Jong-Chen Chen and Ruey-Dong Chen- Toward an evolvable neuromolecular hardware: a hardware design for a multilevel artificial brain with digital circuits

8/3/2019 Jong-Chen Chen and Ruey-Dong Chen- Toward an evolvable neuromolecular hardware: a hardware design for a m

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Toward an evolvable neuromolecular hardware: a hardware

design for a multilevel artificial brain with digital circuits

Jong-Chen Chen, Ruey-Dong Chen

Department of Management Information Systems, National YunLin University of Science

and Technology, Touliu, Taiwan, R.O.C.

Author to whom correspondence should be sent: Jong-Chen Chen

Ph: +886-5-534-2601 ext. 5300 (dept.)+886-5-534-2601 ext. 5332 (office)

+886-5-551-2762 (home)FAX: +886-5-531-2077

email: [email protected]

Running Title: Evolutionary Neural Networks

Revised: Feb., 2001


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Toward an evolvable neuromolecular hardware: a hardware

design for a multilevel artificial brain with digital circuits

Jong-Chen Chen, Ruey-Dong Chen

Department of Management Information Systems, National YunLin University of Science

and Technology, Touliu, Taiwan, R.O.C.

Abstract: A biologically inspired neuromolecular architecture implemented on digital circuits

is proposed in this paper. Digital machines and biological systems provide different modes

of information processing. The former are designed to be effectively programmable, whereas

the latter have self-organizing dynamics. Previously, we developed a multilevel computer

model that captures intra- and interneuronal information processing. The experimental

results showed that this self-organizing model has long-term evolutionary learning capability

that allows it to learn in a continuous manner, and that the function of the system changes as

its structure is altered. Malleability and gradual transformability play an important role in

facilitating evolutionary learning. The implementation of this model on digital circuits

would allow it to perform on a real-time basis and to provide an architectural paradigm for

emerging molecular or neuromolecular electronic technologies.

keywords: evolutionary adaptability, artificial brain, multilevel evolutionary learning,

evolvable hardware


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1. Introduction

Our brain is a highly activated, asynchronous concurrent network. This network has

significant information processing capability that allows us to think, imagine, dream, and so

on. In contrast, conventional digital computers have excellent computational power for

performing an enormous amount of repetitive work and a variety of information processing

tasks ranging over a wide spectrum of applications. Conrad [19] indicated that the major

dichotomy between brains and machines is the ability to evolve versus programmability.

Evolution by variation and selection is the foundation of natures problem-solving

method [23]. In biological systems, functions and structures are closely related [15]. That

is, when the structures of a system are altered, its functions (or behaviors) change accordingly.

Evolvability and a close structure-function relationship provide organisms with the

malleability (gradual transformability) to cope with environmental changes (i.e., noisy

environments) and to learn new survival strategies for uncertain environments (i.e., new

environments). In recent years, the application of evolutionary computational techniques to

different problem domains has gained more attention and grown rapidly. The major

contributions were made by evolutionary optimization procedures [1], the evolutionary

programming approach [29], evolutionary strategies [57,59], and genetic algorithms [30,41].

Unlike biological systems, conventional computers are deficient in coping with problem

change [15,20,79]. A slight modification in a computer program can easily produce an

incorrect program, or a major malfunction. Usually, reprogramming is inevitable with only

a slight change in problem requirements. However, as advocated by Turing [71], there does

exist an effective procedure (or program) that can simulate (or solve) any problem as long as

it can be defined (or described) in a formal, precise manner. This means that conventional

computers have an effective programmability that allows us to simulate any physically

realizable process in nature [16].


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As indicated above, evolvablity is an important feature in our brain that allows

adaptability. Conventional computers have an effective programmability that allows us to

apply them to various problem domains. One of the ultimate goals is to integrate the merits

of information processing mechanisms provided by both brains and computers (which might

be called a brain-like computer) into a system, which might generate synergistic effects that

cannot be performed by a brain or computer alone. However, the principles of biological

information processing in the human brain are not understood completely. While the

success of a real brain-like computer may seem faraway, a feasible approach is to employ

some possible information processing mechanisms understood from our brain, develop a

system based on this, and perform a variety of experiments. A vast number of research

projects have been conducted along this line. This research has included connectionist

models, evolutionary neural models, evolvable hardware, molecular computing, molecular

electronics and neuromolecular systems.

Connectionist models [32,33,36,45,49,73,74], which attempt to use the strength of

connections among neurons to represent information, are the most well-known neural models.

A number of investigators further applied evolutionary learning techniques to connectionist

models [46,58,63-65,75,77,78,81,82] and to intraneuronal models [23,24,46,47]. The

advantages of evolutionary design over human design can be found in Yao and Higushi [80].

The above models have more flexible learning capabilities than classic artificial intelligence

(AI) models and are applicable to a variety of problem domains. However, most models

developed so far are software simulation systems. It is very time-consuming to simulate a

population of networks, in particular an ensemble of evolutionary neural networks. The

studies on evolvable hardware have thus emerged.

As pointed out by Yao [79], there is no unanimous definition of evolvable hardware at

this moment. He defined it as architectures, structures, and functions that can change


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dynamically and autonomously to perform specific tasks, but with a constant hardware

architecture [79]. Simulated evolution and reconfigurable hardware are two major aspects

of evolable hardware. de Garis [25-27] further divided evolvable hardware into two

categories: extrinsic and intrinsic. The former simulates evolution using software while the

latter with hardware.

Sipper et al. [61] proposed two reconfigurable architectures inspired by evolution and

ontogeny. Higuchi and his colleagues [39,40] have been working on the development of

evolvable hardware chips for different applications; an analog chip for cellular phones, a

clock-timing chip for Giga hertz systems, a chip for autonomous reconfiguration control, a

data compression chip, and a chip for controlling robotic hands and navigation. Murakawa

et al. [55] presented an evolvable hardware for neural network applications by reconfiguring

the network topology and node functions in order to adapt the dynamics for a specific

problem domain. de Garis [25-27] developed an artificial brain that can assemble a great

number of cellular automata-based neural net modules and in the future may control the

behavior of a kitten robot.

