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

June 23, 2013 Franco Cortese Comments 0 Comment

Franco Cortese

June 23, 2013

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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].

Summary

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:

UNIMOLECULAR

“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].

B-STAVES:

“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].

MICELLAR

“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].

ANION CONDUCTING CHANNELS:

“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’.

References:

[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: http://www.sciencedirect.com/science/article/pii/S0040402004007690:

[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: https://www.rationalargumentator.com/index/blog/2013/05/gradual-neuron-replacement/

[67] Cortese, F., (2013). Gradual Neuron Replacement for the Preservation of Subjective-Continuity. The Rational Argumentator. Access: https://www.rationalargumentator.com/index/blog/2013/05/gradual-neuron-replacement/

[68] Cortese, F., (2013). Wireless Synapses, Artificial Plasticity, and Neuromodulation. The Rational Argumentator. Access: https://www.rationalargumentator.com/index/blog/2013/05/wireless-synapses/

[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: http://www.ncbi.nlm.nih.gov/pubmed/9663339

Maintaining the Operational Continuity of Replicated Neurons – Article by Franco Cortese

June 4, 2013 Franco Cortese Comments 0 Comment

Franco Cortese

June 3, 2013

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This essay is the tenth 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 nine chapters were previously published on The Rational Argumentator under the following titles:

– Chapter 1: “The Moral Imperative and Technical Feasibility of Defeating Death”
– Chapter 2: “Immortality: Material or Ethereal? Nanotech Does Both!”

– Chapter 3: “Concepts for Functional Replication of Biological Neurons”

– Chapter 4: “Gradual Neuron Replacement for the Preservation of Subjective-Continuity”

– Chapter 5: “Wireless Synapses, Artificial Plasticity, and Neuromodulation”

– Chapter 6: “Mind as Interference with Itself: A New Approach to Immediate Subjective-Continuity”

– Chapter 7: “Neuronal ‘Scanning’ and NRU Integration”

– Chapter 8: “Squishy Machines: Bio-Cybernetic Neuron Hybrids”

– Chapter 9: “Choosing the Right Scale for Brain Emulation”

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Operational Continuity

One of the reasons for continuing conceptual development of the physical-functionalist NRU (neuron-replication-unit) approach, despite the perceived advantages of the informational-functionalist approach, was in the event that computational emulation would either fail to successfully replicate a given physical process (thus a functional-modality concern) or fail to successfully maintain subjective-continuity (thus an operational-modality concern), most likely due to a difference in the physical operation of possible computational substrates compared to the physical operation of the brain (see Chapter 2). In regard to functionality, we might fail to computationally replicate (whether in simulation or emulation) a relevant physical process for reasons other than vitalism. We could fail to understand the underlying principles governing it, or we might understand its underlying principles so as to predictively model it yet still fail to understand how it affects the other processes occurring in the neuron—for instance if we used different modeling techniques or general model types to model each component, effectively being able to predictively model each individually while being unable to model how they affect eachother due to model untranslatability. Neither of these cases precludes the aspect in question from being completely material, and thus completely potentially explicable using the normative techniques we use to predictively model the universe. The physical-functionalist approach attempted to solve these potential problems through several NRU sub-classes, some of which kept certain biological features and functionally replaced certain others, and others that kept alternate biological features and likewise functionally replicated alternate biological features. These can be considered as varieties of biological-nonbiological NRU hybrids that functionally integrate those biological features into their own, predominantly non-biological operation, as they exist in the biological nervous system, which we failed to functionally or operationally replicate successfully.

The subjective-continuity problem, however, is not concerned with whether something can be functionally replicated but with whether it can be functionally replicated while still retaining subjective-continuity throughout the procedure.

This category of possible basis for subjective-continuity has stark similarities to the possible problematic aspects (i.e., operational discontinuity) of current computational paradigms and substrates discussed in Chapter 2. In that case it was postulated that discontinuity occurred as a result of taking something normally operationally continuous and making it discontinuous: namely, (a) the fact that current computational paradigms are serial (whereas the brain has massive parallelism), which may cause components to only be instantiated one at a time, and (b) the fact that the resting membrane potential of biological neurons makes them procedurally continuous—that is, when in a resting or inoperative state they are still both on and undergoing minor fluctuations—whereas normative logic gates both do not produce a steady voltage when in an inoperative state (thus being procedurally discontinuous) and do not undergo minor fluctuations within such a steady-state voltage (or, more generally, a continuous signal) while in an inoperative state. I had a similar fear in regard to some mathematical and computational models as I understood them in 2009: what if we were taking what was a continuous process in its biological environment, and—by using multiple elements or procedural (e.g., computational, algorithmic) steps to replicate what would have been one element or procedural step in the original—effectively making it discontinuous by introducing additional intermediate steps? Or would we simply be introducing a number of continuous steps—that is, if each element or procedural step were operationally continuous in the same way that the components of a neuron are, would it then preserve operational continuity nonetheless?

This led to my attempting to develop a modeling approach aiming to retain the same operational continuity as exists in biological neurons, which I will call the relationally isomorphic mathematical model. The biophysical processes comprising an existing neuron are what implements computation; by using biophysical-mathematical models as our modeling approach, we might be introducing an element of discontinuity by mathematically modeling the physical processes giving rise to a computation/calculation, rather than modeling the computation/calculation directly. It might be the difference between modeling a given program, and the physical processes comprising the logic elements giving rise to the program. Thus, my novel approach during this period was to explore ways to model this directly.

