GRADUAL UPLOADING AS A COGNITION OF MIND
by Algimantas Malickas
Institute Mathematics and Informatics, Akademijos 4, Vilnius, Lithuania E-mail: email@example.com
Gradual uploading could be considered as an alternative to neuron-by-neuron uploading. In this article we discuss some technological aspects of this problem, including computer-brain, brain-computer interfaces and necessary computational power for mind simulation. We interpret the uploading system as a fault tolerant neural network (where damage would be brain death), as a "black box" (brain) imitation system. We use children mind evolution model for the description of uploading system evolution, where the external world is changed to the brain world, receptors - to the brain-computer interface, motor functions - to the computer-brain interface. Speech communication as an interface prototype is discussed.
2. Functional uploading
3. Interpretation of gradual uploading
4. Brain - computer interface.
5. Computer - brain interface.
7. The system of brain cognition
7.1 The principle of world modeling
7.2 Comparison between real world and brain cognition.
7.3 Evolution of the external system
8. Speech as an interface
9. The problem of person identity
The gradual uploading of mind (also known as gradual extension, metamorphosis , soft uploading, brain enhancement) could be evaluated as an alternative of atom-by-atom, or neuron-by-neuron uploading.
The gradual uploading requires addition of artificial neural network (or other means of AI) to the brain. After this, the brain and the external system would operate as one system. During this common coexistence, the memory and other functions of mind would gradually grow into the external system and would survive after the brain's death. Therefore, the death of the brain would not be fatal for the person, i.e. main goal of uploading would be achieved.
The goal of this article is to discuss some ideas, how this problem can be solved, what is going on in this field of research, and what is necessary to do in the future.
2. Functional uploading
The gradual uploading would be uploading of mind functions, not uploading of a morphological brain architectures.
At first, the morphological structure of brain is defined by not only functional necessities. Evolution is very stochastic process and accidental factors contributed a lot to the brain structure.
At second, some functions (homeostasis, most motor reflexes etc.) are not useful for the cyber system, if the 'cyber-body' differs a lot from the human body. In addition, it looks like those functions are not so important for the self-perception.
The mechanisms of brain functioning and functions unimportant for the self-perception can be abolished, adapted or changed. This standpoint could greatly facilitate the solution of the uploading problem.
2. Gradual uploading interpretation
External neural network of the uploading system could be implemented using several ways. The interpretation of performance of this network can be various too:
1. The neural network functions are distributed over the network (over large field of network, at least). In some cases, the neural network is a fault- tolerant i.e. after removal of some neurons, survived part of network can inherit almost complete collection of parent network functions. Under certain conditions similar properties could be used for the separation of external system in case of death of biological brain.
2. The brain can be interpreted as a "black box" with complex inside structure and functional properties. If the sufficient quantity of information about brain properties were obtained, the imitation or reconstruction of these properties would be possible.
3. Some ideas about organization and interpretation of external neural network can be supported using the existing example of powerful neural system - the human brain. Evolution of external system could be interpreted as a child evolution, in this case. Some cognitive science, psychological and pedagogical models can be used for this evolution description. We will discuss this point of view in details later.
In any case, the problem of technical implementation of gradual uploading can be divided into:
external system software problem,
external system hardware problem,
interface computer - brain problem,
interface brain - computer problem.
3. Interface brain - computer.
There are several possibilities of creation of this interface. One way is to implement some electrodes or microchips into the brain. At this time some examples of this interface are available [9,10,11 ]. Main disadvantage of this method is necessity of surgical invasion into the brain.
Another way is to use multichanell electroencephalogram (EEG). Some implementations of EEG brain-computer interfaces are also available at this time . For the EEG, the minimal distance between electrodes must exceed 1 centimeter, and this factor limits a number of electrodes, i.e. EEG interfaces permeability. The EEG could represent brain information from the cortex only. In addition, the EEG channel can represent only a sum of activities of many neurons under channel electrode.
