On communicating with semantic machines

Walter J Freeman

Department of Molecular & Cell Biology

University of California at Berkeley CA 94720-3200

http://sulcus.berkeley.edu

Abstract

Semantics is the essence of human communication. It

concerns the manufacture and use of symbols as

representations to exchange meanings. Information

technology is faced with the problem of using

intelligent machines as intermediaries for interpersonal

communication. The problem of designing such

semantic machines has been intractable because brains

and machines work on very different principles. A

solution to the problem is to describe how brains create

meaning and then express it in information by making a

symbol as a representation to another brain in pairwise

communication. Understanding of the neurodynamics by

which brains create meaning may enable engineers to

build devices with which they can communicate pairwise,

as they do now with colleagues, though not with words,

but with shared actions. 

Key Words: EEG patterns, intentionality,

neurodynamics, phase transitions, semantics

1. Introduction

The most challenging of the three branches of

semiotics is called semantics [5]. It deals with the

relation between meanings and representations, a

relation often referred to in artificial intelligence and

philosophy as 'intention', 'aboutness' (a thought,

belief or memory is 'about' something), or 'symbol

grounding'. Computers are very good in the other

two branches (syntactics, which is the relation of

symbol to symbol as found in dictionaries) and

pragmatics (the relations between symbol and action

like those of traffic signals). They are inept in

semantics. The reason for this ineptitude stems from

deep differences between brains and computers. von

Neumann [19] surmised nearly half a century ago:

"We have now accumulated sufficient evidence

to see that whatever language the central

nervous system is using, it is characterized by

less logical and arithmetic depth than we are

normally used to. ... Thus the outward forms

of our mathematics are not absolutely relevant

from the point of view of evaluating what the

mathematical or logical language truly used by

the central nervous system is. [W]hatever the

system is, it cannot fail to differ considerably

from what we consciously and explicitly

consider as mathematics. p. 81"

Brains are exceedingly capable of grasping the

salient features of complex situations and social

relationships, which are captured in such words as

'value', 'significance', 'import', or 'bottom line', in a

word, 'meaning'. It is my conclusion in this essay

that meanings exist only in brains, where they take

the place of the internal representations that

computers use. My conclusion is based on research

into the spatiotemporal patterns of active states of

brains in animals, that accompany and support the

animals' performance of the cognitive tasks involved

in learning to respond appropriately to simple stimuli

that signify events and circumstances that are vital to

their welfare. I find that sensory cortices receive the

information that the sensory receptors provide from

stimuli, and that this information, once it has arrived

in cortex, triggers the construction of activity patterns

in brains that constitute the meaning of the stimuli.

These patterns over-ride the sensory-driven

information [4], which is then discarded, so that

everything that an animal learns about its

environment has been constructed within its brain

from its own experiences.

In order to translate these findings into terms that

engineers will require to emulate in hardware the

performance of brains in wetware, some further

consideration of the biological basis of meaning is

required. A meaning state is an activity pattern that

occupies the entire available brain [3]. The

construction begins with formation within the animal

of an activity pattern that embodies its immediate

goal, such as food, shelter or a mate, the achievement

of which requires acquisition of information from the

environment. That information is got by intentional

action into the environment, followed by sensory

stimulation and learning from consequences of the

action. A stimulus such as a light, an odor, or a tone

contains information that serves to represent to the

animal the state of its environment. It is a material

object or process that is equivalent to a book, face, or

gesture for humans. It is a part of the environment

that has no meaning in itself. The French poet Paul

Valéry [18] wrote:

"I have already explained what I think of literal

representation; but one cannot insist enough on

this: there is no true meaning of a text. No
author's authority. Whatever he may have

wanted to say, he wrote what he wrote. Once

published, a text is like an implement that

everyone can use as he chooses and according to

his means: it is not certain that the maker could

use it better than someone else. p. 1597."

My analysis of brain activity patterns shows that

sensory cortical activity patterns that are triggered by

stimuli are selected by the stimuli but are determined

by the history and context of the relations of the

individual to the stimuli [4,5]. These brain activity

patterns are states of meaning. They occur in the

dynamic state space of a brain as trajectories of

discrete steps marked by cortical phase transitions.

The patterned active states are called wave packets [2].

