"Neuroscience as a Source of Inspiration for Data Science", Dr Annina Neumann, Head of Data Science at SHS Viveon YouTube Link: https://www.youtube.com/watch?v=XRI7_cygj0M Watch more from Data Natives 2015 here: http://bit.ly/1OVkK2J Visit the conference website to learn more: www.datanatives.io Follow Data Natives: https://www.facebook.com/DataNatives https://twitter.com/DataNativesConf Stay Connected to Data Natives by Email: Subscribe to our newsletter to get the news first about Data Natives 2016: http://bit.ly/1WMJAqS About the Author: Dr. Annina Neumann is Head of Data Science at SHS VIVEON. In her field of work she focuses on the application of algorithms and mathematical methods to generate measureable business benefit. Building up on the study of bioinformatics and neuroscience she has now gained more than 8 years of extensive experience in the field of data analysis in diverse industries. Her professional expertise is driven by an enthusiastic interest in interdisciplinary domains between computer science, mathematics and business.

1. Neuroscience as a Source of Inspiration for Data
Science?
Data Natives Conference
Berlin, 19. November 2015
Dr. Annina Neumann
Head of Data Science
Business Unit Big Data
Annina.Neumann@SHS-VIVEON.com

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Some Basics of Neuroscience
Parietal Lobe
• Movement
• Orientation
• Recognition
• Perception of stimuli
Frontal Lobe
• Reasoning
• Planning
• Parts of speech
• Movement
• Emotions
• Problem solving
Occipital Lobe
• Visual processing
Temporal Lobe
• Perception & recognition
of auditory stimuli
• Memory
• Speech

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A few crossing points between Neuroscience and Data Science
Behaviourism
Turing Machine
and Finite
Automaton
Classical
Cognitive Science
Connectionism
Neural Networks Reinforcement
Learning
…and it goes on
Neuroscience
Cognitive Science
Data Science
Machine Learning

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Connectionism challenges Classicism
Connectionism
Neural
Networks
Use of computers to simulate neural
processes
Classical Cognitive
Science

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Neural Coding Application
Grandmother
Cells
Sparse vs.
Distributed
Coding
• Sparse Coding Algorithms are often applied, e.g. in image
processing and audio processing
BrainCellsforGrandmother
RodrigoQuianQuiroga,ItzhakFried&ChristofKoch
ScientificAmerican308,30-35(2013)
• Sparse Coding can be used in the concepts of „deep learning“
where compact features are learned from data

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A few crossing points between Neuroscience and Data Science
Behaviourism
Turing Machine
and Finite
Automaton
Classical
Cognitive Science
Connectionism
Neural Networks Reinforcement
Learning
…and it goes on
Neuroscience
Cognitive Science
Data Science
Machine Learning

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Back to the beginning…
Behaviourism
Neural
Networks
Reinfocement
Learning
http://hubpages.com/education/Psychology-and-Television-The-
Big-Bang-Theory-Tackles-Operant-Conditioning

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Diskussion
Summary
• Neuroscience invesitgates the brain and its processes, like learning
and reasoning
• In Data Science we use a lot if these findings, e.g. by applying machine
learning algorithms to business problems and „real world“ data
• Neuroscience has been a great source of inspiration for Data Science
– and vice versa!
• Many more opportunities and open questions!
• For example: The humunculus, rules and decision-making
 smart algorithms cannot replace data scientists yet!
Conclusion
Outlook

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Thank you!
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Dr. Annina Neumann
Head of Data Science
Business Unit Big Data
Annina.Neumann@SHS-VIVEON.com

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