Design of Self-Organizing Learning Array for Intelligent Machines

1. Design of Self-Organizing Learning Array for Intelligent Machines Janusz Starzyk School of Electrical Engineering and Computer Science Heidi Meeting June 3 2005 Motivation: How a new understanding of the brain will lead to the creation of truly intelligent machines from J. Hawkins “On Intelligence”

2. Elements of Intelligence • Abstract thinking and action planning • Capacity to learn and memorize useful things • Spatio-temporal memories • Ability to talk and communicate • Intuition and creativity • Consciousness • Emotions and understanding others • Surviving in complex environment and adaptation • Perception • Motor skills in relation to sensing and anticipation

3. Problems of Classical AI • Lack of robustness and generalization • No real-time processing • Central processing of information by a single processor • No natural interface to environment • No self-organization • Need to write software

4. Intelligent Behavior • Emergent from interaction with environment • Based on large number of sparsely connected neurons • Asynchronous • Self-timed • Interact with environment through sensory-motor system • Value driven • Adaptive

5. Design principles of intelligent systems from Rolf Pfeifer “Understanding of Intelligence” Design principles • synthetic methodology • time perspectives • emergence • diversity/compliance • frame-of-reference Agent design complete agent principle cheap design ecological balance redundancy principle parallel, loosely coupled processes sensory-motor coordination value principle

6. The principle of “cheap design” • intelligent agents: “cheap” • exploitation of ecological niche • economical (but redundant) • exploitation of specific physical properties of interaction with real world

7. Principle of “ecological balance” • balance / task distribution between • morphology • neuronal processing (nervous system) • materials • environment • balance in complexity • given task environment • match in complexity of sensory, motor, and neural system

8. The redundancy principle • redundancy prerequisite for adaptive behavior • partial overlap of functionality in different subsystems • sensory systems: different physical processes with “information overlap”

9. Generation of sensory stimulation through interaction with environment • multiple modalities • constraints from morphology and materials • generation of correlations through physical process • basis for cross-modal associations

10. The principle of sensory-motor coordination • Holk Cruse • •no central control • •only local neuronal communication • •global communication through environment • neuronal connections • self-structuring of sensory data through interaction with environment • physical process —not „computational“ • prerequisite for learning

11. The principle of parallel, loosely coupled processes • Intelligent behavior emergent from agent-environment interaction • Large number of parallel, loosely coupled processes • Asynchronous • Coordinated through agent’s –sensory-motor system –neural system –interaction with environment

12. The “value principle” • about motivation • evaluation of actions • frame-of-reference: explicit and implicit values • recent theorizing: information theoretic • (organism tries to mainting “flow of information”)

13. Human Brain at Birth 14 Years Old 6 Years Old Neuron Structure and Self-Organizing Principles 13

14. Neuron Structure and Self-Organizing Principles (Cont’d)

15. Motor cortex Somatosensory cortex Sensory associative cortex Pars opercularis Visual associative cortex Broca’s area Visual cortex Primary Auditory cortex Wernicke’s area Brain Organization

16. Minicolumn Organization and Self Organizing Learning Arrays • V. Mountcastle argues that all regions of the brain perform the same algorithm • SOLAR combines many groups of neurons (minicolumns) in a pseudorandom way • Each microcolumn has the same structure • Thus it performs the same computational algorithm satisfying Mountcastle’s principle • VB Mountcastle (2003). Introduction [to a special issue of Cerebral Cortex on columns]. Cerebral Cortex, 13, 2-4.

17. Cortical Minicolumns “The basic unit of cortical operation is the minicolumn… It contains of the order of 80-100 neurons except in the primate striate cortex, where the number is more than doubled. The minicolumn measures of the order of 40-50 m in transverse diameter, separated from adjacent minicolumns by vertical, cell-sparse zones … The minicolumn is produced by the iterative division of a small number of progenitor cells in the neuroepithelium.” (Mountcastle, p. 2) Stain of cortex in planum temporale.

18. Groupping of Minicolumns Groupings of minicolumns seem to form the physiologically observed functional columns. Best known example is orientation columns in V1. They are significantly bigger than minicolumns, typically around 0.3-0.5 mm and have 4000-8000 neurons Mountcastle’s summation: “Cortical columns are formed by the binding together of many minicolumns by common input and short range horizontal connections. … The number of minicolumns per column varies … between 50 and 80. Long range intracortical projections link columns with similar functional properties.” (p. 3)

19. Sparse Connectivity The brain is sparsely connected. (Unlike most neural nets.) A neuron in cortex may have on the order of 100,000 synapses. There are more than 1010 neurons in the brain. Fractional connectivity is very low: 0.001%. Implications:  • Connections are expensive biologically since they take up space, use energy, and are hard to wire up correctly. • Therefore, connections are valuable. • The pattern of connection is under tight control. • Short local connections are cheaper than long ones. Our approximation makes extensive use of local connections for computation.