Rather than using a host of mathematical operations to model the physical components that themselves give rise to a different type of mathematics, we instead use a modeling approach that maintains a 1-to-1 element or procedural-step correspondence with the level-of-scale that embodies the salient (i.e., aimed-for) computation. My attempts at developing this produced the following approach, though I lack the pure mathematical and computer-science background to judge its true accuracy or utility. The components, their properties, and the inputs used for a given model (at whatever scale) are substituted by numerical values, the magnitude of which preserves the relationships (e.g., ratio relationships) between components/properties and inputs, and by mathematical operations which preserve the relationships exhibited by their interaction. For instance: if the interaction between a given component/property and a given input produces an emergent inhibitory effect biologically, then one would combine them to get their difference or their factors, respectively, depending on whether they exemplify a linear or nonlinear relationship. If the component/property and the input combine to produce emergently excitatory effects biologically, one would combine them to get their sum or products, respectively, depending on whether they increased excitation in a linear or nonlinear manner.

In an example from my notes, I tried to formulate how a chemical synapse could be modeled in this way. Neurotransmitters are given analog values such as positive or negative numbers, the sign of which (i.e., positive or negative) depends on whether it is excitatory or inhibitory and the magnitude of which depends on how much more excitatory/inhibitory it is than other neurotransmitters, all in reference to a baseline value (perhaps 0 if neutral or neither excitatory nor inhibitory; however, we may need to make this a negative value, considering that the neuron’s resting membrane-potential is electrically negative, and not electrochemically neutral). If they are neurotransmitter clusters, then one value would represent the neurotransmitter and another value its quantity, the sum or product of which represents the cluster. If the neurotransmitter clusters consist of multiple neurotransmitters, then two values (i.e., type and quantity) would be used for each, and the product of all values represents the cluster. Each summative-product value is given a second vector value separate from its state-value, representing its direction and speed in the 3D space of the synaptic junction. Thus by summing the products of all, the numerical value should contain the relational operations each value corresponds to, and the interactions and relationships represented by the first- and second-order products. The key lies in determining whether the relationship between two elements (e.g., two neurotransmitters) is linear (in which case they are summed), or nonlinear (in which case they are combined to produce a product), and whether it is a positive or negative relationship—in which case their factor, rather than their difference, or their product, rather than their sum, would be used. Combining the vector products would take into account how each cluster’s speed and position affects the end result, thus effectively emulating the process of diffusion across the synaptic junction. The model’s past states (which might need to be included in such a modeling methodology to account for synaptic plasticity—e.g., long-term potentiation and long-term modulation) would hypothetically be incorporated into the model via a temporal-vector value, wherein a third value (position along a temporal or “functional”/”operational” axis) is used when combining the values into a final summative product. This is similar to such modeling techniques as phase-space, which is a quantitative technique for modeling a given system’s “system-vector-states” or the functional/operational states it has the potential to possess.

How excitatory or inhibitory a given neurotransmitter is may depend upon other neurotransmitters already present in the synaptic junction; thus if the relationship between one neurotransmitter and another is not the same as that first neurotransmitter and an arbitrary third, then one cannot use static numerical values for them because the sequence in which they were released would affect how cumulatively excitatory or inhibitory a given synaptic transmission is.

A hypothetically possible case of this would be if one type of neurotransmitter can bond or react with two or more types of neurotransmitter. Let’s say that it’s more likely to bond or react with one than with the other. If the chemically less attractive (or reactive) one were released first, it would bond anyways due to the absence of the comparatively more chemically attractive one, such that if the more attractive one were released thereafter, then it wouldn’t bond because the original one would have already bonded with the chemically less attractive one.

If a given neurotransmitter’s numerical value or weighting is determined by its relation to other neurotransmitters (i.e., if one is excitatory, and another is twice as excitatory, then if the first was 1.5, the second would be 3—assuming a linear relationship), and a given neurotransmitter does prove to have a different relationship to one neurotransmitter than it does another, then we cannot use a single value for it. Thus we might not be able to configure it such that the normative mathematical operations follow naturally from each other; instead, we may have to computationally model (via the [hypothetically] subjectively discontinuous method that incurs additional procedural steps) which mathematical operations to perform, and then perform them continuously without having to stop and compute what comes next, so as to preserve subjective-continuity.

We could also run the subjectively discontinuous model at a faster speed to account for its higher quantity of steps/operations and the need to keep up with the relationally isomorphic mathematical model, which possesses comparatively fewer procedural steps. Thus subjective-continuity could hypothetically be achieved (given the validity of the present postulated basis for subjective-continuity—operational continuity) via this method of intermittent external intervention, even if we need extra computational steps to replicate the single informational transformations and signal-combinations of the relationally isomorphic mathematical model.

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.

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

Choosing the Right Scale for Brain Emulation – Article by Franco Cortese

June 3, 2013 Franco Cortese Comments 1 comment

Franco Cortese

June 2, 2013

Recommend this page.

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This essay is the ninth 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 eight chapters were previously published on The Rational Argumentator under the following titles:

– Chapter 1: “The Moral Imperative and Technical Feasibility of Defeating Death”
– Chapter 2: “Immortality: Material or Ethereal? Nanotech Does Both!”

– Chapter 3: “Concepts for Functional Replication of Biological Neurons”

– Chapter 4: “Gradual Neuron Replacement for the Preservation of Subjective-Continuity”

– Chapter 5: “Wireless Synapses, Artificial Plasticity, and Neuromodulation”

– Chapter 6: “Mind as Interference with Itself: A New Approach to Immediate Subjective-Continuity”

– Chapter 7: “Neuronal ‘Scanning’ and NRU Integration”

– Chapter 8: “Squishy Machines: Bio-Cybernetic Neuron Hybrids”

***

The two approaches falling within this class considered thus far are (a) computational models that model the biophysical (e.g., electromagnetic, chemical, and kinetic) operation of the neurons—i.e., the physical processes instantiating their emergent functionality, whether at the scale of tissues, molecules and/or atoms, and anything in between—and (b) abstracted models, a term which designates anything that computationally models the neuron using the (sub-neuron but super-protein-complex) components themselves as the chosen model-scale (whereas the latter uses for its chosen model-scale the scale at which physical processes emergently instantiating those higher-level neuronal components exist, such as the membrane and individual proteins forming the transmembrane protein-complexes), regardless of whether each component is abstracted as a normative-electrical-component analogue (i.e., using circuit diagrams in place of biological schematics, like equating the lipid bilayer membrane with a capacitor connected to a variable battery) or mathematical models in which a relevant component or aspect of the neuron becomes a term (e.g., a variable or constant) in an equation.

