SlideShare a Scribd company logo
NEUROINFORMATICS
Presented by
Ranjana N
Dept. of Biotechnology
BIET, Davangere.
Seminar Guide
Mr Prakash K K
Asst. Professor
Department of Biotechnology
BIET, Davangere.
1
AGENDA
 Introduction
 Database development and management
 Overview of NIRS
 Computational Neuroscience
 Current research and applications
 Hurdles faced by emerging
Neuroinformatics
 Conclusion
 References
2
Introduction
 Understanding the human brain is one of the
biggest challenges of the 21st century.
 Abilities such as information perception,
processing, decision-making and action are far
more complicated than the man made systems.
 This presentation focuses on the overview of
Neuroinformatics and its potential applications.
3
Introduction
Neuroscience
 Field of science devoted to the scientific study of
nerves especially their relation to behavior and
learning.
 Deals with the anatomy, physiology, biochemistry
and molecular biology of the nervous system.
Neuroinformatics
 Combination of neuroscience and information
science.
 Deals with the organization of neuroscience data
and application of computational models and
analytical tools. 4
NEUROINFORMATICS
Neuroinformatics
Neuroscience
data and
knowledge bases
of nervous
system
Mathematical
modelling:
Computational
neuroscience
Tools for data
acquisition,
analysis,
visualization and
distribution 5
DEVELOPMENT AND
MANAGEMENT OF DATABASE
6
Contents
 Database
 Collaborative web portal application
 Web-based data entry
 Tablet based data entry
 Quality assurance and quality control
7
8
Neuroinformatics Repository System
Generate
protocol and
provide stimulus
material
Obtain subject
responses
Pass collected
data through
data cleaner
Provide data
models
Perform data
extraction
Implement
data querying
Analyse data
queried
Provide data to
visualization
tools
Generate
reports
End
9
Computational Neuroscience
 Study of the information processing properties of
the neurons.
Neurocomputing Method:
 A logical model is used to map the cognitive
functions of the brain.
 An abstract intelligence theory is developed which
explains the mechanisms of the brain.
 This will enable the development of cognitive
computers that perceive, think and learn.
10
Single Neuron Modelling
 A neuron model is a mathematical description of
the properties of neurons that is designed to
accurately describe and predict biological
process.
 Hodgkin Huxley model
 Wang Buzsaki model
 Cable theory
11
• Hodgkin Huxley model:
 The first mathematical model of action potential.
 It predicts the mechanism of the action potential.
 Wang Buzsaki Model:
 Interaction between the neurons within the
hippocampus and neocortex and their storage,
processing and transmission of information.
12
• Cable theory
 Deals with the biophysical aspects of the neuron.
 This theory takes into account the input
conductance at the base, total surface area of the
tree and the electro tonic length of the dendrite
which depends on its length, diameter and
resistance.
13
Implementation of Computational tools
 Memory and synapses.
 Cognition, discrimination and learning.
Role of their studies in building intelligent
machines.
14
CURRENT RESEARCH AND
EXPERIMENTS
 BRAIN-GENE ONTOLOGY
 HUMAN BRAIN MAPPING
 CURRENT ATLAS TOOLS
15
BRAIN-GENE ONTOLOGY
 Teaching and research tool
 Includes concepts, facts, graphs, animations related
to brain functions, diseases, genetics.
 Computational Neurogenetic Modelling (CNGM).
 Data sources: Gene Ontology, Unified Medical
Language System (UMLS), PubMed, gene
expression databases.
 3D animation of various concepts and data.
Applications of BGO:
 Brain organization and function
 Gene regulatory network
 Simulation modelling 16
HUMAN BRAIN MAPPING
 Set of techniques used to view the structural
and functional aspects of the brain onto a
spatial representation called Maps.
 Study of anatomy and functions of the brain
and spinal cord through the use of imaging
techniques, cellular biology, molecular
genetics, biomedical engineering and
neurophysiology.
17
CURRENT ATLAS TOOLS
TALAIRACH COORDINATE SYSTEM
 3-dimensional system used to map the location
of the brain structures.
 Make inferences about tissue identity by
referring to the atlas.
18
BRAIN-COMPUTER INTERFACE
 Communication pathway between brain and
external device.
 Repairing or assisting in human cognitive or
sensory motor functions.
 Types:
 Invasive- directly into the grey matter
 Partially invasive- inside the skull, outside the
brain
Electrocorticography (ECoG)
 Non invasive- portable; EEG
19
20
HURDLES FACED BY THE
EMERGING NI SYSTEM
 Research culture issues- information not
disclosed
 Meta Data- data about the data
 Tools- expensive software development and
lack of initiative among the programmers.
 Ethical concerns- anonymity and inappropriate
use of information
 Legal Aspects- protection for- the creator, the
user and the person subjected to.
21
CONCLUSION
 Due to the advancement of IT, huge amount of
neuroscience data is being generated, analysed and
interpreted by using computational tools with the help
of Neuroinformatics.
 However, emerging of this new technology poses
various challenges that must be overcome by effective
functioning of NI system.
 Since NI is an emerging field, it is important to
encourage young minds to contribute to this field and
also involve organizations world wide to provide an
international collaboration.
 Nevertheless, Neuroinformatics plays a crucial role in
the modern trend techniques such as human brain
mapping- used in research and crime labs and also
BCIs, which is used in treating various brain diseases,
all ultimately working for the betterment of human
life. 22
REFERENCES
 Wang, Y. (2009), On Abstract Intelligence: Toward a
Unified Theory of Natural, Artificial, Machinable, and
Computational Intelligence, Int’l Journal of Software
 Science and Computational Intelligence, 1(1), 1-18.
 International Neuroinformatics Coordinating Facility:
http://www.incf.org/
 http://www.neuro.gatech.edu/groups/potter/index.html
 Henry J. Bockholt, Mark Scully, William Courtney, Srinivas
Rachakonda, Adam Scott, Arvind Caprihan, Jill Fries, Ravi
Kalyanam, Judith M. Segall, Raul de la Garza, Susan Lane1
and Vince D. Calhoun (2010). Mining the Mind Research
Network
 Pradeep et al. (2013) US States Patent.
23
REFERENCES
 Cannon, R.C., Howell, F.W., Goddard, N.H., De Schutter, E.
(2002) Non-curated distributed databases for experimental
data and models in neuroscience. Network: Computation in
Neural Systems 13, 415-428.
 De Schutter, E. ed. (2000) Computational Neuroscience:
Realistic modelling for experimentalists. Boca Raton, FL:
CRC Press, 2000.
 Eckersley, P., Egan G.F. et al. (2002) Neuroinformatics data
and tools sharing: a discussion paper on ethical and legal
issues”, Journal of Neuroinformatics.
 Lancaster, J.L., Woldorff, M.G., Parsons, L.M., Liotti, M.,
Freitas, C.S., Rainey, L., Kochunov, P.V., Nickerson, D.,
Mikiten, S.A., and Fox, P.T. (2000) Automated Talairach atlas
labels for functional brain mapping. Human Brain
Mapping, 10, 120-31.
24
25