It should be noted that connectionist models, including most evolutionary neural

networks and evolvable hardware, emphasize the connections among neurons based on the

Hebbian rule and omit information processing inside the neurons. Roughly, they consider

the neurons to be simple on/off threshold units with a simple firing rule. The intelligence of

these models is mediated primarily by exchanging signals among neurons. In general, these

models have a common underlying structure (i.e., map to one another). When learning is

completed, input patterns are translated into the strength of the connections among the

neurons. The patterns will interfere with one another because they are coded based on the

strength of the connections among the neurons. This has been called the superposition

problem [14]. This problem becomes worse when the number of patterns to be stored in a


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network increases. Ignoring intraneuronal dynamics is an enormous simplification that

greatly reduces the computational capability of the neurons. The molecular and

neuromolecular models that will be described in this study shift the emphasis to an

intraneuronal form of information processing.

In the early 1970s, Conrad proposed some molecular information processing

architectures motivated from some modes of information processing in the human brain

[10-13]. This line of work was further developed into the idea of molecular computers

[16-18,20,22]. A number of researchers [2,3,42-44,68-70] have tried to develop

carbon-based computing devices (so-called biocomputers) by using actual biological

materials. However, the realization of biocomputers is still in a very early stage for at least

a couple of reasons [42]. First, biological materials have not been considered seriously for

device construction. Secondly, biological materials are too fragile and not durable.

The artificial neuromolecular (ANM) model that we developed earlier [6,7] was

motivated by two molecular architectures [11-13]. This model has three distinguishing

features. The first is that the input-output behavior of the neurons is controlled by complex

internal dynamics that reflect the molecular mechanisms inside real neurons. The second

feature involves neurons that have hierarchical controls that make it possible to manipulate

collections of neurons. Finally, the model is an open evolutionary architecture that has a

rich potential for the evolution of a variety of behaviors that could significantly expand the

problem domains to which neural computing is applicable. In principle, this openness

should allow the model to address a broader class of problems than purely connectionist

models do. However, this is still a virtual machine that runs on top of a serial digital

computer and is therefore subject to practical computational limitations.

Section 2 describes the neuromolecular architecture and the previous experimental

results. Section 3 explains the detailed architecture of the intraneuronal dynamic model


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along with the biological evidence. Section 4 illustrates the evolutionary learning

mechanisms. Section 5 describes a hardware design of the biologically motivated

neuromolecular architecture with digital circuits. Section 6 is our concluding remarks.

2. The ANM system

2.1 Brief description of the system

The ANM system is an artificial brain that provides a rich platform for evolutionary

learning. The artificial brain is comprised of a network of neuron-like modules with internal

dynamics modeled by cellular automata. The dynamics reflect molecular processes believed

to be operative in real neurons, in particular processes connected with second messenger

signals and cytoskeleton-membrane interactions.

The objective is to create a repertoire of special-purpose pattern processors through an

evolutionary search algorithm and then to use memory manipulation algorithms to select

combinations of processors from the repertoires that are capable of performing coherent

functions. The system, as implemented presently, consists of two layers of memory access

neurons (called reference neurons) and one layer of intraneuronal dynamic neurons (called

cytoskeletal neurons) divided into a collection of functionally comparable subnets.

Evolutionary learning can occur at the intraneuronal level through variation-selection in

the cytoskeletal structures responsible for the integration of signals in space and time. The

memory manipulation algorithms that orchestrate the repertoire of neuronal processors also

use evolutionary search procedures, and are well suited for operating in an associative mode

as well.

2.2 Previous experimental results

By adjusting the input/output interfaces, the ANM system has been linked to a number


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of problem domains, including maze navigation, bit pattern recognition, Chinese character

recognition, and chronic hepatitis B diagnosis.

Previous investigations on the malleability of this system showed that its function

changes in accordance with changes in the systems structure [9]. The experimental results

also provided the information about the fitness landscape implicit in the systems structure

that facilitates evolutionary learning [4,9]. The evolution friendliness of this system

increases as its structural complexity increases. This was investigated by adding more types

of cytoskeletal fibers, allowing weaker interactions, and increasing redundancy [7].

The integration of intra- and interneuronal information processing also plays a vital role.

These two types of information processing yield significant computational and learning

synergies [6]. The integrated system effectively employs synergies among different levels

of learning [4]. With the above features, the system is able to learn continuously in complex

problem domains and is effective in coping with problem changes [4,9].

Choosing significant features for differentiating data and insignificant features for

tolerating noise is not an easy problem for any intelligent system. Our experimental results

showed that the system exhibits an effective self-organizing capability in striking a balance

between pattern categorization and pattern generalization [5,8]. In the diagnosis of hepatitis

B patient data application, this system showed itself to be well suited for differentiating

chronic hepatitis B patients from healthy individuals and for investigating what would be the

significant parameters in determining if one is infected with chronic hepatitis B [5].

2.3 The ANM architecture

The artificial brain is comprised of two complementary neuromolecular models:

reference neurons and cytoskeletal neurons. The following only explains the connections

among the neurons and their control mechanisms. Intraneuronal information processing will


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be discussed in section 3.

The neuromolecular architecture has 256 cytoskeletal neurons, divided into eight

comparable subnets. Each subnet consists of 32 cytoskeletal neurons. By comparable

subnets, we mean that the input/output neuronal connections and intraneuronal structures of

each subnet are similar or the same (the detail will be described in the next section). As

shown in Fig. 1, these 256 cytoskeletal neurons are controlled by two layers of reference

neurons (8 high-level reference neurons and 32 low-level reference neurons). Each

high-level reference neuron controls a collection of low-level reference neurons, which in turn

controls a bundle of comparable cytoskeletal neurons. A high-level reference neuron will

therefore control a particular combination of cytoskeletal neurons through low-level reference

neurons.

subnet1

R2 R3

r1 r2 r32

E1 E2 E32

r3low-levelreferenceneurons

high-levelreferenceneurons

R8. . .

