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1 -Developing workflows on the EBRAINS research infrastructure – an early career researchers’ perspective.
1 +Young Researchers EBRAINS Workflows White Paper
Content
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1 -== Download the [[WhitePaper PDF.>>attach:YRW_whitepaper (1).pdf]] ==
1 +== “Young Researchers EBRAINS Workflows White Paper” ==
2 2  
3 -==== ====
4 -
5 -
6 -==== //This online document will be updated as new information is provided by the students.// ====
7 -
8 -== “Developing workflows on
9 -the Ebrains research infrastructure – an early career researchers’ perspective.” ==
10 -
11 -Christian Mata, Christian Stephan-Otto, Nalan Karunanayake, Igori Comarovschii, Niccolò Mattiello,  João Miguel Alves Ferreira,
3 +Christian Mata, Christian Stephan-Otto, Nalan Karunanayake, Igori Comarovschii, Niccolò Mattiello, 
12 12  Katia Djerroud, Nathaniel Adibuer, Aziz Ullah Khan, Claudia Bachmann, Alper Yegenoglu, Sandra Díaz, Marissa Díaz.
13 13  
14 14  September 1, 2023
... ... @@ -17,7 +17,7 @@
17 17  (% style="text-align: center;" %)
18 18  ==== Abstract ====
19 19  
20 -This white paper presents a collection of scientific workflows crafted by students under the guidance of the Scientific Liaison Unit (SLU) within the EBRAINS infrastructure. This document describes how they translated their research goals into structured workflows using a standardized process. The workflows showcased in this paper span varying levels of maturity, from abstract concepts to well-defined requirements within the scientific process. These early career researchers effectively articulated their requirements and harnessed EBRAINS tools to construct scientific workflows. The objective of this white paper is not only to highlight the practical application of SLU-developed tools and methodologies but also to serve as a valuable resource for the EBRAINS community, offering insights into the process of defining and executing scientific goals through modular and traceable workflows.
12 +This white paper presents a collection of scientific workflows crafted by students under the guidance of the Scientific Liaison Unit (SLU) within the EBRAINS infrastructure. This document describes how they translated their research goals into structured workflows using a standardized process. The workflows showcased in this paper span varying levels of maturity, from abstract concepts to well-defined requirements within the scientific process. These young researchers effectively articulated their requirements and harnessed EBRAINS tools to construct scientific workflows. The objective of this white paper is not only to highlight the practical application of SLU-developed tools and methodologies but also to serve as a valuable resource for the EBRAINS community, offering insights into the process of defining and executing scientific goals through modular and traceable workflows.
21 21  
22 22  (% style="text-align: center;" %)
23 23  ==== ====
... ... @@ -33,7 +33,7 @@
33 33  
34 34  In this white paper we cover workflows at different levels of maturity. Some of them are in an abstract stage, where only a general goal or research idea has been identified and the first steps to create a working approach towards it have been taken. Other workflows are more mature and rely on well identified requirements within the scientific journey. These requirements can include: data querying and analysis, model generation, simulation, analysis of experimental and simulated data, visualization, machine learning, interaction with the neurorobotics or the medical informatics platforms, usage of large computing infrastructure, interactive simulations, emulation and simulation on neuromorphic hardware, storage, sharing, secure data transfer, etc.
35 35  
36 -The results section first presents workflows from early career researchers who have limited or no experience with the EBRAINS ecosystem. The early career researchers were guided by the scientific liaison unit of the human brain project and the technical coordination team to describe their requirements and use EBRAINS tools to create both executive and scientific workflows. An executive workflow is the one which describes the interactions between scientists, groups, communities and other stakeholders. A scientific workflow describes the steps to be taken to collect observations, generate hypotheses, design, implement and execute experiments of different kinds, analyze, validate and visualize the results, asses the hypotheses, disseminate and share the results in an interactive manner.
28 +The results section first presents workflows from young researchers who have limited or no experience with the EBRAINS ecosystem. The young researchers were guided by the scientific liaison unit of the human brain project and the technical coordination team to describe their requirements and use EBRAINS tools to create both executive and scientific workflows. An executive workflow is the one which describes the interactions between scientists, groups, communities and other stakeholders. A scientific workflow describes the steps to be taken to collect observations, generate hypotheses, design, implement and execute experiments of different kinds, analyze, validate and visualize the results, asses the hypotheses, disseminate and share the results in an interactive manner.
37 37  
38 38  In a second part of the results we include the EBRAINS showcases, which represent the workflows with higher level of maturity. The EBRAINS showcases are examples of complex research questions which have been addressed using a combination of EBRAINS tools and services and which can be modified, extended or used as a template by end users.
