You signed in with another tab or window. Reload to refresh your session.You signed out in another tab or window. Reload to refresh your session.You switched accounts on another tab or window. Reload to refresh your session.Dismiss alert
{{ message }}
This repository was archived by the owner on Feb 26, 2025. It is now read-only.
title = {A {{Software Framework}} for {{Validating Neuroscience Models}}},
31
-
author = {Appukuttan, Shailesh and Sharma, Lungsi and {Garcia-Rodriguez}, Pedro and Davison, Andrew},
32
-
langid = {english}
33
-
}
34
-
35
-
@article{awileModernizingNEURONSimulator,
29
+
@article{awileModernizingNEURONSimulator2022a,
36
30
title = {Modernizing the {{NEURON Simulator}} for {{Sustainability}}, {{Portability}}, and {{Performance}}},
37
-
author = {Awile, Omar and Kumbhar, Pramod and Cornu, Nicolas and {Dura-Bernal}, Salvador and King, James Gonzalo and Lupton, Olli and Magkanaris, Ioannis and McDougal, Robert A. and Newton, Adam J.H. and Pereira, Fernando and S{\u a}vulescu, Alexandru and Carnevale, Nicholas T. and Lytton, William W. and Hines, Michael L. and Sch{\"u}rmann, Felix},
31
+
author = {Awile, Omar and Kumbhar, Pramod and Cornu, Nicolas and {Dura-Bernal}, Salvador and King, James Gonzalo and Lupton, Olli and Magkanaris, Ioannis and McDougal, Robert A. and Newton, Adam J. H. and Pereira, Fernando and S{\u a}vulescu, Alexandru and Carnevale, Nicholas T. and Lytton, William W. and Hines, Michael L. and Sch{\"u}rmann, Felix},
32
+
year = {2022},
33
+
month = jun,
34
+
journal = {Frontiers in Neuroinformatics},
35
+
volume = {16},
36
+
pages = {884046},
37
+
issn = {1662-5196},
38
+
doi = {10.3389/fninf.2022.884046},
38
39
abstract = {The need for reproducible, credible, multiscale biological modeling has led to the development of standardized simulation platforms, such as the widely-used NEURON environment for computational neuroscience. Developing and maintaining NEURON over several decades has required attention to the competing needs of backwards compatibility, evolving computer architectures, the addition of new scales and physical processes, accessibility to new users, and efficiency and flexibility for specialists. In order to meet these challenges, we have now substantially modernized NEURON, providing continuous integration, an improved build system and release workflow, and better documentation. With the help of a new source-to-source compiler of the NMODL domain-specific language we have enhanced NEURON's ability to run efficiently, via the CoreNEURON simulation engine, on a variety of hardware platforms, including GPUs. Through the implementation of an optimized in-memory transfer mechanism this performance optimized backend is made easily accessible to users, providing training and model-development paths from laptop to workstation to supercomputer and cloud platform. Similarly, we have been able to accelerate NEURON's reaction-diffusion simulation performance through the use of just-in-time compilation. We show that these efforts have led to a growing developer base, a simpler and more robust software distribution, a wider range of supported computer architectures, a better integration of NEURON with other scientific workflows, and substantially improved performance for the simulation of biophysical and biochemical models.},
title = {Pareto Optimality, Economy\textendash Effectiveness Trade-Offs and Ion Channel Degeneracy: Improving Population Modelling for Single Neurons},
300
-
author = {Jedlicka, Peter and Bird, Alexander D and Cuntz, Hermann},
title = {Parameter {{Optimization Using Covariance Matrix Adaptation}}\textemdash{{Evolutionary Strategy}} ({{CMA-ES}}), an {{Approach}} to {{Investigate Differences}} in {{Channel Properties Between Neuron Subtypes}}},
307
301
author = {{J{\c e}drzejewski-Szmek}, Zbigniew and Abrahao, Karina P. and {J{\c e}drzejewska-Szmek}, Joanna and Lovinger, David M. and Blackwell, Kim T.},
title = {Likelihood-Free Inference with Emulator Networks},
380
-
author = {Lueckmann, Jan-Matthis and Bassetto, Giacomo and Karaletsos, Theofanis and Macke, Jakob H},
381
-
