Gradual Uploading Applied to Single Neurons (2008)
In early 2008 I was trying to conceptualize a means of applying the logic of gradual replacement to single neurons under the premise that extending the scale of gradual replacement to individual sections of the neuronal membrane and its integral membrane proteins—thus increasing the degree of graduality between replacement sections—would increase the likelihood of subjective-continuity through substrate transfer. I also started moving away from the use of normative nanotechnology as the technological and methodological infrastructure for the NRUs, as it would delay the date at which these systems could be developed and experimentally verified. Instead I started focusing on conceptualizing systems that electromechanically replicate the functional modalities of the small-scale integral-membrane-components of the neuron. I was calling this approach the “active mechanical membrane” to differentiate it from the electro-chemical-mechanical modalities of the nanotech approach. I also started using MEMS rather than NEMS for the underlying technological infrastructure (because MEMS are less restrictive) while identifying NEMS as preferred.
I felt that trying to replicate the metabolic replacement rate in biological neurons should be the ideal to strive for, since we know that subjective-continuity is preserved through the gradual metabolic replacement (a.k.a. molecular-turnover) that occurs in the existing biological brain. My approach was to measure the normal rate of metabolic replacement in existing biological neurons and the scale at which such replacement occurs (i.e., are the sections being replaced metabolically with single molecules, molecular complexes, or whole molecular clusters?). Then, when replacing sections of the membrane with electromechanical functional equivalents, the same ratio of replacement-section size to replacement-time factor would be applied—that is, the time between sectional replacement would be increased in proportion to how much larger the sectional-replacement section/scale is compared to the existing scale of metabolic replacement-sections/scale. Replacement size/scale is defined as the size of the section being replaced—and so would be molecular complexes in the case of normative metabolic replacement. Replacement time is defined as the interval of time between a given section being replaced and a section that it has causal connection with is replaced; in metabolic replacement it is the time interval between a given molecular complex being replaced and an adjacent (or directly-causally-connected) molecular complex being replaced.
I therefore posited the following formula:
Ta = (Sa/Sb)*Tb,
where Sa is the size of the artificial-membrane-replacement sections, Sb is the size of the metabolic replacement sections, Tb is the time interval between the metabolic replacement of two successive metabolic replacement sections, and Ta is the time interval needing to be applied to the comparatively larger artificial-membrane-replacement sections so as to preserve the same replacement-rate factor (and correspondingly the same degree of graduality) that exists in normative metabolic replacement through the process of gradual replacement on the comparatively larger scale of the artificial-membrane sections.
The use of the time-to-scale factor corresponding with normative molecular turnover or “metabolic replacement” follows from the fact that we know subjective-continuity through substrate replacement is successful at this time-to-scale ratio. However, the lack of a non-arbitrarily quantifiable measure of time and the fact that that time is infinitely divisible (i.e., it can be broken down into smaller intervals to an arbitrarily large degree) logically necessitates that the salient variable is not time, but rather causal interaction between co-affective or “causally coupled” components. Interaction between components and the state transitions each component or procedural step undergo are the only viable quantifiable measures of time. Thus, while time is the relevant variable in the above equation, a better (i.e., more methodologically rigorous) variable would be a measure of either (a) the number of causal interactions occurring between co-affective or “adjacent” components within the interval of replacement time Ta, which is synonymous with the frequency of causal interaction; or (b) the number of state-transitions a given component undergoes within the interval of time Ta. While they should be generally correlative, in that state-transitions are facilitated via causal interaction among components, state-transitions may be a better metric because they allow us to quantitatively compare categorically dissimilar types of causal interaction that otherwise couldn’t be summed into a single variable or measure. For example, if one type of molecular interaction has a greater effect on the state-transitions of either component involved (i.e., facilitates a comparatively greater state-transition) than does another type of molecular interaction, then quantifying a measure of causal interactions may be less accurate than quantifying a measure of the magnitude of state-transitions.