39 39  
... ... @@ -41,7 +41,7 @@
41 41  * (((
42 42  workflows of different readiness levels:
43 43  
44 -1. early-stage workflows: the implementation of the workflow has not started yet or is still in the first phases. Here, mainly early career researchers contributed. If you find a workflow particularly interesting, please do not hesitate to contact the authors.
36 +1. early-stage workflows: the implementation of the workflow has not started yet or is still in the first phases. Here, mainly Young researchers contributed. If you find a workflow particularly interesting, please do not hesitate to contact the authors.
45 45  1. middle-stage workflows: workflows that are currently in the implementation process. Here, authors can share already first experiences with the implementation
46 46  1. late-stage workflows: workflows that have been almost completed. Here, we will also present some showcases of EBRAINS.
47 47  )))
... ... @@ -64,18 +64,15 @@
64 64  
65 65  It can be challenging for new EBRAINS users to determine how EBRAINS products can assist them in realizing their scientific projects. In order to help in this process, the Scientific Liaison Unit of EBRAINS (SLU) has developed a standard description of workflows, which allows them to map the scientific project to software and compute resources and plan out its implementation.
66 66  
67 -Aside from this support, the HBP also organizes outreach activities targeted at different scientific communities. In this regard, a special focus is placed on early career researchers who wish to learn how to use EBRAINS.
59 +Aside from this support, the HBP also organizes outreach activities targeted at different scientific communities. In this regard, a special focus is placed on young researchers who wish to learn how to use EBRAINS.
68 68  
69 -In order to learn how to use EBRAINS, it is helpful to understand how other scientific projects have been translated into EBRAINS workflows. Also in this regard, HBP seeks to support EBRAINS users. Following this idea, we (the authors of this paper) investigated the possibility of writing a white paper that serves as a database of different workflows and making them available to other researchers. In order to collect such use cases, we decided to combine the outreach efforts of the education program and the student ambassadors of the HBP with SLU formalization and technical coordination (TC). A students’ workshop was chosen as the best means of collecting use-cases.
61 +In order to learn how to use EBRAINS, it is helpful to understand how other scientific projects have been translated into EBRAINS workflows. Also in this regard, HBP seeks to support EBRAINS users. Following this idea, we (the authors of this paper) investigated the possibility of writing a white paper that serves as a database of different workflows and making them available to other researchers. In order to collect such usecases, we decided to combine the outreach efforts of the education program and the student ambassadors of the HBP with SLU formalization and technical coordination (TC). A students’ workshop was chosen as the best means of collecting usecases.
70 70  
71 71  With the students’ workshop we wanted to accomplish the following goals:
64 +• Collecting different neuroscientific projects suitable for creating workflows in EBRAINS • Presenting the different EBRAINS tools and computing resources to the students
65 +• Teaching students how to create their own EBRAINS workflows
66 +• Supporting students in creating their first workflows
72 72  
73 -
74 -* • Collecting different neuroscientific projects suitable for creating workflows in EBRAINS.
75 -* Presenting the different EBRAINS tools and computing resources to the students.
76 -* Teaching students how to create their own EBRAINS workflows.
77 -* Supporting students in creating their first workflows.
78 -
79 79  Accordingly we structured the workflow as follows:
80 80  
81 81  In the first part we introduced the different EBRAINS tools and services.
... ... @@ -84,29 +84,23 @@
84 84  
85 85  In the third part we asked students to present their scientific project idea. For this part we asked students to give a presentation prior to the workshop. There was no requirement for the project to have already produced any kind of results or to have used EBRAINS tools. There was only one thing that was important: presenting the scientific idea.
86 86  
87 -How to create EBRAINS workflows from these scientific projects was discussed after the presentations. Here, the mural board were of great assistance and helped the students and tutors to streamline. In this last part of the workshop, we reserved extra time for students to ask questions, seek assistance, and/or present and discuss their first Mural boards.
76 +How to create EBRAINS workflows from these scientific projects was discussed after the presentations. Here, the mural board were of great assistance and helped the students and tutors to streamline. An example of such a filled-out mural board is illustrated in Fig? here a figure is missing with an example of a filled out mir board. In this last part of the workshop, we reserved extra time for students to ask questions, seek assistance, and/or present and discuss their first Mural boards.
88 88  
89 89  The workshop idea proved to be successful. During the 1,5 days of the workshop, we had 9 participants, of which 5 agreed to share their workflows in this paper.
90 90  
91 -This work was made possible by the following groups sharing their workflows, which were subsequently integrated into this work after a long post-processing period, which included many emails between early career researchers and organizers.
80 +This work was made possible by the following groups sharing their workflows, which were subsequently integrated into this work after a long post-processing period, which included many emails between young researchers and organizers.