Other possible technology for the interface could be magnetoencephalography, using SQUID (Superconducting QUantum Interference Device) . This is a most sensitive technology for registration of magnetic fields, at this time.
For the gradual uploading goals, the brain-computer interface should have some specific features. One problem is representation of necessary brain information in the cortex field under EEG, MEG electrodes or near implant. This problem can be partly solved optimizing the location of electrodes according to our current knowledge about cortex structure.
More powerful method would be a brain adaptation itself. The brain - computer interface would have feedback coupling (computer - brain interface), and this fact would ensure good co-operation between brain and uploading system. Through this co-operation, the brain adaptation would happen itself. Besides, some additional means could be implemented for better brain adaptation and those means could create necessary ways to transfer the information into the brain. As one of examples of this adaptation some human learning methods can be considered which relates the artificial electroactivity in the deeper fields of brain with the electroactivity in the fixed field of cortex.
Some examples of human brain adaptation (Braille reading method, the speech of gestures etc.) show that such adaptation can be possible in principle, though these possibilities diminish during the human life.
4. Interface computer - brain
The brain implants could be used for information translation to the brain too [9,10,11]. Other way is to use sensations of human body for the transmission of computer - brain information.
Human body uses many nerves to translate information about external world to the brain. Some of nerves could be used to translate the information from the computer to the brain. One way is to use tactile nerves. In this case, the interface hardware would include a matrix element, each consisting of a small vibrator (like small needle).
The tactile brain input was widely investigated for hearing disorder compensation using tactile sensation. Some bibliography about this studies can be found in [2,3].
Another way to transmit information transmission is to use visual sensations. Such interface would have better information permeability, but it might interfere to world`s visual perception.
Using connection machine the processing speed up to 1.3 * 10^9 synapses per second can be achieved , and using transputer system the processing speed up to 2.7 *10^9 synapses per second can be reached . In principle, the most powerful current parallel hardware (1.8 *10^12 FLOP)  could implement up to 10^10 - 10^11 synapses/second.
The evaluation of the computational power of human brain very uncertain at this time. Some estimates of brain power could be based on the brain synapses number and neurons firing rate. The human brain have a 10^11 neurons and each neuron has average of 10^2 - 10^4 synapses. The average firing rate of brain neurons is about 100-1000 Hz. As result the brain modeling would require the computational power of 10^11 neurons * (10^2-10^4 synapses/neuron) * (100-1000 Hz) = 10^15 - 10^18 synapses/second.
Other estimations  show that the brain power could be between 10^13 and 10^16 synapses/second.
On the other hand, some factors could diminish the necessary computer power. Most artificial neural networks (backpropagation, Hopfield, Kohonen, Grossberg etc.) use neuron activity represented by analog signal amplitude (firing frequency modulation), not a firing signal. Computer simulation in this case is more simple and speedy.
Change of all brain neural network to amplitudical network could be problematic. For example, the temporary pattern of integration/summation is complicated for amplitudical neural networks, but more usual for firing neurons. But some systems (spatial processing parts of visual system, for example) could be changed and simulated more effectively.
The brain have many systems (sensors and motor channels, homeostasis system) which are not that important and could be replaced after uploading. Many sensory - motor tracts would be incompatible with new body properties, many old reflexes would be useless etc. The large sound articulation control network could be replaced to few transistors, for example. The similar changes could be done to most sensory-motorics systems of the brain, and this would let decrease the necessary computer power.
The brain neural networks are redundant. Besides not all neurons in the brain are active at the same time. Those facts could lead to decrease of the necessary computer power.
The evolution of external system will take some time (the children evolution take some years), and during this time computer will become more powerful (child brain after birth has little number of synapses too, but during his evolution this number increases).