abstract = {Approximate Bayesian Computation (ABC) provides methods for Bayesian inference in simulation-based models which do not permit tractable likelihoods. We present a new ABC method which uses probabilistic neural emulator networks to learn synthetic likelihoods on simulated data \textendash{} both `local' emulators which approximate the likelihood for specific observed data, as well as `global' ones which are applicable to a range of data. Simulations are chosen adaptively using an acquisition function which takes into account uncertainty about either the posterior distribution of interest, or the parameters of the emulator. Our approach does not rely on user-defined rejection thresholds or distance functions. We illustrate inference with emulator networks on synthetic examples and on a biophysical neuron model, and show that emulators allow accurate and efficient inference even on problems which are challenging for conventional ABC approaches.},
382
-
langid = {english}
383
-
}
384
-
385
372
@article{maki-marttunenStepwiseNeuronModel2018,
386
373
title = {A Stepwise Neuron Model Fitting Procedure Designed for Recordings with High Spatial Resolution: {{Application}} to Layer 5 Pyramidal Cells},
387
374
shorttitle = {A Stepwise Neuron Model Fitting Procedure Designed for Recordings with High Spatial Resolution},
abstract = { Neurons encounter unavoidable evolutionary trade-offs between multiple tasks. They must consume as little energy as possible while effectively fulfilling their functions. Cells displaying the best performance for such multi-task trade-offs are said to be Pareto optimal, with their ion channel configurations underpinning their functionality. Ion channel degeneracy, however, implies that multiple ion channel configurations can lead to functionally similar behaviour. Therefore, instead of a single model, neuroscientists often use populations of models with distinct combinations of ionic conductances. This approach is called population (database or ensemble) modelling. It remains unclear, which ion channel parameters in the vast population of functional models are more likely to be found in the brain. Here we argue that Pareto optimality can serve as a guiding principle for addressing this issue by helping to identify the subpopulations of conductance-based models that perform best for the trade-off between economy and functionality. In this way, the high-dimensional parameter space of neuronal models might be reduced to geometrically simple low-dimensional manifolds, potentially explaining experimentally observed ion channel correlations. Conversely, Pareto inference might also help deduce neuronal functions from high-dimensional Patch-seq data. In summary, Pareto optimality is a promising framework for improving population modelling of neurons and their circuits. }
title = {Flexible Statistical Inference for Mechanistic Models of Neural Dynamics},
345
-
author = {Lueckmann, Jan-Matthis and Goncalves, Pedro J and Bassetto, Giacomo and {\"O}cal, Kaan and Nonnenmacher, Marcel and Macke, Jakob H},
346
-
abstract = {Mechanistic models of single-neuron dynamics have been extensively studied in computational neuroscience. However, identifying which models can quantitatively reproduce empirically measured data has been challenging. We propose to overcome this limitation by using likelihood-free inference approaches (also known as Approximate Bayesian Computation, ABC) to perform full Bayesian inference on single-neuron models. Our approach builds on recent advances in ABC by learning a neural network which maps features of the observed data to the posterior distribution over parameters. We learn a Bayesian mixture-density network approximating the posterior over multiple rounds of adaptively chosen simulations. Furthermore, we propose an efficient approach for handling missing features and parameter settings for which the simulator fails, as well as a strategy for automatically learning relevant features using recurrent neural networks. On synthetic data, our approach efficiently estimates posterior distributions and recovers ground-truth parameters. On in-vitro recordings of membrane voltages, we recover multivariate posteriors over biophysical parameters, which yield model-predicted voltage traces that accurately match empirical data. Our approach will enable neuroscientists to perform Bayesian inference on complex neuron models without having to design model-specific algorithms, closing the gap between mechanistic and statistical approaches to single-neuron modelling.},
title = {Efficient {{Low-Pass Dendro-Somatic Coupling}} in the {{Apical Dendrite}} of {{Layer}} 5 {{Pyramidal Neurons}} in the {{Anterior Cingulate Cortex}}},
352
345
author = {Marti Mengual, Ulisses and Wybo, Willem A.M. and Spierenburg, Lotte J.E. and Santello, Mirko and Senn, Walter and Nevian, Thomas},
