2. The architecture
As indicated earlier, the ANM system com- prises two types of neurons: cytoskeletal neurons
and reference neurons. Cytoskeletal neurons are capable of performing spatiotemporal input – out-
put transduction while reference neurons are re- sponsible for assembling cytoskeletal neurons into
groups for specific tasks. In this section, we first describe the implementation of cytoskeletal neu-
rons. Secondly, we explain the mechanisms of reference neurons. Thirdly, we illustrate the in-
put – output interfaces of the ANM system. Fi- nally, we specify the learning algorithms that
mold the system for a specific task.
2
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1
. Cytoskeletal neurons or enzymatic neurons
The ANM system has 256 cytoskeletal neurons, divided into eight comparable subnets. Each sub-
net consists of 32 cytoskeletal neurons. By com- parable subnets, we mean that the interneuronal
connections and intraneuronal structures of each subnet are similar. Our implementation of cy-
toskeletal dynamics is designed to capture the features of signal integration. When an external
signal impinges on the membrane of a neuron, it will initiate a cytoskeletal signal flow. For exam-
ple, in Fig. 1, 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 microtubule associated protein MAP linking these components together. For example,
in Fig. 1, 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 travel-
ling along the eighth column. We assumed that the interactions between two neighboring compo-
nents 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 com-
ponents transmit signals at different speeds. For example, C1 components transmit signals at the
slowest speed. By contrast, C3 components trans- mit signals at the fastest speed. The transmitting
speed of the C2 components is intermediate be- tween that of the C1 and C3 components.
When a requisite spatiotemporal combination of cytoskeletal signals arrives at the site of a
readout enzyme, the neuron will fire. For exam- ple, in Fig. 1, 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, acti-
vated 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
Fig. 1. Structures of 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. A readin enzyme converts an external signal
into a cytoskeletal signal. Specific combinations of cytoskeletal signals will activate a readout enzyme, which in turn causes the
neuron to fire. The neighbors of an edge site are determined in a wrap-around fashion. A MAP links two neighboring compo-
nents of different types together.
Fig. 2. Transition rules of the components. s1, s2, and s3 indicate a signal from a highly activated component C1,
component C2, and component C3, respectively. For example, if component C1 in the state q0 receives an S2 signal it will
enter the moderately activated state q2. If it then receives an S3 signal it will enter the more activated state q3.
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.
The matter given above explains how the fea- tures of signal integration are captured in the
cytoskeleton. The following explains how cy- toskeletal dynamics are implemented with cellular
automata. Each cytoskeletal component has six possible states: quiescent q0, active with increas-
ing 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 qr 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 summation of all
stimuli received from its active neighboring com- ponents with each component type having its
own update time. The detailed state transition rules are illustrated in Fig. 2. A component in the
refractory state will go to the quiescent state at its next update time. A component in the refractory
state is not affected by its neighboring compo- nents until its refractory period is over.
2
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2
. Reference neurons Two layers of reference neurons serve to assem-
ble cytoskeletal neurons into a collection capable of performing a required task Fig. 3. Each high-
level reference neuron controls a collection of low-level reference neurons, while each low-level
reference neuron in turn controls a bundle of comparable cytoskeletal neurons. Thus, the firing
of a high-level reference neuron will also fire the low-level reference neurons controlled by it, which
in turn fires a particular combination of cytoskele- tal neurons.
2
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3
. ANM input – output interface This system had 64 receptor neurons and 32
effector neurons when it was first constructed Chen and Conrad, 1994a. The patterns of neu-
ronal connections of each comparable subnet are the same Fig. 4. This ensures that comparable
cytoskeletal neurons in each subnet i.e. neurons
Fig. 3. 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 low-
level reference neurons.
the readout enzyme sitting at the same location. The activation of the latter will in turn cause the
Fig. 4. Input – output interface of the ANM system. The connections between receptor neuron and cytoskeletal neuron layers are randomly decided initially, and will vary as learning proceeds. The connections between cytoskeletal neuron and effector neuron
layers are fixed.
having similar intraneuronal structures will re- ceive the same inputs from receptor neurons and
that the system 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 compet-
ing subnet. We note that an effector neuron fires when one of its controlling cytoskeletal neurons
fires.
2
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4
. Multile6el learning Five levels of evolutionary learning are allowed
in this system. They are at the levels of initiating signal flows controlled by readin enzymes, re-
sponding to signal flows controlled by readout enzymes, controlling signal flows controlled by
MAPs, transmitting signal flows controlled by cytoskeletal
components, and
grouping cy-
toskeletal neurons controlled by reference neu- rons. The first four levels are intraneuronal,
occurring inside cytoskeletal neurons. The last level is interneuronal, occurring at the level of the
connections between low-level reference neurons and high-level reference neuron layers.
Evolutionary learning at the cytoskeletal neu- ron layer has three major steps. We will evaluate
the performance of each subnet first. Three best- performing subnets are then selected. Finally, the
readout enzyme patterns, readin enzymes, MAPs, and components are copied with variation from
the best-performing subnets to lesser-performing subnets. The variation depends on which level of
evolutionary learning is operative. An example of the three steps given above is shown in Fig. 5.
Evolutionary learning at the reference neuron level also occurs in three steps. First, cytoskeletal
neurons, controlled by each high-level reference neuron, are activated in sequence to evaluate their
performance. Second, the patterns of neural activ- ities controlled by best-performing reference neu-
rons are copied to lesser performing reference neurons. Finally, lesser-performing reference neu-
rons control slight variations of the neural group- ing controlled by the best-performing reference
neurons, assuming that some errors occur during
Fig. 5. Evolutionary learning at the cytoskeletal neuron layer.
Fig. 6. Evolutionary learning at the reference neuron layer.
Fig. 7. Sequence of opening of learning levels.
the copy process. Fig. 6 shows an example of the three steps given above.
In the current implementation, only one level is opened for learning at a time, while other levels
are turned off. Each level is opened for 16 learn- ing 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. 7.
3. Application problem domains
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