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Intimations of Imitations: Visions of Cellular Prosthesis and Functionally Restorative Medicine – Article by Franco Cortese

Intimations of Imitations: Visions of Cellular Prosthesis and Functionally Restorative Medicine – Article by Franco Cortese

The New Renaissance Hat
Franco Cortese
June 23, 2013

In this essay I argue that technologies and techniques used and developed in the fields of Synthetic Ion Channels and Ion-Channel Reconstitution, which have emerged from the fields of supramolecular chemistry and bio-organic chemistry throughout the past 4 decades, can be applied towards the purpose of gradual cellular (and particularly neuronal) replacement to create a new interdisciplinary field that applies such techniques and technologies towards the goal of the indefinite functional restoration of cellular mechanisms and systems, as opposed to their current proposed use of aiding in the elucidation of cellular mechanisms and their underlying principles, and as biosensors.

In earlier essays (see here and here) I identified approaches to the synthesis of non-biological functional equivalents of neuronal components (i.e., ion-channels, ion-pumps, and membrane sections) and their sectional integration with the existing biological neuron — a sort of “physical” emulation, if you will. It has only recently come to my attention that there is an existing field emerging from supramolecular and bio-organic chemistry centered around the design, synthesis, and incorporation/integration of both synthetic/artificial ion channels and artificial bilipid membranes (i.e., lipid bilayer). The potential uses for such channels commonly listed in the literature have nothing to do with life-extension, however, and the field is, to my knowledge, yet to envision the use of replacing our existing neuronal components as they degrade (or before they are able to), rather seeing such uses as aiding in the elucidation of cellular operations and mechanisms and as biosensors. I argue here that the very technologies and techniques that constitute the field (Synthetic Ion Channels & Ion-Channel/Membrane Reconstitution) can be used towards the purposes of indefinite longevity and life-extension through the iterative replacement of cellular constituents (particularly the components comprising our neurons – ion-channels, ion-pumps, sections of bi-lipid membrane, etc.) so as to negate the molecular degradation they would have otherwise eventually undergone.

While I envisioned an electro-mechanical-systems approach in my earlier essays, the field of Synthetic Ion-Channels from the start in the early 1970s applied a molecular approach to the problem of designing molecular systems that produce certain functions according to their chemical composition or structure. Note that this approach corresponds to (or can be categorized under) the passive-physicalist sub-approach of the physicalist-functionalist approach (the broad approach overlying all varieties of physically embodied, “prosthetic” neuronal functional replication) identified in an earlier essay.

The field of synthetic ion channels is also referred to as ion-channel reconstitution, which designates “the solubilization of the membrane, the isolation of the channel protein from the other membrane constituents and the reintroduction of that protein into some form of artificial membrane system that facilitates the measurement of channel function,” and more broadly denotes “the [general] study of ion channel function and can be used to describe the incorporation of intact membrane vesicles, including the protein of interest, into artificial membrane systems that allow the properties of the channel to be investigated” [1]. The field has been active since the 1970s, with experimental successes in the incorporation of functioning synthetic ion channels into biological bilipid membranes and artificial membranes dissimilar in molecular composition and structure to biological analogues underlying supramolecular interactions, ion selectivity, and permeability throughout the 1980s, 1990s, and 2000s. The relevant literature suggests that their proposed use has thus far been limited to the elucidation of ion-channel function and operation, the investigation of their functional and biophysical properties, and to a lesser degree for the purpose of “in-vitro sensing devices to detect the presence of physiologically active substances including antiseptics, antibiotics, neurotransmitters, and others” through the “… transduction of bioelectrical and biochemical events into measurable electrical signals” [2].

Thus my proposal of gradually integrating artificial ion-channels and/or artificial membrane sections for the purpose of indefinite longevity (that is, their use in replacing existing biological neurons towards the aim of gradual substrate replacement, or indeed even in the alternative use of constructing artificial neurons to — rather than replace existing biological neurons — become integrated with existing biological neural networks towards the aim of intelligence amplification and augmentation while assuming functional and experiential continuity with our existing biological nervous system) appears to be novel, while the notion of artificial ion-channels and neuronal membrane systems ion in general had already been conceived (and successfully created/experimentally verified, though presumably not integrated in vivo).