It was during the process of trying to formulate different ways of mathematically (and otherwise computationally) modeling neurons or sub-neuron regions that I laid the conceptual embryo of the first new possible basis for subjective-continuity: the notion of operational isomorphism.

A New Approach to Subjective-Continuity Through Substrate Replacement

There are two other approaches to increasing the likelihood of subjective-continuity, each based on the presumption of two possible physical bases for discontinuity, that I explored during this period. Note that these approaches are unrelated to graduality, which has been the main determining factor impacting the likelihood of subjective-continuity considered thus far. The new approaches consist of designing the NRUs so as to retain the respective postulated physical bases for subjective-continuity that exist in the biological brain. Thus they are unrelated to increasing the efficacy of the gradual-replacement procedure itself, instead being related to the design requirements of functional-equivalents used to gradually replace the neurons that maintain immediate subjective-continuity.

Operational Isomorphism

Whereas functionality deals only with the emergent effects or end-product of a given entity or process, operationality deals with the procedural operations performed so as to give rise to those emergent effects. A mathematical model of a neuron might be highly functionally equivalent while failing to be operationally equivalent in most respects. Isomorphism can be considered a measure of “sameness”, but technically means a 1-to-1 correspondence between the elements of two sets (which would correspond with operational isomorphism) or between the sums or products of the elements of two sets (which would correspond with functional isomorphism, using the definition of functionality employed above). Thus, operational isomorphism is the degree with which the sub-components (be they material as in entities or procedural as in processes) of the two larger-scale components, or the operational modalities possessed by each respective collection of sub-components, are equivalent.

To what extent does the brain possess operational isomorphism? It seems to depend on the scale being considered. At the highest scale, different areas of the nervous system are classed as systems (as in functional taxonomies) or regions (as in anatomical taxonomies). At this level the separate regions (i.e., components of a shared scale) differ widely from one another in terms of operational-modality; they process information very differently from the way other components on the same scale process information. If this scale was chosen as the model-scale of our replication-approach and the preceding premise (that the physical basis for subjective-continuity is the degree of operational isomorphism between components at a given scale) is accepted, then we would in such a case have a high probability of replicating functionality, but a low probability of retaining subjective-continuity through gradual replacement. This would be true even if we used the degree of operational isomorphism between separate components as the only determining factor for subjective-continuity, and ignored concerns of graduality (e.g., the scale or rate—or scale-to-rate ratio—at which gradual substrate replacement occurs).

Contrast this to the molecular scale, where the operational modality of each component (being a given molecule) and the procedural rules determining the state-changes of components at this scale are highly isomorphic. The state-changes of a given molecule are determined by molecular and atomic forces. Thus if we use an informational-functionalist approach, choose a molecular scale for our model, and accept the same premises as the first example, we would have a high probability of both replicating functionality and retaining subjective-continuity through gradual replacement because the components (molecules) have a high degree of operational isomorphism.

Note that this is only a requirement for the sub-components instantiating the high-level neural regions/systems that embody our personalities and higher cognitive faculties such as the neocortex — i.e., we wouldn’t have to choose a molecular scale as our model scale (if it proved necessary for the reasons described above) for the whole brain, which would be very computationally intensive.

So at the atomic and molecular scale the brain possesses a high degree of operational isomorphism. On the scale of the individual protein complexes, which collectively form a given sub-neuronal component (e.g., ion channel), components still appear to possess a high degree of operational isomorphism because all state-changes are determined by the rules governing macroscale proteins and protein-complexes (i.e., biochemistry and particularly protein-protein interactions); by virtue of being of the same general constituents (amino acids), the factors determining state-changes at this level are shared by all components at this scale. The scale of individual neuronal components, however, seems to possess a comparatively lesser degree of operational isomorphism. Some ion channels are ligand-gated while others are voltage-gated. Thus, different aspects of physicality (i.e., molecular shape and voltage respectively) form the procedural-rules determining state-changes at this scale. Since there are now two different determining factors at this scale, its degree of operational isomorphism is comparatively less than the protein and protein-complex scale and the molecular scale, both of which appear to have only one governing procedural-rule set. The scale of individual neurons by contrast appears to possess a greater degree of operational isomorphism; every neuron fires according to its threshold value, and sums analog action-potential values into a binary output (i.e., neuron either fires or does not). All individual neurons operate in a highly isomorphic manner. Even though individual neurons of a given type are more operationally isomorphic in relation to each other than with a neuron of another type, all neurons regardless of type still act in a highly isomorphic manner. However, the scale of neuron-clusters and neural-networks, which operate and communicate according to spatiotemporal sequences of firing patterns (action-potential patterns), appears to possess a lesser degree of operational isomorphism compared to individual neurons, because different sequences of firing patterns will mean a different thing to two respective neural clusters or networks. Also note that at this scale the degree of functional isomorphism between components appears to be less than their degree of operational isomorphism—that is, the way each cluster or network operates is more similar in relation to each other than is their actual function (i.e., what they effectively do). And lastly, at the scale of high-level neural regions/systems, components (i.e., neural regions) differ significantly in morphology, in operationality, and in functionality; thus they appear to constitute the scale that possesses the least operational isomorphism.