. . .

subnet2

E1 E2 E32. . .

subnet8

E1 E2 E32. . .. . .

R1

. . .

cytoskeletal

neurons

Fig. 1. Connections between reference and cytoskeletal neuron layers. Low-level reference

neurons select cytoskeletal neurons in each subnet that have similar cytoskeletal structures.

High-level reference neurons select different combinations of the low-level reference neurons.

The reference neuron scheme [13] is a memory manipulation model. This approach

correlates with some suggested hippocampal function mechanisms. These mechanisms

involve synaptic facilitation, as in Hebbian models. A reference neuron will load all of the

firing neurons that it contacts at the same time. Subsequent firing of a reference neuron will

thus fire (rekindle) all of the neurons that it loaded previously. This mechanism makes it


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possible to store a single experience rapidly. The reference neuron scheme supports

time-ordered memories, content-addressable memories, associative memories, control of

circuit selection, and neuron orchestration. With these mechanisms it is possible to build up

complex association structures. Circuit selection and neuron orchestration are the only

memory functions that are used in our model (to be explained below).

Reference neurons can be used to control network selection. Signals emanating from

reference neurons inhibit and excite a set of networks in a manner that allows only one to be

active at any instant in time. This feature is important when we need to evaluate the

performance of each comparable subnet individually and alternately.

Orchestration is an adaptive process mediated by varying neurons in the assembly that

selects good performing combinations of neurons. The objective of orchestration is to select

an assembly of neurons that allows for performing input/output pattern transduction. In the

ANM system, orchestration occurs between high-level and low-level reference neurons. We

note that only cytoskeletal neurons selected by reference neurons are allowed to perform

pattern transduction.

2.4 Input-output interface

This system had 64 receptor neurons and 32 effector neurons when first constructed [6].

The neuronal connection patterns of each comparable subnet are the same (Fig. 2). This

ensures that comparable cytoskeletal neurons in each subnet (i.e., neurons having similar

intraneuronal structures) will receive the same inputs from receptor neurons and that the

systems outputs are the same when the firing patterns of each subnet are the same. Each

effector neuron is controlled by eight comparable cytoskeletal neurons (i.e., one from each

comparable subnet). We note that an effector neuron fires when one of its controlling

cytoskeletal neurons fires.


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In each input pattern, the first firing effector neuron is recorded. The initial effector

neuron group firing is defined as the output associated with an input pattern. When the

initial effector neuron-firing group is the same as the group determined by a particular

problem domain, the system makes a correct response. The greater the number of correct

responses made by the system, the higher its fitness. The overall architecture of the ANM

system is shown in Fig. 3.

. . . . . . . . . .I2

cytoskeletal

neurons

I1 I64

E32E1 E2 E32E1 E2

subnet1 subnet2

effector

neurons O2O1

receptor

neurons

. . .

. . . . . .

I4I3

O32

Fig. 2 Input/output interface of comparable cytoskeletal subnets. The connections between

receptor neuron and cytoskeletal neuron layers are randomly decided initially, but vary as

learning proceeds. The connections between cytoskeletal neuron and effector neuron layers

are fixed.


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cytoskeletal

effector neuronsI1

I2

O2

O32

ANM

first firingeffector neuron

receptor neurons

ref. neurons

neuronsOk

samegroup

yes

correct

classification

each

pattern

no

group of a pattern

select

.

.

.

wrong

classification

I64

.

.

.

O1

Fig. 3. Overall architecture of the ANM system

3. Intraneuronal dynamics

3.1 Biological evidence

Experimental studies utilizing a variety of techniques suggest that chemical and

molecular processes within neurons play a significant role in controlling neural firing

[28,37,50-53]. Rapid depolarizing effects induced by the microinjection of second

messenger molecules (cAMP) led to the suggestion that the cytoskeletal motions influence

ion channels [52,53]. Presumably cAMP acts on microtubule associated proteins to trigger

signal flow in the cytoskeleton or to alter the flow of signals arising from other sources.

This conclusion is supported by ultrafast electron microscopic studies that correlate ion

channel activity with cytoskeletal dynamics [54].

The cytoskeleton has three major components: microtubules, microfilaments (e.g., actin

filaments), and intermediate filaments (referred to as neurofilaments in neurons).

Microtubules and microfilaments, composed of simple tublin polymers (alpha and beta) and

actins respectively, might interact with one another via microtubule associated proteins


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[34,35,56,60]. Likewise, intermediate filaments could interact with microtubules and

microfilaments via some of their binding proteins [62,66,67,72].

However, the real interaction among the three major filaments of the cytoskeleton is not

at present well understood. The cytoskeleton extends throughout the cell and underlies the

membrane. It is capable of exhibiting structural changes associated with

polymerization-depolymerization processes [54]. Conformational switching [38],

propagating conformational changes [21], vibratory motions of the sound wave type [53],

electric-dipole oscillations of the Frhlich type [31,37], and membrane mediated interactions

[48] have also been suggested as possibilities. These and other mechanisms could

conceivably coexist, allowing for different modes of signal transmission. Obviously the

cytoskeleton is an extremely complex system.

3.2 The cytoskeletal neuron model

The cytoskeletal neuron is motivated by the biological evidence described above. It is

simulated with a two-dimensional grid. Each grid square is referred to as a compartment.

Signals impinging on a neuron are transduced into the cytoskeletal signal flows. When a

compartment of a cytoskeletal neuron receives an external signal, a cytoskeletal signal will be

generated in a component of the cytoskeleton and transmitted to its neighboring

compartments at a specific rate. In the meantime, the signal will decrease over time. When

a cytoskeletal component is activated and there are some kinases sitting in the same

compartment, a cytoskeletal neuron will fire.