The way in which they are made by the selforganizing brain dynamics that controls behavior is a

pivotal topic in this essay.

The dynamics of brains that creates meaning can

be emulated in computer models of brain function

[4,12,15]. This step requires that a major problem be

addressed: the relation between representation and

meaning in brain function. The Shannon-Weaver

information theory is representational, because it

divorces meaning from information and therefore does

not apply directly to brains. Shannon [16] wrote:

"The fundamental problem of communication is

that of reproducing at one point either exactly or

approximately a message selected at another

point. Frequently the messages have meaning;

that is they refer to or are correlated according to

some system with certain physical or conceptual

entities. These semantic aspects of

communication are irrelevant to the engineering

problem. p. 380."

In Section 2, I sketch some of the principal

elements of communication, as a basis for discussing

a pathway toward solutions through a better

understanding of the biological basis of meanings,

which grows from behavioral actions. Meaning

stems not from the rule-driven operations between

symbols embedded within syntactical systems such as

computers, nor from conventional 'computing with

words'. it stems from shared actions. In Section 3, I

summarize the main observations on sensory cortical

wave packets. In Section 4, I enlarge the description

to include the limbic system and the origin of

intentional behavior. In Section 5, I take up the

critical difference between linear and circular causality

that underlies the distinction between deterministic

and self-organizing systems. In Section 6, I discuss

in more detail the relations between meaning and

representation. In Section 7, I summarize.

2. Communication by representations

Operational discreteness is essential for

communication in dialogue. A pair of brains can act,

sense, and construct in alternation with respect to

each other, just as dogs circle, and as two humans

plan, speak, listen, and hear. Consider brains A and

B interacting [Fig. 1], where A-B are parent-child,

wife-husband, rabbit-dog, philosopher-biologist,

neuroscientist -rabbit, etc. A has a thought that

constitutes some meaning M(a). In accordance with

this meaning A acts to shape a bit of matter in the

world (a trace of ink on paper, a vibration of air, a set

of keystrokes on e-mail, movements of the face, etc.)

to create a representation (a sign or symbol for

humans, merely a sign for animals, in both cases,

information) directed at B, R(ab). B is impacted by

this shaped matter and is induced by thought to create

a meaning M(b). So B acts to shape a bit of matter

in accordance with M(b) in a representation R(ba),

which impacts on A to induce M(a+1).

Fig. 1. The schematic shows the roles of

representation in the communication of

meaning between individuals by the exchange

of information through use of representations.

A method is proposed to replace one of the

communicants with a machine

And so on. Already by this description there is

implicit recognition of a discrete recurrent flow of

conversation like the tides, so that meanings M(i)'s

as constructions of thoughts become the internal

active states, and the R(ij)'s as attributes of matter

become the external representations. The interchange

requires a coordinated succession of phase transitions

in both communicants. By its relatively fixed nature

an :external" representation can be used over and over,

just as we use a letter, word, ideograph or equation.

It cannot be said to contain or carry meaning, since

the meanings are located uniquely inside A and B and

not between them. The same R induces different

meanings M(i) in any other subject C who may

intercept the transmission of a representation. The

objects that are used to communicate are shaped by

meanings that are constructed in A and B iteratively,

and they induce the constructions of meaning in B

and A alternately. If communication is successful,
then the internal meanings will come transiently into

harmony, as manifested by cooperative behavior such

as dancing, walking in step, shaking hands,

exchanging notes, ringing bells, etc. Symbols

persist in books and stone tablets, while minds

fluctuate and evolve until they die.

3. Observations on the electric fields, the

electroencephalogram (EEG)

A biological approach to the problem of meaning is

to study the evolution of minds and brains, on the

premise that animals have minds that are prototypic

of our own, and that their brains and behaviors tell us

what essential properties are common to animal and

human minds. Experimental measurements of brain

activity (EEG) that follows sensory stimulation of

animals show that sensory cortices engage in

construction of activity patterns in response to

stimuli [2]. The operations are not those that are

characteristic of computers, which include filtering,

storing, retrieval, template matching, pattern

completion, gradient descent, or correlation

mechanisms. Each construct is by a phase transition,

in which a sensory cortex switches abruptly from one

basin of attraction to another, thereby changing one

spatial pattern instantly to another like a succession

of frames in a cinema.