92 92  
93 93  * Research Center Sant Joan de Déu in Spain, Christian Mata and Christian Stephan-Otto (work- flow 2)
94 94  * Nalan Kraunanayake, a PhD in Biomedical Engineering and Prof. Dr. Stanislav S. Makhanov both working at Thammasat University in Thailand (workflow 3)
95 95  * Igori Comarovschii, Mickkel Vinding and Prof. Daniel Lundqvist from the Karolinska Institute as well as Pascal Helson for the KTH Royal Institute of Technology in Sweden (workflow 4)
96 96  
86 +
97 97  • PhD student Niccolò Mattiello of Gerardo Biella’s group at the University of Pavia (workflow 5)
98 98  
99 -• Katia Djerround, a master student at the Algerian University of Science and Technology Houari Boumediene, Nathaniel Adibuer, a research assistant at the University of Ghana, and Aziz Ullah Khan, a professional engineer.
89 +• Katia Djerround, a master student at the Algerian University of Science and Technology Houari Boumediene, Nathaniel Adibuer, a research assistant at the University of Ghana, and Aziz Ullah Khan, a professional engineer from X
100 100  
91 +Figure 2: Mural board presented to the students during the workshop with an invite to fill it out. Each field on the board had a short caption and a short explanation of what to fill in: Team: the team name, the members and and one sentence per each group member to introduce themselves; Background:Explaining the main trends of the state of the art to address the research question the reasearchers are working on; Problem: Decomposing the main challenge into subproblems; Vision: Where does the research group want to go? What are dreams of the researchers for future researchers working in your field?; Impact: What difference will the researchers make? Who else will benefit from the designed workflow?; Solution elements: Brainstorm the elements which would be key to solve your problem; Challenges:What is missing to achieve ther researcher’s vision and can they address this using EBRAINS tools?; Workflow: Associating solution elements to EBRAINS components, and ordering them in a workflow; the construction of diagrams make use of the symbols described in 1.
101 101  
102 -(% style="text-align:center" %)
103 -[[image:Marissa Diaz_2023-03-02_14-43-52.png]]
104 -
105 -(% class="small" %)Figure 2: Mural board presented to the students during the workshop with an invite to fill it out. Each field on the board had a short caption and a short explanation of what to fill in: Team: the team name, the members and and one sentence per each group member to introduce themselves.
106 -
107 -
108 -Background: Explaining the main trends of the state of the art to address the research question the reasearchers are working on; Problem: Decomposing the main challenge into subproblems; Vision: Where does the research group want to go? What are dreams of the researchers for future researchers working in your field?; Impact: What difference will the researchers make? Who else will benefit from the designed workflow?; Solution elements: Brainstorm the elements which would be key to solve your problem; Challenges:What is missing to achieve the researcher’s vision and can they address this using EBRAINS tools?; Workflow: Associating solution elements to EBRAINS components, and ordering them in a workflow; the construction of diagrams make use of the symbols described in 1.
109 -
110 110  In the following chapter, the scientific projects and the corresponding workflows are explained. An overview of the tools used in these workshops is given in the table 1. Tools are categorized here according to their fields of application. A short description of each tool is provided, along with a link to a more detailed explanation.
111 111  
112 112  (% style="text-align: center;" %)
... ... @@ -116,77 +116,25 @@
116 116  
117 117  One of the use-cases, for example, focuses on establishing a methodology to facilitate the creation of 6
118 118  
119 -brain atlases for a particular target group. Other studies investigate phenomena like contour grouping, oscillation in Parkinson’s Disease, cell and network properties of the entorhinal cortex, and the use of imaging signals, specifically the EEG, to improve brain-computer interfaces.
102 +brain atlases for a particular target group. Other studies investigate phenomena like contour grouping, oscillation in Parkinson’s Disease, cell and network properties of the entorhinal cortex, and the use of imaging signals, specifically the EEG, to improve brain-computer interfaces. [This needs to be extended to show cases and other cases]
120 120  
121 121  In order to help the reader to quickly identify the most relevant use cases, we will briefly sketch each use case in a couple of sentences. This will include a quick overview of the use case content, the data it treats, EBRAINS tools used, maturity level achieved so far and information on the research group.
122 122  
123 -subsection 3.2 The researchers at Research Center Sant Joan de Déu in Spain, Christian Mata and Christian Stephan-Otto, desire to create anatomical brain templates of specific human subgroups from images in the BIDS format. Among the tools they are using are the Knowledge Graph, Quick NII, and the Brain Atlas of EBRAINS.
