19.05.2021
| Inside Data Science |
“The Brain is the Ultimate Data Scientist”
Iris Grothe shares fascinating insights into her research in neuroscience and talks about the importance of big data and data science.
What topics are you currently working on in your research?
One of the major themes in our group is to try to understand signal routing in the brain. A typical neuron in the brain is connected to about 1000 other neurons. How can a neuron block incoming signals that are not relevant at the moment and preferentially process more important information? It seems that the dynamics of the interactions between groups of neurons play a crucial role in selective signal gating: a neuron that receives multiple inputs will rather process the inputs coming from sender neurons that are rhythmically active in a coherent manner with the receiver. We try to understand this rhythmic synchrony between sender and receiver in more detail: how is it controlled? How generalizable is it to different brain areas and different layers of the cortex? Can we manipulate it?
How important is data to your research?
Very important because we generate a lot!
We record the signals of single brain cells and large groups of them simultaneously. All of these are basically continuous electrical time series. Continuous for the duration of the session of course. We record such time series with high temporal and spatial resolution (up to 500 contact points). Because we record every day a few hours, we generate a few GB a day. Each day.
What role does data science play in your research? Do you see yourself more as a user, a method developer, a basic researcher, or perhaps something completely different?
I would describe myself definitely as a user. Or maybe not even a real user yet. In fact, we are so far applying rather classical inferential statistical methods to our data. This is partly related to the questions we are interested in in systems neuroscience: we want to know how the brain mechanistically implements the processes that we humans with a brain describe as cognitive processes. We want to understand how the brain “really” does it. When applying data science to neuroscience we often see interesting ideas develop about how the brain potentially might do it, but not how it is mechanistically implemented in the brain.
Another reason is that I am simply not trained in it. So it is great that the Data Science Center started with courses as well!
Which data science methods and technologies are in the focus of your research or could also become interesting in the future?
Since I focus on the visual system in my work there is an almost natural link to image processing, object recognition, eye gaze analyses, etc. We can for example present a lot of images and record the eye gaze signal or the brain signal simultaneously. Machine learning techniques can be applied to that data, for example to extract features in the brain signals that lead to recognition of the object.
I think that vice versa understanding the brain better can inspire data science. The brain is the ultimate data scientist, it continuously tries to extract useful knowledge from the information, the data, that it is given. Maybe understanding the brain better can help us towards explainable AI as well.
What are your main challenges in dealing with data?
The human brain consists of about 86 000 000 000 neurons, each connects to about 1000 others. Although it is definitely not possible yet to measure them all at the same time, even not in smaller animal models, the aim is to measure as many as possible to understand the processing in these dense networks better. The data that we thereby generate are impressively complex. At the end of the day, we have tens to thousands of simultaneously recorded time-series, each is a stream of the messages that one neuron sends to another. The statistics of these signals change over time. The strength of the activities spread over multiple orders of magnitude and their patterns vary. We need to multiply this by sessions, participants, etc. What we need are “data-neuroscientists” to develop the right tools to understand how all these neurons work together
And finally, what is your personal motivation for joining the Data Science Center?
I am actually interested in all the Data Science Center has to offer: we can really use some of your computing power, further I hope to get some adequate training in order to be able to apply data science tools in the future. And last but definitely not least: I am really looking forward to potentially fruitful collaborations with other groups at the university. I think we have interesting data to share and I am sure there are some people around that have know-how about different ways to analyze our data. Maybe we can even start a collaborative project a data-neuroscience one!
You can learn more about Iris’ activities in her talk
“How the Brain Processes Big Data“ in the Data Science Forum on 20.05.2021.
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19.05.2021 | Inside Data Science
“The Brain is the Ultimate Data Scientist”
Iris Grothe shares fascinating insights into her research in neuroscience and talks about the importance of big data and data science.
What topics are you currently working on in your research?
One of the major themes in our group is to try to understand signal routing in the brain. A typical neuron in the brain is connected to about 1000 other neurons. How can a neuron block incoming signals that are not relevant at the moment and preferentially process more important information? It seems that the dynamics of the interactions between groups of neurons play a crucial role in selective signal gating: a neuron that receives multiple inputs will rather process the inputs coming from sender neurons that are rhythmically active in a coherent manner with the receiver. We try to understand this rhythmic synchrony between sender and receiver in more detail: how is it controlled? How generalizable is it to different brain areas and different layers of the cortex? Can we manipulate it?
How important is data to your research?
Very important because we generate a lot!
We record the signals of single brain cells and large groups of them simultaneously. All of these are basically continuous electrical time series. Continuous for the duration of the session of course. We record such time series with high temporal and spatial resolution (up to 500 contact points). Because we record every day a few hours, we generate a few GB a day. Each day.
What role does data science play in your research? Do you see yourself more as a user, a method developer, a basic researcher, or perhaps something completely different?
I would describe myself definitely as a user. Or maybe not even a real user yet. In fact, we are so far applying rather classical inferential statistical methods to our data. This is partly related to the questions we are interested in in systems neuroscience: we want to know how the brain mechanistically implements the processes that we humans with a brain describe as cognitive processes. We want to understand how the brain “really” does it. When applying data science to neuroscience we often see interesting ideas develop about how the brain potentially might do it, but not how it is mechanistically implemented in the brain.
Another reason is that I am simply not trained in it. So it is great that the Data Science Center started with courses as well!
Which data science methods and technologies are in the focus of your research or could also become interesting in the future?
Since I focus on the visual system in my work there is an almost natural link to image processing, object recognition, eye gaze analyses, etc. We can for example present a lot of images and record the eye gaze signal or the brain signal simultaneously. Machine learning techniques can be applied to that data, for example to extract features in the brain signals that lead to recognition of the object.
I think that vice versa understanding the brain better can inspire data science. The brain is the ultimate data scientist, it continuously tries to extract useful knowledge from the information, the data, that it is given. Maybe understanding the brain better can help us towards explainable AI as well.
What are your main challenges in dealing with data?
The human brain consists of about 86 000 000 000 neurons, each connects to about 1000 others. Although it is definitely not possible yet to measure them all at the same time, even not in smaller animal models, the aim is to measure as many as possible to understand the processing in these dense networks better. The data that we thereby generate are impressively complex. At the end of the day, we have tens to thousands of simultaneously recorded time-series, each is a stream of the messages that one neuron sends to another. The statistics of these signals change over time. The strength of the activities spread over multiple orders of magnitude and their patterns vary. We need to multiply this by sessions, participants, etc. What we need are “data-neuroscientists” to develop the right tools to understand how all these neurons work together
And finally, what is your personal motivation for joining the Data Science Center?
I am actually interested in all the Data Science Center has to offer: we can really use some of your computing power, further I hope to get some adequate training in order to be able to apply data science tools in the future. And last but definitely not least: I am really looking forward to potentially fruitful collaborations with other groups at the university. I think we have interesting data to share and I am sure there are some people around that have know-how about different ways to analyze our data. Maybe we can even start a collaborative project a data-neuroscience one!
You can learn more about Iris’ activities in her talk
“How the Brain Processes Big Data“ in the Data Science Forum on 20.05.2021.
Interviewee:
Dr. Iris Grothe
Cognitive Neurophysiology Lab
FB 02 – Biology / Chemistry
grothe@uni-bremen.de
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