Modelling human choices: MADeM and decision‑making

Research supported by FAPESP 2015/50122-0 and DFG-GRTK 1740/2. RP and AR are also part of the Research, Innovation and Dissemination Center for Neuromathematics FAPESP grant (2013/07699-0). RP is supported by a FAPESP scholarship (2013/25667-8). ACR is partially supported by a CNPq fellowship (grant 306251/2014-0)

Modeling growth of cultured neocortical neuronal networks: Comparison of the simulation tools CX3D and NETMORPH

Background and aims: Development of neuronal morphology and formation of networks is an elaborate process determining the information processing capabilities of the formed network. The early phases of this process are readily studied in vitro, but are so far incompletely understood. Computational modeling can help in revealing the mechanisms of development of neuronal networks. The aim of this work was to compare two recently published simulators of neuronal growth, CX3D and NETMORPH, by implementing in them a model of growth of neurons in culture.Methods: Three different models were built and simulated with parameter values determined based on the literature. The ?rst model has 100 interconnected neurons and was simulated on both CX3D and NETMORPH varying the neurite growth rate parameters. The second model has 1000 neurons and was simulated with NETMORPH. The third model has 100 neurons and was simulated with CX3D varying the attraction parameter. Sholl analysis was used to analyze the produced single neuron morphologies, and graph theoretical measures (in-degree, shortest path length,motifs) were used to quantify features of the simulated neuronal networks.Results: Based on the quanti?cation of numbers of synapses, CX3D simulations produced neuronal networks closest to the experimentally observed ones, but Sholl analysis revealed in some cases unrealistic features in morphologies of neurites. Also, the simulations were computationally heavy. NETMORPH also produced realistic results, but with excessive numbers of synapses per neuron. The simulations, however, were lighter. Proportional to the number of synapses, the graph theoretical measures behaved similarly with both simulation tools.Conclusions: Implementing the same model in both CX3D and NETMORPH is not straightforward. Thus, they also produced differing results with the same set of simulated parameters. However, with a correct choice of parameters, both simulation tools are capable of producing qualitatively similar results. Both tools produce results that are in the range of experimentally observed values. The different nature of the tools suggest different applications. CX3D would be suited for models that have molecular guidance cue diffusion, and NETMORPH for graph theoretical studies of large neuronal networks. Asiasanat:neuronal cell cultures, neuronal networks, simulation tools, computational neuroscienc

Effect of Frontal Transcranial Direct Current Stimulation on Word Fluency in Healthy Adults

Introduction: Transcranial direct current stimulation (tDCS) is a noninvasive and well tolerated method for stimulating the brain in a subthreshold manner. It has shown some promise e.g. in treatment of major depressive disorder. The prefrontal cortex is an interesting target for tDCS studies, since the executive functions it performs are compromised in many diseases of the brain. Verbal fluency tasks are one way of measuring executive functions, albeit inherently being a combined task that measures several other functions, such as verbal ability, as well. In this study, we wanted to explore whether tDCS targeted to the dorsolateral prefrontal cortex influences performance in phonemic and semantic word fluency tasks in healthy adults. Materials and methods: 23 healthy participants, aged 21-34 years, were randomized into two groups, one receiving active tDCS stimulation and the other one receiving sham stimulation. They performed a one-minute phonemic and semantic fluency test before (session 1) and after (session 2) performing Executive reaction time test, a computer-based test engaging several executive functions simultaneously, during which the active or sham stimulation was administered. The number of words produced during the verbal fluency tests was analyzed for the full one-minute test period, and in 15 second intervals using analysis of variance and Student’s T-test. Results: The semantic fluency task proved to be easier for the participants, as expected. There was also a tendency to perform better in session 2 (post-stimulation) than session 1 (pre-stimulation) in both fluency tasks and in both active and sham stimulation groups, implying learning. Interestingly, there was a statistically significant difference in the semantic fluency test session 2 (post-stimulation) second quarter (15-30 s from the beginning of the test) between the number of words produced by the active and sham stimulation groups with those having received tDCS producing more words. Conclusions: The results indicate significant learning in repeated verbal fluency tasks influencing the assessment of an intervention on executive functions. tDCS improved verbal fluency in the second quarter of the fluency test. We speculate based on the exponential decay curve of performance in the fluency task that the second quarter is most dependent on executive functions, and thus subtle alterations in executive functions may be more easily detected during this quarter. This is in contrast to the first quarter that relies on semiautomatic access of frequent words rather than effortful retrieval of infrequent words. Furthermore, it may be that in the third and fourth quarter vocabulary may be the limiting factor on the performance rather than the efficiency of executive functions. Thus, while caution is warranted and these preliminary results should be confirmed in future studies, it is possible that there was a subtle improvement in executive functions due to tDCS that was observed only in the second quarter of the fluency task