The transitions in the primary sensory cortices

(visual, auditory, somatic and olfactory [1]) are shaped

by interactions with the limbic system, which

establish multimodal unity, selective attention, and

the intentionality of percepts. The interactions of the

several sensory cortices and the limbic system lead to

goal-directed actions in time and space. Each cortical

phase transition involves synaptic changest

hroughout the forebrain that constitute learning, so

that a unified and global trajectory is formed

cumulatively by each brain over its lifetime. Each

spatial pattern reflects the content of past and present

experience [5], that is, a meaning.

The most important experimental finding is that

the neuroactivity patterns in sensory cortex, which

form during perception of conditioned stimuli by the

animals, are not invariant with respect to unchanging

physicochemical stimuli. The brain activity patterns

are found to change slightly and cumulatively with

any change in the significance of the stimuli, such as

by changing the reinforcement, or with the addition of

new stimuli [4]. From numerous tests of this kind

the conclusion is drawn that brain patterns reflect the

value and significance of the stimuli for the animals,

not fixed memory traces.

Each pattern forming in response to the

presentation of a stimulus is freshly constructed by

chaotic dynamics in the sensory cortex, in

cooperation with input from the limbic system that

implements the supporting processes of attention and

intention. It expresses the history, existing state, and

intent of the animal rather than the actual incident

stimulus. The patterns cannot be representations of

meanings of stimuli, either. They are observable

manifestations from the material substrate of the

meanings that are induced by stimuli, or that emerge

from self-induced instabilities in the sensory and

limbic systems. Their trajectory constitutes the

evolution of a brain in its growth of experience [13].

Similarly, a semantic device must be given

opportunity to practice, experience, and grow in

abilities to communicate.

The mechanism by which the formation of a

wave packet is triggered is of particular interest.

When an animal or human receives sensory

information, it is carried not by any small number of

axons from receptors but by a massive barrage of

action potentials. A glimpse of a face, for example,

includes all of the detectors for motions, contours,

colors, and binocular disparities of the face, and also

whatever background against which the face is

glimpsed, such as a crowd, a factory or a battlefield.

The process in mammals involves a dozen or more

specialized areas in each sensory cortex that process

the sensory information, with multiple feedback

pathways among them.

Despite this enormous complexity, recognition

occurs within half a second. The mechanism

suggested by EEG analysis is that an entire sensory

cortex is destabilized by input that is gated by a rapid

eye movement (a microsaccade), or its equivalent in

other sensory systems such as a sniff or a finger

motion. When it is destabilized, the cortex jumps

from one state to another. The transition is

completed within 3-7 msec of onset [4, 7]. It is

followed within 25-35 msec by the formation of a

spatial pattern of amplitude modulation [AM, Fig. 2]

of a chaotic carrier wave that persists for 80-100

msec. The AM pattern is accompanied by a spatial

pattern of phase modulation [PM, Fig. 2] that is

radially symmetric with a fixed phase velocity in all

directions. The PM pattern is measured by fitting to

it a cone in the 2 dimensions of the brain surface [7].

These two features, the AM and PM patterns,

serve to characterize the spatial and temporal location,

size, duration and content of the wave packet that is

triggered by sensory input. The AM pattern

manifests the meaning of the stimulus, not the

stimulus in itself, because the AM pattern changes

when the context or significance of the stimulus is

changed [4] [Fig. 2, right]. In contrast, the location

and sign of the apex (maximal lead or lag) of the PM
cone are random variables that do not reflect the

properties of the stimulus that evoked it or its

meaning for the subject [Fig. 3] [1,4,7].

Fig. 2. Left. EEG traces from an 8x8 electrode

array (4x4 mm) in 100 msec burst from olfactory

bulb after band pass filtering in the gamma range

(20-80 Hz). Middle. Spatial AM patterns from

RMS amplitudes on first trial set with control

and odor amyl acetate. Right. Two weeks later

in session 3 the stimuli were the same, but the

AM patterns had evolved to new forms, showing

that they are not representations. They are

context dependent and individualized for each

animal, and they change with variations in the

reinforcement.

