|
| 4. |
|
|
| 5. |
- Cooray, CN, et al.
(författare)
-
Noise induced quiescence of epileptic spike generation in patients with epilepsy
- 2021
-
Ingår i: Journal of computational neuroscience. - : Springer Science and Business Media LLC. - 1573-6873 .- 0929-5313. ; 49:1, s. 57-67
-
Tidskriftsartikel (refereegranskat)abstract
- Clinical scalp electroencephalographic recordings from patients with epilepsy are distinguished by the presence of epileptic discharges i.e. spikes or sharp waves. These often occur randomly on a background of fluctuating potentials. The spike rate varies between different brain states (sleep and awake) and patients. Epileptogenic tissue and regions near these often show increased spike rates in comparison to other cortical regions. Several studies have shown a relation between spike rate and background activity although the underlying reason for this is still poorly understood. Both these processes, spike occurrence and background activity show evidence of being at least partly stochastic processes. In this study we show that epileptic discharges seen on scalp electroencephalographic recordings and background activity are driven at least partly by a common biological noise. Furthermore, our results indicate noise induced quiescence of spike generation which, in analogy with computational models of spiking, indicate spikes to be generated by transitions between semi-stable states of the brain, similar to the generation of epileptic seizure activity. The deepened physiological understanding of spike generation in epilepsy that this study provides could be useful in the electrophysiological assessment of different therapies for epilepsy including the effect of different drugs or electrical stimulation.
|
|
| 6. |
- Diehl, Stefan, et al.
(författare)
-
Efficient simulations of tubulin-driven axonal growth
- 2016
-
Ingår i: Journal of Computational Neuroscience. - : Springer Science and Business Media LLC. - 1573-6873 .- 0929-5313. ; 41:1, s. 45-63
-
Tidskriftsartikel (refereegranskat)abstract
- This work concerns efficient and reliable numerical simulations of the dynamic behaviour of a moving-boundary model for tubulin-driven axonal growth. The model is nonlinear and consists of a coupled set of a partial differential equation (PDE) and two ordinary differential equations. The PDE is defined on a computational domain with a moving boundary, which is part of the solution. Numerical simulations based on standard explicit time-stepping methods are too time consuming due to the small time steps required for numerical stability. On the other hand standard implicit schemes are too complex due to the nonlinear equations that needs to be solved in each step. Instead, we propose to use the Peaceman–Rachford splitting scheme combined with temporal and spatial scalings of the model. Simulations based on this scheme have shown to be efficient, accurate, and reliable which makes it possible to evaluate the model, e.g. its dependency on biological and physical model parameters. These evaluations show among other things that the initial axon growth is very fast, that the active transport is the dominant reason over diffusion for the growth velocity, and that the polymerization rate in the growth cone does not affect the final axon length.
|
|
| 7. |
- Halnes, Geir, et al.
(författare)
-
Modelling and sensitivity analysis of the reactions involving receptor, G-protein and effector in vertebrate olfactory receptor neurons
- 2009
-
Ingår i: Journal of Computational Neuroscience. - : Springer Science and Business Media LLC. - 0929-5313 .- 1573-6873. ; 27:3, s. 471-491
-
Tidskriftsartikel (refereegranskat)abstract
- 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.
|
|
| 8. |
- Hammarlund, Per, et al.
(författare)
-
Large neural network simulations on multiple hardware platforms
- 1998
-
Ingår i: Journal of Computational Neuroscience. - 0929-5313 .- 1573-6873. ; 5:4, s. 443-459
-
Tidskriftsartikel (refereegranskat)abstract
- 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.
|
|
| 9. |
- Huss, Mikael, et al.
(författare)
-
An experimentally constrained computational model of NMDA oscillations in lamprey CPG neurons
- 2008
-
Ingår i: Journal of Computational Neuroscience. - : Springer Science and Business Media LLC. - 0929-5313 .- 1573-6873. ; 15:1, s. 108-121
-
Tidskriftsartikel (övrigt vetenskapligt/konstnärligt)abstract
- 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.
|
|
| 10. |
- Kozlov, Alexander, et al.
(författare)
-
Modeling of substance P and 5-HT induced synaptic plasticity in the lamprey spinal CPG : Consequences for network pattern generation
- 2001
-
Ingår i: Journal of Computational Neuroscience. - 0929-5313 .- 1573-6873. ; 11:2, s. 183-200
-
Tidskriftsartikel (refereegranskat)abstract
- 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.
|
|