The field of Functionally Restorative Medicine (and the orphan sub-field of whole-brain gradual-substrate replacement, or “physically embodied” brain-emulation, if you like) can take advantage of the decades of experimental progress in this field, incorporating both the technological and methodological infrastructures used in and underlying the field of Ion-Channel Reconstitution and Synthetic/Artificial Ion Channels & Membrane-Systems (and the technologies and methodologies underlying their corresponding experimental-verification and incorporation techniques) for the purpose of indefinite functional restoration via the gradual and iterative replacement of neuronal components (including sections of bilipid membrane, ion channels, and ion pumps) by MEMS (micro-electrocal-mechanical systems) or more likely NEMS (nano-electro-mechanical systems).

The technological and methodological infrastructure underlying this field can be utilized for both the creation of artificial neurons and for the artificial synthesis of normative biological neurons. Much work in the field required artificially synthesizing cellular components (e.g., bilipid membranes) with structural and functional properties as similar to normative biological cells as possible, so that the alternative designs (i.e., dissimilar to the normal structural and functional modalities of biological cells or cellular components) and how they affect and elucidate cellular properties, could be effectively tested. The iterative replacement of either single neurons, or the sectional replacement of neurons with synthesized cellular components (including sections of the bi-lipid membrane, voltage-dependent ion-channels, ligand-dependent ion channels, ion pumps, etc.) is made possible by the large body of work already done in the field. Consequently the technological, methodological, and experimental infrastructures developed for the fields of Synthetic Ion Channels and Ion-Channel/Artificial-Membrane Reconstitution can be utilized for the purpose of (a) iterative replacement and cellular upkeep via biological analogues (or not differing significantly in structure or functional and operational modality to their normal biological counterparts) and/or (b) iterative replacement with non-biological analogues of alternate structural and/or functional modalities.

Rather than sensing when a given component degrades and then replacing it with an artificially-synthesized biological or non-biological analogue, it appears to be much more efficient to determine the projected time it takes for a given component to degrade or otherwise lose functionality, and simply automate the iterative replacement in this fashion, without providing in vivo systems for detecting molecular or structural degradation. This would allow us to achieve both experimental and pragmatic success in such cellular prosthesis sooner, because it doesn’t rely on the complex technological and methodological infrastructure underlying in vivo sensing, especially on the scale of single neuron components like ion-channels, and without causing operational or functional distortion to the components being sensed.

A survey of progress in the field [3] lists several broad design motifs. I will first list the deign motifs falling within the scope of the survey, and the examples it provides. Selections from both papers are meant to show the depth and breadth of the field, rather than to elucidate the specific chemical or kinetic operations under the purview of each design-variety.

For a much more comprehensive, interactive bibliography of papers falling within the field of Synthetic Ion Channels or constituting the historical foundations of the field, see Jon Chui’s online biography here, which charts the developments in this field up until 2011.

First Survey

Unimolecular ion channels:

Examples include (a) synthetic ion channels with oligocrown ionophores, [5] (b) using a-helical peptide scaffolds and rigid push–pull p-octiphenyl scaffolds for the recognition of polarized membranes, [6] and (c) modified varieties of the b-helical scaffold of gramicidin A [7].

Barrel-stave supramolecules:

Examples of this general class falling include voltage-gated synthetic ion channels formed by macrocyclic bolaamphiphiles and rigidrod p-octiphenyl polyols [8].

Macrocyclic, branched and linear non-peptide bolaamphiphiles as staves:

Examples of this sub-class include synthetic ion channels formed by (a) macrocyclic, branched and linear bolaamphiphiles, and dimeric steroids, [9] and by (b) non-peptide macrocycles, acyclic analogs, and peptide macrocycles (respectively) containing abiotic amino acids [10].

