Machine learning in neuroscience
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1 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 24 th, Madrid B. Mihaljevic, L. Rodriguez-Lujan (UPM) Apr, / 48
2 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, / 48
3 Computational Intelligence Group At Artificial Intelligence Department, School of Computer Science Since full professors, 1 associate professor, 1 post-doc, and 11 PhD students B. Mihaljevic, L. Rodriguez-Lujan (UPM) Apr, / 48
4 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, / 48
5 A useful tool B. Mihaljevic, L. Rodriguez-Lujan (UPM) Apr, / 48
6 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 X 1 X 2 X 3 X 4 X A D B C B U Goal: learn some function over X Related terms: data mining, pattern recognition, data science... B. Mihaljevic, L. Rodriguez-Lujan (UPM) Apr, / 48
7 Tasks Classification (discrete target variable) Regression (real-valued target variable) Clustering (hidden discrete target variable) Others: collaborative filtering, market basket analysis, etc. wiki/file:social_red.jpg B. Mihaljevic, L. Rodriguez-Lujan (UPM) Apr, / 48
8 Multiple models Some of them: P(x, c) = P(c)P(x 1 c)p(x 2 c)p(x 3 c)p(x 4 c)p(x 5 c) Naive Bayes k nearest neighbors p(c x, w) = Ber(y sigm(x T x)) Logistic regression Hastie, T., Tibshirani, R., Friedman, J., (2009). The elements of statistical learning (Vol. 2, No. 1). New York: Springer Decision tree B. Mihaljevic, L. Rodriguez-Lujan (UPM) Apr, / 48
9 Toolbox Many different tools to extract models from data Optimization (often heuristic) Combinatorial Continuous Information theory Probability theory and statistics Inherent uncertainty (e.g., noise; prediction confidence)... B. Mihaljevic, L. Rodriguez-Lujan (UPM) Apr, / 48
10 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, / 48
11 Underpinning: conditional independence Many random variables: intractable distributions 20 binary variables mean parameters in the joint distribution Fortunately, some variables are sometimes independent of others 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(X 1, X 2, X 3..., X n ) = P(X 1 )P(X 2 X 1 )P(X 3,..., X n X 1, X 2 ) B. Mihaljevic, L. Rodriguez-Lujan (UPM) Apr, / 48
12 Representation Directed acyclic graph Nodes = variables Arcs encode conditional independencies A local distribution for each parents values combination P(x) = n i=1 P(x i pa(x i )) Can greatly reduce number of parameters B. Mihaljevic, L. Rodriguez-Lujan (UPM) Apr, / 48
13 Inference B. Mihaljevic, L. Rodriguez-Lujan (UPM) Apr, / 48
14 Inference B. Mihaljevic, L. Rodriguez-Lujan (UPM) Apr, / 48
15 Some research topics Learning from data NP-hard in the general case Conditional-independence tests Structure scoring (optimization) Inference NP-complete in the general case Exact Approximate Classifiers Specialized learning algorithms Non-standard local probability distributions Hybrid networks Mixtures of polynomials Directional variables B. Mihaljevic, L. Rodriguez-Lujan (UPM) Apr, / 48
16 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, / 48
17 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) Apr, / 48
18 Representation and methods Different ways to represent directional data Directional probability distributions B. Mihaljevic, L. Rodriguez-Lujan (UPM) Apr, / 48
19 Research topics in CIG Bayesian networks Different local distributions Multi-dimensional classifiers Learning classifiers Big Data... Heuristic optimization Multi-objetive Estimation of distribution algorithms (probabilistic evolutionary) Applications Neuroscience Scientometrics Bioinformatics B. Mihaljevic, L. Rodriguez-Lujan (UPM) Apr, / 48
20 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, / 48
21 Projects and collaborations Projects Collaborations Companies B. Mihaljevic, L. Rodriguez-Lujan (UPM) Apr, / 48
22 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, / 48
23 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 kilometers of wiring (myelinated white fibers) B. Mihaljevic, L. Rodriguez-Lujan (UPM) Apr, / 48
24 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, / 48
25 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, / 48
26 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) Apr, / 48
27 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) Apr, / 48
28 Towards a consensus in naming Differences among experts Six clusters of experts (Bayesian network clustering) B. Mihaljevic, L. Rodriguez-Lujan (UPM) Apr, / 48
29 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, / 48
30 Morphological simulation Denditric trees Why so different denditric tree shapes? Determine interconnectivity and functional roles B. Mihaljevic, L. Rodriguez-Lujan (UPM) Apr, / 48
31 Morphological simulation Variables More than 40 variables Evidence and construction variables B. Mihaljevic, L. Rodriguez-Lujan (UPM) Apr, / 48
32 Morphological simulation B. Mihaljevic, L. Rodriguez-Lujan (UPM) Apr, / 48
33 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, / 48
34 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) Apr, / 48
35 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, / 48
36 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, / 48
37 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) Apr, / 48
38 Example 20 points where we are interested in building a forest of three trees B. Mihaljevic, L. Rodriguez-Lujan (UPM) Apr, / 48
39 Application to Neuroscience Applied to optimal neuronal wiring B. Mihaljevic, L. Rodriguez-Lujan (UPM) Apr, / 48
40 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, / 48
41 Medical applications Medical decision support systems: Neonatal jaundice treatment B. Mihaljevic, L. Rodriguez-Lujan (UPM) Apr, / 48
42 Other applications DNA microarray analysis Immunology Alzheimer Parkinson B. Mihaljevic, L. Rodriguez-Lujan (UPM) Apr, / 48
43 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, / 48
44 Integration B. Mihaljevic, L. Rodriguez-Lujan (UPM) Apr, / 48
45 BN & Big data B. Mihaljevic, L. Rodriguez-Lujan (UPM) Apr, / 48
46 Big data in neuroscience Functional Magnetic Resonance Imaging (fmri) Single Photon Emission Computed Tomography (SPECT) B. Mihaljevic, L. Rodriguez-Lujan (UPM) Apr, / 48
47 Contact us! Summer School 2015 Computational Intelligence Group B. Mihaljevic, L. Rodriguez-Lujan (UPM) Apr, / 48
48 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 24 th, Madrid B. Mihaljevic, L. Rodriguez-Lujan (UPM) Apr, / 48
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