In this way the rate of gradual replacement, despite being on a scale larger than normative metabolic replacement, would hypothetically follow the same degree of graduality with which biological metabolic replacement occurs. This was meant to increase the likelihood of subjective-continuity through a substrate-replacement procedure (both because it is necessarily more gradual than gradual replacement of whole individual neurons at a time, and because it preserves the degree of graduality that exists through the normative metabolic replacement that we already undergo).
Replicating Neuronal Membrane and Integral Membrane Components
Thus far there have been 2 main classes of neuron-replication approach identified: informational-functionalist and physical-functionalist, the former corresponding to computational and simulation/emulation approaches and the latter to physically embodied, “prosthetic” approaches.
The physicalist-functionalist approach, however, can at this point be further sub-divided into two sub-classes. The first can be called “cyber-physicalist-functionalist”, which involves controlling the artificial ion-channels and receptor-channels via normative computation (i.e., an internal CPU or controller-circuit) operatively connected to sensors and to the electromechanical actuators and components of the ion and receptor channels (i.e., sensing the presence of an electrochemical gradient or difference in electrochemical potential [equivalent to relative ionic concentration] between the respective sides of a neuronal membrane, and activating the actuators of the artificial channels to either open or remain closed, based upon programmed rules). This sub-class is an example of a cyber-physical system, which designates any system with a high level of connection or interaction between its physical and computational components, itself a class of technology that grew out of embedded systems, which designates any system using embedded computational technology and includes many electronic devices and appliances.
This is one further functional step removed from the second approach, which I was then simply calling the “direct” method, but which would be more accurately called the passive-physicalist-functionalist approach. Electronic systems are differentiated from electric systems by being active (i.e., performing computation or more generally signal-processing), whereas electric systems are passive and aren’t meant to transform (i.e., process) incoming signals (though any computational system’s individual components must at some level be comprised of electric, passive components). Whereas the cyber-physicalist-functionalist sub-class has computational technology controlling its processes, the passive-physicalist-functionalist approach has components emergently constituting a computational device. This consisted of providing the artificial ion-channels with a means of opening in the presence of a given electric potential difference (i.e., voltage) and the receptor-channels with a means of opening in response to the unique attributes of the neurotransmitter it corresponds to (such as chemical bonding as in ligand-based receptors, or alternatively in response to its electrical properties in the same manner – i.e., according to the same operational-modality – as the artificial ion channels), without a CPU correlating the presence of an attribute measured by sensors with the corresponding electromechanical behavior of the membrane needing to be replicated in response thereto. Such passive systems differ from computation in that they only require feedback between components, wherein a system of mechanical, electrical, or electromechanical components is operatively connected so as to produce specific system-states or processes in response to the presence of specific sensed system-states of its environment or itself. An example of this in regards to the present case would be constructing an ionic channel from piezoelectric materials, such that the presence of a certain electrochemical potential induces internal mechanical strain in the material; the spacing, dimensions and quantity of segments would be designed so as to either close or open, respectively, as a single unit when eliciting internal mechanical strain in response to one electrochemical potential while remaining unresponsive (or insufficiently responsive—i.e., not opening all the way) to another electrochemical potential. Biological neurons work in a similarly passive way, in which systems are organized to exhibit specific responses to specific stimuli in basic stimulus-response causal sequences by virtue of their own properties rather than by external control of individual components via CPU.
However, I found the cyber-physicalist approach preferable if it proved to be sufficient due to the ability to reprogram computational systems, which isn’t possible in passive systems without necessitating a reorganization of the component—which itself necessitates an increase in the required technological infrastructure, thereby increasing cost and design-requirements. This limit on reprogramming also imposes a limit on our ability to modify and modulate the operation of the NRUs (which will be necessary to retain the function of neural plasticity—presumably a prerequisite for experiential subjectivity and memory). The cyber-physicalist approach also seemed preferable due to a larger degree of variability in its operation: it would be easier to operatively connect electromechanical membrane components (e.g., ionic channels, ion pumps) to a CPU, and through the CPU to sensors, programming it to elicit a specific sequence of ionic-channel opening and closing in response to specific sensor-states, than it would be to design artificial ionic channels to respond directly to the presence of an electric potential with sufficient precision and accuracy.