Dimeric steroid staves:

Examples of this sub-class include channels using polydroxylated norcholentriol dimers [11].

p-Oligophenyls as staves in rigid-rod ß-barrels:

Examples of this sub-class include “cylindrical self-assembly of rigid-rod ß-barrel pores preorganized by the nonplanarity of p-octiphenyl staves in octapeptide-p-octiphenyl monomers” [12].

Synthetic polymers:

Examples of this sub-class include synthetic ion channels and pores comprised of (a) polyalanine, (b) polyisocyanates, (c) polyacrylates, [13] formed by (i) ionophoric, (ii) ‘smart’, and (iii) cationic polymers [14]; (d) surface-attached poly(vinyl-n-alkylpyridinium) [15]; (e) cationic oligo-polymers [16], and (f) poly(m-phenylene ethylenes) [17].

Helical b-peptides (used as staves in barrel-stave method):

Examples of this class include cationic b-peptides with antibiotic activity, presumably acting as amphiphilic helices that form micellar pores in anionic bilayer membranes [18].

Monomeric steroids:

Examples of this sub-class include synthetic carriers, channels and pores formed by monomeric steroids [19], synthetic cationic steroid antibiotics that may act by forming micellar pores in anionic membranes [20], neutral steroids as anion carriers [21], and supramolecular ion channels [22].

Complex minimalist systems:

Examples of this sub-class falling within the scope of this survey include ‘minimalist’ amphiphiles as synthetic ion channels and pores [23], membrane-active ‘smart’ double-chain amphiphiles, expected to form ‘micellar pores’ or self-assemble into ion channels in response to acid or light [24], and double-chain amphiphiles that may form ‘micellar pores’ at the boundary between photopolymerized and host bilayer domains and representative peptide conjugates that may self-assemble into supramolecular pores or exhibit antibiotic activity [25].

Non-peptide macrocycles as hoops:

Examples of this sub-class falling within the scope of this survey include synthetic ion channels formed by non-peptide macrocycles acyclic analogs [26] and peptide macrocycles containing abiotic amino acids [27].

Peptide macrocycles as hoops and staves:

Examples of this sub-class include (a) synthetic ion channels formed by self-assembly of macrocyclic peptides into genuine barrel-hoop motifs that mimic the b-helix of gramicidin A with cyclic ß-sheets. The macrocycles are designed to bind on top of channels and cationic antibiotics (and several analogs) are proposed to form micellar pores in anionic membranes [28]; (b) synthetic carriers, antibiotics (and analogs), and pores (and analogs) formed by macrocyclic peptides with non-natural subunits. Certain macrocycles may act as ß-sheets, possibly as staves of ß-barrel-like pores [29]; (c) bioengineered pores as sensors. Covalent capturing and fragmentations have been observed on the single-molecule level within engineered a-hemolysin pore containing an internal reactive thiol [30].


Thus even without knowledge of supramolecular or organic chemistry, one can see that a variety of alternate approaches to the creation of synthetic ion channels, and several sub-approaches within each larger ‘design motif’ or broad-approach, not only exist but have been experimentally verified, varietized, and refined.

Second Survey

The following selections [31] illustrate the chemical, structural, and functional varieties of synthetic ions categorized according to whether they are cation-conducting or anion-conducting, respectively. These examples are used to further emphasize the extent of the field, and the number of alternative approaches to synthetic ion-channel design, implementation, integration, and experimental verification already existent. Permission to use all the following selections and figures was obtained from the author of the source.

There are 6 classical design-motifs for synthetic ion-channels, categorized by structure, that are identified within the paper:

A: Unimolecular macromolecules,
B: Complex barrel-stave,
C: Barrel-rosette,
D: Barrel hoop, and
E: Micellar supramolecules.

Cation Conducting Channels:


“The first non-peptidic artificial ion channel was reported by Kobuke et al. in 1992” [33].

