Data driven building of realistic neuron model using IBEA and CMA evolution strategies

Damart, Tanguy; Geit, Werner Van; Markram, Henry

doi:10.1145/3377929.3398161

Cited by 3 publications

(9 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Our finding that CMAES performs well in a variety of different tasks is supported by several other studies. In particular, CMAES and IBEA have been compared on data-driven neuronal models, and CMAES generally delivered better final scores [41]. CMAES was also found to be efficient and robust in a study that involved fitting the biophysical parameters of models of striatal neurons [42].…”

Section: Discussionmentioning

confidence: 99%

Evaluation and comparison of methods for neuronal parameter optimization using the Neuroptimus software framework

Mohasci,

Torok,

Saray

et al. 2024

Preprint

View full text Add to dashboard Cite

Finding optimal parameters for detailed neuronal models is a ubiquitous challenge in neuroscientific research. Recently, manual model tuning has been replaced by automated parameter search using a variety of different tools and methods. However, using most of these software tools and choosing the most appropriate algorithm for a given optimization task require substantial technical expertise, which prevents the majority of researchers from using these methods effectively. To address these issues, we developed a generic platform (called Neuroptimus) that allows users to set up neural parameter optimization tasks via a graphical interface, and to solve these tasks using a wide selection of state-of-the-art parameter search methods implemented by five different Python packages. Neuroptimus also offers several features to support more advanced usage, including the ability to run most algorithms in parallel, which allows it to take advantage of high-performance computing architectures. We used the common interface provided by Neuroptimus to conduct a detailed comparison of more than twenty different algorithms (and implementations) on six distinct benchmarks that represent typical scenarios in neuronal parameter search. We quantified the performance of the algorithms in terms of the best solutions found and in terms of convergence speed. We identified several algorithms, including covariance matrix adaptation evolution strategy and particle swarm optimization, that consistently found good solutions in all of our use cases. By contrast, some other algorithms including all local search methods provided good solutions only for the simplest use cases, and failed completely on more complex problems. Finally, we created an online database that allows uploading, querying and analyzing the results of optimization runs performed by Neuroptimus, which enables all researchers to update and extend the current benchmarking study. The tools and analysis we provide should aid members of the neuroscience community to apply parameter search methods more effectively in their research.

show abstract

Section: Discussionmentioning

confidence: 99%

Evaluation and comparison of methods for neuronal parameter optimization using the Neuroptimus software framework

Mohasci,

Torok,

Saray

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…The optimization was performed using the BluePyOpt python package [70] whose optimization module relies on the DEAP Python package [25]. We extended the package and implemented an hybrid CMA optimization strategy [38, 19] tasked to both: minimize the sum of the scores defined as , where and were the experimental mean and standard deviation for e-feature i , respectively, and f i was the feature value computed on the model to evaluate. maximize the hyper-volume of the Pareto front formed by the current population of models [9]. At each generation, all models in the population of size λ = 20 were ranked for both criteria, and a mixed rank was obtained following the formula …”

Section: Methodsmentioning

confidence: 99%

“…sections: the third strategy lies in between the single and all strategies. In this case, we manually selected N sections extracellular areas of several electrodes (10)(11)(12)(13)(14)(15)(16)(17)(18)(19)(20) that corresponded to different regions of the neuron (e.g., dendritic, perisomatic, axon initial segment areas). For each of these sections, the cosine distance between the simulated and experimental features was used as a score.…”

Section: Combining Patch-clamp and Hd-mea Data For Fitting Multicompa...mentioning

confidence: 99%

“…For each optimization strategy, we ran 10 optimizations, each starting from a different seed using a covariance matrix adaptation (CMA) optimizer (see Methods -Optimization algorithm for details) [19]. Figure 4A shows the fitness of the 10 optimized solutions for each strategy with respect to intracellular (left) and extracellular features (right -features were computed with the all strategy for Conversely, the single strategy yielded the best performance with respect to extracellular features, while the worst performance was achieved by using the soma strategy.…”

Section: Validation Of the Multi-modal Fitting Strategy On A Ground-t...mentioning

confidence: 99%

Section: Optimization Algorithmmentioning

confidence: 99%

See 2 more Smart Citations

A multi-modal fitting approach to construct single-neuron models with patch clamp and high-density microelectrode arrays

Buccino

Damart²,

Bartram

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

In computational neuroscience, multicompartment models are among the most biophysically realistic representations of single neurons. Constructing such models usually involves the use of the patch-clamp technique to record somatic voltage signals under different experimental conditions. The experimental data are then used to fit the many parameters of the model. While patching of the soma is currently the gold-standard approach to build multicompartment models, several studies have also evidenced a richness of dynamics in dendritic and axonal sections. Recording from the soma alone makes it hard to observe and correctly parameterize the activity of non-somatic compartments. In order to provide a richer set of data as input to multicompartment models, we here investigate the combination of somatic patch-clamp recordings with recordings of high-density micro-electrode arrays (HD-MEAs). HD-MEAs enable the observation of extracellular potentials and neural activity of neuronal compartments at sub-cellular resolution. In this work, we introduce a novel framework to combine patch-clamp and HD-MEA data to construct multicompartment models. We first validate our method on a ground-truth model with known parameters and show that the use of features extracted from extracellular signals, in addition to intracellular ones, yields models enabling better fits than using intracellular features alone. We also demonstrate our procedure using experimental data by constructing cell models from in vitro cell cultures. The proposed multi-modal fitting procedure has the potential to augment the modeling efforts of the computational neuroscience community and to provide the field with neuronal models that are more realistic and can be better validated.

show abstract

A Multimodal Fitting Approach to Construct Single-Neuron Models With Patch Clamp and High-Density Microelectrode Arrays

Buccino,

Damart,

Bartram

et al. 2024

Neural Computation

Self Cite

View full text Add to dashboard Cite

In computational neuroscience, multicompartment models are among the most biophysically realistic representations of single neurons. Constructing such models usually involves the use of the patch-clamp technique to record somatic voltage signals under different experimental conditions. The experimental data are then used to fit the many parameters of the model. While patching of the soma is currently the gold-standard approach to build multicompartment models, several studies have also evidenced a richness of dynamics in dendritic and axonal sections. Recording from the soma alone makes it hard to observe and correctly parameterize the activity of nonsomatic compartments. In order to provide a richer set of data as input to multicompartment models, we here investigate the combination of somatic patch-clamp recordings with recordings of high-density microelectrode arrays (HD-MEAs). HD-MEAs enable the observation of extracellular potentials and neural activity of neuronal compartments at subcellular resolution. In this work, we introduce a novel framework to combine patch-clamp and HD-MEA data to construct multicompartment models. We first validate our method on a ground-truth model with known parameters and show that the use of features extracted from extracellular signals, in addition to intracellular ones, yields models enabling better fits than using intracellular features alone. We also demonstrate our procedure using experimental data by constructing cell models from in vitro cell cultures. The proposed multimodal fitting procedure has the potential to augment the modeling efforts of the computational neuroscience community and provide the field with neuronal models that are more realistic and can be better validated.

show abstract

Data driven building of realistic neuron model using IBEA and CMA evolution strategies

Cited by 3 publications

References 6 publications

Evaluation and comparison of methods for neuronal parameter optimization using the Neuroptimus software framework

Evaluation and comparison of methods for neuronal parameter optimization using the Neuroptimus software framework

A multi-modal fitting approach to construct single-neuron models with patch clamp and high-density microelectrode arrays

A Multimodal Fitting Approach to Construct Single-Neuron Models With Patch Clamp and High-Density Microelectrode Arrays

Contact Info

Product

Resources

About