Download Machine learning in neuroscience - Computational Intelligence Group

Machine learning in neuroscience
Bojan Mihaljevic, Luis Rodriguez-Lujan
Computational Intelligence Group
School of Computer Science, Technical University of Madrid
2015 IEEE Iberian Student Branch Congress
April 24th , Madrid
B. Mihaljevic, L. Rodriguez-Lujan (UPM)
Apr, 2015
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Outline
1
Introduction
2
Methods
Machine learning
Bayesian networks
Directional statistics
3
Applications
Introduction to neuroscience
Neuron classification
Morphological simulation
Soma and spines
DRCMST
Other applications
4
Future work
B. Mihaljevic, L. Rodriguez-Lujan (UPM)
Apr, 2015
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Computational Intelligence Group
At Artificial Intelligence Department, School of Computer Science
Since 2008
2 full professors, 1 associate professor, 1 post-doc, and 11 PhD
students
B. Mihaljevic, L. Rodriguez-Lujan (UPM)
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Outline
1
Introduction
2
Methods
Machine learning
Bayesian networks
Directional statistics
3
Applications
Introduction to neuroscience
Neuron classification
Morphological simulation
Soma and spines
DRCMST
Other applications
4
Future work
B. Mihaljevic, L. Rodriguez-Lujan (UPM)
Apr, 2015
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A useful tool
http://en.wikipedia.org/wiki/File:No-spam.png http://commons.wikimedia.org/wiki/File:Logo_Youtube.svg
B. Mihaljevic, L. Rodriguez-Lujan (UPM)
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Data-driven
Learn from data what you cannot program (well) explicitly
Large amounts of data these days
Typically, we assume data comes as attribute values
X1
1.40
-0.31
-0.01
X2
A
B
B
X3
10003
2039
7383
X4
-24
21
70
X5
D
C
U
Goal: learn some function over X
Related terms: data mining, pattern recognition, data science...
B. Mihaljevic, L. Rodriguez-Lujan (UPM)
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Tasks
Classification (discrete target
variable)
Regression (real-valued target
variable)
Clustering (hidden discrete target
variable)
Others: collaborative filtering,
market basket analysis, etc.
B. Mihaljevic, L. Rodriguez-Lujan (UPM)
http://commons.wikimedia.org/
wiki/File:Social_Red.jpg
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Multiple models
Some of them:
P(x, c) = P(c)P(x1 |c)P(x2 |c)P(x3 |c)P(x4 |c)P(x5 |c)
Naive Bayes
k nearest neighbors
p(c | x, w) = Ber (y | sigm(xT x))
Logistic regression
Hastie, T., Tibshirani, R., Friedman, J., (2009).
The elements of statistical learning (Vol. 2, No.
1). New York: Springer
B. Mihaljevic, L. Rodriguez-Lujan (UPM)
Decision tree
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Toolbox
Many different tools to extract models from data
Optimization (often heuristic)
I
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Combinatorial
Continuous
Information theory
Probability theory and statistics
I
Inherent uncertainty (e.g., noise; prediction confidence)
...
