Title: ARTIFICIAL NEURON TRANSPLANTATION – A TREATMENT FOR SHIZOPHRENIA
Authors: S.HARISUDHAN , 3rd Year B.E, Civil Engineering Department
M.NARMATHA, 3rd Year B.E, Computer Science Engineering Department
College: Indra Ganesan College of Engineering , Trichy
ABSTRACT:
Schizophrenia is one of the major brain diseases for which there is no effective treatment even today. Basically, schizophrenia is caused when the neurons are affected. It leads in to hallucination and constant paranoia. Also schizophrenia has been a burden on our society. Currently, 65.7million individuals are living with schizophrenia. Therefore, this paper deals with the effective treatment for schizophrenia by the transplantation of affected neurons with artificial neurons. This work involves pinpointing the neurons that are responsible for the symptoms related to schizophrenia and also creating the artificial neurons. The faulty neurons which cause schizophrenia are replaced by artificial neurons to emulate the behavior of the biological neuron. As a result, the adverse impacts of schizophrenia on human beings get reduced.
KEY TERMS: Schizophrenia, artificial neuron, transplantation, behavior.
OBJECTIVE:
To compute an artificial neuron that can be replaced by a faulty neuron as an effective treatment for schizophrenia.
1. INTRODUCTION:
Schizophrenia is a brain disorder plagued with hallucinations and paranoia. It has been a burden to our society as 65.7million individuals are living with schizophrenia. And even now there is no effective treatment for it. In general, schizophrenia causes damage to the neurons. So considering the effective treatment for it, the faulty neurons are replaced by the artificial neurons.
Artificial neurons simplify the behavior of human brain. A neuron can be defined as a black box with few inputs and outputs. For example, CCortex building by CorticalDB, which is a massive spiking neural network simulation of the human cortex and peripheral systems.
2. BEHAVIOUR OF A NEURON:
First, a neuron must be studied and its parts and behavior should be understood. A neuron comprises of a three parts namely, Soma, Dendrites and Axon. Soma denotes the body of the neuron. Dendrites are branches that transmit the action potentials to the soma from other neurons. Axon is a branch that transmits the action potential from the neuron to other cell.
When a neuron is fired, it generates the action potential that is transmitted and processed in the soma of the next neuron. Soma considers all the input and makes a non-linear addition which gives the membrane potential of neuron that is transmitted along the axon to target neurons. One of their characteristics is their capacity of learning for which the inputs have adaptive weights.
There are two potentials namely presynaptic and postsynaptic potentials. Presynaptic potentials are nervous impulses that arrive to the neuron. Postsynaptic potentials are actions generated in a neuron propagated to the rest of neurons.
2.1 ANALYSIS OF THE PROBLEM:
It is to make a model that implements the behavior of a neuron. This model has some inputs that emulate the dendrites of the neuron and a pulse will be obtained as a response.
In general, artificial neurons are modeled using digital signals and computed with the transfer function as mentioned in the above diagram.
2.2 SPIKE Vs PULSE:
The below spike shows the wave form of the real neuron. For modeling it is transferred into the digital signals with the Boolean logic operation. The diagram is represented as follows:
In the above diagram, spike is represented in digital pulse form for modeling. It leaves the output as logic ‘1’ when the neuron is active and logic ‘0’ when the neuron is inactive.
2.3 NEURONAL IMPLEMENTATION:
The implementation of neuronal model will be carried out in VHDL which is used to design digital circuits, for that, all signals will be digital signals.
One of the main characteristics of the neural networks is the ability of learning. For neurons, learning is the modification of induced behavior for the interaction with the environment and establishing new response models to extern stimulus. The knowledge is represented in the weights of the connections between the neurons. The process of learning implies some numbers of changes in these connections. The neural network learns modifying the values of the weights of the network.
2.4 SYSTEM COMPONENTS:
The neuronal network system must have the following components.
i) 3 inputs referred to the dendrites from other neurons.
ii) 1 output referred to the axon. When the output would be fired, the output will have to generate a pulse to propagate it to other neurons through axon.
FIRGURE.3: Basic components of a neuron.
With the help of transfer function, summations of weights are computed from those inputs in order to give an output.
3. FUNCTIONS OF A NEURON:
When a spike arrives, the soma has two functions, to generate the potential action according to the input and to compare if the addition of all potential actions in this instance of time is over a threshold, in that case, it will have to generate a pulse through the axon.
FIGURE.4: Basic functions of a neuron.
From the above figure, it is clearly shown that each dendrite needs a potential generator.