106 +subsection 3.2 The researchers at Research Center Sant Joan de Déu in Spain, Christian Mata and Christian Stephan-Otto, desire to create anatomical brain templates of specific human subgroups from images in the BIDS format. Among the tools they are using are the Knowledge Graph, Quick NII, and the Brain Atlas of EBRAINS. [The maturity level of this project is probably already advanced but this is something we have to ask them]
124 124  
125 -subsection 3.3 In the third workflow, Nalan Kraunanayake, a PhD in Biomedical Engineering and Prof. Dr. Stanislav S. Makhanov both working at Thammasat University in Thailand, present a project in which contour grouping is investigated. Using robot simulations of visual neuro function, the group explores how local and global stimulus properties affect contour grouping. Here, EBRAINS tools and services such as the NRP, NEST and L2L support this research.
108 +subsection 3.3 In the third workflow, Nalan Kraunanayake, a PhD in Biomedical Engineering and Prof. Dr. Stanislav S. Makhanov both working at Thammasat University in Thailand, present a project in which contour grouping is investigated. Using robot simulations of visual neuro function, the group explores how local and global stimulus properties affect contour grouping. Here, EBRAINS tools and services such as the NRP, NEST and L2L support this research. [The maturity level of this project is not known - we have to ask them]
126 126  
127 127  subsection 3.4 Igori Comarovschii, Mickkel Vinding and Prof. Daniel Lundqvist from the Karolinska Institute as well as Pascal Helson for the KTH Royal Institute of Technology in Sweden present a work- flow that investigates beta oscillations in Parkinson’s Disease and the possibility of DBS treatment. To this end, a multiscale model is constructed based on MEG data collected under different experimental conditions. In particular the effect of DBS should be studied in this model. Thus, the overall goal is to create a workflow that leads from raw MEG data to a prediction of how MEG activity is influenced by DBS. This is accomplished with the aid of BIDS manager, Knowledge Graph, TVB, TVB imaging pipeline, NEST, and L2L. [The maturity level of this project is probably already advanced but this is something we have to ask them]
128 128  
129 -subsection 3.5 PhD student Niccolò Mattiello of Gerardo Biella’s group at the University of Pavia is investigating the different functional interactions of heterogeneous neurons in the entorhinal and perirhinal cortex. With this aim in mind he first focuses on single neuron model properties, collecting and analysing the data with the help of Neo, Elephant, Elephant Vis, BluePyEfe and Neuro Tech Mesh and, in a subsequent step investigates how the different experimental results influence the neuron’s activity in a simulated environment using Arbor, Neuron, BluePyEfe, BluePyOpt, L2L, Validation Framework and Hippo Unit. In a second step a larger cortical network is considered. Here data analysis can be carried out via Nutil and VisuAlign, Ilastik and Neuro Tech Mesh. For the creation of a model of a large network and its simulation, Brain Scafold Builder, Arbor, Core Neuron ViSimple, Neuroscheme, BluePyOpt, L2L, and Network Unit are considered.
112 +subsection 3.5 PhD student Niccolò Mattiello of Gerardo Biella’s group at the University of Pavia is investigating the different functional interactions of heterogeneous neurons in the entorhinal and perirhinal cortex. With this aim in mind he first focuses on single neuron model properties, collecting and analysing the data with the help of Neo, Elephant, Elephant Vis, BluePyEfe and Neuro Tech Mesh and, in a subsequent step investigates how the different experimental results influence the neuron’s activity in a simulated environment using Arbor, Neuron, BluePyEfe, BluePyOpt, L2L, Validation Framework and Hippo Unit. In a second step a larger cortical network is considered. Here data analysis can be carried out via Nutil and VisuAlign, Ilastik and Neuro Tech Mesh. For the creation of a model of a large network and its simulation, Brain Scafold Builder, Arbor, Core Neuron ViSimple, Neuroscheme, BluePyOpt, L2L, and Network Unit are considered. [The maturity level of this project is not known - we have to ask him]
130 130  
131 131  Worflow 6:Katia Djerround, a master student at the Algerian University of Science and Technology Houari Boumediene, Nathaniel Adibuer, a research assistant at the University of Ghana, and Aziz Ullah Khan, a professional engineer from X, aim to develop a systematic approach for utilizing EEG data in Brain Computer Interfaces (BCI). A GDPR compliant data storage location combined with a standard data processing procedure should be made possible using the Health data cloud and tools like Frites and Neo. As a second step, the potential of MEG data for steering a robot is examined using the NRP. Finally, the loop is closed by exploring the potential of EEG signals to interact with a robotic application via a TVB simulation embedded in a NRP environment. Currently, this project is in the planning stage.
132 132  
133 -
134 134  (% style="text-align: center;" %)
135 135  **3.1 Workflow 1**
136 136  
137 -3.1.1 Team
119 +This workflow will be added and updated on the second version of this withepeper.