I will now illustrate the concept of operational isomorphism using the physical-functionalist and the informational-functionalist NRU approaches, respectively, as examples. In terms of the physical-functionalist (i.e., prosthetic neuron) approach, both the passive (i.e., “direct”) and CPU-controlled sub-classes, respectively, are operationally isomorphic. An example of a physical-functionalist NRU that would not possess operational isomorphism is one that uses a passive-physicalist approach for the one type of component (e.g., voltage-gated ion channel) and a CPU-controlled/cyber-physicalist approach [see Part 4 of this series] for another type of component (e.g., ligand-gated ion channel)—on that scale the components act according to different technological and methodological infrastructures, exhibit different operational modalities, and thus appear to possess a low degree of operational isomorphism. Note that the concern is not the degree of operational isomorphism between the functional-replication units and their biological counterparts, but rather with the degree of operational isomorphism between the functional-replication units and other units on the same scale.

Another possibly relevant type of operational isomorphism is the degree of isomorphism between the individual sub-components or procedural-operations (i.e., “steps”) composing a given component, designated here as intra-operational isomorphism. While very similar to the degree of isomorphism for the scale immediately below, this differs from (i.e., is not equivalent to) such a designation in that the sub-components of a given larger component could be functionally isomorphic in relation to each other without being operationally isomorphic in relation to all other components on that scale. The passive sub-approach of the physical-functionalist approach would possess a greater degree of intra-operational isomorphism than would the CPU-controlled/cyber-physicalist sub-approach, because presumably each component would interact with the others (via physically embodied feedback) according to the same technological and methodological infrastructure—be it mechanical, electrical, chemical, or otherwise. The CPU-controlled sub-approach by contrast would possess a lesser degree of intra-operational-isomorphism, because the sensors, CPU, and the electric or electromechanical systems, respectively (the three main sub-components for each singular neuronal component—e.g., an artificial ion channel), operate according to different technological and methodological infrastructures and thus exhibit alternate operational modalities in relation to eachother.

In regard to the informational-functionalist approach, an NRU model that would be operationally isomorphic is one wherein, regardless of the scale used, the type of approach used to model a given component on that scale is as isomorphic with the ones used to model other components on the same scale as is possible. For example, if one uses a mathematical model to simulate spiking regions of the dendritic spine, then one shouldn’t use a non-mathematical (e.g., strict computational-logic) approach to model non-spiking regions of the dendritic spine. Since the number of variations to the informational-functionalist approach is greater than could exist for the physical-functionalist approach, there are more gradations to the degree of operational isomorphism. Using the exact same branches of mathematics to mathematically model the two respective components would incur a greater degree of operational isomorphism than if we used alternate mathematical techniques from different disciplines to model them. Likewise, if we used different computational approaches to model the respective components, then we would have a lesser degree of operational isomorphism. If we emulated some components while merely simulating others, we would have a lesser degree of operational isomorphism than if both were either strictly simulatory or strictly emulatory.

If this premise proves true, it suggests that when picking the scale of our replication-approach (be it physical-functionalist or informational-functionalist), we choose a scale that exhibits operational isomorphism—for example, the molecular scale rather than the scale of high-level neural-regions, and that we don’t use widely dissimilar types of modeling techniques to model one component (e.g., a molecular system) than we do for another component on the same scale.

Note that unlike operational-continuity, the degree of operational isomorphism was not an explicit concept or potential physical basis for subjective-continuity at the time of my working on immortality (i.e., this concept wasn’t yet fully fleshed out in 2010), but rather was formulated in response to going over my notes from this period so as to distill the broad developmental gestalt of my project; though it appears to be somewhat inherent (i.e., appears to be hinted at), it hasn’t been explicitized until relatively recently.

The next chapter describes the rest of my work on technological approaches to techno-immortality in 2010, focusing on a second new approach to subjective-continuity through a gradual-substrate-replacement procedure, and concluding with an overview of the ways my project differs from the other techno-immortalist projects.

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

Squishy Machines: Bio-Cybernetic Neuron Hybrids – Article by Franco Cortese

May 26, 2013 Franco Cortese Comments 0 Comment

Franco Cortese

May 25, 2013

Recommend this page.

******************************

This essay is the eighth 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 seven chapters were previously published on The Rational Argumentator under the following titles:

– Chapter 1: “The Moral Imperative and Technical Feasibility of Defeating Death”
– Chapter 2: “Immortality: Material or Ethereal? Nanotech Does Both!”

– Chapter 3: “Concepts for Functional Replication of Biological Neurons”

– Chapter 4: “Gradual Neuron Replacement for the Preservation of Subjective-Continuity”

– Chapter 5: “Wireless Synapses, Artificial Plasticity, and Neuromodulation”

– Chapter 6: “Mind as Interference with Itself: A New Approach to Immediate Subjective-Continuity”

– Chapter 7: “Neuronal ‘Scanning’ and NRU Integration”

***

By 2009 I felt the major classes of physicalist-functionalist replication approaches to be largely developed, producing now only potential minor variations in approach and procedure. These developments consisted of contingency plans in the case that some aspect of neuronal operation couldn’t be replicated with alternate, non-biological physical systems and processes, based around the goal of maintaining those biological (or otherwise organic) systems and processes artificially and of integrating them with the processes that could be reproduced artificially.

2009 also saw further developments in the computational approach, where I conceptualized a new sub-division in the larger class of the informational-functionalist (i.e., computational, which encompasses both simulation and emulation) replication approach, which is detailed in the next chapter.

Developments in the Physicalist Approach

During this time I explored mainly varieties of the cybernetic-physical functionalist approach. This involved the use of replicatory units that preserve certain biological aspects of the neuron while replacing certain others with functionalist replacements, and other NRUs that preserved alternate biological aspects of the neuron while replacing different aspects with functional replacements. The reasoning behind this approach was twofold. The first was that there was a chance, no matter how small, that we might fail to sufficiently replicate some relevant aspect(s) of the neuron either computationally or physically by failing to understand the underlying principles of that particular sub-process/aspect. The second was to have an approach that would work in the event that there was some material aspect that couldn’t be sufficiently replicated via non-biological physically embodied systems (i.e., the normative physical-functionalist approach).