“The channel contained “an amphiphilic ion pair consisting of oligoether-carboxylates and mono– (or di-) octadecylammoniumcations. The carboxylates formed the channel core and the cations formed the hydrophobic outer wall, which was embedded in the bilipid membrane with a channel length of about 24 to 30 Å. The resultant ion channel, formed from molecular self-assembly, is cation-selective and voltage-dependent” [34].

“Later, Kokube et al. synthesized another channel comprising of resorcinol-based cyclic tetramer as the building block. The resorcin-[4]-arenemonomer consisted of four long alkyl chains which aggregated to form a dimeric supramolecular structure resembling that of Gramicidin A” [35]. “Gokel et al. had studied [a set of] simple yet fully functional ion channels known as “hydraphiles” [39].

“An example (channel 3) is shown in Figure 1.6, consisting of diaza-18-crown-6 crown ether groups and alkyl chains as side arms and spacers. Channel 3 is capable of transporting protons across the bilayer membrane” [40].

“A covalently bonded macrotetracycle (Figure 1.8) had shown to be about three times more active than Gokel’s ‘hydraphile’ channel, and its amide-containing analogue also showed enhanced activity” [44].

“Inorganic derivative using crown ethers have also been synthesized. Hall et al. synthesized an ion channel consisting of a ferrocene and 4 diaza-18-crown-6 linked by 2 dodecyl chains (Figure 1.9). The ion channel was redox-active as oxidation of the ferrocene caused the compound to switch to an inactive form” [45].


“These are more difficult to synthesize [in comparison to unimolecular varieties] because the channel formation usually involves self-assembly via non-covalent interactions” [47].“A cyclic peptide composed of even number of alternating D– and L-amino acids (Figure 1.10) was suggested to form barrel-hoop structure through backbone-backbone hydrogen bonds by De Santis” [49].

“A tubular nanotube synthesized by Ghadiri et al. consisting of cyclic D and L peptide subunits form a flat, ring-shaped conformation that stack through an extensive anti-parallel ß-sheet-like hydrogen bonding interaction (Figure 1.11)” [51].

“Experimental results have shown that the channel can transport sodium and potassium ions. The channel can also be constructed by the use of direct covalent bonding between the sheets so as to increase the thermodynamic and kinetic stability” [52].

“By attaching peptides to the octiphenyl scaffold, a ß-barrel can be formed via self-assembly through the formation of ß-sheet structures between the peptide chains (Figure 1.13)” [53].

“The same scaffold was used by Matile et al. to mimic the structure of macrolide antibiotic amphotericin B. The channel synthesized was shown to transport cations across the membrane” [54].

“Attaching the electron-poor naphthalene diimide (NDIs) to the same octiphenyl scaffold led to the hoop-stave mismatch during self-assembly that results in a twisted and closed channel conformation (Figure 1.14). Adding the complementary dialkoxynaphthalene (DAN) donor led to the cooperative interactions between NDI and DAN that favors the formation of barrel-stave ion channel.” [57].


“These aggregate channels are formed by amphotericin involving both sterols and antibiotics arranged in two half-channel sections within the membrane” [58].

“An active form of the compound is the bolaamphiphiles (two-headed amphiphiles). Figure 1.15 shows an example that forms an active channel structure through dimerization or trimerization within the bilayer membrane. Electrochemical studies had shown that the monomer is inactive and the active form involves dimer or larger aggregates” [60].


“A highly active, anion selective, monomeric cyclodextrin-based ion channel was designed by Madhavan et al. (Figure 1.16). Oligoether chains were attached to the primary face of the ß-cyclodextrin head group via amide bonds. The hydrophobic oligoether chains were chosen because they are long enough to span the entire lipid bilayer. The channel was able to select “anions over cations” and “discriminate among halide anions in the order I- > Br- > Cl- (following Hofmeister series)” [61].

“The anion selectivity occurred via the ring of ammonium cations being positioned just beside the cyclodextrin head group, which helped to facilitate anion selectivity. Iodide ions were transported the fastest because the activation barrier to enter the hydrophobic channel core is lower for I- compared to either Br- or Cl-” [62]. “A more specific artificial anion selective ion channel was the chloride selective ion channel synthesized by Gokel. The building block involved a heptapeptide with Proline incorporated (Figure 1.17)” [63].