A kinase thus serves as a readout enzyme that can recognize a subset of input patterns.

Adding or deleting a kinase will as a consequence add or delete the set of patterns to which a

neuron responds. All input patterns in space and time that trigger a neuron to fire are

grouped as its recognition set. Relocating a kinase to a neighboring compartment could in


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some cases hold the set of patterns recognized by a neuron constant, but in general it would

alter the input-output behavior of neurons by advancing or delaying its firing timing. The

power of a cytoskeletal neuron is that it is capable of transducing a set of spatiotemporal input

patterns into temporal output patterns.

The following explains how the signal integration features in the cytoskeleton are

captured. As indicated above, a cytoskeletal signal flow is initiated when an external signal

impinges on the membrane of a neuron. For example, in Fig. 4, the activation of the readin

enzyme at location (2,2) will trigger a cytoskeletal signal flow transmitted along the second

column of the C2 components, starting from location (2,2) and running to location (8,2).

An activated component will affect the state of the various types of neighboring

components if there is a MAP (microtubule associated protein) linking these components

together. For example, in Fig. 4, the activation of the readin enzyme at location (3,7) will

trigger a cytoskeletal signal flow transmitted along the seventh column of the C1 components,

starting from location (3,7) and running to location (6,7). When the signal arrives at

location (4,7), it will activate the component at location (4,8) via the MAP. The activation

of this component will in turn trigger a signal flow travelling along the eighth column. We

assumed that the interactions between two neighboring components are asymmetrical. That

is, the activated component at location (4,8) is not sufficient to activate the component at

location (4,7). The other assumption was that different types of components transmit

signals at different speeds. For example, C1 components transmit signals at the slowest

speed. By contrast, C3 components transmit signals at the fastest speed. The transmittion

speed of the C2 components is intermediate, between that of the C1 and C3 components.


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MAP' ' ' ' ' 'readout

enzyme

1

2

3

4

5

6

7

8

i

location (i, j)

C1

C1

C1

C1

C1

C2

C2

C2

C2

C2

C1

C1

C1

C1

C1

C3

C3

C3

C3

C3

C2

C2

C1

C1

C1

C1

C1

C2

C2

C2

C2

C1

C1

C1

C1

C3

C3

C3

C3

C3

C3

readin

enzyme

6 71 2 3 4 5 8

Fig. 4. A cytoskeletal neuron. Each grid location, referred to as a site, has at most one of

three types of components: C1, C2, or C3. Some sites may not have any component at all.

Readin enzymes could reside at the same site as any one of the above components. Readout

enzymes are only allowed to reside at the site of a C1 component. Each site has eight

neighboring sites. The neighbors of an edge site are determined in a wrap-around fashion.

Two neighboring components of different types may be linked by a MAP.


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When a requisite spatiotemporal combination of cytoskeletal signals arrives at a readout

enzyme site, the neuron will fire. For example, in Fig. 4, there are three possible signal

flows that might reach and activate the readout enzyme at location (8,3). The first signal

flow is the one transmitted along the second column, activated either by the readin enzyme at

location (2,2) or by the enzyme at location (3,2). The second signal flow transmits along

the third column, activated by the enzyme at location (4,3). The third signal flow transmits

along the fourth column, activated either by the readin enzyme at location (1,4) or by the

enzyme at location (4,4). When two out of the three signal flows reach location (8,3) within

a short period of time, they will activate the readout enzyme sitting at the same location.

The activation of the latter will in turn cause the neuron to fire. However, the neuron might

fire at different times for two reasons. First, signals are transmitted at different speeds along

different types of components. Secondly, signals may be initiated by different readin

enzymes.

We have explained how to capture the signal integration feature in the cytoskeleton.

The following explains how cytoskeletal dynamics are implemented with cellular automata.

Each cytoskeletal component has six possible states: quiescent (q0), active with increasing

levels of activity (q1, q2, q3, and q4), and refractory (qr). A component in the highly active

state (q3 or q4) will return to the refractory state at the next update time for that component

type. The next state for a less active component (q0, q1, or q2) depends on the sum of all

stimuli received from its active neighboring components (with each component type having

its own update time). The detailed state transition rules are illustrated in Fig. 5. A

component in the refractory state will go into the quiescent state at its next update time. A

component in the refractory state is not affected by its neighboring components until its

refractory period is over.


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(a) C1 component

q0

q2

q1

q3

q4

s2

s1 s1, s2, s3

s1s3s3

s2

s3

s1, s2

(b) C2 component

q0

q2

q1 q 3,q4

s3

s1, s2 s1, s2, s3

s1, s2, s3

(c) C3 component

q 0q2

q1 q3,q4

s1, s2, s3

s1, s2, s3

s1, s2, s3

Fig. 5. Transition rules of the components. S1, S2, and S3 indicate a signal from a highly

activated component C1, C2, and C3, respectively. For example, if C1 in the state q0 receives

an S2 signal it will enter the moderately activated state q2. If it then receives an S3 signal itwill enter the more activated state q3.


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4. Multilevel learning

Six levels of evolutionary learning are allowed in this system. They are at the initiating

signal-flow level (controlled by readin enzymes), responding to signal-flow level (controlled

by readout enzymes), controlling signal-flow level (controlled by MAPs), transmission

signal-flow level (controlled by cytoskeletal components), responding to external-stimuli

level (determined by the pattern of connections to receptor neurons), and

cytoskeletal-neurons-group level (controlled by reference neurons). The first four levels are

intraneuronal and occur inside cytoskeletal neurons, whereas the last two levels are

interneuronal.

Intraneuronal evolutionary learning has three major steps (Fig. 6). The performance of

each subnet is evaluated first. Then, the three best-performing subnets are selected.