However, these varieties were conceived of in case we couldn’t replicate certain components successfully (i.e., without functional divergence). The chances of preserving subjective-continuity in such circumstances are increased by the number of varieties we have for this class of model (i.e., different arrangements of mechanical replacement components and biological components), because we don’t know which we would fail to functionally replicate.

This class of physical-functionalist model can be usefully considered as electromechanical-biological hybrids, wherein the receptors (i.e., transporter proteins) on the post-synaptic membrane are integrated with the artificial membrane and in coexistence with artificial ion-channels, or wherein the biological membrane is retained while the receptor and ion-channels are replaced with functional equivalents instead. The biological components would be extracted from the existing biological neurons and reintegrated with the artificial membrane. Otherwise they would have to be synthesized via electromechanical systems, such as, but not limited to, the use of chemical stores of amino-acids released in specific sequences to facilitate in vivo protein folding and synthesis, which would then be transported to and integrated with the artificial membrane. This is better than providing stores of pre-synthesized proteins, due to more complexities in storing synthesized proteins without decay or functional degradation over storage-time, and in restoring them from their “stored”, inactive state to a functionally-active state when they were ready for use.

During this time I also explored the possibility of using the neuron’s existing protein-synthesis systems to facilitate the construction and gradual integration of the artificial sections with the existing lipid bilayer membrane. Work in synthetic biology allows us to use viral gene vectors to replace a given cell’s constituent genome—and consequently allowing us to make it manufacture various non-organic substances in replacement of the substances created via its normative protein-synthesis. We could use such techniques to replace the existing protein-synthesis instructions with ones that manufacture and integrate the molecular materials constituting the artificial membrane sections and artificial ion-channels and ion-pumps. Indeed, it may even be a functional necessity to gradually replace a given neuron’s protein-synthesis machinery with protein-synthesis-based machinery for the replacement, integration and maintenance of the non-biological sections’ material, because otherwise those parts of the neuron would still be trying to rebuild each section of lipid bilayer membrane we iteratively remove and replace. This could be problematic, and so for successful gradual replacement of single neurons, a means of gradually switching off and/or replacing portions of the cell’s protein-synthesis systems may be required.

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

Neuronal “Scanning” and NRU Integration – Article by Franco Cortese

May 24, 2013 Franco Cortese Comments 2 comments

Franco Cortese

May 23, 2013

Recommend this page.

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This essay is the seventh 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 six chapters were previously published on The Rational Argumentator under the following titles:

– Chapter 1: “The Moral Imperative and Technical Feasibility of Defeating Death”
– Chapter 2: “Immortality: Material or Ethereal? Nanotech Does Both!”

– Chapter 3: “Concepts for Functional Replication of Biological Neurons”

– Chapter 4: “Gradual Neuron Replacement for the Preservation of Subjective-Continuity”

– Chapter 5: “Wireless Synapses, Artificial Plasticity, and Neuromodulation”

– Chapter 6: “Mind as Interference with Itself: A New Approach to Immediate Subjective-Continuity“

***

I was planning on using the NEMS already conceptually developed by Robert Freitas for nanosurgery applications (to be supplemented by the use of MEMS if the technological infrastructure was unavailable at the time) to take in vivo recordings of the salient neural metrics and properties needing to be replicated. One novel approach was to design the units with elongated, worm-like bodies, disposing the computational and electromechanical apparatus within the elongated body of the unit. This sacrifices width for length so as to allow the units to fit inside the extra-cellular space between neurons and glial cells as a postulated solution to a lack of sufficient miniaturization. Moreover, if a unit is too large to be used in this way, extending its length by the same proportion would allow it to then operate in the extracellular space, provided that its means of data-measurement itself weren’t so large as to fail to fit inside the extracellular space (the span of ECF between two adjacent neurons for much of the brain is around 200 Angstroms).

I was planning on using the chemical and electrical sensing methodologies already in development for nanosurgery as the technological and methodological infrastructure for the neuronal data-measurement methodology. However, I also explored my own conceptual approaches to data-measurement. This consisted of detecting variation of morphological features in particular, as the schemes for electrical and chemical sensing already extant seemed either sufficiently developed or to be receiving sufficient developmental support and/or funding. One was the use of laser-scanning or more generally radiography (i.e., sonar) to measure and record morphological data. Another was a device that uses a 2D array of depressible members (e.g., solid members attached to a spring or ratchet assembly, which is operatively connected to a means of detecting how much each individual member is depressed—such as but not limited to piezoelectric crystals that produce electricity in response and proportion to applied mechanical strain). The device would be run along the neuronal membrane and the topology of the membrane would be subsequently recorded by the pattern of depression recordings, which are then integrated to provide a topographic map of the neuron (e.g., relative location of integral membrane components to determine morphology—and magnitude of depression to determine emergent topology). This approach could also potentially be used to identify the integral membrane proteins, rather than using electrical or chemical sensing techniques, if the topologies of the respective proteins are sufficiently different as to be detectable by the unit (determined by its degree of precision, which typically is a function of its degree of miniaturization).