20. Introducing Self-Organizing Learning Array SOLAR • SOLAR is a regular array of identical processing cells, connected to programmable routing channels. • Each cell in the array has ability to self-organize by adapting its functionality in response to information contained in its input signals. • Cells choose their input signals from the adjacent routing channels and send their output signals to the routing channels. • Processing cells can be structured to implement minicolumns

21. SOLAR Hardware Architecture

22. SOLARRouting Scheme

23. PCB SOLAR XILINX VIRTEX XCV 1000

24. System SOLAR

25. Wiring in SOLAR Initial wiring and final wiring selection for credit card approval problem

26. SOLAR Classification Results

27. Associative SOLAR

28. Associations made in SOLAR

29. Sensors Actuators Defining Simple Brain Reactive Associations Sensory Inputs Motor Outputs

30. Simple Brain Properties • Interacts with environment through sensors and actuators • Uses distributed processing in sparsely connected neurons • Uses spatio-temporal associative learning • Uses feedback for input prediction and screening input information for novelty

31. Sensors Actuators Brain Structure with Value System Value System Action Planning Reinforcement Signal Anticipated Response Sensory Inputs Motor Outputs

32. Brain Structure with Value System Properties • Interacts with environment through sensors and actuators • Uses distributed processing in sparsely connected neurons • Uses spatio-temporal associative learning • Uses feedback for input prediction and screening input information for novelty • Develops an internal value system to evaluate its state in environment using reinforcement learning • Plans output actions for each input to maximize the internal state value in relation to environment • Uses redundant structures of sparsely connected processing elements

33. Value System in Reinforcement Learning Control States Controller Environment Value System Optimization Reinforcement Signal

34. Value System Action Planning Sensors Actuators Artificial Brain Organization Understanding Decision making Anticipated Response Reinf. Signal Motor Outputs Sensory Inputs

35. Artificial Brain Organization • Learning should be restricted to unexpected situation or reward • Anticipated response should have expected value • Novelty detection should also apply to the value system • Need mechanism to improve and compare the value

36. Value System Action Planning Sensors Actuators Artificial Brain Organization Understanding Improvement Detection Expectation Comparison Inhibition Novelty Detection Anticipated Response Reinf. Signal Motor Outputs Sensory Inputs

37. Artificial Brain Organization • Anticipated response block should learn the response that improves the value • A RL optimization mechanism may be used to learn the optimum response for a given value system and sensory input • Random perturbation should be applied to the optimum response to explore possible states and learn their the value • New situation will result in new value and WTA will chose the winner

38. Artificial Brain Organization Positive Reinforcement Negative Reinforcement Sensory Inputs Motor Outputs

39. Artificial Brain Selective Processing • Sensory inputs are represented by more and more abstract features in the sensory inputs hierarchy • Possible implementation is to use winner takes all or Hebbian circuits to select the best match • Random wiring may be used to preselect sensory features • Uses feedback for input prediction and screening input information for novelty • Uses redundant structures of sparsely connected processing elements

40. Microcolumn Organization superneuron WTA Positive Reinforcement Negative Reinforcement WTA WTA Sensory Inputs Motor Outputs

41. Superneuron Organization • Each microcolumn contains a number of superneurons • Within each microcolumn, superneurons compete on different levels of signal propagation • Superneuron contains a predetermined configuration of • Sensory (blue) • Motor and (yellow) • Reinforcement neurons (positive green and negative red) • Superneurons internally organize to perform operations of • Input selection and recognition • Association of sensory inputs • Feedback based anticipation • Learning inhibition • Associative value learning, and • Value based motor activation

42. Superneuron Organization • Sensory neurons are primarily responsible for providing information about environment • They receive inputs from sensors or other sensory neurons on lower level • They interact with motor neurons to represent action and state of environment • They provide an input to reinforcement neurons • They help to activate motor neurons • Motor neurons are primarily responsible for activation of motor functions • They are activated by reinforcement neurons with the help from sensory neurons • They activate actuators or provide an input to lower level motor neurons • They provide an input to sensory neurons • Reinforcement neurons are primarily responsible for building the internal value system • They receive inputs from reinforcement learning sensors or other reinforcement neurons on lower level • They receive inputs from sensory neurons • They provide an input to motor neurons • They help to activate sensory neurons

43. S1h S2h WTA S2 S3 WTA WTA S1 Sensory Neurons Interactions

44. WTA WTA WTA Sensory Neurons Functions • Sensory neurons are responsible for • Representation of inputs from environment • Interactions with motor functions • Anticipation of inputs and screening for novelty • Selection of useful information • Identifying invariances • Making spatio-temporal associations

45. Sensory Neurons Functions Sensory neurons • Represent inputs from environment by • Responding to activation from lower level (summation) • Selecting most likely scenario (WTA) • Interact with motor functions by • Responding to activation from motor outputs (summation) • Anticipate inputs and screen for novelty by • Correlation to sensory inputs from higher level • Inhibition of outputs to higher level • Select useful information by • Correlating its outputs with reinforcement neurons • Identify invariances by • Making spatio-temporal associations betweenneighbor sensory neurons

47. From Apparent Mess

48. To Clear Mind Organization WTA WTA WTA WTA