Finally, the readout enzyme, readin enzyme, MAP, or component patterns are copied (with

variation) from the best-performing subnets to the lesser-performing subnets, depending on

which level of evolution is occurring. Evolutionary learning at the level of responding to

external stimuli comprises three steps, too (Fig. 6). As above, the performance of each

subnet is evaluated first. Then, the three best-performing subnets are selected. Finally, the

connections between receptor neuron and cytoskeletal neuron layer patterns are copied (with

variation) from the best-performing subnets to the lesser-performing subnets.

Evolutionary learning at the reference neuron level also comprises three steps (Fig. 7).

First, cytoskeletal neurons controlled by each high-level reference neuron (through low-level

reference neurons) are activated in sequence for evaluating their performance. Secondly,

the patterns of neural activities controlled by the best-performing reference neurons are

copied to the lesser-performing reference neurons. Finally, the lesser-performing reference

neurons control slight variations in the neural groups controlled by the best-performing

reference neurons, assuming that some errors occur during the copy process.


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In the current implementation, only one level is opened for learning at a time while the

other levels are turned off. Each level is opened for 16 learning cycles. Our approach is to

turn on each level in an alternating manner until the simulation is terminated. The level

opening learning sequence is shown in Fig. 8. We note that the segregation in time

described above does not mean that the fitness assigned to the reference neurons is

independent of the properties of the cytoskeletal neurons. Evolutionary learning at the

cytoskeletal neuron level alters the performance characteristics of the collection of neurons

(or combination of bundles) that the reference neurons control. This alters the fitness of the

collection and therefore the fitness of the reference neuron that provides access to this

collection. Also, it should be noted that the mechanism of controlling the evolutionary

process does not have to be rigid. Indeed, it would be interesting to investigate the impacts

of varying the number of learning cycles assigned to each level and the level opening

sequence on the learning in the future.

Previous experimental results [4,7] showed that the information processing capability of

this system increases as more levels of learning are allowed. We further examined what

levels of evolution contribute most to the learning. The experimental result [4] showed that

the contributions are made by several levels of evolution in the early stage of learning, and

that fitness increases only at certain levels in the later stage of learning. This suggested that

synergy only occurs in a selective manner. However, it is rather difficult to determine what

kind of contribution made by each individual level of learning for the following two reasons.

First, the significance of each level varies as input data (or problem domains) change.

Secondly, synergies among different levels of learning suggest that learning at one level open

up opportunities for another. It is thus that we are not able to assign appropriate credit to

each individual level.


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subnet1

a. evaluate

E3 E4E1 E2

subnet2

E3 E4E1 E2

subnet1

b. copy

E3 EE1 E2

subnet2

E3 EE1 E2

readin, readout, MAP, component,connections to receptor neurons

subnet1

c. vary

E3 E4E1 E2

subnet2

E3 E4E1 E2

variant

Fig. 6. Evolutionary learning at the cytoskeletal neuron layer.

R 1 R 2

r1 r2 r3 r4

re fe re nc e ne uro ns

(a )

low-le ve lre fe re nc e ne uro ns

high-le ve l

R 1 R 2

r1 r2 r3 r4


(b )


high-le ve l

R 1 R 2

r1 r2 r3 r4


(c)


high-le ve l

vari ant

Fig. 7. Evolutionary learning at the reference neuron layer.

timeref. neuron

(readout)

16 cycles

ref. neuron

cytoskeletal neuron

(receptor neuron)

16 cycles 16 cycles

ref. neuron

cytoskeletal neuron

(MAP)

16 cycles 16 cycles

ref. neuron

cytoskeletal neuron

(component)

16 cycles 16 cycles

16 cycles

cytoskeletal neuron

(readin)

Fig. 8. Sequence of opening of learning levels.

5. Digital hardware

In this section, we will explain a hardware design of the central architecture of the ANM

system (i.e., cytoskeletal neurons and reference neurons) on digital circuits.


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5.1 Cytoskeletal neurons

As shown in Fig. 4, the cytoskeleton is represented with a 2-D (8X8) grid structure.

Cytoskeletal dynamics were simulated with 2-D cellular automata [76]. Each grid location

is simulated by a clocked sequential circuit (referred to as a processing unit, PU). In total,

there are sixty-four synchronous PUs for each cytoskeletal neuron. Each PU has 8

neighboring PUs. The neighbors of an edge PU are determined in a wrap-around fashion.

For any two neighboring PUs, there are two possible unidirectional connections between

them (i.e., one and its opposite directions). This allows each PU to take signals from and

send outputs to its eight neighboring PUs.

Each PU consists of three departments: input, process, and output (Fig. 9). The input

department receives information from its neighboring PUs and sends its outputs to the process

department. The latter integrates signals from either its input department or receptor neurons

into an output signal for the output department, which in turn sends its outputs to all

neighboring PUs.

As indicated earlier, the cytoskeleton model includes the following components:

microtubules, neurofilaments, microfilaments, microtubule associated proteins (MAPs),

readin enzymes, and readout enzymes. The following explains how to implement each of

these components on digital circuits. It should be noted that our aim in this study was to

provide the basic layout for implementing the ANM with conventional digital circuits. An

optimized circuit design layout has not been completed yet.


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input

dept.

rocessin unit PU

process

dept.

output

dept./8

/8

/3

/1

neighboring

PUs

neighboring

PUs

process

dept.

bounderaccumulatorDPG /1

/3

input

control

process

control

output

controlcontrol

dept.

. . .

. . .

. . .

. . .

.

.

.

.

.

.

Fig. 9. Conceptual architecture of a cytoskeletal neuron.

5.1.1 Input department

As indicated earlier, the input department plays the role of converting signals from

neighboring PUs into signals for the process department. It has two major functions. The

first is to determine the type of influence a neighboring signal has on the current PU. The

second function is to control the signal conversion timing.