138 138  
139 -• João Miguel Alves Ferreira - molecular and translational neuroscience,  PhD.
140 -
141 -3.1.2 Background
142 -
143 -There have been several projects in the past relating the effect of cultural activities in the wellbeing of patients suffering from a variety of disorders including anxiety and depression.
144 -
145 -3.1.3 Problem
146 -
147 -Many patients who suffer from depression, anxiety, etc. are prescribed drugs to treat the symptoms of these disorders.
148 -
149 -However, it is possible that a large part of these patients do not really have neurological disorders and 
150 -Could get real improvement in their overall wellbeing by changing their environment and lifestye.
151 -
152 -3.1.4 Vision
153 -
154 -Develop and test a new treatment program focused on inserting patients with different disorders such as depression and anxiety into a culturally rich and nurturing environment in order to eliminate/reduce the need for drugs.
155 -
156 -Create a modus operandi which can be transformed into a course which can be shared and taught to other museums / cultural centers all around the world on how to help people with therapeutic protocols to enhance the quality of life.
157 -
158 -3.1.5 Impact
159 -
160 -This innovative perspective on the treatment of these disorders could: 
161 -• Reduce the dependency of patents on drugs.
162 -• Have a better and more sustainable effect on the relief of these disorders, 
163 -• Provide a more economic way to deal with mental disorders. 
164 -• Enhance our understanding of the effects of environmental changes in the function of our brains.
165 -
166 -3.1.6 Solution elements
167 -
168 -Instead of prescribing drugs, get the patient involved into a well being program which embeds her/him into a new culturally rich environment, being it a museum or a botanical garden, 
169 -Personalize it and prove that has a real effect. 
170 -In order to formulate a scientific background for the effects of such innovative protocol, there should be a link to neurobiology, brain network plasticity, etc.
171 -• Measure some levels of anxiety hormones before and after the program 
172 -• Measure other kinds of hormones like serotonine and oxitosin during and after the program. 
173 -• Do some statistics on the data and see if the course of treatment is helping the people overcome the disorder.
174 -• We can also do psychological batteries of tests.
175 -• Create a collaborative network with neuroscientists to research the potential links of these environmental changes in network function at different scales.
176 -
177 -• Explore the possibilty of conducting an imaging study (e.g MRI) and see if the program alters the brain activity at arge scale (and link to altered hormone levels)
178 -
179 -• Explore the possibility of creating models which link mental states to neuroscience models of brain activity.
180 -
181 -3.1.7 Challenges
182 -
183 -• Figure out how to connect the mental disorders withthe neurobiology underneath.
184 -• Find good measures of success for the effects ofthe new treatment that can be reproduced in a reliable and robust way.
185 -• Develop a strong community to support the implementation of such approaches into everyday treatment plans.
186 -
187 -3.1.8 Workflow
188 -
189 -
190 190  (% style="text-align: center;" %)
191 191  **3.2 Workflow 2**
192 192  
... ... @@ -219,7 +219,7 @@
219 219  
220 220  3.2.7 Challenges
221 221  
222 -EBRAINS’ tools offer many solutions and the main challenge is to correctly choose a specific application. The tool that could be useful for this purpose is the Multiscale Atlas of the Human Brain.
153 +EBRAINS’ tools offer many solutions and the main challenge is to correctly choose a specific applica- tion. The tool that could be useful for this purpose is the Multiscale Atlas of the Human Brain.
223 223  
224 224  For this reason, being able to create a repository of cases validated by experts and to create a specific database to be able to access specific cases represents one of the main objectives.
225 225  
... ... @@ -390,7 +390,7 @@
390 390  
391 391  3.5.2 Background
392 392  
393 -The main research question I’m working on is to understand the neural mechanisms underlying the functions carried out by the enthorinal cortex (e.g. spatial navigation, time coding of events) and perirhinal cortex (e.g. multisensory integration, information gating towards hippocampus during memory formation). What I want to understand is how the heterogeneous populations of neurons (mainly from an electrophysiological perspective, but it could be also extended to a molecular point of view) located in those areas orchestrate in order to create structures that can perform the previously mentioned functions. In other words, how each of those neurons with its properties and its connections takes part to the process. In literature are reported a wealth of experimental data describing the electrophysiological behavior of the different neural populations, their modulation by different neurotransmitters and their anatomical connections. These experimental data are collected thanks to electrophysiological recordings as patch-clamp recordings performed at a single neuron resolution in mouse/rat brain slices or extracellular recordings performed both in vivo and in brain slices. Other experiments commonly conducted are imaging experiments as confocal microscopy acquisitions and calcium imaging microscopy performed both in vivo and in brain slices. At last, some experiments involved in assessing the contribution of a certain neurotransmitter or a specific neural population are performed with behavioral tests. But what is missing is a clear vision on how all those aspects at a single neuron level contribute to the dynamics at a network level. What could help to achieve this wide perspective of the neural dynamics is a neural network that as a scaffold gathers all the experimental evidences at a single neuron level. That could be a key point to make a step forward in understanding how the brain performs those functions and at same point this kind of network could be a useful resource both for the design of new experiments and the development of new paradigms for bio-inspired machine learning and neuromorphic architectures. At the moment different projects aimed in developing neural networks are going on but none of them involves the previously mentioned cortical structures and that’s the reason why I’m interested in design and carry on this workflow. Similar projects carried on by the Human Brain Project can support the feasibility and the potentials of the project I’m proposing, in particular, I’m referring to all the projects focused to model the cerebellum (as the ones conducted by the Egido D’Angelo’s Lab at the University of Pavia). That projects proved to be very useful to make some step forward in better understanding the cerebellar functions and also develop new neurorobotic prototypes.
