Add 3 papers mentioning/using BPO to wiki

Author: Jaquier Aurélien Tristan

misc/github_wiki/bibtex/mentions_BPO.bib

@@ -488,4 +488,33 @@ @misc{yegenogluExploringHyperparameterSpaces2022
   keywords = {Computer Science - Neural and Evolutionary Computing}
 }
 
+@article{erikssonCombiningHypothesisDatadriven2022,
+  title = {Combining Hypothesis- and Data-Driven Neuroscience Modeling in {{FAIR}} Workflows},
+  author = {Eriksson, Olivia and Bhalla, Upinder Singh and Blackwell, Kim T and Crook, Sharon M and Keller, Daniel and Kramer, Andrei and Linne, Marja-Leena and Saudargien{\.e}, Ausra and Wade, Rebecca C and Hellgren Kotaleski, Jeanette},
+  year = {2022},
+  month = jul,
+  journal = {eLife},
+  volume = {11},
+  pages = {e69013},
+  issn = {2050-084X},
+  doi = {10.7554/eLife.69013},
+  abstract = {Modeling in neuroscience occurs at the intersection of different points of view and approaches. Typically, hypothesis-\-driven modeling brings a question into focus so that a model is constructed to investigate a specific hypothesis about how the system works or why certain phenomena are observed. Data-d\- riven modeling, on the other hand, follows a more unbiased approach, with model construction informed by the computationally intensive use of data. At the same time, researchers employ models at different biological scales and at different levels of abstraction. Combining these models while validating them against experimental data increases understanding of the multiscale brain. However, a lack of interoperability, transparency, and reusability of both models and the workflows used to construct them creates barriers for the integration of models representing different biological scales and built using different modeling philosophies. We argue that the same imperatives that drive resources and policy for data \textendash{} such as the FAIR (Findable, Accessible, Interoperable, Reusable) principles \textendash{} also support the integration of different modeling approaches. The FAIR principles require that data be shared in formats that are Findable, Accessible, Interoperable, and Reusable. Applying these principles to models and modeling workflows, as well as the data used to constrain and validate them, would allow researchers to find, reuse, question, validate, and extend published models, regardless of whether they are implemented phenomenologically or mechanistically, as a few equations or as a multiscale, hierarchical system. To illustrate these ideas, we use a classical synaptic plasticity model, the Bienenstock\textendash Cooper\textendash Munro rule, as an example due to its long history, different levels of abstraction, and implementation at many scales.},
+  langid = {english}
+}
+
+@article{jedlickaParetoOptimalityEconomy2022,
+  title = {Pareto Optimality, Economy\textendash Effectiveness Trade-Offs and Ion Channel Degeneracy: Improving Population Modelling for Single Neurons},
+  shorttitle = {Pareto Optimality, Economy\textendash Effectiveness Trade-Offs and Ion Channel Degeneracy},
+  author = {Jedlicka, Peter and Bird, Alexander D. and Cuntz, Hermann},
+  year = {2022},
+  month = jul,
+  journal = {Open Biology},
+  volume = {12},
+  number = {7},
+  pages = {220073},
+  issn = {2046-2441},
+  doi = {10.1098/rsob.220073},
+  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.},
+  langid = {english}
+}
 

misc/github_wiki/bibtex/uses_BPO.bib

@@ -517,4 +517,16 @@ @article{yaoReducedInhibitionDepression2022
   langid = {english}
 }
 
+@article{huntStrongReliableSynaptic2022,
+  title = {Strong and Reliable Synaptic Communication between Pyramidal Neurons in Adult Human Cerebral Cortex},
+  author = {Hunt, Sarah and Leibner, Yoni and Mertens, Eline J and {Barros-Zulaica}, Natal{\'i} and Kanari, Lida and Heistek, Tim S and Karnani, Mahesh M and Aardse, Romy and Wilbers, Ren{\'e} and Heyer, Djai B and Goriounova, Natalia A and Verhoog, Matthijs B and {Testa-Silva}, Guilherme and Obermayer, Joshua and Versluis, Tamara and {Benavides-Piccione}, Ruth and {de Witt-Hamer}, Philip and Idema, Sander and Noske, David P and Baayen, Johannes C and Lein, Ed S and DeFelipe, Javier and Markram, Henry and Mansvelder, Huibert D and Sch{\"u}rmann, Felix and Segev, Idan and {de Kock}, Christiaan P J},
+  year = {2022},
+  month = jul,
+  journal = {Cerebral Cortex},
+  pages = {bhac246},
+  issn = {1047-3211, 1460-2199},
+  doi = {10.1093/cercor/bhac246},
+  abstract = {Abstract             Synaptic transmission constitutes the primary mode of communication between neurons. It is extensively studied in rodent but not human neocortex. We characterized synaptic transmission between pyramidal neurons in layers 2 and 3 using neurosurgically resected human middle temporal gyrus (MTG, Brodmann area 21), which is part of the distributed language circuitry. We find that local connectivity is comparable with mouse layer 2/3 connections in the anatomical homologue (temporal association area), but synaptic connections in human are 3-fold stronger and more reliable (0\% vs 25\% failure rates, respectively). We developed a theoretical approach to quantify properties of spinous synapses showing that synaptic conductance and voltage change in human dendritic spines are 3\textendash 4-folds larger compared with mouse, leading to significant NMDA receptor activation in human unitary connections. This model prediction was validated experimentally by showing that NMDA receptor activation increases the amplitude and prolongs decay of unitary excitatory postsynaptic potentials in human but not in mouse connections. Since NMDA-dependent recurrent excitation facilitates persistent activity (supporting working memory), our data uncovers cortical microcircuit properties in human that may contribute to language processing in MTG.},
+  langid = {english}
+}