As noted above, each PUhas 8 neighboring PUs. There are eight D-latches designed to

hold information coming from neighboring PUs (one latch for each PU). The information

held in each D-latch is decoded by a corresponding 2x4 decoder for determining the type of

influence a neighboring signal has on the current PU.

As indicated in section 3, biological evidence suggests that the cytoskeleton is comprised

of three types of fibers: microtubules, microfilaments, and neurofilaments. Our assumption

[6] was that the cytoskeletal fibers play the role of signal transmission and integration, which

in turn controls the firing activity of a neuron. In addition, we assumed that signals

transmitted along microtubules (denoted by C1 in Fig. 4) represent major signal flows in the

cytoskeletal neuron and have the greatest impact on the other two types of components. In

contrast, signals transmitted along microfilaments (denoted by C3 in Fig. 4) play the role of


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modulating major signal flows in the cytoskeletal neuron and have the least impact on the

other two types of components. Neurofilaments also serve as the role of modulating major

signals, but with more impact on the other two types of components than neurofilaments. In

summary, the types of influence for signals from neighboring fibers are divided into three

categories: strong, intermediate, and weak (denoted by S, I, and Win Fig. 10, respectively), as

shown in Table 1.

2 X 4decoder

.

.

.

Dflip-flop

D

flip-flop

PU120

21

E unused

S

I

W

unused

2 X 4decoder

20

21

E

.

.

.

PU8

.

.

.

input

dept.

process

dept.

3 X 8decoderRCTR DIV

counter

S0S1S2

.

.

.clk

input

control

M1 M16

M17 M24I1

I8

evolve at

MAP level

evolve at

component

level

Fig. 10. Input department.

Table 1. Influence type of a neighboring signal on a PU

type of current PUtype of aneighboring PU C1 C2 C3

C1 strong strong strong

C2 intermediate strong strong

C3 weak intermediate strong

For each connection, two bits are used to specify the influence of a neighboring signal on

a processing unit. For eight neighboring connections, sixteen bits are required (denoted by

M1-M16 in Fig. 10). As indicated earlier, we allow evolutionary learning to occur at the


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cytoskeletal component level. That is, the component type of each PU is allowed to change

as learning proceeds. Indirectly, this would change the signal influence type from and to the

neighboring PUs. For example, lets assume that there is a PUwhose component type is C1.

As shown in Table 1, it has the greatest impact on its neighboring PUs. However, its impact

becomes much smaller if its component type is altered from C1 into C3. This belongs to the

first level of learning in this system.

For any two neighboring PUs, the connection is defaulted if they belong to the same

component type. This would allow signals to transmit along the same component type. If

they belong to different types, the connection is set only when there is a MAP linking them

together. For every possible connection to a neighboring PU, one bit is needed to indicate

whether there is a connection between them. Eight bits are required to setup the MAP

connection pattern to the eight neighbors (denoted by M17-M24). The MAP pattern linking

different types of PUs is allowed to change as evolutionary learning proceeds. This belongs

to the second level of learning.

The other function of the input department is to control the timing of signal conversion

for each neighboring signal arriving at the input department into signals for the process

department. The input department polls these latches in sequence such that only one is

allowed to perform signal conversion at a time. A counter starting from 0 to 7 is used to

control the timing of signal conversion. We note that signal conversion does not have to be

done in a sequential manner. Instead, it might be implemented with parallel digital circuits.

This would speed up the response time, but requires a more complicated circuit design.

5.1.2 Process department

The process department has three components: DPG (digital pulse generator),

accumulator, and bounder (Fig. 11). The DPG is responsible for converting signals from


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either receptor neurons or neighboring PUs (through input department) into a sequence of

binary signals for the accumulator. The latter adds up these binary signals by using a 3-bit

binary counter and then sends the outputs to the bounder. The bounder will increase by 1 if

it receives a 1 signal from the DPG. The bounder is used to determine whetheror not a

neuron is ready for firing.

output

dept./1

process dept.

bounderaccumulatorDP G /1

/3

S

receptor neurons

IW

M25 M88

evolve at receptor tocytoskeletal neuron

connection level. . . I64I2I1 M26 . . .

M89

evolve at

readin enzymelevel

input

dept.

Fig. 11. Conceptual architecture of the process department.

As indicated above, the DPG receives signals from either receptor neurons or its

neighboring PUs. The pattern of connections between receptor neurons and each PU might

vary during the course of learning. In the current implementation, sixty-four bits (denoted

by M25-M88) are employed to represent the connections between receptor neurons and each

PU(one bit for each receptor neuron). This belongs to the third level of learning.

As mentioned earlier, a cytoskeletal signal is initiated when a readin enzyme receives an

external signal from any one of these 64 receptor neurons. In other words, there will be no

signal initiated if there is no readin enzyme sitting at the same site. As a consequence, the

existence of a readin enzyme will directly determine whether external signals arriving from

receptor neurons are allowed to convert into cytoskeletal signals. Changing the pattern of


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readin enzymes will thus control the pattern of inputs into the cytoskeletal neurons. This

belongs to the fourth level of learning.

As shown in section 3.2, each cytoskeletal component has six possible states: quiescent

(q0), active with increasing levels of activity (q1, q2, q3, and q4), and refractory (qr). In the

current version of this model, a 3-bit binary counter is employed to represent the state of a

cytoskeletal component. The counter with 0 represents state q0, 1 represents state q1, 2

represents state q2, 3 represents state q3, 4 represents state q4, 5 represents state qr, and the

remaining two values are unused. The counter starts from 0 and increments by one when it

receives a 1 pulse from the DPG. After the count of 4, the counter will stay at the same

state until its next update time. A component in states q3 or q4 will go into the refractory

state (qr) at its next update time, and then go into the quiescent state (q0) at the following

update time.