3.1 SPLITTED POTENTIAL GENERATOR:
Each dendrite needs an alpha and a timer block. Therefore, the potential generator is split into alpha and a timer block. The potential generator is in function of time. So there is a need for two blocks namely, alpha and timer. The timer is to synchronize the signal and the alpha is to make operations.
FIGURE.5: Components of a potential generator.
The above diagram clearly depicts that the potential generator constitutes a timer and an alpha block.
3.2 INHIBIT BLOCK:
If a neuron is unable to generate another spike and if it is fired, it has the duration of about 2ms known as refractory period.
FIGURE.6: Inhibit block
To solve this problem, there is an inhibit block that works as a timer. When it is fired, it inhibits and reset any count. When the counter overloads, it disables the inhibit signal. Then the potential generator is able to generate the potential when a pulse arrives again.
3.3 TIMER:
We need a system that when it detects a pulse (the rise edge), starts a count of real time. It will also have an enable input, when this input is active, the timer will stop count, and the count would be reset. When this signal will be inactive the timer will be able to count again when receive other rise edge.
FIGURE.7: Timer block function
Inputs: Input1: External input from others neurons
CLK: External input, used to synchronize the count.
Enable: Internal input, from the inhibit block.
Outputs: Count1: Internal output, to alpha block.
4. SIGNIFICANCE OF TRANSPLANTATION:
This is an amazing phenomenon for the artificial neurons that would replace our patient’s damaged brain cells. They would have the opportunity to connect with the rest of the brain and create a united system as before. Unfortunately, it is likely that these connections would differ from the connections that she previously had formed. This difference in connections could result in an entirely new personality than that of the woman that she was before becoming ill. This method focuses on the importance of demonstrating a moral character and the use of experience and education in order to arrive at an ethically acceptable decision. It also highlights the significance of “the responsibility to do good where possible”. It does not get much better than giving the gift of life to someone after they have had to live without it.
This is an amazing phenomenon for the artificial neurons that would replace our patient’s damaged brain cells. They would have the opportunity to connect with the rest of the brain and create a united system as before. Unfortunately, it is likely that these connections would differ from the connections that she previously had formed. This difference in connections could result in an entirely new personality than that of the woman that she was before becoming ill. This method focuses on the importance of demonstrating a moral character and the use of experience and education in order to arrive at an ethically acceptable decision. It also highlights the significance of “the responsibility to do good where possible”. It does not get much better than giving the gift of life to someone after they have had to live without it.
5. CONCLUSION:
As “engineering is really about enhancement”, specifically, “many enhancements are absolutely necessary, such as those that provide healthier bodies and minds. If we do not act soon to deal with some of these challenges, there is a cost to certain members of society”. Although the application of artificial neurons would have the potential to cure our schizophrenic patient, it is also possible that she would be transformed into a new woman because the neurons would be entirely new to her brain. Our brains develop over time and are constantly molded by our experiences. Every time we practice an old skill or learn a new one, existing neural connections are strengthened and, over time, neurons create more connections to other neurons”.
6. ACKNOWLEDGEMENTS:
We are very thankful to YUVA ENGINEERS for giving us a platform to execute our paper. We would also like to thank our management who inspired us a lot for doing this work.
7. REFERENCES:
(2010). “World Alzheimer Report: The Global Economic Impact of Dementia.” Alzheimer ’s disease International.(pdf). http://www.alz.co.uk/research/files/WorldAlzheimerReport2 010.pdf. p. 6
C. Freudenrich and R. Boyd. (2001). “How Your Brain Works.” How Stuff Works. (Online article). http://science.howstuffworks.com/life/inside-the mind/human-brain/brain1.htm. p. 2
D. Vallero. (2007). Biomedical Ethics for Engineers. Burlington, MA: Academic Press. (Print book). pp. 123-126
O. Sacks. (2010). “This Year, Change Your Mind.” The New York Times. (Online article).
http://www.nytimes.com/2011/01/01/opinion/01sacks.html? Page wanted=all&_r=0. n. pag.
(2013). “Novel Neurotechnologies: Intervening in the Brain.” Nuffield Council on Bioethics. (Print report). pp. 76- 84
Strange. (2013). “DARPA Unveils Atlas Humanoid Robot.” PC Magazine. (Online article). http://www.pcmag.com/article2/0,2817,2421687,00.asp. n. pag.
T. McVeigh. (2013). “Killer robots must be stopped, say campaigners.” The Guardian. (Online article).
http://www.theguardian.com/technology/2013/feb/23/stopkiller-robots. n. pag.
http://www.corticaldb.com/projects.asp, May 2008.
Perry, Douglas L. 1998, VHDL, third edition, McGraw-Hill, New York, USA, ISBN 0-07-049436-3