324 +The main research question I’m working on is to understand the neural mechanisms underlying the functions carried out by the enthorinal cortex (e.g. spatial navigation, time coding of events) and perirhinal cortex (e.g. multisensory integration, information gating towards hippocampus during memory formation). What I want to understand is how the heterogeneous populations of neurons (mainly from an electrophysiological perspective, but it could be also extended to a molecular point of view) located in those areas orchestrate in order to create structures that can perform the previously mentioned functions. In other words, how each of those neurons with its properties and its connections takes part to the process. In literature are reported a wealth of experimental data describing the electrophysiological behaviour of the different neural populations, their modulation by different neurotransmitters and their anatomical connections. These experimental data are collected thanks to electrophysiological recordings as patch-clamp recordings performed at a single neuron resolution in mouse/rat brain slices or extracellular recordings performed both in vivo and in brain slices. Other experiments commonly conducted are imaging experiments as confocal microscopy acquisitions and calcium imaging microscopy performed both in vivo and in brain slices. At last, some experiments involved in assessing the contribution of a certain neurotransmitter or a specific neural population are performed with behavioural tests. But what is missing is a clear vision on how all those aspects at a single neuron level contribute to the dynamics at a network level. What could help to achieve this wide perspective of the neural dynamics is a neural network that as a scaffold gathers all the experimental evidences at a single neuron level. That could be a key point to make a step forward in understanding how the brain performs those functions and at same point this kind of network could be a useful resource both for the design of new experiments and the development of new paradigms for bio-inspired machine learning and neuromorphic architectures. At the moment different projects aimed in developing neural networks are going on but none of them involves the previously mentioned cortical structures and that’s the reason why I’m interested in design and carry on this workflow. Similar projects carried on by the Human Brain Project can support the feasibility and the potentials of the project I’m proposing, in particular, I’m referring to all the projects focused to model the cerebellum (as the ones conducted by the Egido D’Angelo’s Lab at the University of Pavia). That projects proved to be very useful to make some step forward in better understanding the cerebellar functions and also develop new neurorobotic prototypes.
394 394  
395 395  3.5.3 Problem
396 396  
... ... @@ -417,11 +417,11 @@
417 417  
418 418  The third point will require the following daily activities:
419 419  
420 -• Collect and analyze experimental data (electrophysioloogical, behavioral, imaging data) that can be used to validate the network previously built
351 +• Collect and analyse experimental data (electrophysioloogical, behavioural, imaging data) that can be used to validate the network previously built
421 421  
422 422  3.5.4 Vision
423 423  
424 -What I would like to achieve with the future projects I’ll be working on is to develop a model of perirhinal and entorhinal cortex that could let us visualize at a detailed cellular level the neural dynamics that are going on in those area both at rest and during specific tasks. To a far extent what I would like to achieve is to have a clear vision on how these networks of single ”computational” units brings to the emergence of the complex behaviors we experience in our daily lives in order to achieve a complete reductionist description of those phenomena
355 +What I would like to achieve with the future projects I’ll be working on is to develop a model of perirhinal and entorhinal cortex that could let us visualize at a detailed cellular level the neural dynamics that are going on in those area both at rest and during specific tasks. To a far extent what I would like to achieve is to have a clear vision on how these networks of single ”computational” units brings to the emergence of the complex behaviours we experience in our daily lives in order to achieve a complete reductionist description of those phenomena
425 425  
426 426  3.5.5 Impact
427 427  
... ... @@ -436,7 +436,7 @@
436 436  
437 437  Analysis optimization: something that could help not only to speed up the advancement in this specific project but experimental research in general would be the optimization of the analysis tools. Based on my personal experience the analysis of electrophysiological data still involves time-consuming steps that might be optimized. In order to achieve this goal I think it might be useful relying more in open access tools as the ones offered in the EBRAINS platform. Thanks to those tools and the opportunity to collaborate with the EBRAINS group and the other members of the community it would be possible to develop better performing tools.
