Neural optimization: Understanding trade-offs with Pareto theory

Pallasdies, Fabian; Norton, Philipp; Schleimer, Jan-Hendrik; Schreiber, Susanne

doi:10.1016/j.conb.2021.08.008

Cited by 23 publications

(38 citation statements)

References 58 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…maximization of efficient wiring), whereas minimization of conduction time can be seen as effective signal propagation [43,69,70] (see also fig. 1 in [34]). A more recent publication has confirmed and extended findings of Cuntz et al [68,70] showing that by implementing optimal solutions for the trade-off between cable length and conduction time, dendritic trees seem to achieve Pareto optimality [71].…”

Section: Evolutionary Trade-offs Between Functionality (Effectiveness...mentioning

confidence: 99%

“…Already the first landmark studies have shown that many disparate configurations of synaptic and intrinsic conductances are able to generate similar neuronal behaviour as well as similar (functional) network behaviour [2] (see also [29][30][31][32][33]). Therefore, although in this article we focus on degeneracy of intrinsic ion channels at the cellular level, the concepts of degeneracy and Pareto optimality [34] can be extended also to extrinsic synaptic channels and to the level of neuronal circuits.…”

Section: Ion Channel Degeneracy In Population Models Of Neuronsmentioning

confidence: 99%

“…Aside from that, ParTI is based on statistical inference of Pareto optimality [99] comparing the set of solutions to randomly shuffled data under the assumption that phenotypic traits are independent and the data are uncorrelated. It has been pointed out that this may not be the case in biological datasets due to phylogenetic correlations of traits [111,112] (see also [34]). This would make the identification of Pareto fronts in high-dimensional datasets prone to errors.…”

Section: Pareto Inference For Deducing Neuronal Functions From High-d...mentioning

confidence: 99%

“…See also Pallasdies et al . [34].

Figure 4Evolutionary trade-offs between different tasks reduce the performance space to a Pareto front.…”

Section: Multi-objective Pareto Optimality As a Geometrically Elegant...mentioning

confidence: 99%

“…Based on Alon [39]. Caveat: if measured neuronal phenotypes do not lie on a Pareto front predicted by a Pareto analysis, this could mean that the analysis neglected some important tasks or that neurons are not Pareto optimal for the studied tasks (see [34]).

Figure 5Trade-offs between 2 or 3 tasks reduce parameter space to low-dimensional Pareto fronts in the form of a line or a triangle, respectively. ( a ) Archetypes are located at peaks of a task performance.…”

Section: Multi-objective Pareto Optimality As a Geometrically Elegant...mentioning

confidence: 99%

See 4 more Smart Citations

Pareto optimality, economy–effectiveness trade-offs and ion channel degeneracy: improving population modelling for single neurons

2022

View full text Add to dashboard Cite

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.

show abstract

Section: Evolutionary Trade-offs Between Functionality (Effectiveness...mentioning

confidence: 99%

Section: Ion Channel Degeneracy In Population Models Of Neuronsmentioning

confidence: 99%

Section: Pareto Inference For Deducing Neuronal Functions From High-d...mentioning

confidence: 99%

“…See also Pallasdies et al . [34].

Figure 4Evolutionary trade-offs between different tasks reduce the performance space to a Pareto front.…”

Section: Multi-objective Pareto Optimality As a Geometrically Elegant...mentioning

confidence: 99%

Section: Multi-objective Pareto Optimality As a Geometrically Elegant...mentioning

confidence: 99%

See 3 more Smart Citations

Pareto optimality, economy–effectiveness trade-offs and ion channel degeneracy: improving population modelling for single neurons

2022

View full text Add to dashboard Cite

show abstract

Rethinking retinoic acid self-regulation: A signaling robustness network approach

Fainsod,

Vadigepalli

2024

Current Topics in Developmental Biology

View full text Add to dashboard Cite

Multi-objective robust optimization design framework for low-pollution emission burners

Hu,

Tao,

Ying

et al. 2024

Chemical Engineering Research and Design

View full text Add to dashboard Cite

Neural optimization: Understanding trade-offs with Pareto theory

Cited by 23 publications

References 58 publications

Pareto optimality, economy–effectiveness trade-offs and ion channel degeneracy: improving population modelling for single neurons

Pareto optimality, economy–effectiveness trade-offs and ion channel degeneracy: improving population modelling for single neurons

Rethinking retinoic acid self-regulation: A signaling robustness network approach

Multi-objective robust optimization design framework for low-pollution emission burners

Contact Info

Product

Resources

About