Cellular Prosthesis: Inklings of a New Interdisciplinary Approach

The paper cites “nanoreactors for catalysis and chemical or biological sensors” and “interdisciplinary uses as nano –filtration membrane, drug or gene delivery vehicles/transporters as well as channel-based antibiotics that may kill bacterial cells preferentially over mammalian cells” as some of the main applications of synthetic ion-channels [65], other than their normative use in elucidating cellular function and operation.

However, I argue that a whole interdisciplinary field and heretofore-unrecognized new approach or sub-field of Functionally Restorative Medicine is possible through taking the technologies and techniques involved in constructing, integrating, and experimentally verifying either (a) non-biological analogues of ion-channels and ion-pumps (thus trans-membrane membrane proteins in general, also sometimes referred to as transport proteins or integral membrane proteins) and membranes (which include normative bilipid membranes, non-lipid membranes and chemically-augmented bilipid membranes), and (b) the artificial synthesis of biological analogues of ion-channels, ion-pumps and membranes, which are structurally and chemically equivalent to naturally-occurring biological components but which are synthesized artificially – and applying such technologies and techniques toward the purpose the gradual replacement of our existing biological neurons constituting our nervous systems – or at least those neuron-populations that comprise the neocortex and prefrontal cortex, and through iterative procedures of gradual replacement thereby achieving indefinite longevity. There is still work to be done in determining the comparative advantages and disadvantages of various structural and functional (i.e., design) motifs, and in the logistics of implanting the iterative replacement or reconstitution of ion-channels, ion-pumps and sections of neuronal membrane in vivo.

The conceptual schemes outlined in Concepts for Functional Replication of Biological Neurons [66], Gradual Neuron Replacement for the Preservation of Subjective-Continuity [67] and Wireless Synapses, Artificial Plasticity, and Neuromodulation [68] would constitute variations on the basic approach underlying this proposed, embryonic interdisciplinary field. Certain approaches within the fields of nanomedicine itself, particularly those approaches that constitute the functional emulation of existing cell-types, such as but not limited to Robert Freitas’s conceptual designs for the functional emulation of the red blood cell (a.k.a. erythrocytes, haematids) [69], i.e., the Resperocyte, itself should be seen as falling under the purview of this new approach, although not all approaches to Nanomedicine (diagnostics, drug-delivery and neuroelectronic interfacing) constitute the physical (i.e. electromechanical, kinetic, and/or molecular physically embodied) and functional emulation of biological cells.

The field of functionally-restorative medicine in general (and of nanomedicine in particular) and the fields of supramolecular and organic chemistry converge here, where these technological, methodological, and experimental infrastructures developed in the fields of Synthetic Ion-Channels and Ion Channel Reconstitution can be employed to develop a new interdisciplinary approach that applies the logic of prosthesis to the cellular and cellular-component (i.e., sub-cellular) scale; same tools, new use. These techniques could be used to iteratively replace the components of our neurons as they degrade, or to replace them with more robust systems that are less susceptible to molecular degradation. Instead of repairing the cellular DNA, RNA, and protein transcription and synthesis machinery, we bypass it completely by configuring and integrating the neuronal components (ion-channels, ion-pumps, and sections of bilipid membrane) directly.

Thus I suggest that theoreticians of nanomedicine look to the large quantity of literature already developed in the emerging fields of synthetic ion-channels and membrane-reconstitution, towards the objective of adapting and applying existing technologies and methodologies to the new purpose of iterative maintenance, upkeep and/or replacement of cellular (and particularly neuronal) constituents with either non-biological analogues or artificially synthesized but chemically/structurally equivalent biological analogues.

This new sub-field of Synthetic Biology needs a name to differentiate it from the other approaches to Functionally Restorative Medicine. I suggest the designation ‘cellular prosthesis’.