As shown in Table 1, there are three types of signals that the DPG might receive. In the

current implementation, we assume that the DPG will generate one, two, and three pulses for

the accumulator when it receives a weak, intermediate, and strong signal, respectively. The

DPG has three 6-bit parallel-load/serial-out registers that load data into the registers in

parallel and then send these bits out one at a time. For example, in Fig. 12, the first 6-bit

register will load the data 101010 in parallel (three 1s represent three 1 pulses will be

generated), and then send these bits out one at a time.

As mentioned earlier, the accumulator will send its outputs to the bounder. The latter is

used for determining whether a PU is ready for sending outputs to its neighboring PUs or

firing a neuron. As shown in Fig. 12, the bounder has two inputs: P and Q. Input P takes

signals from the accumulator while Q is a fixed threshold set up by the system in advance.

Currently, the threshold value is set at 011, representing the highly active state q3. There

are three possible cases between P and Q. When P is less than Q, there is no output


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generated from the bounder. When P is greater than or equal to Q, this means that the PU is

ready for sending outputs to its neighboring PUs. Specifically, the neuron will fire when P

is greater than Q and there is a readout enzyme sitting at the same site. As indicated earlier,

only the first firing effector neuron is recorded as an output associated with each input pattern.

As a consequence, all PUs will be reset to their initial states when there is a cytoskeletal

neuron firing. Through changing the pattern of readout enzymes, we can control the output

pattern of a cytoskeletal neuron. Like readin enzymes, the pattern of readout enzymes is

allowed to change as learning proceeds. This belongs to the fifth level of learning.

In addition to the above three major components, the process department has a

controller with two functions (see and in Fig. 12). First, it will control the accumulator

countdown at discrete instants of time. For example, an accumulator in the moderately

activated state q2 will go to the slightly activated state q1 at the next update time if it receives

no signal. Similarly, an accumulator in the slightly activated state q1 will go to the quiescent

state q0 if it receives no signal. An accumulator in the refractory state is not affected by its

neighbors until its refractory period is over, and will go into the quiescent state at its next

update time. The refractory state is necessary to ensure unidirectional propagation.

Secondly, the process department controls the update timing of the accumulator state.

Indirectly, this controls the signal transfer timing from the accumulator to the bounder, which

in turn determines the PU transmission speed. As indicated earlier, different types of

components transmit signals at different speeds. We assume that C1 and C3 components

(PUs) transmit signals at the slowest and fastest speed, respectively. The transmission speed

of C2 components is intermediate between that of C1 and C3 components. Our somewhat

arbitrary choice is that C3 transmits signals to its neighboring components on the fastest time

scale. C2 transmits slightly slower than twice the C3 rate. C1 transmits slightly slower than

twice the C2 rate.


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0 1 2 3 4 5

clk

0 1 2 3 4 5

0 1 2 3 4 5

W

I

S

inputdept.

101010

101000

100000

DPG

Up

Clear

accumulator

/1

/1

/1

3-bit

binary

counter/3

/3

/3

3-bit

comparator

PQ

P>Q

P=Q

M90

bounder

firing

clk

output

dept.

011

parallel load/serial out

evolve at readoutenzyme level

Down

Fig. 12. Detailed architecture of the process department.

5.1.3 Output department

As indicated earlier, there are two unidirectional connections between a PU and its

neighboring PUs. In section 5.1.1, we explained that M17-M24 (representing the pattern of

MAPs) controls the pattern of signals from neighboring PUs to a specific PU. Similarly, we

need one bit to indicate whether a PU should send outputs to its neighboring PUs. In total,

eight bits are required (denoted by M91-M98), as shown in Fig. 13. As indicated earlier, the

connection is defaulted for any two neighboring PUs of the same type. If they belong to

different types, the connection is set only if there is a MAP linking them together. As

described in the input department, the MAP pattern is allowed to change as learning proceeds.


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output dept.

process

dept.

PU1

PU8

..

....

M91 M98evolve at

MAP level. . .

Fig. 13. Output department.

5.1.4 Preliminary result

To evaluate the performance of the above digital circuits, each PU was simulated and

tested with MAX PLUS II system, a digital circuit simulation tool developed by Altera

Corporation (San Jose, CA). The result showed that these circuits function as expected.

The simulation results were consistent with those of the ANM system constructed previously.

At this stage, we have not yet performed a complete set of experiments to report in the present

paper.

5.2 Reference Neurons

As shown in Fig. 1, cytoskeletal neurons are controlled by two levels of reference

neurons. A low-level reference neuron contacts all cytoskeletal neurons in a given class (i.e.,

neurons belonging to the same bundle). A high-level reference neuron contacts subsets of

the low-level reference neurons. In the current implementation, the connections between the

two levels of reference neurons are allowed to change when learning proceeds. This belongs

to the sixth level of learning. We note that the connections between low-level reference

neuron and cytoskeletal neuron layers are held constant.


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5.3 Learning Mechanisms

We have shown in section 5.1 that each PU is controlled by 98 bits of memory (M1-M16

for determining the type of influence for signals from neighboring PUs, M17-M24 and M91-M98

for setting up the MAP patterns, M25-M88 for choosing stimuli from receptor neurons, M89 and

M90 for deciding the existence of readin and readout enzymes, respectively). For each

cytoskeletal neuron implemented with 8x8 cellular automata, around 6.4 kilobits of memory

are required. As mentioned earlier, the ANM system has 256 cytoskeletal neurons in the

current implementation. In total, this would require slightly more than 1.6 megabits of

memory (i.e., 256x6400), which is around 200k bytes. When learning proceeds at the level

of cytoskeleton neurons, the performance of each subnet is evaluated first. Then, the

variations from the bit positions representing the best-performing subnets are copied to the

lesser-performing subnets. As shown in Fig. 14, the above process is repeated until the

system is terminated.