The constructional and data-measurement units would also rely on the technological and methodological infrastructure for organization and locomotion that would be used in normative nanosurgery. I conceptually explored such techniques as the use of a propeller, the use of pressure-based methods (i.e., a stream of water acting as jet exhaust would in a rocket), the use of artificial cilia, and the use of tracks that the unit attaches to so as to be moved electromechanically, which decreases computational intensiveness – a measure of required computation per unit time – rather than having a unit compute its relative location so as to perform obstacle-avoidance and not, say, damage in-place biological neurons. Obstacle-avoidance and related concerns are instead negated through the use of tracks that limit the unit’s degrees of freedom—thus preventing it from having to incorporate computational techniques of obstacle-avoidance (and their entailed sensing apparatus). This also decreases the necessary precision (and thus, presumably, the required degree of miniaturization) of the means of locomotion, which would need to be much greater if the unit were to perform real-time obstacle avoidance. Such tracks would be constructed in iterative fashion. The constructional system would analyze the space in front of it to determine if the space was occupied by a neuron terminal or soma, and extrude the tracks iteratively (e.g., add a segment in spaces where it detects the absence of biological material). It would then move along the newly extruded track, progressively extending it through the spaces between neurons as it moves forward.

Non-Distortional in vivo Brain “Scanning”

A novel avenue of enquiry that occurred during this period involves counteracting or taking into account the distortions caused by the data-measurement units on the elements or properties they are measuring and subsequently applying such corrections to the recording data. A unit changes the local environment that it is supposed to be measuring and recording, which becomes problematic. My solution was to test which operations performed by the units have the potential to distort relevant attributes of the neuron or its environment and to build units that compensate for it either physically or computationally.

If we reduce how a recording unit’s operation distorts neuronal behavior into a list of mathematical rules, we can take the recordings and apply mathematical techniques to eliminate or “cancel out” those distortions post-measurement, thus arriving at what would have been the correct data. This approach would work only if the distortions are affecting the recorded data (i.e., changing it in predictable ways), and not if they are affecting the unit’s ability to actually access, measure, or resolve such data.

The second approach applies the method underlying the first approach to the physical environment of the neuron. A unit senses and records the constituents of the area of space immediately adjacent to its edges and mathematically models that “layer”; i.e., if it is meant to detect ionic solutions (in the case of ECF or ICF), then it would measure their concentration and subsequently model ionic diffusion for that layer. It then moves forward, encountering another adjacent “layer” and integrating it with its extant model. By being able to sense iteratively what is immediately adjacent to it, it can model the space it occupies as it travels through that space. It then uses electric or chemical stores to manipulate the electrical and chemical properties of the environment immediately adjacent to its surface, so as to produce the emergent effects of that model (i.e., the properties of the edges of that model and how such properties causally affect/impact adjacent sections of the environment), thus producing the emergent effects that would have been present if the NRU-construction/integration system or data-measuring system hadn’t occupied that space.

The third postulated solution was the use of a grid comprised of a series of hollow recesses placed in front of the sensing/measuring apparatus. The grid is impressed upon the surface of the membrane. Each compartment isolates a given section of the neuronal membrane from the rest. The constituents of each compartment are measured and recorded, most probably via uptake of its constituents and transport to a suitable measuring apparatus. A simple indexing system can keep track of which constituents came from which grid (and thus which region of the membrane they came from). The unit has a chemical store operatively connected to the means of locomotion used to transport the isolated membrane-constituents to the measuring/sensing apparatus. After a given compartment’s constituents are measured and recorded, the system then marks its constituents (determined by measurement and already stored as recordings by this point of the process), takes an equivalent molecule or compound from a chemical inventory, and replaces the substance it removed for measurement with the equivalent substance from its chemical inventory. Once this is accomplished for a given section of membrane, the grid then moves forward, farther into the membrane, leaving the replacement molecules/compounds from the biochemical inventory in the same respective spots as their original counterparts. It does this iteratively, making its way through a neuron and out the other side. This approach is the most speculative, and thus the least likely to be used. It would likely require the use of NEMS, rather than MEMS, as a necessary technological infrastructure, if the approach were to avoid becoming economically prohibitive, because in order for the compartment-constituents to be replaceable after measurement via chemical store, they need to be simple molecules and compounds rather than sections of emergent protein or tissue, which are comparatively harder to artificially synthesize and store in working order.

***

In the next chapter I describe the work done throughout late 2009 on biological/non-biological NRU hybrids, and in early 2010 on one of two new approaches to retaining subjective-continuity through a gradual replacement procedure, both of which are unrelated to concerns of graduality or sufficient functional equivalence between the biological original and the artificial replication-unit.

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

Concepts for Functional Replication of Biological Neurons – Article by Franco Cortese

May 19, 2013 Franco Cortese Comments 0 Comment

Franco Cortese

May 18, 2013

Recommend this page.

******************************

This essay is the third 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 two chapters were previously published on The Rational Argumentator as “The Moral Imperative and Technical Feasibility of Defeating Death” and “Immortality: Material or Ethereal? Nanotech Does Both!“.

***

The simplest approach to the functional replication of biological neurons I conceived of during this period involved what is normally called a “black-box” model of a neuron. This was already a concept in the wider brain-emulation community, but I was yet to find out about it. This is even simpler than the mathematically weighted Artificial Neurons discussed in the previous chapter. Rather than emulating or simulating the behavior of a neuron, (i.e, using actual computational—or more generally signal—processing) we (1) determine the range of input values that a neuron responds to, (2) stimulate the neuron at each interval (the number of intervals depending on the precision of the stimulus) within that input-range, and (3) record the corresponding range of outputs.

This reduces the neuron to essentially a look-up-table (or, more formally, an associative array). The input ranges I originally considered (in 2007) consisted of a range of electrical potentials, but later (in 2008) were developed to include different cumulative organizations of specific voltage values (i.e., some inputs activated and others not) and finally the chemical input and outputs of neurons. The black-box approach was eventually seen as being applied to the sub-neuron scale—e.g., to sections of the cellular membrane. This creates a greater degree of functional precision, bringing the functional modality of the black-box NRU-class in greater accordance with the functional modality of biological neurons. (I.e., it is closer to biological neurons because they do in fact process multiple inputs separately, rather than singular cumulative sums at once, as in the previous versions of the black-box approach.) We would also have a higher degree of variability for a given quantity of inputs.

