Arbib: Neural Computing, Round 3 1 From Brain Operating Principles to Computer Technology Michael A. Arbib Computer Science, Neuroscience and the USC Brain

Arbib: Neural Computing, Round 3 1

From Brain Operating Principles to Computer Technology

Michael A. ArbibComputer Science, Neuroscience

and the USC Brain ProjectUniversity of Southern California

Los Angeles, CA [email protected]


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

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 small piece of A Purkinje Cell (PC) cerebellar cortex

A Case Study: Cerebellum

Cerebellar Cortex

Cerebellar Nucleus

Motor Pattern Generators

Input Patterns and Context Error Signals

InhibitorySculpting

Modulation

Motor Output


The Cerebellar Module: The Microcomplex

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 PFPC synapses• (plus further subtleties!!)

The Microcomplex: a patch of cerebellar cortex and the nuclear cells it inhibits; modulating the activity of one MPG

to the MPG


The Role of the CerebellumModulation and coordination of MPGs is also critical for motor

skill learning

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.

CerebellarMicrocomplex A

MPG A

CerebellarMicrocomplex B

MPG B

Parallelfibers

MotorPattern

Generators


Lessons from the Cerebellum

This brief review of the cerebellum shows three things:

1) The special type of learning involved learning how to reduce errors by adjusting the inhibitory sculpting to apply in different contexts

2) The immense subtlety of individual neurons, 3) 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

Winner-Take-AllExtensively used in several models

Dynamic re-mapping Double saccade Path integration for locomotion

Reinforcement learningActor-critic modelHierarchical 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?

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

But there’s a paradox:

millions or billions

15 to 25

8 plus 2

six billion


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 technology will 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

The goal: Extending brain-style computing by augmenting self-organization with “wrappings-based” programming to 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

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

F5 - grasp commands inpremotor cortexGiacomo Rizzolatti

AIP - grasp affordancesin parietal cortexHideo Sakata

A recurring theme of mammalian “brain design”:parietal affordances are coupled to frontal motor schemas


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

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.

Monkey [Not to scale] Human

Homology


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


Tracking

Bottom-Up Attention Target acquisition saccades

Top-Down Attention Locating a designated targetFovea versus PeripheryRetinal coordinates Other reference framesSensor Fusion: Cues from different sensor setsSmooth PursuitSocial: Invoking extra cameras for different viewsGeneralizing 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?


Room Strategy:

“Body” is fixed, it’s circum global

Sensors may (a) move (b) form transientcoalitions. BOP: Sensor fusion

Sensor data must be linked to room coordinates and/or effector coordinates

Reach for what the eye is looking atDeploy locomotion as needed.

Brain

World inside

Brain

World inside

World outside

BrainBody

Move body in world, &Move sensors a little on body:Turn eyes, head, move arm, hand.

World outside

BrainBody

Move body in world, &Move sensors a little on body:Turn eyes, head, move arm, hand.

Relate sensors to body frame

Bio Strategy:Moving sensors to achieve a viewpoint

A Room with a View:Allocentric and Egocentric Coordinates


An Invitation

To learn more about this subject, takeCS564: Brain Theory and Artificial Intelligencenext Fall.

…. and read the book!!

Documents

Arbib: Neural Computing, Round 3 1 From Brain Operating Principles to Computer Technology Michael A. Arbib Computer Science, Neuroscience and the USC Brain