When learning proceeds at the level of reference neurons, only the connections among

the two levels of reference neurons are allowed to change in the course of learning. That is,

each high-level reference neuron is allowed to change its 32 low-level reference neurons

selection. For each high-level reference neuron, 32 bits are needed to specify the pattern of

connections to the 32 low-level reference neurons. In total, 256 bits (m1-m256) are needed for

the eight high-level reference neurons. As learning proceeds, the cytoskeletal neurons

selected by each high-level reference neuron are evaluated first. We note that the

performance (or fitness) of each high-level reference neuron is determined by the cytoskeletal

neurons it selects. The variations from those bits representing the best-performing

reference neurons are copied to the lesser-performing reference neurons. As shown in Fig.

15, the above process is repeated until the system is terminated.


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Generate the initial repertoire of cytoskeletal neurons Mijk (i: subnet number; j: neuronnumber; k: memory bit number)

Repeat

Evaluate the performance of each subnet

(For each input pattern, a subnet makes a correct response when the first effectorneuron-firing group is the same as the group determined by a specific problem

domain. The greater the number of correct responses made by a subnet, the

higher its fitness. The detailed procedure of evaluating the performance of a

subnet is shown in Fig. 3.)

Select three best-performing subnets

Copy Mxjk to Myjk(x: best-performing subnet; y: lesser-performing subnet; j: 1,,32; k: 1,,98)

Mutate Myjk(y: lesser-performing subnet; j: 1,,32; k depends on which learning level is

operative. The range of k is:

from 1 to 16 if evolves at the component level

from 17 to 24 and from 91 to 98 if evolves at the MAP level

from 25 to 88 if evolves at the rec/cyto connection level

89 ` if evolves at the readin enzyme level90 if evolves at the readout enzyme level)

Until learnin ob ective com lete or maximum learnin time reaches

Fig. 14. Evolutionary learning at the cytoskeletal neuron level.

Generate the initial repertoire of high-level reference neurons mi (i: memory bit

number)

Repeat

Evaluate the performance of each high-level reference neuron

(The fitness of a reference neuron is determined by the performance of the

cytoskeletal neurons that it selects.)

Select three best-performing high-level reference neurons

Copy mx to my(x: best-performing ref. neurons; y: lesser-performing ref. neurons)

Mutate my (y: lesser-performing ref. neurons)

Until learning objective complete or maximum learning time reaches

Fig. 15. Evolutionary learning at the reference neuron level


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6. Conclusions

Evolution is the essence of biological systems for high adaptability. Digital machines

are designed to be effectively programmable. The ANM system that we developed [6,7] is a

biologically inspired neuromolecular architecture that attempts to capture certain biological

information processing features. Evolutionary adaptability is one of the significant features

captured in this architecture. Previously, this architecture was implemented using computer

programs. Whereas a computer simulation of such a multilevel parallel network is very

time-consuming, we demonstrated a hardware design of this architecture using conventional

digital circuits. Our ultimate goal is to build actual hardware (or better,

molecularware/neuromolecularware) that is natural to the biological processing mode.

Adaptability is a very broad term. It might be defined as the capacity to continue to

function in an unknown or uncertain environment [15]. Generalization can be said as a

specific kind of adaptability. By generalization, we mean the ability to group different

patterns in a natural way in accordance with some underlying structural or functional

principles [8]. Previous experimental results [4,6,8] demonstrated that this system exhibits

some degrees of effective generalization capability in which intraneuronal dynamics plays a

significant role. However, this was still a very limited approach to generalization since high

level cognitive processes were not taken into account.

In this system, a cytoskeletal neuron with a particular integrative dynamics and readout

enzyme distribution will recognize some families of input patterns (i.e., it will recognize a

family of input patterns that are variant in space and time). The input patterns recognized by

a cytoskeletal neuron will be generalized in a more selective way than a simple threshold

neuron. Furthermore, the manner of generalization can be altered by changing its integrative

dynamics. This capability is advantageous for handling problems with environmental

ambiguity. Cytoskeleltal neurons may be trained to recognize sets of input patterns through


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an evolutionary learning algorithm. If overgeneralization occurs, the neuron will lose its

pattern processing specificity since every pattern will trigger its firing. Conversely, if a

cytoskeletal neuron is trained to recognize only a single pattern, it will be overly specific and

rigid. In this case it will lose its capability for recognizing input patterns that are variable in

space and time. It is important to strike a balance between these two extremes.

The ability to generalize is clearly necessary for dealing with variable or noisy

environments. The problem is that dynamics that allow for effective generalization of some

classes of environments necessarily preclude effective generalization for other classes. We

call this the interference problem. Dealing with this problem requires an effective

evolutionary learning algorithm. It also requires the learning algorithm to proceed with a

suitable neuronal architecture, including both the internal structures and neuronal dynamics,

and memory mechanisms that link neurons into coherent groups. It is essential that the

architecture allow for the evolution of a repertoire of special purpose neurons with dynamics

that have different generalization properties and a linking mechanism that allows for

orchestration of this repertoire. Our model opens up such a rich evolutionary possibility.

The model is clearly much more complex than conventional connectionist models. We

can regard it on the one hand as a tool for examining the nature of biological processing itself,

and on the other as a tool that is capable of yielding practical benefits. This paper is our first

attempt to develop a neuromolecular architecture on digital circuits. The detailed (or more

effective) design of this hardware is still under investigation. We expect that the realization

of this architecture on digital circuits would allow the system to perform on a real-time basis.

It would indeed expand the application domains. Future work includes widening the

dynamic capabilities of the neurons, utilizing the associative memory capability in

combination with evolutionary learning, porting evolved neurons with useful pattern

processing capabilities to special silicon hardware, using the system as an architectural


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paradigm for emerging molecular electronic technologies, and employing the system as a

vehicle for obtaining a clearer understanding of the role of intraneuronal mechanisms in brain

functions.

Acknowledgment

This paper is dedicated to the memory of Professor Michael Conrad, a pioneer in the field

of molecular computing.

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