Effect of Frontal Transcranial Direct Current Stimulation on Word Fluency in Healthy Adults

Introduction: Transcranial direct current stimulation (tDCS) is a noninvasive and well tolerated method for stimulating the brain in a subthreshold manner. It has shown some promise e.g. in treatment of major depressive disorder. The prefrontal cortex is an interesting target for tDCS studies, since the executive functions it performs are compromised in many diseases of the brain. Verbal fluency tasks are one way of measuring executive functions, albeit inherently being a combined task that measures several other functions, such as verbal ability, as well. In this study, we wanted to explore whether tDCS targeted to the dorsolateral prefrontal cortex influences performance in phonemic and semantic word fluency tasks in healthy adults. Materials and methods: 23 healthy participants, aged 21-34 years, were randomized into two groups, one receiving active tDCS stimulation and the other one receiving sham stimulation. They performed a one-minute phonemic and semantic fluency test before (session 1) and after (session 2) performing Executive reaction time test, a computer-based test engaging several executive functions simultaneously, during which the active or sham stimulation was administered. The number of words produced during the verbal fluency tests was analyzed for the full one-minute test period, and in 15 second intervals using analysis of variance and Student’s T-test. Results: The semantic fluency task proved to be easier for the participants, as expected. There was also a tendency to perform better in session 2 (post-stimulation) than session 1 (pre-stimulation) in both fluency tasks and in both active and sham stimulation groups, implying learning. Interestingly, there was a statistically significant difference in the semantic fluency test session 2 (post-stimulation) second quarter (15-30 s from the beginning of the test) between the number of words produced by the active and sham stimulation groups with those having received tDCS producing more words. Conclusions: The results indicate significant learning in repeated verbal fluency tasks influencing the assessment of an intervention on executive functions. tDCS improved verbal fluency in the second quarter of the fluency test. We speculate based on the exponential decay curve of performance in the fluency task that the second quarter is most dependent on executive functions, and thus subtle alterations in executive functions may be more easily detected during this quarter. This is in contrast to the first quarter that relies on semiautomatic access of frequent words rather than effortful retrieval of infrequent words. Furthermore, it may be that in the third and fourth quarter vocabulary may be the limiting factor on the performance rather than the efficiency of executive functions. Thus, while caution is warranted and these preliminary results should be confirmed in future studies, it is possible that there was a subtle improvement in executive functions due to tDCS that was observed only in the second quarter of the fluency task

Modeling growth of cultured neocortical neuronal networks: Comparison of the simulation tools CX3D and NETMORPH

Background and aims: Development of neuronal morphology and formation of networks is an elaborate process determining the information processing capabilities of the formed network. The early phases of this process are readily studied in vitro, but are so far incompletely understood. Computational modeling can help in revealing the mechanisms of development of neuronal networks. The aim of this work was to compare two recently published simulators of neuronal growth, CX3D and NETMORPH, by implementing in them a model of growth of neurons in culture.Methods: Three different models were built and simulated with parameter values determined based on the literature. The ?rst model has 100 interconnected neurons and was simulated on both CX3D and NETMORPH varying the neurite growth rate parameters. The second model has 1000 neurons and was simulated with NETMORPH. The third model has 100 neurons and was simulated with CX3D varying the attraction parameter. Sholl analysis was used to analyze the produced single neuron morphologies, and graph theoretical measures (in-degree, shortest path length,motifs) were used to quantify features of the simulated neuronal networks.Results: Based on the quanti?cation of numbers of synapses, CX3D simulations produced neuronal networks closest to the experimentally observed ones, but Sholl analysis revealed in some cases unrealistic features in morphologies of neurites. Also, the simulations were computationally heavy. NETMORPH also produced realistic results, but with excessive numbers of synapses per neuron. The simulations, however, were lighter. Proportional to the number of synapses, the graph theoretical measures behaved similarly with both simulation tools.Conclusions: Implementing the same model in both CX3D and NETMORPH is not straightforward. Thus, they also produced differing results with the same set of simulated parameters. However, with a correct choice of parameters, both simulation tools are capable of producing qualitatively similar results. Both tools produce results that are in the range of experimentally observed values. The different nature of the tools suggest different applications. CX3D would be suited for models that have molecular guidance cue diffusion, and NETMORPH for graph theoretical studies of large neuronal networks. Asiasanat:neuronal cell cultures, neuronal networks, simulation tools, computational neuroscienc

