How to Make a Computer Think for You

1. How to Make a Computer Think for You Jeff Knisley, The Institute for Quantitative Biology, East Tennessee State University ALABAMA MAA STATE DINNER AND LECTURE, Feb, 2006

2. Soma Dendrites Synapses Axon This is a Neuron Signals Propagate from Dendrites to Soma Signals Decay at Soma if below a Certain threshold

3. Signals May Arrive Close Together If threshold exceeded, then neuron “fires,” sending a signal along its axon.

4. Neurons Form Networks

5. Artificial Neural Network (ANN) • Made of artificial neurons, each of which • Sums inputs from other neurons • Compares sum to threshold • Sends signal to other neurons if above threshold • Synapses have weights • Model relative ion collections • Model efficacy (strength) of synapse

6. Artificial Neuron Nonlinear firing function . . .

7. Firing Functions are Sigmoidal

8. Choose ith neuron at random and calculate its new state Hopfield Network Imagine Complete Connectivity with weights wij between ith and jth neurons Blue = 1 White = 0

9. Energy Define the energy to be Theorem: If the weights are symmetric, then the Energy decreases each time a neuron fires.

10. Applications: • Handwriting Recognition • http://faculty.etsu.edu/knisleyj/neural/neuralnet3.htm • Universal Classifier • http://faculty.etsu.edu/knisleyj/neural/neuralnet4.htm • Expert Systems • Rule-based rather than sequential programs • Air traffic control, Industrial Controls • Robotics • Usually using a 3-layer network

11. 3 Layer Neural Network The output layer may consist of a single neuron Output Input Hidden (is usually much larger)

12. Neural Nets can “Think” • A Neural Network can “think” for itself • Can be trained (programmed) to make decisions • Can be trained to classify information This tiny 3-Dimensional Artificial Neural Network, modeled after neural networks in the human brain, is helping machines better visualize their surroundings. http://www.nasa.gov/vision/universe/roboticexplorers/robots_like_people.html

13. The Mars Rovers • Must choose where to explore • Programmed to avoid “rough” terrain • Programmed to choose “smooth” terrain • ANN decides between “rough” and “smooth” • “rough” and “smooth” are ambiguous • Programmingis by means of many “examples”(lessons)

14. 1 2 3 4 Robot 5 6 7 8 Illustration: Colors = Terrains • As a robot moves, it defines 8 squares of size A that define directions it can move in • It should avoid red (rough) terrain • It should prefer green (smooth) terrain • It should be indifferent to blue (normal) terrain • It is impossible to program every possible shade and variation • Instead, a neural network is constructed • Terrain Block – Color class input/output patterns are used to train the network

15. “Houston, we have a problem!” 0.1 0 0 0.9 0.1 0 Train ANN to Classify Colors Training Set Input OutputTerrain <R,G,B> Red <1,0,0> Green <0,1,0> Blue ColorClass <0,0,1> TerrainExamples <1,0,0> Hidden <0,0,0>

16. ANN’s Can Also Think for Us • Mars Rovers do what Humans can do better • They do not “learn” on their own • They are taught by Humans who could make the same or better decisions in their place • They can use their “learning” independently • What about problems Humans can’t solve at all • Neural Networks can be used as savants dedicated to a single problem too complex for humans to decipher • Examples • Agent-Based Modeling • RNA, Protein Structures, DNA analysis • Data Mining

17. Division of Labor in Wasps Nests • A Wasp can alternate between laborer, water forager, and pulp forager • Hypothesis: Individual wasps choose roles so that total water in the nest reaches equilibrium • Hypothesis: Pulp production is maximized when total water in the nest is stable • Limited confirmation of Hypotheses • (Karsai and Wenzel, 2002) System of 3 ode’s • (Karsai, *Phillips, and Knisley, 2005) Computer Simulation

18. ANN Wasp Societies • Problems with current models • Role assigned randomly w.r.t. a Weibull distribution • Parameters selected so model “works” • ANN: Role is a decision of each wasp • A wasp is a special type of Artificial Neuron • Wasp decides its own role • Wasp learns to make good choices • System is deterministic yet unpredictable

19. “I’m empty, I need water!” “No luck, so I’m a water forager.”

20. “I’m so good at getting water, I think I should go forage for pulp.” “I have water!”

21. Division of Labor by choice? • Numbers of foragers goes up and down • But water level in nest becomes stable

22. Data Mining • We often consider very large data sets • Microarrays contain about 20,000 data points • Typical studies use 70 – 100 microarrays • Most of the data is not relevant • ANN’s can be trained to find hidden patterns • Input layer = Genes • Network is trained repeatedly with microarrays collected in various physiological states • ANN’s predict which genes are responsible for a given state

23. ANN’s in Data Mining • Each neuron acts as a “linear classifier” • Competition among neurons via nonlinear firing function = “local linear classifying” • Method for Genes: • Train Network until it can classify between control and experimental groups • Eliminating weights sufficiently close to 0 does not change local classification scheme • First results obtained with a Perceptron ANN

24. Simple Perceptron Model x1 = Gene 1 w1 x2 = Gene 2 w2 wn xn = Gene n The output is the “physiological state” due to the relative gene expression levels used as inputs.