More Related Content

What's hot

Human genome
Human genomeHuman genome
Human genome
shoaa311
 
Use of Nanotechnology in Diagnosis and Treatment of Cancer
Use of Nanotechnology in Diagnosis and Treatment of CancerUse of Nanotechnology in Diagnosis and Treatment of Cancer
Use of Nanotechnology in Diagnosis and Treatment of Cancer
Anas Indabawa
 

What's hot (20)

Bioinformatics
BioinformaticsBioinformatics
Bioinformatics
 
AI in Bioinformatics
AI in BioinformaticsAI in Bioinformatics
AI in Bioinformatics
 
Structure Based Drug Design
Structure Based Drug DesignStructure Based Drug Design
Structure Based Drug Design
 
Human genome
Human genomeHuman genome
Human genome
 
Genomics
GenomicsGenomics
Genomics
 
AI for drug discovery
AI for drug discoveryAI for drug discovery
AI for drug discovery
 
Drug Discovery: Proteomics, Genomics
Drug Discovery: Proteomics, GenomicsDrug Discovery: Proteomics, Genomics
Drug Discovery: Proteomics, Genomics
 
Bioinformatics and Drug Discovery
Bioinformatics and Drug DiscoveryBioinformatics and Drug Discovery
Bioinformatics and Drug Discovery
 
Systems biology
Systems biologySystems biology
Systems biology
 
Uses of Artificial Intelligence in Bioinformatics
Uses of Artificial Intelligence in BioinformaticsUses of Artificial Intelligence in Bioinformatics
Uses of Artificial Intelligence in Bioinformatics
 