The randomness of the sign implies that the apex

cannot signify a pacemaker for the oscillation, which

in any case is aperiodic. The proposed explanation is

that the phase gradient manifests the formation of a

wave packet by a 1st order phase transition [9], for

which the location of the apex reveals the site of

nucleation, and the velocity conforms to the finite

rate of spread of the state change in a distributed

medium, in the case of cortex by the conduction

velocities of axons running parallel to the pial surface

[7].

The phase gradient shows that the populations of

neurons in the wave packet are not oscillating in

phase at zero lag, but that they do so with leads or

lags that increase with distance from the apex. This

feature provides a soft boundary condition for the

wave packet, which can be specified by the half-power

diameter. The mode and 95% inclusion diameters are

shown by circles in Fig. 4, which are superimposed

on a diagram of the rabbit forebrain as seen from

above. Wave packets having these properties were

found in all of the sensory cortices examined. The

8x8 electrode arrays were placed on the primary

sensory receiving areas, as shown by the rectangles

indicating the size and locations of electrode arrays.

The 64 electrodes were used to record the EEGs from

the sensory cortices, in order to calculate the AM and

PM patterns in the cortical activity.

Fig. 3. Phase distributions were measured

with respect to the phase of the spatial ensemble

average at the surface of the olfactory bulb and

fitted with a cone in spherical coordinates. The

sketch is a projection of the outline of the bulb

as it would appear on looking through the left

bulb onto the array on the left lateral surface of

the bulb. A representative set of isophase

contours is at intervals of 0.25 radians/mm. The

locations of the apices of the cones on the surface

of the sphere (2.5 mm in radius) are plotted from

the center of the array to the antipode. The square

outlines the electrode array. The standard error of

location of points was twice the radius of the

dots. From Freeman and Baird [6].

Fig. 4 . The outline shows the left cerebral

hemisphere of the rabbit as seen from above.

The rectangles show the approximate locations of

the 8x8 arrays placed on the olfactory bulb (OB),

prepyriform cortex (PPC), somatomotor cortex

(SOM), auditory cortex (AUD), and visual cortex

(VIS), and a 2x8 array on the entorhinal cortex

(ENT). The inner circle shows the modal

diameter of phase cones. The outer circle shows

the diameter including 95% of cases. The

vertical line is the zero stereotaxic reference.

Diameter inner circle: 15 mm. Adapted from

Barrie et al. [1].
The massive size of the areas of cooperation

leading to formation of wave packets provides an

explanation for the speed with which pattern

recognition takes place. The immense cloud of action

potentials that is driven by sensory input from a

stimulus undergoes a 1st order phase transition that is

equivalent to formation of a rain drop from a cloud of

water molecules. In the process of the formation of a

condensation disk, a spatial pattern of output is

selected as the phase transition places the sensory

cortex into the basin of an attractor that has been

selected by the stimulus. The process repeats at

frame rates between 2 and 7 Hz, as shown by the

covariance of the successive phase cones with the low

frequency oscillations in the theta range of the EEG

[7].

4. Neural base of intentional action

The making of a representation is an intentional

action. All intentional actions begin with the

construction of patterns of neural activity in the

limbic system, which has been shown by use of

lesions and by comparative neuroanatomy and

behavior to be a product of the limbic system [11].

In mammals all sensory input is delivered to the

entorhinal cortex, which is the main source of input

to the hippocampus, and the main target of

hippocampal output [Fig. 5]. Goal-directed action

must take place in time and space, and the requisite

organ for the orientation is the hippocampus with its

'short term memory' and 'cognitive map'.

Fig. 5. A schematic diagram summarizes the

main elements in the dynamics of intentional

behavior to aid in the design of a KIV device

capable of intentional action, including semantic

communication. From Freeman [6].

For example, hunger is an emergent pattern of

neuroactivity that expresses the requirements of brains

and bodies for metabolic fuel and building material.

It induces a phase transition in the neural populations

of the forebrain under the influence of sensory stimuli

from the gut and the brain's own chemoreceptors for

its chemical state. It is also shaped by

neurohormones from nuclei in the brain stem. The

emergent pattern impacts the brain stem and spinal

cord, leading to stereotypic searching movements that

are adapted to the immediately surrounding world.