438 438  
439 -Data similarity/Quantitative approximation: Using data from already better described areas might be a solution to cope with the lack of some experimental data during the early stages of the project and preventing the risk of delays in the development of computational models. For example, after appropriate evaluations and metanalysis some electrophysiological data describing hippocampal features shared with the neurons in the area of my interest might be used as preliminary data for the single neuron model development. In this way both the computational flow and the experimental flow can advance at the same time. Once the quantitative data are collected by the experimental flow they will the replace the ones that were used in the early stages.
370 +Data similarity/Quantitative approximation: Using data from already better described areas might be a solution to cope with the lack of some experimental data duringthe early stages of the project and preventing the risk of delays in the development of computational models. For example, after appropriate evaluations and metanalysis some electrophysiological data describing hippocampal features shared with the neurons in the area of my interest might be used as preliminary data for the single neuron model development. In this way both the computational flow and the experimental flow can advance at the same time. Once the quantitative data are collected by the experimental flow they will the replace the ones that were used in the early stages.
440 440  
441 441  Iterative workflow and intermediate deliverables achievement: Since the project can’t be carried out as a single huge project, in order to be sustainable an iterative step workflow design might be the key to achieve the final result. So instead of collecting all the experimental results and input them in the workflow all at once, a good solution might be input the experimental data concerning some specific feature and at the end of the process release an updated version of the neural network implementing some additional feature in comparison to the previous one and then repeat the process. For example, in the first cycle I would like to implement the ”computational” operations performed by the dendrites and the spatial distribution of the different neural populations. So I’ll achieve a first updated network and I could assess its contribution in improving performances in neurorobotics and ML algorithms and in better describing the activity in those cortical areas. Then in a second cycle I could repeat the process in order to implement in the network additional information concerning the synaptic connectivity of the network. On a third cycle I could implement additional data concerning the modulation of certain neurons by a certain neurotransmitter.
442 442  
... ... @@ -446,9 +446,9 @@
446 446  
447 447  The challenges that will be faced during this phase will be:
448 448  
449 -* Lack of some specific experimental data. As I mentioned in the background section there is already a large amount of data describing the behaviors and connections of the populations in my regions of interest, but there are still some aspects that require additional quantitative data in order to develop a model that reliably implements those features
380 +* Lack of some specific experimental data. As I mentioned in the background section there is already a large amount of data describing the behaviours and connections of the populations in my regions of interest, but there are still some aspects that require additional quantitative data in order to develop a model that reliably implements those features
450 450  * This aspect brings two additional challenges that must be faced, time consuming processing during data analysis and the risk of some bottle-necks in the process. The necessity to collect additional experimental data might cause a delay in the progression of the computational models development
451 -* Developing a neural network made of detailed neurons require a considerable amount of computational resources.
382 +* Developing a neural network made of detailed neurons require a considerable amount of compu- tational resources.
452 452  * (((
453 453  Time and Money. Not as technical as the previous challenges but as important as the other are two issues that every project has to face: the time required to advance across all the steps and achieve the final goal, and the the necessity of fundings to keep the workflow going on
454 454  
... ... @@ -472,13 +472,13 @@
472 472  3.6.1 Team
473 473  
474 474  * Katia Djerround, Bachelor in Biochemistry and Master in Neurobiology.
475 -* Nathaniel Adibuer, research and teaching assistant at the University of Ghana, Biomedical Engineering department
406 +* Nathaniel Adibuer, research and teaching assistant at the University of Ghana, Biomedical En- gineering department
476 476  * (((
477 477  Aziz Ullah Khan: professional Engineer, Master’s degree in Electrical Engineering
478 478  
479 479  3.6.2 Background
480 480  
481 -In the recent years, study of the BCI (Brain Computer Interface) technology based on EEG (Electroencephalography) has become one of the important parts in the biomedicine engineering. With this technique, people can control equipment or do very basic communication through their brains instead of language or physical actions [61] Present-day BCIs determine the intent of the user from a variety of different electrophysiological signals. These signals include slow cortical potentials, P300 potentials, and mu or beta rhythms recorded from the scalp, and cortical neuronal activity recorded by implanted electrodes [62]. They are translated in real-time into commands that operate a computer display or other device.