Machine learning in biology
Machine learning in biologyMachine learning in biology
Machine learning in biology
 
Ai in drug discovery and drug development
Ai in drug discovery and drug developmentAi in drug discovery and drug development
Ai in drug discovery and drug development
 
Computer Aided Drug Design
Computer Aided Drug DesignComputer Aided Drug Design
Computer Aided Drug Design
 
Nanomedicine
NanomedicineNanomedicine
Nanomedicine
 
Organs on chip ppt
Organs on chip pptOrgans on chip ppt
Organs on chip ppt
 
Use of Nanotechnology in Diagnosis and Treatment of Cancer
Use of Nanotechnology in Diagnosis and Treatment of CancerUse of Nanotechnology in Diagnosis and Treatment of Cancer
Use of Nanotechnology in Diagnosis and Treatment of Cancer
 
Organ on a chip- replacement of laboratory animal
Organ on a chip- replacement of laboratory animalOrgan on a chip- replacement of laboratory animal
Organ on a chip- replacement of laboratory animal
 
Bioinformatics
BioinformaticsBioinformatics
Bioinformatics
 
Role of bioinformatics and pharmacogenomics in drug discovery
Role of bioinformatics and pharmacogenomics in drug discoveryRole of bioinformatics and pharmacogenomics in drug discovery
Role of bioinformatics and pharmacogenomics in drug discovery
 
Organ-on-chip
Organ-on-chipOrgan-on-chip
Organ-on-chip
 

Similar to NEUROINFORMATICS

Human brain project 2010
Human brain project 2010Human brain project 2010
Human brain project 2010
Karlos Svoboda
 
STUDY AND IMPLEMENTATION OF ADVANCED NEUROERGONOMIC TECHNIQUES
STUDY AND IMPLEMENTATION OF ADVANCED NEUROERGONOMIC TECHNIQUES STUDY AND IMPLEMENTATION OF ADVANCED NEUROERGONOMIC TECHNIQUES
STUDY AND IMPLEMENTATION OF ADVANCED NEUROERGONOMIC TECHNIQUES
acijjournal
 
Brain Computer Interface Next Generation of Human Computer Interaction
Brain Computer Interface Next Generation of Human Computer InteractionBrain Computer Interface Next Generation of Human Computer Interaction
Brain Computer Interface Next Generation of Human Computer Interaction
Saurabh Giratkar
 
Brain computer interfaces in medicine
Brain computer interfaces in medicineBrain computer interfaces in medicine
Brain computer interfaces in medicine
Karlos Svoboda
 
PPT on mind reading computer
 PPT on mind reading computer PPT on mind reading computer
PPT on mind reading computer
Anjali Agarwal
 

Similar to NEUROINFORMATICS (20)

Human brain project 2010
Human brain project 2010Human brain project 2010
Human brain project 2010
 
Brain-Chips.pptx
Brain-Chips.pptxBrain-Chips.pptx
Brain-Chips.pptx
 
Current trends in cognitive science and brain computing research 18th june 2020
Current trends in cognitive science and brain computing research 18th june 2020Current trends in cognitive science and brain computing research 18th june 2020
Current trends in cognitive science and brain computing research 18th june 2020
 
Seminar blue brain
Seminar blue brainSeminar blue brain
Seminar blue brain
 
STUDY OF BRAIN MACHINE INTERFACE SYSTEM
STUDY OF BRAIN MACHINE INTERFACE SYSTEMSTUDY OF BRAIN MACHINE INTERFACE SYSTEM
STUDY OF BRAIN MACHINE INTERFACE SYSTEM
 
STUDY AND IMPLEMENTATION OF ADVANCED NEUROERGONOMIC TECHNIQUES
STUDY AND IMPLEMENTATION OF ADVANCED NEUROERGONOMIC TECHNIQUES STUDY AND IMPLEMENTATION OF ADVANCED NEUROERGONOMIC TECHNIQUES
STUDY AND IMPLEMENTATION OF ADVANCED NEUROERGONOMIC TECHNIQUES
 
Albus
AlbusAlbus
Albus
 
An Overview On Neural Network And Its Application
An Overview On Neural Network And Its ApplicationAn Overview On Neural Network And Its Application
An Overview On Neural Network And Its Application
 
Brain Computer Interface Next Generation of Human Computer Interaction
Brain Computer Interface Next Generation of Human Computer InteractionBrain Computer Interface Next Generation of Human Computer Interaction
Brain Computer Interface Next Generation of Human Computer Interaction
 