In the cyber-physicalist-functionalist approach the membrane material is constructed so as to be (a) electrically insulative, while (b) remaining thin enough to act as a capacitor via the electric potential differential (which is synonymous with voltage) between the two sides of the membrane.
The ion-channel replacement units consisted of electromechanical pores that open for a fixed amount of time in the presence of an ion gradient (a difference in electric potential between the two sides of the membrane); this was to be accomplished electromechanically via a means of sensing membrane depolarization (such as through the use of reference electrodes) connected to a microcircuit (or nanocircuit, hereafter referred to as a CPU) programmed to open the electromechanical ion-channels for a length of time corresponding to the rate of normative biological repolarization (i.e., the time it takes to restore the membrane polarization to the resting-membrane-potential following an action-potential), thus allowing the influx of ions at a rate equal to the biological ion-channels. Likewise sections of the pre-synaptic membrane were to be replaced by a section of inorganic membrane containing units that sense the presence of the neurotransmitter corresponding to the receptor being replaced, which were to be connected to a microcircuit programmed to elicit specific changes (i.e., increase or decrease in ionic permeability, such as through increasing or decreasing the diameter of ion-channels—e.g., through an increase or decrease in electric stimulation of piezoelectric crystals, as described above—or an increase or decrease in the number of open channels) corresponding to the change in postsynaptic potential in the biological membrane resulting from postsynaptic receptor-binding. This requires a bit more technological infrastructure than I anticipated the ion-channels requiring.
While the accurate and active detection of particular types and relative quantities of neurotransmitters is normally ligand-gated, we have a variety of potential, mutually exclusive approaches. For ligand-based receptors, sensing the presence and steepness of electrochemical gradients may not suffice. However, we don’t necessarily have to use ligand-receptor fitting to replicate the functionality of ligand-based receptors. If there is a difference in the charge (i.e., valence) between the neurotransmitter needing to be detected and other neurotransmitters, and the degree of that difference is detectable given the precision of our sensing technologies, then a means of sensing a specific charge may prove sufficient. I developed an alternate method for ligand-based receptor fitting in the event that sensing-electric charge proved insufficient, however. Different chemicals (e.g., neurotransmitters, but also potentially electrolyte solutions) have different volume-to-weight ratios. We equip the artificial-membrane sections with an empty compartment capable of measuring the weight of its contents. Since the volume of the container is already known, this would allow us to identify specific neurotransmitters (or other relevant molecules and compounds) based on their unique weight-to-volume ratio. By operatively connecting the unit’s CPU to this sensor, we can program specific operations (i.e., receptor opens allowing entry for fixed amount of time, or remains closed) in response to the detection of specific neurotransmitters. Though it is unlikely to be necessitated, this method could also work for the detection of specific ions, and thus could work as the operating mechanism underlying the artificial ion-channels as well—though this would probably require higher-precision volume-to-weight comparison than is required for neurotransmitters.