Feedback from the muscles and joints to the

somatosensory cortex provides confirmation whether

the intended actions are taking place. The impact of

the movements of the body on sensory input is

conveyed to the visual, auditory and olfactory

systems. All of these perceptual constructs, that are

triggered by sensory stimuli and are dependent on

prior learning, are transmitted to the limbic system,

specifically to the entorhinal cortex, where they are

combined. When, for example, an animal detects an

odor of food, it must hold the immediate memory of

the concentration, move, take another sniff, and

compare the two concentrations in order to decide

which way to move next. The difference in strength

has no meaning, unless the animal has a record of

where it was when it sensed the first concentration,

which way it moved, when the second sample was

taken, and to where. This information provides a

basis for determining distance and direction in its

environment from itself to its intended goal. These

basic operations of intentional behavior are properties

of the limbic system. The same requirements hold

for all distance receptors, so it is understandable that

evolution has led to multimodal sensory convergence

that performs space-time integration on the

multisensory percept, the Gestalt, not on its

components prior to their assembly. These

operations are already commonplace in robotics,

though less so their integration into goal states.

In the description thus far the flow of neural

activity is counterclockwise through loops from

sensory systems to motor systems, then through

proprioceptive and exteroceptive loops outside the

brain back to the sensory systems. Within the brain

there is a clockwise loop that sustains the flow of

activity constituting reafference (the leftward arrows

from "motor systems" through "entorhinal cortex" to

"sensory" systems, then rightward to close the loops).

When a motor act is initiated by the limbic system, it

issues a command as an activity pattern descending

into the brain stem and spinal cord. Copies of this

activity pattern are sent clockwise along these internal

pathways to all of the sensory systems by the

entorhinal cortex. These 'efference copies' [17]

prepare the sensory processors for the impact on the

sensory systems of the movements of the eyes, head,

ears, and body and, most importantly, the efference

copies sensitize the sensory cortices selectively by

shaping their attractor landscapes to respond only to

stimuli that are appropriate for the goal toward which

the action has been directed. The efference copy has

also been denoted as a 'sense of effort' [3]. They are
the essence of selective attention. These concepts are

familiar in feedback control; they need to be

generalized in the context of intentional behavior.

5. Linear versus circular causality in selforganizing systems

The conventional view of sensory cortical function

holds that stimuli activate receptors, which transmit

information to sensory cortex through a linear causal

chain, leading eventually to a motor response to the

initiating stimulus. Contrariwise, modeling with

nonlinear dynamics shows that the stimulus is

typically not the initiating event. Rather it is the

search for the stimulus that arises in conjunction with

an evolving goal in the limbic system, which

emerges in a recurrent manner from prior search and

its results. This is circular causality at the level of

intentional behavior [10].

Much lower in the hierarchy of brain

organization is another instance of circular causality.

This is the creation of the wave packet in the primary

sensory cortex, which consists of the destabilization

of a pre-existing mesoscopic state by the introduction

of intense barrages of microscopic sensory input. In

this case the transition from a prior basin of attraction

to a new one, which has been facilitated by limbic

modulation, is guided by the sensory input that

activates a learned nerve cell assembly comprising a

small subset of cortical neurons. The input from the

receptors includes both the expected stimulus and the

massive receptor discharge evoked by everything that

is in the background. The total receptor input forces

the instability and triggers the phase transition, and

the nerve cell assembly that is activated by the

expected stimulus selects the basin of attraction that

captures the cortical system. Then the entire domain

of the primary sensory cortex transits to the pattern

that emerges as the system converges to the attractor

in the basin, which in the words of Haken [10]

"enslaves" the whole set of cortical neurons by acting

as an "order parameter". This new active state has

been characterized by Ilya Prigogine [14] as a

"dissipative structure", that constitutes, in his words,

the "emergence of order out of chaos".

The similarity of the properties of neural activity

in the various parts of the limbic system to those in

the primary sensory cortices [1,4] indicates that

populations of neurons there also maintain global

attractors, which are accessed by nonlinear phase

transitions, and which are responsible for the genesis

of goal states, their motor patterns controlling goaldirected actions, and the reafference patterns that

prepare the sensory cortices for the consequences of

those actions.