Brain computer interfaces in medicine
Brain computer interfaces in medicineBrain computer interfaces in medicine
Brain computer interfaces in medicine
 
Brain computer Interface
Brain computer InterfaceBrain computer Interface
Brain computer Interface
 
blue brain
blue brainblue brain
blue brain
 
[IJET V2I2P20] Authors: Dr. Sanjeev S Sannakki, Ms.Anjanabhargavi A Kulkarni
[IJET V2I2P20] Authors: Dr. Sanjeev S Sannakki, Ms.Anjanabhargavi A Kulkarni[IJET V2I2P20] Authors: Dr. Sanjeev S Sannakki, Ms.Anjanabhargavi A Kulkarni
[IJET V2I2P20] Authors: Dr. Sanjeev S Sannakki, Ms.Anjanabhargavi A Kulkarni
 
Brain computer interaction and medical access to the brain
Brain computer interaction and medical access to the brainBrain computer interaction and medical access to the brain
Brain computer interaction and medical access to the brain
 
Smart Brain Wave Sensor for Paralyzed- A Real Time Implementation
Smart Brain Wave Sensor for Paralyzed- A Real Time ImplementationSmart Brain Wave Sensor for Paralyzed- A Real Time Implementation
Smart Brain Wave Sensor for Paralyzed- A Real Time Implementation
 
PPT on mind reading computer
 PPT on mind reading computer PPT on mind reading computer
PPT on mind reading computer
 
