From Brain Operating Principles to Computer Technology

1. From Brain Operating Principles to Computer Technology Michael A. ArbibComputer Science, Neuroscience and the USC Brain ProjectUniversity of Southern CaliforniaLos Angeles, CA 90089-2520arbib@pollux.usc.edu

2. Rounds of Neural Computing • Round 1: • Hebbian adaptation and Perceptrons: adaptation and self-organization in neural networks • Reinvigorated in the 1980s as work on reinforcement learning and backpropagation extended earlier insights. • Round 2: • Compartmental modeling of the neuron • Mead (1989) built on his earlier work on digital VLSI (Mead and Conway) to show how to exploit neuromorphic function in highly parallel analog VLSI

3. A Prospectus for Round 3 • Analyzing the architecture of the primate brain • to extract neural information processing principles and • translate them into biologically-inspired operating systems and computer architectures • interplay between feedforward and feedback pathways • sharing of neural resources between perception and action • the role of plasticity in sensory, motor and “central” processing • A Contrast: • Rounds 1 and 2 are based on brain capabilities that come from [components of] single neurons (synaptic plasticity and dendritic tree complexity, respectively) • Work on Round 3 will develop the theme that many of the most interesting capabilities of the brain result not just from the individual component mechanisms, but from large scale organization as well

4. A Case Study: Cerebellum Cerebellar Cortex Inhibitory Sculpting Cerebellar Nucleus Modulation Motor Pattern Generators A small piece of A Purkinje Cell (PC) cerebellar cortex Input Patterns and Context Error Signals Motor Output

5. The Cerebellar Module: The Microcomplex to the MPG • The Microcomplex: a patch of cerebellar cortex and the nuclear cells it inhibits; modulating the activity of one MPG • Schematic of our cerebellar model: • Inputs arrive via mossy fibers (MF); • nuclear cells (NUC) generate output; • training signals are carried by climbing fibers (CF) from the inferior olive (IO) which depress the strength of PFPC synapses • (plus further subtleties!!)

6. The Role of the CerebellumModulation and coordination of MPGs is also critical for motor skill learning Parallel fibers Cerebellar Microcomplex A Cerebellar Microcomplex B Motor Pattern Generators MPG A MPG B • Hypothesis: The cerebellum adjust the parameters of MPGs • To tune MPGs: adjusting metrics within a movement • To coordinate MPGs: grading the coordination between motor components Plasticity within this system provides subtle parameter adjustment dependent on an immense wealth of context. In most cases, the tuning often depends crucially on the uniquely rich combinatorics of mossy fibers and granule cells, and so cannot be replaced by processing in other regions.

7. Lessons from the Cerebellum • This brief review of the cerebellum shows three things: • The special type of learning involved  learning how to reduce errors by adjusting the inhibitory sculpting to apply in different contexts • The immense subtlety of individual neurons, • The way these details are all embedded within a high-level architecture. • Points (1), (2) and (3) correspond to what I call Rounds 1, 2 and 3 of neural computing.

8. A Few Brain Operating Principles • Winner-Take-All Extensively used in several models • Dynamic re-mapping • Double saccade • Path integration for locomotion • Reinforcement learning • Actor-critic model • Hierarchical Reinforcement learning • Competitive Queuing • Attention control: the combination of a “saliency map” with an “inhibition of return” mechanism forms the basic mechanism for controlling attention deployment in contemporary computational models of focal visual attention • Parallel recall from long term memory

9. What is a Database in the Brain? • Classic Database Style: • Maintain (perhaps in a federation of databases) a coherent set of correct up-to-date master data • Provide a set of “Views” customized to different users • Passive data with external inference engines • The Brain’s “Database Style” is Cooperative Computation: • Maintain different views of the “data” as separate entities each separately updatable by experience • Coordinate views (more or less) as they are dynamically integrated for action in novel situations • Active schemas which integrate data and the processes for deploying them