I soon chanced upon literature dealing with MEMS (micro-electro-mechanical systems) and NEMS (nano-electro-mechanical systems), which eventually led me to nanotechnology and its use in nanosurgery in particular. I saw nanotechnology as the preferred technological infrastructure regardless of the approach used; its physical nature (i.e., operational and functional modalities) could facilitate the electrical and chemical processes of the neuron if the physicalist-functionalist (i.e., physically embodied or ‘prosthetic’) approach proved either preferable or required, while the computation required for its normative functioning (regardless of its particular application) assured that it could facilitate the informationalist-functionalist (i.e., computational emulation or simulation) of neurons if that approach proved preferable. This was true of MEMS as well, with the sole exception of not being able to directly synthesize neurotransmitters via mechanosynthesis, instead being limited in this regard to the release of pre-synthesized biochemical inventories. Thus I felt that I was able to work on conceptual development of the methodological and technological infrastructure underlying both (or at least variations to the existing operational modalities of MEMS and NEMS so as to make them suitable for their intended use), without having to definitively choose one technological/methodological infrastructure over the other. Moreover, there could be processes that are reducible to computation, yet still fail to be included in a computational emulation due to our simply failing to discover the principles underlying them. The prosthetic approach had the potential of replicating this aspect by integrating such a process, as it exists in the biological environment, into its own physical operation, and perform iterative maintenance or replacement of the biological process, until such a time as to be able to discover the underlying principles of those processes (which is a prerequisite for discovering how they contribute to the emergent computation occurring in the neuron) and thus for their inclusion in the informationalist-functionalist approach.

Also, I had by this time come across the existing approaches to Mind-Uploading and Whole-Brain Emulation (WBE), including Randal Koene’s minduploading.org, and realized that the notion of immortality through gradually replacing biological neurons with functional equivalents wasn’t strictly my own. I hadn’t yet come across Kurzweil’s thinking in regard to gradual uploading described in The Singularity is Near (where he suggests a similarly nanotechnological approach), and so felt that there was a gap in the extant literature in regard to how the emulated neurons or neural networks were to communicate with existing biological neurons (which is an essential requirement of gradual uploading and thus of any approach meant to facilitate subjective-continuity through substrate replacement). Thus my perceived role changed from the father of this concept to filling in the gaps and inconsistencies in the already-extant approach and in further developing it past its present state. This is another aspect informing my choice to work on and further varietize both the computational and physical-prosthetic approach—because this, along with the artificial-biological neural communication problem, was what I perceived as remaining to be done after discovering WBE.

The anticipated use of MEMS and NEMS in emulating the physical processes of the neurons included first simply electrical potentials, but eventually developed to include the chemical aspects of the neuron as well, in tandem with my increasing understanding of neuroscience. I had by this time come across Drexler’s Engines of Creation, which was my first introduction to antecedent proposals for immortality—specifically his notion of iterative cellular upkeep and repair performed by nanobots. I applied his concept of mechanosynthesis to the NRUs to facilitate the artificial synthesis of neurotransmitters. I eventually realized that the use of pre-synthesized chemical stores of neurotransmitters was a simpler approach that could be implemented via MEMS, thus being more inclusive for not necessitating nanotechnology as a required technological infrastructure. I also soon realized that we could eliminate the need for neurotransmitters completely by recording how specific neurotransmitters affect the nature of membrane-depolarization at the post-synaptic membrane and subsequently encoding this into the post-synaptic NRU (i.e., length and degree of depolarization or hyperpolarization, and possibly the diameter of ion-channels or differential opening of ion-channels—that is, some and not others) and assigning a discrete voltage to each possible neurotransmitter (or emergent pattern of neurotransmitters; salient variables include type, quantity and relative location) such that transmitting that voltage makes the post-synaptic NRU’s controlling-circuit implement the membrane-polarization changes (via changing the number of open artificial-ion-channels, or how long they remain open or closed, or their diameter/porosity) corresponding to the changes in biological post-synaptic membrane depolarization normally caused by that neurotransmitter.

In terms of the enhancement/self-modification side of things, I also realized during this period that mental augmentation (particularly the intensive integration of artificial-neural-networks with the existing brain) increases the efficacy of gradual uploading by decreasing the total portion of your brain occupied by the biological region being replaced—thus effectively making that portion’s temporary operational disconnection from the rest of the brain more negligible to concerns of subjective-continuity.

While I was thinking of the societal implications of self-modification and self-modulation in general, I wasn’t really consciously trying to do active conceptual work (e.g., working on designs for pragmatic technologies and methodologies as I was with limitless-longevity) on this side of the project due to seeing the end of death as being a much more pressing moral imperative than increasing our degree of self-determination. The 100,000 unprecedented calamities that befall humanity every day cannot wait; for these dying fires it is now or neverness.

Virtual Verification Experiments

The various alternative approaches to gradual substrate-replacement were meant to be alternative designs contingent upon various premises for what was needed to replicate functionality while retaining subjective-continuity through gradual replacement. I saw the various embodiments as being narrowed down through empirical validation prior to any whole-brain replication experiments. However, I now see that multiple alternative approaches—based, for example, on computational emulation (informationalist-functionalist) and physical replication (physicalist-functionalist) (these are the two main approaches thus far discussed) would have concurrent appeal to different segments of the population. The physicalist-functionalist approach might appeal to wide numbers of people who, for one metaphysical prescription or another, don’t believe enough in the computational reducibility of mind to bet their lives on it.

