NEUROINFORMATICS

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

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

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

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