Computational Models for Calcium-Mediated Astrocyte Functions

The computational neuroscience field has heavily concentrated on the modeling of neuronal functions, largely ignoring other brain cells, including one type of glial cell, the astrocytes. Despite the short history of modeling astrocytic functions, we were delighted about the hundreds of models developed so far to study the role of astrocytes, most often in calcium dynamics, synchronization, information transfer, and plasticity in vitro, but also in vascular events, hyperexcitability, and homeostasis. Our goal here is to present the state-of-the-art in computational modeling of astrocytes in order to facilitate better understanding of the functions and dynamics of astrocytes in the brain. Due to the large number of models, we concentrated on a hundred models that include biophysical descriptions for calcium signaling and dynamics in astrocytes. We categorized the models into four groups: single astrocyte models, astrocyte network models, neuron-astrocyte synapse models, and neuron-astrocyte network models to ease their use in future modeling projects. We characterized the models based on which earlier models were used for building the models and which type of biological entities were described in the astrocyte models. Features of the models were compared and contrasted so that similarities and differences were more readily apparent. We discovered that most of the models were basically generated from a small set of previously published models with small variations. However, neither citations to all the previous models with similar core structure nor explanations of what was built on top of the previous models were provided, which made it possible, in some cases, to have the same models published several times without an explicit intention to make new predictions about the roles of astrocytes in brain functions. Furthermore, only a few of the models are available online which makes it difficult to reproduce the simulation results and further develop the models. Thus, we would like to emphasize that only via reproducible research are we able to build better computational models for astrocytes, which truly advance science. Our study is the first to characterize in detail the biophysical and biochemical mechanisms that have been modeled for astrocytes

Reproducibility and Comparability of Computational Models for Astrocyte Calcium Excitability

The scientific community across all disciplines faces the same challenges of ensuring accessibility, reproducibility, and efficient comparability of scientific results. Computational neuroscience is a rapidly developing field, where reproducibility and comparability of research results have gained increasing interest over the past years. As the number of computational models of brain functions is increasing, we chose to address reproducibility using four previously published computational models of astrocyte excitability as an example. Although not conventionally taken into account when modeling neuronal systems, astrocytes have been shown to take part in a variety of in vitro and in vivo phenomena including synaptic transmission. Two of the selected astrocyte models describe spontaneous calcium excitability, and the other two neurotransmitter-evoked calcium excitability. We specifically addressed how well the original simulation results can be reproduced with a reimplementation of the models. Additionally, we studied how well the selected models can be reused and whether they are comparable in other stimulation conditions and research settings. Unexpectedly, we found out that three of the model publications did not give all the necessary information required to reimplement the models. In addition, we were able to reproduce the original results of only one of the models completely based on the information given in the original publications and in the errata. We actually found errors in the equations provided by two of the model publications; after modifying the equations accordingly, the original results were reproduced more accurately. Even though the selected models were developed to describe the same biological event, namely astrocyte calcium excitability, the models behaved quite differently compared to one another. Our findings on a specific set of published astrocyte models stress the importance of proper validation of the models against experimental wet-lab data from astrocytes as well as the careful review process of models. A variety of aspects of model development could be improved, including the presentation of models in publications and databases. Specifically, all necessary mathematical equations, as well as parameter values, initial values of variables, and stimuli used should be given precisely for successful reproduction of scientific results.publishedVersionPeer reviewe

Challenges in Reproducibility, Replicability, and Comparability of Computational Models and Tools for Neuronal and Glial Networks, Cells, and Subcellular Structures