The construction of a device that can simulate the

creative dynamics of the brain has been based

primarily on the dynamics and architecture of the

olfactory system, both in software [8,12] and in

hardware [4,15]. The basic unit of the construction is

a neuron population called a KO set, that is roughly

equivalent to an average neuron [2]. Its timedependent dynamics is governed by a linear 2nd order

ordinary differential equation that is evaluated by

fitting its solution as a sum of two exponentials to

derive the coefficients for the rate of rise of the

impulse response and the passive decay rate of the

membranes. Its input is provided by lines that

terminate in simulated synapses represented by gain

coefficients that are subject to change by learning, and

its output is bounded by a static nonlinear gain curve,

which is the derivative of the sigmoid curve [6]

relating dendritic current amplitude to pulse density

output of the population. An interactive population

of excitatory neurons is called a KIe set, and is made

by feedback connections between two excitatory KOe

sets in positive excitatory feedback. Similarly a KIi

set is made by feedback connections between two KOi

sets in positive feedback. KI sets have zero and nonzero point attractors and can generate sustained

excitatory and inhibitory biases. A KII set is made

by negative feedback connections between a KIe set

and a KIi set. It is has both point and limit cycle

attractors.

The interconnection and interaction of three KII

sets with distributed feedback delays forms a KIII set,

that is capable of aperiodic, nonconvergent, sustained

output governed by a chaotic attractor, in addition to

outputs governed by point and limit cycle attractors.

The nonlinear gain curve, which is the derivative of

the sigmoid function, governs pulse density in

relation to dendritic current density [2,4,8]. The

attractor landscape determines the spatial patterns of

EEG of sensory cortices, which are experimentally

observed from 8x8 arrays of recording electrodes with

a simulation using a KIII set embodying an 8x8 array

of coupled KII sets in the OB layer of a KIII set [8].

Each node in Fig. 5 is equivalent to a KIII set. The

cooperative synaptic interactions among them support

a KIV set, which is responsible for primitive forms

of intentional behavior.

6. A hypothesis on the relations of

meanings and representations

The idea is proposed that representations are formed

by the motor systems through the forward,

counterclockwise flow of neural activity. The motor

commands are formed at the mesoscopic level by the

interactions of neurons and neuronal populations,

under the 'enslaving' influence of the global attractor
landscape of the KIV set. The commands place the

motor systems of the brain stem and spinal cord into

appropriate basins of attraction. The representations

emerge as spatiotemporal patterns of activity in the

effectors of the body (the musculoskeletal apparatus,

the autonomic nervous system, and the

neuroendocrine nuclei), which respond to the volleys

of action potentials from motor neurons at the

microscopic level, i.e. single motor units activated in

concert. The movements of the body, supported by

the autonomic and neuroendocrine back-ups, express

the internal meaning states by gestures, vocalizations,

shaped objects, etc. These actions change the sensory

inflow of the actor in a goal-directed manner. The

actions elicit sensory feedback not only to the

individual in action. The representations, as intended,

activate patterns of receptor discharge in other beings,

that place their sensory cortices into the expected

basins of attraction. The receivers likewise create

patterns of meaning, that lead to up-dates in their

limbic systems, re-formation of motor commands,

and behaviors that re-transmit representations to the

original actor. Thereby, the circular causal chain is

maintained between two or more communicators.

The internal clockwise back flow of neural

activity serves as an order parameter to modulate and

shape the neural activity patterns of the sensory

cortices, which transmit the states of their neural

populations before and after the expected inputs have

occurred, and also if they do not occur as expected, or

at all. Modulation comprises not only the reafference

but also the exteroceptive and proprioceptive feedback

as well. I infer that the organisms constructing and

transmitting representations cannot know their

meanings until the sensory consequences have been

delivered to their own limbic systems. More

generally, a poet, painter, or scientist cannot know

the meaning of his or her creation until after the act

has been registered as an act of the self, nor until the

listeners and viewers have responded with reciprocal

representations of their own, each with meaning

unique to the recipients.

Why do brains work this way? Animals and

humans survive and flourish in an infinitely complex

world despite having finite brains. Their mode of

coping is to construct hypotheses in the form of

neural activity patterns and test them by movements

into the environment. All that they can know is the

hypotheses they have constructed, tested, and either

accepted or rejected [5,13]. The same limitation is

currently encountered in the failure of machines to

function in environments that are not circumscribed

and drastically reduced in complexity from the real

world. Truly flexible and adaptive intelligence

operating in realistic environments cannot flourish

without meaning.