10. The Claim: Brain Operating Principles have much to offer for future Computer Technology • But there’s a paradox: • millions or billions • 15 to 25 • 8 plus 2 • six billion

11. Point and Counterpoint • I argue • for the promise for computer science of developing an explicit formulation of the brain’s approach to “reusable computing” by adding evolutionary refinements to augment available circuitry to handle new tasks • that what is known about the organization and architecture of these capabilities is also critical to the development of a new approach to computer architecture and operating systems. • However, new architectural developments will include, but not be restricted by, biological principles: • Example: the inclusion of a non-biological reflection technologywill allow the re-use of biological computing strategies in a way that in biology is available only on an evolutionary time scale.

12. Programmable, Reflective Self-Organization • The goal: Extendingbrain-style computingby augmentingself-organizationwith “wrappings-based” programmingto mobilize resources for each new problem • Seeking to exploit an understanding of how the brain marshals the specialized capabilities of different subsystems such as • multiple levels of sensory analysis and integration • declarative and episodic memory • planning and motor control • emotion and social interaction • language and other communication interfaces • Issue: How can we have the wrappings/high-level specifications (the essence of a reflective architecture) keep track of the distributed self-organization of successful systems so that emergent resources can be recognized as providing approximate solutions to subproblems? • Aim: To have novel problems programmed by negotiating assemblages of resources …

13. Focusing on the Mirror • We now consider a dramatic pattern of "re-use" in a neural architecture, focused on a whole progression of neural systems concerned with behaviors ranging from • the visual control of grasping to • the mirror system: action recognition and even • the mirror system hypothesis: human language • Prior and continuing research • modeling the primate mirror system • extending the Mirror System Hypothesis • Round 3 of Neural Computation: • Studying how sensory, planning and executive stages of neural processing converge in a flexible manner to yield a very powerful integrated system • Building on this to translate high-level neural computation principles into new computer systems and architectures.

14. Visual Control of Grasping A recurring theme of mammalian “brain design”: parietal affordances are coupled tofrontal motor schemas AIP - grasp affordances in parietal cortex Hideo Sakata F5 - grasp commands in premotor cortex Giacomo Rizzolatti

15. A Mirror Neuron Rizzolatti, Fadiga, Gallese, and Fogassi, 1995: Premotor cortex and the recognition of motor actions This neuron is active for both execution and observation of a precision pinch

16. A New Approach to the Evolution of Human Language Homology Monkey [Not to scale] Human • Monkey F5 is homologous to human Broca’s area • Rizzolatti, G, and Arbib, M.A., 1998, Language Within Our Grasp, Trends in Neuroscience, 21(5):188-194. • Thus suggest an evolutionary basis for language parity rooting speech in communication based on manual gesture.

17. The Room-Brain is Sometimes Like a Colony of Reassignable Brains

18. Tracking • Bottom-Up Attention  Target acquisition  saccades • Top-Down Attention  Locating a designated target • Fovea versus Periphery • Retinal coordinates  Other reference frames • Sensor Fusion: Cues from different sensor sets • Smooth Pursuit • Social: Invoking extra cameras for different views • Generalizing the BOPs: • Going from 2 eyes to n cameras • What happens when you need to keep track of more objects and agents than you have cameras?

19. A Room with a View:Allocentric and Egocentric Coordinates Bio Strategy: Moving sensors to achieve a viewpoint World outside World outside Brain Brain Body Body Brain Brain Relate sensors to body frame World inside World inside Move body in world, & Move body in world, & Move sensors a little on body: Move sensors a little on body: Turn eyes, head, move arm, hand. Turn eyes, head, move arm, hand. Room Strategy: “Body” is fixed, it’s circum global Reach for what the eye is looking at Sensors may (a) move (b) form transientcoalitions. BOP: Sensor fusion Sensor data must be linked to room coordinates and/or effector coordinates Deploy locomotion as needed.

20. An Invitation • To learn more about this subject, take • CS564: Brain Theory and Artificial Intelligence • next Fall. • …. and read the book!!