25. Simple Perceptron Model • Features • The wi measure gene “significance” • Detects genes across n samples & references • Ref: Artificial Neural Networks for Reducing the Dimensionality of Gene Expression Data, A. Narayanan, et al. 2004. • Drawbacks • The Perceptron is a globaly linear classifier (i.e., only classifies linearly separable data) • We are now using a more sophisticated model

26. Linearly Separable Data Separation using Hyperplanes

27. Data that Cannot be separated Linearly

28. How do we select w’s • Define an energy function • x1,…,xn are inputs, y = output • t1,…,tm are the outputs associated with the patterns to be “learned” • y = s( S wixi - q) for a perceptron • Key: Neural networks minimize energy

29. Back Propagation • Minimize the Energy Function • Choose wi so that • In practice, this is hard • Back Propagation: s = s(1-s) • For each pattern ti • Feed Forward and Calculate E • Increment weights using a delta rule: • Repeat until E is sufficiently close to 0

30. Perceptron for Microarray DataMining • Remove % of genes with synaptic weights that are close to 0 • Create ANN classifier on reduced arrays • Repeat 1 and 2 until only the genes that most influence the classifer problem remain Remaining genes are most important in classifying experimentals versus controls

31. Functional Viewpoint • ANN is a mapping f: Rn→ R • Can we train perceptron so that f(x1,…,xn) =1 if x vector is from a control and f(x1,…,xn) =0 if x is from an experimental? • Answer: Yes if data can be linearly separated, but no otherwise unless we use better ANN’s! • General ANN’s also have problems • Spurious states: (sometimes ANN’s get the wrong answer) • Hard Margins: Training set must be perfect

32. Multilayer Network . . . . . .

33. How do we select w’s • Define an energy function • t vectors are the information to be “learned” • Neural networks minimize energy • The “information” in the network is equivalent to the minima of the total squared energy function

34. Back Propagation • Minimize the Energy Function • Choose wj and aj so that • In practice, this is hard • Back Propagation with cont. sigmoidal • Feed Forward and Calculate E • Modify weights using a d rule • Repeat until E is sufficiently close to 0

35. ANN as Classifer • (Cybenko) For any e>0, the function f(x1,…,xn) =1 if x vector is from a control and f(x1,…,xn) =0 if x is from an experimental can be approximated to within e by a multilayer neural network. • The weights no longer have the one-to-one correspondence to genes, so we test significance using Monte Carlo techniques.

36. ANN and Monte Carlo Methods • Monte Carlo methods have been a big success story with ANN’s • Error estimates with network predictions • ANN’s are very fast in the forward direction • Example: ANN+MC implement and outperform Kalman Filters (recursive linear filters used in Navigation and elsewhere)(De Freitas J. F. G., et. al., 2000)

37. Recall: Multilayer Network . . . . . . ajcorrespond to genes, but do not directly depend on a single gene. N Genes N node Hidden Layer

38. Naïve Monte Carlo ANN Method • Randomly choose subset S of genes • Train using Back Propagation • Prune based on values of wj (or aj , or both) • Repeat 2-3 until a small subset of S remains • Increase “count” of genes in small subset • Repeat 1-5 until each gene has 95% probability of appearing at least some minimum number of times in a subset • Most frequent genes are the predicted

39. Additional Considerations • If a gene is up-regulated or down-regulated for a certain condition, then put it into a subset in step 1 with probability 1. • This is a simple-minded Bayesian method. Bayesian analysis can make it much better. • Algorithm distributes naturally across a multi-processor cluster or machine • Choose the subsets first • Distribute subsets to different machines • Tabulate the results from all the machines

40. Summary • ANN’s are designed to make decisions in similar fashion to how we make decisions • In this way, they can “think” for themselves • Can be considered supplements to existing hardware and software tools • The ability of ANN’s to make decisions allows them to think for us as well! • They can find patterns in large data sets that we humans would likely never uncover • They can “think” for days/months on end

41. Any Questions?

42. References Cybenko, G. Approximation by Superpositions of a sigmoidal function, Mathematics of Control, Signals, and Systems, 2(4),1989, p. 303-314. De Freitas J. F. G., et. al. Sequential Monte Carlo Methods To Train Neural Network Models. Neural Computation, Volume 12, Number 4, 1 April 2000, pp. 955-993(39) L. Glenn and J. Knisley, Solutions for Transients in Arbitrarily Branching and Tapering Cables, Modeling in the Neurosciences: From Biological Systems to Neuromimetic Robotics, ed. Lindsay, R., R. Poznanski, G.N.Reeke, J.R. Rosenberg, and O.Sporns, CRC Press, London, 2004. A. Narayan, et. al Artificial Neural Networks for Reducing the Dimensionality of Gene Expression Data. Neurocomputing, 2004.