B. Mihaljevic, L. Rodriguez-Lujan (UPM)
Apr, 2015
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Outline
1
Introduction
2
Methods
Machine learning
Bayesian networks
Directional statistics
3
Applications
Introduction to neuroscience
Neuron classification
Morphological simulation
Soma and spines
DRCMST
Other applications
4
Future work
B. Mihaljevic, L. Rodriguez-Lujan (UPM)
Apr, 2015
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Underpinning: conditional independence
Many random variables: intractable distributions
I
20 binary variables mean 220 − 1 parameters in the joint distribution
Fortunately, some variables are sometimes independent of others
I
E.g., if I know that it is very warm, then knowing that it is summer
might not make it more likely that many people will be on the beach
Factor a joint distribution into smaller local ones
P(X1 , X2 , X3 . . . , Xn ) = P(X1 )P(X2 | X1 )P(X3 , . . . , Xn | X1 , X2 )
B. Mihaljevic, L. Rodriguez-Lujan (UPM)
Apr, 2015
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Representation
Directed acyclic graph
I
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Nodes = variables
Arcs encode
conditional
independencies
A local distribution for
each parents’ values
combination
P(x) =
Qn
i=1 P(xi
| pa(xi ))
Can greatly reduce number of parameters
http://commons.wikimedia.org/wiki/File:SimpleBayesNet.svg
B. Mihaljevic, L. Rodriguez-Lujan (UPM)
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Inference
B. Mihaljevic, L. Rodriguez-Lujan (UPM)
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Inference
B. Mihaljevic, L. Rodriguez-Lujan (UPM)
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Some research topics
Learning from data
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NP-hard in the general case
Conditional-independence tests
Structure scoring (optimization)
Inference
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NP-complete in the general case
Exact
Approximate
Classifiers
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Specialized learning algorithms
”Non-standard” local probability distributions
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Hybrid networks
Mixtures of polynomials
Directional variables
B. Mihaljevic, L. Rodriguez-Lujan (UPM)
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Outline
1
Introduction
2
Methods
Machine learning
Bayesian networks
Directional statistics
3
Applications
Introduction to neuroscience
Neuron classification
Morphological simulation
Soma and spines
DRCMST
Other applications
4
Future work
B. Mihaljevic, L. Rodriguez-Lujan (UPM)
Apr, 2015
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Directional statistics
Deal with directions, axes, rotations
Cannot be studied as regular real-valued variables. Periodicity
Real world data: Wind, animal behaviour, neuroscience, ...
B. Mihaljevic, L. Rodriguez-Lujan (UPM)
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Representation and methods
Different ways to represent directional data
Directional probability distributions
B. Mihaljevic, L. Rodriguez-Lujan (UPM)
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Research topics in CIG
Bayesian networks
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Different local distributions
Multi-dimensional classifiers
Learning classifiers
Big Data
...
Heuristic optimization
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Multi-objetive
Estimation of distribution algorithms (probabilistic evolutionary)
Applications
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Neuroscience
Scientometrics
Bioinformatics
B. Mihaljevic, L. Rodriguez-Lujan (UPM)
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Outline
1
Introduction
2
Methods
Machine learning
Bayesian networks
Directional statistics
3
Applications
Introduction to neuroscience
Neuron classification
Morphological simulation
Soma and spines
DRCMST
Other applications
4
Future work
B. Mihaljevic, L. Rodriguez-Lujan (UPM)
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Projects and collaborations
Projects
Collaborations
Companies
B. Mihaljevic, L. Rodriguez-Lujan (UPM)
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Outline
1
Introduction
2
Methods
Machine learning
Bayesian networks
Directional statistics
3
Applications
Introduction to neuroscience
Neuron classification
Morphological simulation
Soma and spines
DRCMST
Other applications
4
Future work
B. Mihaljevic, L. Rodriguez-Lujan (UPM)
Apr, 2015
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The brain
Scientific study of the nervous system. Molecular and cellular
neuroscience
We do not study the brain at macro level (yet)...
... but on a micro scale: Neurons
100 billion neurons in the brain
180.000 kilometers of wiring (myelinated white fibers)
B. Mihaljevic, L. Rodriguez-Lujan (UPM)
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Neurons
Three main parts: Soma, dendrites and axon
Neurons communicate with each other using electro-chemical signals
Significant differences between neurons
B. Mihaljevic, L. Rodriguez-Lujan (UPM)
Apr, 2015
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Outline
1
Introduction
2
Methods
Machine learning
Bayesian networks
Directional statistics
3
Applications
Introduction to neuroscience
Neuron classification
Morphological simulation
Soma and spines
DRCMST
Other applications
4
Future work
B. Mihaljevic, L. Rodriguez-Lujan (UPM)
Apr, 2015
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’Gardener’ Classification
There is an accepted catalogue of neuron types and names
But lack of a consistent terminology
Every neuroanatomist has is own classification scheme
B. Mihaljevic, L. Rodriguez-Lujan (UPM)
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Towards a consensus in naming
Learning from the experts
Gather data from 42 experts
Learn a model (Bayesian network) for each expert
B. Mihaljevic, L. Rodriguez-Lujan (UPM)
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Towards a consensus in naming
Differences among experts
Six clusters of experts (Bayesian network clustering)
B. Mihaljevic, L. Rodriguez-Lujan (UPM)
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Outline
1
Introduction
2
Methods
Machine learning
Bayesian networks
Directional statistics
3
Applications
Introduction to neuroscience
Neuron classification
Morphological simulation
Soma and spines
DRCMST
Other applications
4
Future work
B. Mihaljevic, L. Rodriguez-Lujan (UPM)
Apr, 2015
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Morphological simulation
Denditric trees
Why so different denditric tree shapes?