6. The system of brain cognition
6.1 The principle of world modeling
Born child has sensors, effectors, motivation, sufficiently powerful neural network (which has necessary architecture for self-organization). Via sensors and effectors child has contact with the environment (physical and social). During the children evolution, brain creates internal model of external world. Most of intellectual functions of any person can be interpreted as information operations in that model . The same principle can be realized in the cyber system too, if the system has all necessary attributes (contact with environment, motivation etc.). Some studies in the fields of neural networks and AI [15,16] show how model of external world could be formed.
6.2 Real world and brain cognition comparison
The external world of the cyber system could not only be a physical world. If the sensors and effectors of cyber system were changed to brain - computer and computer - brain interfaces (i. e. the system would be connected with human brain "world"), the system would create an internal model of this brain (like as model of the physical world).
The "brain world" would not be very different from the real world. Some similarities:
1. The real world cognition is implemented via sensations.
The brain cognition would be implemented via external system sensations - brain-computer interface.
2. Brain have a possibility to change the real world.
The external system would use the signal to the brain (feedback coupling) for the brain change.
3. For the real world description the brain use the sensoric patterns, symbolic categories, combinations of patterns and categories, relationships between patterns and categories.
For the "brain world" description would used patterns from brain-computer interface, symbolic categories, combination of these, relations between these patterns and categories.
2. Many uncertainties exist in the information from the real world. Many uncertainties exist in the information from the brain.
On the other hand, there are some differences in the case of external system:
1. There are only indirect possibilities to influence the brain evolution process. In case of the external system it is possible to influence evolution of every single neuron.
2. The real world cognition is formed from two main parts: sensoric cognition (unprocessed information) and symbolic cognition (this information are processed during society evolution). Very roughly, these could be interpreted as neural network learning with supervisor: the sensor information would be neural network input, the symbolic information (notions, for example) - learning target.
In the case of external system, the "sensor" and "learning target" inputs would be received from the same environment - the human brain. The brain information contains not only unorganized sensor information (like as physical world), but the more organized, abstract information too. This information could substitute the symbolic information in the case of real world.
6.3 Evolution of the external system
In the cognitive level, the evolution of this external system will depend from initial and latest motivation of the system. The control of this motivation would allow to control an evolution of the external system. Inborn motivation is a very simple: stress or satisfaction. Genetic defines only which sensation is stress and which is satisfaction. The child evolution up to about two years could be described as a sensor stage. In this stage most sensors categories are memorized only. The motivation of the child are reference to these created categories.
After sensoric stage the semantic connections between categories are created. Through self-organizing processes and social influence (speech mainly) the abstraction process of categories starts. The latest motivation of child is reference to new, more abstract categories.
The external system could implement similar model. It is necessary to have some initial ("genetic") motives for the evolution of this system. Because the receptors of system would be connected to the human brain, the initial motive could be defined as some certain state of the brain, for example satisfaction. This state must be defined before system initialization, i.e. is necessary to know what brain output the satisfaction has. This properties could be defined subjectively or objectively, for example using some psychological or biometrics tests (like false detector).
In the sensoric stage the external system would create some categories of "brain world" and use these categories to define further motives of system In this stage, the external system have to reply clearly to the concrete brain state.
Later, the categories of the "brain world" would be related via semantic connections. Symbolic brain information would be used for category abstraction processes. During those processes the motivation of the system could be improved, using these new categories.
This is a very rough description only. Real processes would be much complicated. An external system abstraction, rule extraction functions, and, the most important the human brain adaptation must be investigated.
The possibilities of operation of external system after brain death could be supported by hemispherectomy - human brain hemisphere cortex removal in the tumor case. After the removal the brain`s ability survives, though some specifics hemisphere functions could be lost .
It is necessary to plan, how the external system will contact with external world after brain`s death. This problem could be solved using artificial sensors and effectors, artificial means of initial processing of sensor or motor information, which would connect the external system after the brain`s death. Another way is to organize the sensor and motor channels during system evolution. In this case it is necessary to plan, how the additional external world sensation would influence the extension system - brain evolution.