412 +In the recent years, study of the BCI (Brain Computer Interface) technology based on EEG (Elec- troencephalography) has become one of the important parts in the biomedicine engineering. With this technique, people can control equipment or do very basic communication through their brains instead of language or physical actions [61] Present-day BCIs determine the intent of the user from a variety of different electrophysiological signals. These signals include slow cortical potentials, P300 potentials, and mu or beta rhythms recorded from the scalp, and cortical neuronal activity recorded by implanted electrodes [62]. They are translated in real-time into commands that operate a computer display or other device.
482 482  )))
483 483  * (((
484 484  Figure 6: Enthorial cortex simulation model
... ... @@ -488,7 +488,7 @@
488 488  
489 489  3.6.3 Problem
490 490  
491 -• Data collection: finding a place in which the sensitive data can be stored in a systematic way • Creating a data processing pipeline that fullfills the following criteria:
422 +• Data collection: finding a place in which the sensitive data can be stored in a systematic way • Creating a data processing pipeline that fullfils the following criteria:
492 492  
493 493  – It can be applied to raw data of different experimental conditions and data formats – Allows to include various analysis techniques (such as AI) in a modular way
494 494  – Allows to compare the results of different imaging modalities
... ... @@ -561,13 +561,13 @@
561 561  * summary: in this white paper we wanted to provide an overview of example workflow of different levels of activities which leverage the EBRAINS infrastructure to do innovative reasearch (of course, some more sentences of what we did, workflows covering areas a,b,c
562 562  * short paragraph about the workflows
563 563  * focussing on communalites of the workflows (data manipulation, integration of modelling cycles, producing some meaning output that can be shared with community, and other meta patterns
564 -* impact of this work: highlighting the importance of early career researchers on the codesign of the research infrastructures, we would like to invite more early career researchers to integrate this methodologies of stating their scientific journeys in which they hopefully make use of EBRAINS tools and shape it, also highlight the template, having a methodology in a clear way is a path towards a common language between disciplines and also to open science and to have more standardized solution that are reproducible and more robust
565 -* education related to brain-sciences would benefit from integrating these type of concept and best practices, making visible to the students to the platforms available in science, students can integrate them in their science
566 -* PIs should encourage the usage of platforms, in order to really make the best out of the European Commission to build this infrastructure the research groups should encourage the usage of these methods/tools/standards and even become part of the developer community which also helps for the long-term stability of the infrastructure itself as a community effort
495 +* impact of this work: highlighting the importance of young researchers on the codesign of the research infrastructures, we would like to invite more young researchers to integrate this metholo- gies of stating their scientific journeys in which they hopefully make use of EBRAINS tools and shape it, also highlight the template, having a methologolgy in a clear way is a path towards a common language between diciplines and also to open science and to have more standardized solution that are reproducible and more robust
496 +* education related to brainsciences would benefit from integrating these type of concept and best practices, making visible to the students to the platforms available in science, students can integrate them in their science
497 +* PIs should encourage the usage of platforms, in order to really make the best out of the European Commision to build this infrastructure the research groups should encourage the usage of these methods/tools/standards and even become part of the developer community which also helps for the longterm stability of the infrastructure itself as a community effort
567 567  
568 -• short comings: in these use-cases we have only covered a subset of the capabilities of EBRAINS and the community is invited to explore/ extent these use-cases with different tools and services inside and outside of the EBRAINS platform; there are other tools, services and platforms designed to research of the brain (e.g open brain, Patraig Gleeson) we have not made an explicit comparison with the possibilities the platform offers, however these platforms can also host tools and services from EBRAINS; most of use-cases are in the initial state, ideally the science will be implemented later; not all use-cases covered in EBRAINS
499 +• shortcommings: in these usecases we have only covered a subset of the capabilities of EBRAInS and the community is invited to explore/ extent these usecases with different tools and services inside and outside of the EBRAINS platform; there are other tools, services and platforms designed to research of the brain (e.g open brain, Patraig Gleeson) we have not made an explicit comparrison with the possibilities the platform offers, however these platforms can also host tools and services from EBRAINS; most of usecases are in the initial state, ideally the science will be implemented later; not all usecases covered in EBRAINS
569 569  
570 -z outlook at some point it would be interesting to have the possibility to derive the connection between the tools, not only in a diagram but with automatic links provided by the infrastructure; share the experience of the use-cases and use this knowledge to improve the infrastructure; tracking system would be nice also to guarantee a more intense collaboration
501 +z outlook at some point it would be interesting to have the possibility to derive the connection between the tools, not only in a diagram but with automatic links provided by the infrastructure; share the experience of the usecases and use this knowledge to improve the infrastructure; tracking system would be nice also to guarantee a more intense collaboration
571 571  
572 572  (% style="text-align: center;" %)
573 573  ==== 5 References ====
YRW_whitepaper (1).pdf
Author
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1 -XWiki.marissadiazpier
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1 -1.3 MB
Content