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From Brain Operating Principles to Computer Technology. Michael A. Arbib Computer Science, Neuroscience and the USC Brain Project University of Southern California Los Angeles, CA 90089-2520 arbib@pollux.usc.edu. Rounds of Neural Computing. Round 1:

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from brain operating principles to computer technology

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

rounds of neural computing
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
a prospectus for round 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
a case study cerebellum
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

the cerebellar module the microcomplex
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!!)
slide6
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.

lessons from the cerebellum
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.
a few brain operating principles
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
what is a database in the brain
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
the claim brain operating principles have much to offer for future computer technology
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
point and counterpoint
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.
programmable reflective self organization
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 …
focusing on the mirror
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.
visual control of grasping
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

slide15

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

a new approach to the evolution of human language
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.

tracking
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?
a room with a view allocentric and egocentric coordinates
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.

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