title = {Single-Neuron Models Linking Electrophysiology, Morphology and Transcriptomics across Cortical Cell Types},
470
-
author = {Nandi, Anirban and Chartrand, Tom and Geit, Werner Van and Buchin, Anatoly and Yao, Zizhen and Lee, Soo Yeun and Wei, Yina and Kalmbach, Brian and Lee, Brian and Lein, Ed and Berg, Jim and S{\"u}mb{\"u}l, Uygar and Koch, Christof and Anastassiou, Costas A},
471
-
abstract = {Identifying the cell types constituting brain circuits is a fundamental question in neuroscience and motivates the generation of taxonomies based on electrophysiological, morphological and molecular single cell properties. Establishing the correspondence across data modalities and understanding the underlying principles has proven challenging. Bio-realistic computational models offer the ability to probe cause-and-effect and have historically been used to explore phenomena at the single-neuron level. Here we introduce a computational optimization workflow used for the generation and evaluation of more than 130 million single neuron models with active conductances. These models were based on 230 in vitro electrophysiological experiments followed by morphological reconstruction from the mouse visual cortex. We show that distinct ion channel conductance vectors exist that distinguish between major cortical classes with passive and h-channel conductances emerging as particularly important for classification. Next, using models of genetically defined classes, we show that differences in specific conductances predicted from the models reflect differences in gene expression in excitatory and inhibitory cell types as experimentally validated by single-cell RNA-sequencing. The differences in these conductances, in turn, explain many of the electrophysiological differences observed between cell types. Finally, we show the robustness of the herein generated single-cell models as representations and realizations of specific cell types in face of biological variability and optimization complexity. Our computational effort generated models that reconcile major single-cell data modalities that define cell types allowing for causal relationships to be examined.},
title = {Transient, {{Consequential Increases}} in {{Extracellular Potassium Ions Accompany Channelrhodopsin2 Excitation}}},
477
463
author = {Octeau, J. Christopher and Gangwani, Mohitkumar R. and Allam, Sushmita L. and Tran, Duy and Huang, Shuhan and {Hoang-Trong}, Tuan M. and Golshani, Peyman and Rumbell, Timothy H. and Kozloski, James R. and Khakh, Baljit S.},
title = {Overexpression of {{UCP4}} in Astrocytic Mitochondria Prevents Multilevel Dysfunctions in a Mouse Model of {{Alzheimer}}'s Disease},
535
-
author = {Rosenberg, Nadia and Reva, Maria and Restivo, Leonardo and Briquet, Marc and Bernardinelli, Yann and Rocher, Anne-B{\'e}reng{\`e}re and Markram, Henry and Chatton, Jean-Yves},
536
-
abstract = {Alzheimer's disease (AD) is becoming increasingly prevalent worldwide. It represents one of the greatest medical challenge as no pharmacologic treatments are available to prevent disease progression. Astrocytes play crucial functions within neuronal circuits by providing metabolic and functional support, regulating interstitial solute composition, and modulating synaptic transmission. In addition to these physiological functions, growing evidence points to an essential role of astrocytes in neurodegenerative diseases like AD. Early stage AD is associated with hypometabolism and oxidative stress. Contrary to neurons that are vulnerable to oxydative stress, astrocytes are particularly resistant to mitochondrial dysfunction and are therefore more resilient cells. In our study, we leveraged astrocytic mitochondrial uncoupling and examined neuronal function in the 3xTg AD mouse model. We overexpressed the mitochondrial uncoupling protein 4 (UCP4), which has been shown to improve neuronal survival in vitro. We found that this treatment efficiently prevented alterations of hippocampal metabolite levels observed in AD mice, along with hippocampal atrophy and reduction of basal dendrite arborization of subicular neurons. This approach also averted aberrant neuronal excitability observed in AD subicular neurons and preserved episodic-like memory in AD mice assessed in a spatial recognition task. These findings show that targeting astrocytes and their mitochondria is an effective strategy to prevent the decline of neurons facing AD-related stress at the early stages of the disease.},
0 commit comments