The possibility to replicate and reproduce published research results is one of the biggest challenges in all areas of science. In computational neuroscience, there are thousands of models available. However, it is rarely possible to reimplement the models based on the information in the original publication, let alone rerun the models just because the model implementations have not been made publicly available. We evaluate and discuss the comparability of a versatile choice of simulation tools: tools for biochemical reactions and spiking neuronal networks, and relatively new tools for growth in cell cultures. The replicability and reproducibility issues are considered for computational models that are equally diverse, including the models for intracellular signal transduction of neurons and glial cells, in addition to single glial cells, neuron-glia interactions, and selected examples of spiking neuronal networks. We also address the comparability of the simulation results with one another to comprehend if the studied models can be used to answer similar research questions. In addition to presenting the challenges in reproducibility and replicability of published results in computational neuroscience, we highlight the need for developing recommendations and good practices for publishing simulation tools and computational models. Model validation and flexible model description must be an integral part of the tool used to simulate and develop computational models. Constant improvement on experimental techniques and recording protocols leads to increasing knowledge about the biophysical mechanisms in neural systems. This poses new challenges for computational neuroscience: extended or completely new computational methods and models may be required. Careful evaluation and categorization of the existing models and tools provide a foundation for these future needs, for constructing multiscale models or extending the models to incorporate additional or more detailed biophysical mechanisms. Improving the quality of publications in computational neuroscience, enabling progressive building of advanced computational models and tools, can be achieved only through adopting publishing standards which underline replicability and reproducibility of research results

Computational modeling of neuronastrocyte interactions: evolution, reproducibility, comparability and future development of models

no. P135. 27th annual computational neuroscience meeting (CNS 2018), Seattle, WA, USA. 13-18 July 2018, part 1: meeting abstractsAstrocyte research has turned out to be a fascinating and popular research field with two groups of researchers having opposite opinions about the importance of astrocytes in brain information processing and plasticity [1–3]. We believe that computational modeling of the biophysics of neuron-astrocyte interactions can greatly help address the dilemma.We have therefore, as the first ones, characterized, categorized, and evaluated in detail more than a hundred published computational models of single astrocytes, astrocyte networks, neuron-astrocyte synapses, and neuron-astrocyte networks [4] as well as studied the reproducibility and comparability of some of the models [5]. Based on this knowledge and additional experimental findings, we have constructed and implemented new neuron-astrocyte synapse models [6]. In this study, we propose to gather the state-of-the-art experimental and computational knowledge to help guide the future astrocyte research. Two of the most important challenges in experimental work on astrocytes are the lack of selective pharmacological tools and the partially contradictory results obtained in in vivo and in vitro studies [1–3]. In computational studies on astrocyte, the most important challenges are the creationof new models without clear explanation how they differ from the previously published models and what new predictions the models make [4]. Furthermore, combining unclearly given model details in the publications with nonexistent online model implementations make the reproducibility and comparability studies as well as the development of previously published models impossible, or at least difficult [4, 5]. We want to emphasize the importance of using common description formats for defining the models in the publications and description languages for exchanging the models through online repositories. Our overall goal is to develop both detailed and reduced models of neuron- astrocyte interactions for different brain areasLietuvos sveikatos mokslų universitetasTaikomosios informatikos katedraVytauto Didžiojo universiteta

Modeling neuron-astrocyte interactions: towards understanding synaptic plasticity and learning in the brain

Online ISBN 978-3-319-63312-1. Knygos DOI: 10.1007/978-3-319-63312-1_14. Serija: Lecture Notes in Computer Science, vol. 10362Spiking neural networks represent a third generation of artificial neural networks and are inspired by computational principles of neurons and synapses in the brain. In addition to neuronal mechanisms, astrocytic signaling can influence information transmission, plasticity and learning in the brain. In this study, we developed a new computational model to better understand the dynamics of mechanisms that lead to changes in information processing between a postsynaptic neuron and an astrocyte. We used a classical stimulation protocol of long-term plasticity to test the model functionality. The long-term goal of our work is to develop extended synapse models including neuron-astrocyte interactions to address plasticity and learning in cortical synapses. Our modeling studies will advance the development of novel learning algorithms to be used in the extended synapse models and spiking neural networks. The novel algorithms can provide a basis for artificial intelligence systems that can emulate the functionality of mammalian brainLietuvos sveikatos mokslų universitetasTaikomosios informatikos katedraVytauto Didžiojo universiteta