T. SUN ET AL.

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Not all the crazy growth of trees is suit with the neuron

growth regulations. This is created by the suppose 4. In

the growth of real neuron, each tree will not be limited

until reach the certain degree. The most external surface

growth is random so this is the most important reason.

The length of some rooms is too long. If set one thresh-

old to chose from the obeyed random number of normal-

ity distribution, it can avoid this unmoral condition.

Nevertheless, the threshold cannot determine that not set

this subject.

3. Neuron Growth Prediction and Its

Influence of Geome tric Characteristics

According to the research of neuron morphologic, dif-

ferent function of neuron has huge differences in size,

shape and complexity [4]. The axon is long in common.

It is separate from axon hillock with uniformity diameter.

The commence part is the begin part. Apart from the

soma some distances, we will get the myelin sheath,

which called nerve fiber. The soma differences are huge.

The small diameter is only 5 - 6 and the large one

could be even 100 and more. The dendrite form,

number and length are different. Dendrite most be branch,

it can transform to the somas after stimulation. Axons are

funicular. The terminal have branches and called axon

terminal. Axons transform from soma to the terminal.

Usually, one neuron has one or more dendrites. Never-

theless, axon has only one. The bigger neuron is the

longer the axon is. Now divide the neuron growth proc-

ess into exponential phase, stationary phase and death

phase. Target is as the length of neuron dendrite. Sepa-

rately describe the growth change and impact of geomet-

ric characteristic in different phases. Here we predict the

following things:

μm

μm

Exponential phase: The half diameter is small. Never-

theless, it will grow more branches with obviously

changes. With the increase of the diameter, the broadcast

consume of neuron signal will increase all the same. At

the same time, the metabolic costs also increase the same.

The change of form will influence the geometrical form

and it might have mistakes.

Stationary phase: the space form of neuron is ensuring.

Under the rich growth, there will not have a change in a

period [5]. However, on the limited environment, the

neuron will limit the growth even grow back. So the en-

sure form data should reflects the geometric form. It is

the best periods of neuron form and functional predic-

tion.

Death phase: when the neuron and neighboring somas

is same to the certain extent, the neuron growth will be

limited. Some will back growth and the whole neuron

structure will change more and not suit for the neuron

identification and classification.

4. Conclusions

Neuron growth will bring the series change of geometri-

cal characteristics. Especially the growth of dendrite and

axon will change the space characteristic and geometric

characteristic obviously. This article is through the iden-

tification of spastics regulations and builds the prediction

model. The growth prediction of combine neuron struc-

ture knowledge, take the advantage of growth period and

describe the form structure and function such as growth,

death, branch and so on. Use the rate distribution and

random process theory to analyze the neuron topological

structure regulation. Through the model identification

and abstract the growth regulation. The model applies

Monte Carlo to imitate the neuron growth. For the close

soma part, dendrite growths get the better imitation.

However, far side room growth cannot control reason-

able. There has no whole reflect condition of neuron

growth. If want to do the further research, the initial

thinking is increasing with the growth configuration and

reduction.

5. References

[1] T. Sun, L. Lin and Q. Y. Huang, “Research of Neuron

Typological Classification and Identification Questions,”

(Committed)

[2] NeuroMorpho.Org. 2010.

http://neuromorpho.org/neuroMorpho/index.jsp

[3] Y. D. Zhang, P. F. Zhu and Z. G. Wang, “Research of

Neuron Regulation about Cultivate Rat Hypothalamus,”

Journal of Traumatic Surgery, Vol. 6, No. 1, 2004, pp.

45-47.

[4] M. B. Kerry, “Quantifying Neuronal Size: Summing up

Trees and Splitting the Branch Difference,” Seminars in

Cell & Developmental Biology, Vol. 19, No. 6, 2008, pp.

485-493. doi:10.1016/j.semcdb.2008.08.005

[5] G. A. Ascoli, et al., “Generation, Description and Storage

of Dendritic Morphology Data,” Philosophical Transac-

tions of the Royal Society B, Vol. 356, No. 1412, 2001, pp.

1131-1145. doi:10.1098/rstb.2001.0905