[1] Williams (1994)., An introduction to the methods available for ion channel reconstitution. in D.C Ogden Microelectrode techniques, The Plymouth workshop edition, CambridgeCompany of Biologists.

[2] Tomich, J., Montal, M. (1996). U.S Patent No. 5,16,890. Washington, DC: U.S. Patent and Trademark Office.

[3] Matile, S., Som, A., & Sorde, N. (2004). Recent synthetic ion channels and pores. Tetrahedron, 60(31), 6405–6435. ISSN 0040–4020, 10.1016/j.tet.2004.05.052. Access:

[4] XIAO, F., (2009). Synthesis and structural investigations of pyridine-based aromatic foldamers.

[5] Ibid., p. 6411.

[6] Ibid., p. 6416.

[7] Ibid., p. 6413.

[8] Ibid., p. 6412.

[9] Ibid., p. 6414.

[10] Ibid., p. 6425.

[11] Ibid., p. 6427.

[12] Ibid., p. 6416.

[13] Ibid., p. 6419.

[14] Ibid.

[15] Ibid.

[16] Ibid., p. 6419.

[17] Ibid.

[18] Ibid., p. 6421.

[19] Ibid., p. 6422.

[20] Ibid.

[21] Ibid.

[22] Ibid.

[23] Ibid., p. 6423.

[24] Ibid.

[25] Ibid.

[26] Ibid., p. 6426.

[27] Ibid.

[28] Ibid., p. 6427.

[29] Ibid., p. 6327.

[30] Ibid., p. 6427.

[31] XIAO, F. (2009). Synthesis and structural investigations of pyridine-based aromatic foldamers.

[32] Ibid., p. 4.

[33] Ibid.

[34] Ibid.

[35] Ibid.

[36] Ibid., p. 7.

[37] Ibid., p. 8.

[38] Ibid., p. 7.

[39] Ibid.

[40] Ibid.

[41] Ibid.

[42] Ibid.

[43] Ibid., p. 8.

[44] Ibid.

[45] Ibid., p. 9.

[46] Ibid.

[47] Ibid.

[48] Ibid., p. 10.

[49] Ibid.

[50] Ibid.

[51] Ibid.

[52] Ibid., p. 11.

[53] Ibid., p. 12.

[54] Ibid.

[55] Ibid.

[56] Ibid.

[57] Ibid.

[58] Ibid., p. 13.

[59] Ibid.

[60] Ibid., p. 14.

[61] Ibid.

[62] Ibid.

[63] Ibid., p. 15.

[64] Ibid.

[65] Ibid.

[66] Cortese, F., (2013). Concepts for Functional Replication of Biological Neurons. The Rational Argumentator. Access:

[67] Cortese, F., (2013). Gradual Neuron Replacement for the Preservation of Subjective-Continuity. The Rational Argumentator. Access:

[68] Cortese, F., (2013). Wireless Synapses, Artificial Plasticity, and Neuromodulation. The Rational Argumentator. Access:

[69] Freitas Jr., R., (1998). “Exploratory Design in Medical Nanotechnology: A Mechanical Artificial Red Cell”. Artificial Cells, Blood Substitutes, and Immobil. Biotech. (26): 411–430. Access:

Gradual Neuron Replacement for the Preservation of Subjective-Continuity – Article by Franco Cortese

Gradual Neuron Replacement for the Preservation of Subjective-Continuity – Article by Franco Cortese

The New Renaissance Hat
Franco Cortese
May 19, 2013
This essay is the fourth chapter in Franco Cortese’s forthcoming e-book, I Shall Not Go Quietly Into That Good Night!: My Quest to Cure Death, published by the Center for Transhumanity. The first three chapters were previously published on The Rational Argumentator as “The Moral Imperative and Technical Feasibility of Defeating Death”, “Immortality: Material or Ethereal? Nanotech Does Both!, and “Concepts for Functional Replication of Biological Neurons“.

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, 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.

Franco is an Advisor for Lifeboat Foundation (on its Futurists Board and its Life Extension Board) and contributes regularly to its blog.


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.