This assembly of interacting wave packets may

be seen as a mechanism supporting consciousness,

which, in the neurodynamic view, is a spatiotemporal

pattern of activity that occupies the entire forebrain.

It is an internal state variable that has a trajectory

composed of a sequence of transitory states that

correspond to awareness. Its regulatory role

transcends that of the operator in a thermostat,

although they share the properties that instantiate the

difference between the state of the environment and

expectation, such as a sensed temperature and a set

point, and that initiates corrective action respectively

by intentional action or by turning a heater on or off.

The difference is that the simple machine state

variable has little history and no capacities for

learning or determining its own set point, but the

principle is the same: the internal state is a form of

energy, an operator, a predictor of the future, and a

carrier of information that is available to the system

as a whole. The feedback device is a prototype, an

evolutionary precursor, not to be confused with

awareness, any more than tropism in plants and

bacteria is to be confused with intentionality. In

animals and humans, the operations and informational

contents of this global state variable constitute the

experience of causation.

7. Summary

Semantics deals with the relation between meanings

and representations, widely known as intention,

'aboutness', or the symbol grounding problem.

Brains obtain information about their environments

through the consequences of the intentional actions

that they execute using their bodies.

Studies of the spatiotemporal patterns of

electroencephalographic (EEG) potentials that are

induced by conditioned stimuli in the primary sensory

and limbic cortices of trained animals have shown

that the information thus obtained is used to construct

meanings and is then discarded. Computers use

representations for information processing and

symbol manipulation, but brains have no internal

representations. They deploy dynamic neural

operators in the form of neural activity patterns that

construct and implement meaning but not

information. Observers can describe these patterns as

information, but that does not imply that the brains

do so, or need to. Brains construct external

representations of their meanings in the form of

shaped objects or movements as their mechanism for

expressing their internal states. Examples are facial

expressions and gestures in animals and words in

humans. Those material constructs are made with the

intent to elicit meaning in other brains, but they have

no meanings in themselves and do not carry meanings
as if they were buckets or placards. Meanings can

only exist in brains, because each meaning expresses

the entire history and experience of an individual. It

is an activity pattern that occupies the entire available

brain, constituting a location in the dynamic state

space of a brain. EEG data indicate that neural

patterns of meanings in each brain are based in

mesoscopic wave packets that follow trajectories in

discrete steps. Each step is demarcated by a 1st order

phase transition that enables formation of

spatiotemporal patterns of chaotic oscillations in the

gamma range. Amplitude modulation of the carrier

wave is the mode of expressing meanings. These

wave packets do not represent external objects; they

embody and implement the meanings for each

individual of his or her interactions with the

environment. 

Engineers who propose to make semantic

machines are faced with the task of defining meaning,

which at present exists only in brains, and then with

the task of learning how to design machines that can

make or cause meaning in themselves. The

requirements on network models to simulate the

chaotic dynamics of brains include global though

sparse connectivity, continuous time dynamics, and

distributed spatial functions in two-dimensional arrays

of nonlinear integrators. Digital hardware may suffice

to emulate the biological functions of sensory cortex

in brains by use of nonlinear difference equations as

in KIII sets [8], provided that the problems can be

solved of attractor crowding and numerical

instabilities that inhere in digital representations of

chaotic dynamics [12]. Digital simulation is a useful

step toward analog simulation in VLSI [15], by

means of which to attain the computational speed that

will be required for real-time operation of the device.

In this way, the next step toward machine meaning

can be to use a KIII model of a sensory cortex as an

interface between the unconstrained real world, which

is infinitely complex, and the finite state automaton

that will rely on a dedicated digital computing system

as the main support for its artificial intelligence.

That is, a model from brain dynamics can provide the

eyes and ears for a conventional computer, that can

enable the device to interface effectively with the

infinitely complex environment that it will share

with its designers, and about which it can

communicate its views.

Acknowledgment

This work was supported in part by a NASA grant

(NCC 2-1244). A preliminary version of this work

appeared in Freeman [5].

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