Blue Brain
Blue BrainBlue Brain
Blue Brain
 
Pallavi ranjan
Pallavi ranjanPallavi ranjan
Pallavi ranjan
 
ANN presentataion
ANN presentataionANN presentataion
ANN presentataion
 
Cognitive systems
Cognitive  systemsCognitive  systems
Cognitive systems
 

NEUROINFORMATICS

  • 1. NEUROINFORMATICS Presented by Ranjana N Dept. of Biotechnology BIET, Davangere. Seminar Guide Mr Prakash K K Asst. Professor Department of Biotechnology BIET, Davangere. 1
  • 2. AGENDA  Introduction  Database development and management  Overview of NIRS  Computational Neuroscience  Current research and applications  Hurdles faced by emerging Neuroinformatics  Conclusion  References 2
  • 3. Introduction  Understanding the human brain is one of the biggest challenges of the 21st century.  Abilities such as information perception, processing, decision-making and action are far more complicated than the man made systems.  This presentation focuses on the overview of Neuroinformatics and its potential applications. 3
  • 4. Introduction Neuroscience  Field of science devoted to the scientific study of nerves especially their relation to behavior and learning.  Deals with the anatomy, physiology, biochemistry and molecular biology of the nervous system. Neuroinformatics  Combination of neuroscience and information science.  Deals with the organization of neuroscience data and application of computational models and analytical tools. 4
  • 5. NEUROINFORMATICS Neuroinformatics Neuroscience data and knowledge bases of nervous system Mathematical modelling: Computational neuroscience Tools for data acquisition, analysis, visualization and distribution 5
  • 7. Contents  Database  Collaborative web portal application  Web-based data entry  Tablet based data entry  Quality assurance and quality control 7
  • 8. 8
  • 9. Neuroinformatics Repository System Generate protocol and provide stimulus material Obtain subject responses Pass collected data through data cleaner Provide data models Perform data extraction Implement data querying Analyse data queried Provide data to visualization tools Generate reports End 9
  • 10. Computational Neuroscience  Study of the information processing properties of the neurons. Neurocomputing Method:  A logical model is used to map the cognitive functions of the brain.  An abstract intelligence theory is developed which explains the mechanisms of the brain.  This will enable the development of cognitive computers that perceive, think and learn. 10
  • 11. Single Neuron Modelling  A neuron model is a mathematical description of the properties of neurons that is designed to accurately describe and predict biological process.  Hodgkin Huxley model  Wang Buzsaki model  Cable theory 11
  • 12. • Hodgkin Huxley model:  The first mathematical model of action potential.  It predicts the mechanism of the action potential.  Wang Buzsaki Model:  Interaction between the neurons within the hippocampus and neocortex and their storage, processing and transmission of information. 12
  • 13. • Cable theory  Deals with the biophysical aspects of the neuron.  This theory takes into account the input conductance at the base, total surface area of the tree and the electro tonic length of the dendrite which depends on its length, diameter and resistance. 13
  • 14. Implementation of Computational tools  Memory and synapses.  Cognition, discrimination and learning. Role of their studies in building intelligent machines. 14
  • 15. CURRENT RESEARCH AND EXPERIMENTS  BRAIN-GENE ONTOLOGY  HUMAN BRAIN MAPPING  CURRENT ATLAS TOOLS 15
  • 16. BRAIN-GENE ONTOLOGY  Teaching and research tool  Includes concepts, facts, graphs, animations related to brain functions, diseases, genetics.  Computational Neurogenetic Modelling (CNGM).  Data sources: Gene Ontology, Unified Medical Language System (UMLS), PubMed, gene expression databases.  3D animation of various concepts and data. Applications of BGO:  Brain organization and function  Gene regulatory network  Simulation modelling 16
  • 17. HUMAN BRAIN MAPPING  Set of techniques used to view the structural and functional aspects of the brain onto a spatial representation called Maps.  Study of anatomy and functions of the brain and spinal cord through the use of imaging techniques, cellular biology, molecular genetics, biomedical engineering and neurophysiology. 17
  • 18. CURRENT ATLAS TOOLS TALAIRACH COORDINATE SYSTEM  3-dimensional system used to map the location of the brain structures.  Make inferences about tissue identity by referring to the atlas. 18
  • 19. BRAIN-COMPUTER INTERFACE  Communication pathway between brain and external device.  Repairing or assisting in human cognitive or sensory motor functions.  Types:  Invasive- directly into the grey matter  Partially invasive- inside the skull, outside the brain Electrocorticography (ECoG)  Non invasive- portable; EEG 19
  • 20. 20
  • 21. HURDLES FACED BY THE EMERGING NI SYSTEM  Research culture issues- information not disclosed  Meta Data- data about the data  Tools- expensive software development and lack of initiative among the programmers.  Ethical concerns- anonymity and inappropriate use of information  Legal Aspects- protection for- the creator, the user and the person subjected to. 21
  • 22. CONCLUSION  Due to the advancement of IT, huge amount of neuroscience data is being generated, analysed and interpreted by using computational tools with the help of Neuroinformatics.  However, emerging of this new technology poses various challenges that must be overcome by effective functioning of NI system.  Since NI is an emerging field, it is important to encourage young minds to contribute to this field and also involve organizations world wide to provide an international collaboration.  Nevertheless, Neuroinformatics plays a crucial role in the modern trend techniques such as human brain mapping- used in research and crime labs and also BCIs, which is used in treating various brain diseases, all ultimately working for the betterment of human life. 22
  • 23. REFERENCES  Wang, Y. (2009), On Abstract Intelligence: Toward a Unified Theory of Natural, Artificial, Machinable, and Computational Intelligence, Int’l Journal of Software  Science and Computational Intelligence, 1(1), 1-18.  International Neuroinformatics Coordinating Facility: http://www.incf.org/  http://www.neuro.gatech.edu/groups/potter/index.html  Henry J. Bockholt, Mark Scully, William Courtney, Srinivas Rachakonda, Adam Scott, Arvind Caprihan, Jill Fries, Ravi Kalyanam, Judith M. Segall, Raul de la Garza, Susan Lane1 and Vince D. Calhoun (2010). Mining the Mind Research Network  Pradeep et al. (2013) US States Patent. 23
  • 24. REFERENCES  Cannon, R.C., Howell, F.W., Goddard, N.H., De Schutter, E. (2002) Non-curated distributed databases for experimental data and models in neuroscience. Network: Computation in Neural Systems 13, 415-428.  De Schutter, E. ed. (2000) Computational Neuroscience: Realistic modelling for experimentalists. Boca Raton, FL: CRC Press, 2000.  Eckersley, P., Egan G.F. et al. (2002) Neuroinformatics data and tools sharing: a discussion paper on ethical and legal issues”, Journal of Neuroinformatics.  Lancaster, J.L., Woldorff, M.G., Parsons, L.M., Liotti, M., Freitas, C.S., Rainey, L., Kochunov, P.V., Nickerson, D., Mikiten, S.A., and Fox, P.T. (2000) Automated Talairach atlas labels for functional brain mapping. Human Brain Mapping, 10, 120-31. 24
  • 25. 25