7. Speech as a interface
The example of the gradual uploading rudiments could be speech communication between persons. The human speech could be interpreted as a interface between two brains, though it does not have the necessary rate for uploading. At average, human being uses about 10^4 notions - words of the speech. The speed of speech is about 2 - 5 words per second. The word information capacity is about about 20 bites, roughly (1 word from 10^4 is 13 -14 bites plus 6 bites of grammatical information). So, the speech information permeability would be about 40 - 100 bites per second. In spite of low speed, the communication results are very good. Large part of our knowledge is acquired by using speech or reading. Sometimes, after long contact with other person we can observe phenomena, which could be interpreted as rudiments of uploading or merging.
8. The problem of person identify
The very important goal of gradual uploading is the subjective perception of continuity of person, during all evolution of system.
It is possible, that the brain and the external system could work as one system. The fighter pilot perceive machine like part of his body. The external system would have more communication channels and would have longer contact with the brain (it would be best if that contact could be unbroken). Therefore, the subjective self-perception of person could be not only be one's brain perception, but the common brain - external system perception.
During the gradual uploading the common brain - external system neural system would be dramatically changed and these changes could be interpreted as person identity change. But these changes would have the evolutional character during the time of gradual uploading procedure.
During some years the person neural system will be changed in any case (with or without gradual uploading). Some new impressions, emotionality, motor skills etc. would be acquired, some - lost. But these changes have the evolutional character and this fact assures the subjective perception continuity of person identity.
The most drastic leap would be death of part of common neural system - biological brain. Fortunately, the human brain have unconscious states like sleep. During these states the external system could continue her activities. When the brain would wake up, these impressions could be interpreted like as "impressions after brain`s death". Such experience could decrease the psychological problems.
Such person identity perception mechanism would be unique property of gradual uploading. In the neuron-by-neuron uploading case this problem can arise much more strongly.
It is necessary to mention some problems necessary to solve for gradual uploading implementation.
1. Although some examples of world modeling is created at this time, is necessary to perform many investigations for the creation of brain power cognition system. Necessary computer power for mind simulation is unavailable too, though the difference is not very large.
2. For the external system creation it is necessary to create some conception of what brain functions should be uploaded to the external system, what functions could be imitated and what functions wouldn't be necessary for the person identity and functioning.
Meantime the same problem would be faced in the atom-by-atom or neuron-by-neuron uploading case.
3. Very important problem is the necessity to create information transmission channels into the brain. Some methods need to be created for this adaptation.
4. Other problem for the interface is brain-computer and computer-brain interface's permeability. For good brain - external system co-operation we need to have sufficient information exchange rate. The question what rate is necessary, and what rate the interfaces would have, could predetermine usefulness of these interfaces.
Although the gradual uploading problem is a very complicated, many components of gradual uploading systems could be available at this time. Some of them must be adapted for these purposes, some of them must be improved, but there is no need for any technological qualitative leaps for its implementation.
I greatly appreciate very useful discussions with Anders Sandberg and Joseph Strout, which gave me a better understanding of this problem.
4. Marvin L. Minsky, Matter, Mind and Models, "Semantic Information Processing," (Marvin Minsky, Ed.) MIT Press, 1968
5. Floyd E. Bloom, Arlyne Lazerson, Laura Hofstadter, Brain, Mind and Behavior, New York, 1985.
6. Alexander Singer, Exploiting the Inherent Parallelism of Artificial Neural networks to Achieve 1300 Million Interconnects per Second, International Neural Networks conference, Paris July 9-13, 1990
7. Thomas Donaldson, Metamorphosis An Alternative To Uploading, Cryonics, May 1990
8. H.P. Siemon, A. Ultsch, Kohonen networks on transputers: implementation and animation, International Neural Networks conference, Paris July 9-13, 1990
15.Uwe R. Zimmer, Robust World-Modelling and Navigation in a Real World, NeuroComputing, Vol. 13, Nos. 2-4, 1996