These experiments originally consisted of applying sensors to a given biological neuron, and constructing NRUs based on a series of variations on the two main approaches, running each and looking for any functional divergence over time. This is essentially the same approach outlined in the WBE Roadmap, which I was yet to discover at this point, that suggests a validation approach involving experiments done on single neurons before moving on to the organismal emulation of increasingly complex species up to and including the human. My thinking in regard to these experiments evolved over the next few years to also include the some novel approaches that I don’t think have yet been discussed in communities interested in brain-emulation.

An equivalent physical or computational simulation of the biological neuron’s environment is required to verify functional equivalence, as otherwise we wouldn’t be able to distinguish between functional divergence due to an insufficient replication-approach/NRU-design and functional divergence due to difference in either input or operation between the model and the original (caused by insufficiently synchronizing the environmental parameters of the NRU and its corresponding original). Isolating these neurons from their organismal environment allows the necessary fidelity (and thus computational intensity) of the simulation to be minimized by reducing the number of environmental variables affecting the biological neuron during the span of the initial experiments. Moreover, even if this doesn’t give us a perfectly reliable model of the efficacy of functional replication given the amount of environmental variables one expects a neuron belonging to a full brain to have, it is a fair approximator. Some NRU designs might fail in a relatively simple neuronal environment and thus testing all NRU designs using a number of environmental variables similar to the biological brain might be unnecessary (and thus economically prohibitive) given its cost-benefit ratio. And since we need to isolate the neuron to perform any early non-whole-organism experiments (i.e., on individual neurons) at all, having precise control over the number and nature of environmental variables would be relatively easy, as this is already an important part of the methodology used for normative biological experimentation anyways—because lack of control over environmental variables makes for an inconsistent methodology and thus for unreliable data.

And as we increase to the whole-network and eventually organismal level, a similar reduction of the computational requirements of the NRU’s environmental simulation is possible by replacing the inputs or sensory mechanisms (from single photocell to whole organs) with VR-modulated input. The required complexity and thus computational intensity of a sensorially mediated environment can be vastly minimized if the normative sensory environment of the organism is supplanted with a much-simplified VR simulation.

Note that the efficacy of this approach in comparison with the first (reducing actual environmental variables) is hypothetically greater because going from simplified VR version to the original sensorial environment is a difference, not of category, but of degree. Thus a potentially fruitful variation on the first experiment (physical reduction of a biological neuron’s environmental variables) would be not the complete elimination of environmental variables, but rather decreasing the range or degree of deviation in each variable, including all the categories and just reducing their degree.

Anecdotally, one novel modification conceived during this period involves distributing sensors (operatively connected to the sensory areas of the CNS) in the brain itself, so that we can viscerally sense ourselves thinking—the notion of metasensation: a sensorial infinite regress caused by having sensors in the sensory modules of the CNS, essentially allowing one to sense oneself sensing oneself sensing.

Another is a seeming refigurement of David Pearce’s Hedonistic Imperative—namely, the use of active NRU modulation to negate the effects of cell (or, more generally, stimulus-response) desensitization—the fact that the more times we experience something, or indeed even think something, the more it decreases in intensity. I felt that this was what made some of us lose interest in our lovers and become bored by things we once enjoyed. If we were able to stop cell desensitization, we wouldn’t have to needlessly lose experiential amplitude for the things we love.

In the next chapter I will describe the work I did in the first months of 2008, during which I worked almost wholly on conceptual varieties of the physically embodied prosthetic (i.e., physical-functionalist) approach (particularly in gradually replacing subsections of individual neurons to increase how gradual the cumulative procedure is) for several reasons:

The original utility of ‘hedging our bets’ as discussed earlier—developing multiple approaches increases evolutionary diversity; thus, if one approach fails, we have other approaches to try.

I felt the computational side was already largely developed in the work done by others in Whole-Brain Emulation, and thus that I would be benefiting the larger objective of indefinite longevity more by focusing on those areas that were then comparatively less developed.

The perceived benefit of a new approach to subjective-continuity through a substrate-replacement procedure aiming to increase the likelihood of gradual uploading’s success by increasing the procedure’s cumulative degree of graduality. The approach was called Iterative Gradual Replacement and consisted of undergoing several gradual-replacement procedures, wherein the class of NRU used becomes progressively less similar to the operational modality of the original, biological neurons with each iteration; the greater the number of iterations used, the less discontinuous each replacement-phase is in relation to its preceding and succeeding phases. The most basic embodiment of this approach would involve gradual replacement with physical-functionalist (prosthetic) NRUs that in turn are then gradually replaced with informational-physicalist (computational/emulatory) NRUs. My qualms with this approach today stem from the observation that the operational modalities of the physically embodied NRUs seem as discontinuous in relation to the operational modalities of the computational NRUs as the operational modalities of the biological neurons does. The problem seems to result from the lack of an intermediary stage between physical embodiment and computational (or second-order) embodiment.

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

Bibliography

Embedded Processor. (2013). In Encyclopædia Britannica. Retrieved from http://www.britannica.com/EBchecked/topic/185535/embedded-processor

Jerome, P. (1980). Recording action potentials from cultured neurons with extracellular microcircuit electrodes. Journal or Neuroscience Methods, 2 (1), 19-31.

Wolf, W. & (March 2009). Cyber-physical Systems. In Embedded Computing. Retrieved February 28, 2013 from http://www.jiafuwan.net/download/cyber_physical_systems.pdf

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

June 23, 2013 Franco Cortese Comments 0 Comment

First Survey

Summary

Second Survey

Cellular Prosthesis: Inklings of a New Interdisciplinary Approach

References:

Maintaining the Operational Continuity of Replicated Neurons – Article by Franco Cortese

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Choosing the Right Scale for Brain Emulation – Article by Franco Cortese

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Squishy Machines: Bio-Cybernetic Neuron Hybrids – Article by Franco Cortese

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Neuronal “Scanning” and NRU Integration – Article by Franco Cortese

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Concepts for Functional Replication of Biological Neurons – Article by Franco Cortese

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