Sectional Integration with Biological Neurons
Integrating replacement-membrane sections with adjacent sections of the existing lipid bilayer membrane becomes a lot less problematic if the scale at which the membrane sections are handled (determined by the size of the replacement membrane sections) is homogenous, as in the case of biological tissues, rather than molecularly heterogeneous—that is, if we are affixing the edges to a biological tissue, rather than to complexes of individual lipid molecules. Reasons for hypothesizing a higher probability for homogeneity at the replacement scale include (a) the ability of experimenters and medical researchers to puncture the neuronal membrane with a micropipette (so as to measure membrane voltage) without rupturing the membrane beyond functionality, and (b) the fact that sodium and potassium ions do not leak through the gaps between the individual bilipid molecules, which would be present if it were heterogeneous at this scale. If we find homogeneity at the scale of sectional replacement, we can use more normative means of affixing the edges of the replacement membrane section with the existing lipid bilayer membrane, such as micromechanical fasteners, adhesive, or fusing via heating or energizing. However, I also developed an approach applicable if the scale of sectional replacement was found to be molecular and thus heterogeneous. We find an intermediate chemical that stably bonds to both the bilipid molecules constituting the membrane and the molecules or compounds constituting the artificial membrane section. Note that if the molecules or compounds constituting either must be energized so as to put them in an abnormal (i.e., unstable) energy state to make them susceptible to bonding, this is fine so long as the energies don’t reach levels damaging to the biological cell (or if such energies could be absorbed prior to impinging upon or otherwise damaging the biological cell). If such an intermediate molecule or compound cannot be found, a second intermediate chemical that stably bonds with two alternate and secondary intermediate molecules (which themselves bond to either the biological membrane or the non-biological membrane section, respectively) can be used. The chances of finding a sequence of chemicals that stably bond (i.e., a given chemical forms stable bonds with the preceding and succeeding chemicals in the sequence) increases in proportion to the number of intermediate chemicals used. Note that it might be possible to apply constant external energization to certain molecules so as to force them to bond in the case that a stable bond cannot be formed, but this would probably be economically prohibitive and potentially dangerous, depending on the levels of energy and energization-precision.
I also worked on the means of constructing and integrating these components in vivo, using MEMS or NEMS. Most of the developments in this regard are described in the next chapter. However, some specific variations on construction procedure were necessitated by the sectional-integration procedure, which I will comment on here. The integration unit would position itself above the membrane section. Using the data acquired by the neuron data-measurement units, which specify the constituents of a given membrane section and assign it a number corresponding to a type of artificial-membrane section in the integration unit’s section-inventory (essentially a store of stacked artificial-membrane-sections). A means of disconnecting a section of lipid bilayer membrane from the biological neuron is depressed. This could be a hollow rectangular compartment with edges that sever the lipid bilayer membrane via force (e.g., edges terminate in blades), energy (e.g., edges terminate in heat elements), or chemical corrosion (e.g., edges coated with or secrete a corrosive substance). The detached section of lipid bilayer membrane is then lifted out and compacted, to be drawn into a separate compartment for storing waste organic materials. The artificial-membrane section is subsequently transported down through the same compartment. Since it is perpendicular to the face of the container, moving the section down through the compartment should force the intra-cellular fluid (which would have presumably leaked into the constructional container’s internal area when the lipid bilayer membrane-section was removed) back into the cell. Once the artificial-membrane section is in place, the preferred integration method is applied.
Sub-neuronal (i.e., sectional) replacement also necessitates that any dynamic patterns of polarization (e.g., an action potential) are continuated during the interval of time between section removal and artificial-section integration. This was to be achieved by chemical sensors (that detect membrane depolarization) operatively connected to actuators that manipulate ionic concentration on the other side of the membrane gap via the release or uptake of ions from biochemical inventories so as to induce membrane depolarization on the opposite side of the membrane gap at the right time. Such techniques as partially freezing the cell so as to slow the rate of membrane depolarization and/or the propagation velocity of action potentials were also considered.
The next chapter describes my continued work in 2008, focusing on (a) the design requirements for replicating the neural plasticity necessary for memory and subjectivity, (b) the active and conscious modulation and modification of neural operation, (c) wireless synaptic transmission, (d) on ways to integrate new neural networks (i.e., mental amplification and augmentation) without disrupting the operation of existing neural networks and regions, and (e) a gradual transition from or intermediary phase between the physical (i.e., prosthetic) approach and the informational (i.e., computational, or mind-uploading proper) approach.
Franco Cortese is an editor for Transhumanity.net, as well as one of its most frequent contributors. He has also published articles and essays on Immortal Life and The Rational Argumentator. He contributed 4 essays and 7 debate responses to the digital anthology Human Destiny is to Eliminate Death: Essays, Rants and Arguments About Immortality.
Churchland, P. S. (1989). Neurophilosophy: Toward a Unified Science of the Mind/Brain. MIT Press, p. 30.
Pribram, K. H. (1971). Languages of the Brain: Experimental Paradoxes and Principles in Neuropsychology. New York: Prentice Hall/Brandon House.