Determine interconnectivity and functional roles
B. Mihaljevic, L. Rodriguez-Lujan (UPM)
Apr, 2015
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Morphological simulation
Variables
More than 40 variables
Evidence and construction variables
B. Mihaljevic, L. Rodriguez-Lujan (UPM)
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Morphological simulation
B. Mihaljevic, L. Rodriguez-Lujan (UPM)
Apr, 2015
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Outline
1
Introduction
2
Methods
Machine learning
Bayesian networks
Directional statistics
3
Applications
Introduction to neuroscience
Neuron classification
Morphological simulation
Soma and spines
DRCMST
Other applications
4
Future work
B. Mihaljevic, L. Rodriguez-Lujan (UPM)
Apr, 2015
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Soma spatial characterization
Descriptors based on the level curves of a level set function
Hybrid Gaussian and angular Bayesian network
B. Mihaljevic, L. Rodriguez-Lujan (UPM)
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Spines
Related with brain functions like learning and memory
3D active contours to repair fragmented spines
Hybrid spatial DBN
B. Mihaljevic, L. Rodriguez-Lujan (UPM)
Apr, 2015
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Outline
1
Introduction
2
Methods
Machine learning
Bayesian networks
Directional statistics
3
Applications
Introduction to neuroscience
Neuron classification
Morphological simulation
Soma and spines
DRCMST
Other applications
4
Future work
B. Mihaljevic, L. Rodriguez-Lujan (UPM)
Apr, 2015
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Main idea
Degree-constrained minimum spanning tree
Degree constraints
Restrict the role of the nodes in the tree to root, intermediate or
leaf node
Novel permutation-based representation to encode forests of
DRCMST
B. Mihaljevic, L. Rodriguez-Lujan (UPM)
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Example
20 points where we are interested in building a forest of three trees
B. Mihaljevic, L. Rodriguez-Lujan (UPM)
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Application to Neuroscience
Applied to optimal neuronal wiring
B. Mihaljevic, L. Rodriguez-Lujan (UPM)
Apr, 2015
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Outline
1
Introduction
2
Methods
Machine learning
Bayesian networks
Directional statistics
3
Applications
Introduction to neuroscience
Neuron classification
Morphological simulation
Soma and spines
DRCMST
Other applications
4
Future work
B. Mihaljevic, L. Rodriguez-Lujan (UPM)
Apr, 2015
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Medical applications
Medical decision support systems: Neonatal jaundice treatment
B. Mihaljevic, L. Rodriguez-Lujan (UPM)
Apr, 2015
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Other applications
DNA microarray analysis
Immunology
Alzheimer
Parkinson
B. Mihaljevic, L. Rodriguez-Lujan (UPM)
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Outline
1
Introduction
2
Methods
Machine learning
Bayesian networks
Directional statistics
3
Applications
Introduction to neuroscience
Neuron classification
Morphological simulation
Soma and spines
DRCMST
Other applications
4
Future work
B. Mihaljevic, L. Rodriguez-Lujan (UPM)
Apr, 2015
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Integration
B. Mihaljevic, L. Rodriguez-Lujan (UPM)
Apr, 2015
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BN & Big data
B. Mihaljevic, L. Rodriguez-Lujan (UPM)
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Big data in neuroscience
Functional Magnetic Resonance
Imaging (fMRI)
Single Photon Emission
Computed Tomography
(SPECT)
B. Mihaljevic, L. Rodriguez-Lujan (UPM)
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Contact us!
Summer School 2015
Computational Intelligence Group
[email protected]
[email protected]
http://cig.fi.upm.es
B. Mihaljevic, L. Rodriguez-Lujan (UPM)
Apr, 2015
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Machine learning in neuroscience
Bojan Mihaljevic, Luis Rodriguez-Lujan
Computational Intelligence Group
School of Computer Science, Technical University of Madrid
2015 IEEE Iberian Student Branch Congress
April 24th , Madrid
B. Mihaljevic, L. Rodriguez-Lujan (UPM)
Apr, 2015
48 / 48