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

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

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

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

The New Renaissance Hat
Franco Cortese
May 23, 2013
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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:
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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.

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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 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.

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

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

The New Renaissance Hat
Franco Cortese
May 18, 2013
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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!“.
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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 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.

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

Immortality: Material or Ethereal? Nanotech Does Both! – Article by Franco Cortese

Immortality: Material or Ethereal? Nanotech Does Both! – Article by Franco Cortese

The New Renaissance Hat
Franco Cortese
May 11, 2013
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This essay is the second 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 chapter was previously published on The Rational Argumentator as “The Moral Imperative and Technical Feasibility of Defeating Death“.

In August 2006 I conceived of the initial cybernetic brain-transplant procedure. It originated from a very simple, even intuitive sentiment: if there were heart and lung machines and prosthetic organs, then why couldn’t these be integrated in combination with modern (and future) robotics to keep the brain alive past the death of its biological body? I saw a possibility, felt its magnitude, and threw myself into realizing it. I couldn’t think of a nobler quest than the final eradication of involuntary death, and felt willing to spend the rest of my life trying to make it happen.

First I collected research on organic brain transplantation, on maintaining the brain’s homeostatic and regulatory mechanisms outside the body (or in this case without the body), on a host of prosthetic and robotic technologies (including sensory prosthesis and substitution), and on the work in Brain-Computer-Interface technologies that would eventually allow a given brain to control its new, non-biological body—essentially collecting the disparate mechanisms and technologies that would collectively converge to facilitate the creation of a fully cybernetic body to house the organic brain and keep it alive past the death of its homeostatic and regulatory organs.

I had by this point come across online literature on Artificial Neurons (ANs) and Artificial Neural Networks (ANNs), which are basically simplified mathematical models of neurons meant to process information in a way coarsely comparable to them. There was no mention in the literature of integrating them with existing neurons or for replacing existing neurons towards the objective of immortality ; their use was merely as an interesting approach to computation particularly optimal to certain situations. While artificial neurons can be run on general-purpose hardware (massively parallel architectures being the most efficient for ANNs, however), I had something more akin to neuromorphic hardware in mind (though I wasn’t aware of that just yet).

At its most fundamental level, Artificial Neurons need not even be physical at all. Their basic definition is a mathematical model roughly based on neuronal operation – and there is nothing precluding that model from existing solely on paper, with no actual computation going on. When I discovered them, I had thought that a given artificial neuron was a physically-embodied entity, rather than a software simulation. – i.e., an electronic device that operates in a way comparable to biological neurons.  Upon learning that they were mathematical models however, and that each AN needn’t be a separate entity from the rest of the ANs in a given AN Network, I saw no problem in designing them so as to be separate physical entities (which they needed to be in order to fit the purposes I had for them – namely, the gradual replacement of biological neurons with prosthetic functional equivalents). Each AN would be a software entity run on a piece of computational substrate, enclosed in a protective casing allowing it to co-exist with the biological neurons already in-place. The mathematical or informational outputs of the simulated neuron would be translated into biophysical, chemical, and electrical output by operatively connecting the simulation to an appropriate series of actuators (which could range from being as simple as producing electric fields or currents, to the release of chemical stores of neurotransmitters) and likewise a series of sensors to translate biophysical, chemical, and electrical properties into the mathematical or informational form they would need to be in to be accepted as input by the simulated AN.

Thus at this point I didn’t make a fundamental distinction between replicating the functions and operations of a neuron via physical embodiment (e.g., via physically embodied electrical, chemical, and/or electromechanical systems) or via virtual embodiment (usefully considered as 2nd-order embodiment, e.g., via a mathematical or computational model, whether simulation or emulation, run on a 1st-order physically embodied computational substrate).

The potential advantages, disadvantages, and categorical differences between these two approaches were still a few months away. When I discovered ANs, still thinking of them as physically embodied electronic devices rather than as mathematical or computational models, I hadn’t yet moved on to ways of preserving the organic brain itself so as to delay its organic death. Their utility in constituting a more permanent, durable, and readily repairable supplement for our biological neurons wasn’t yet apparent.

I initially saw their utility as being intelligence amplification, extension and modification through their integration with the existing biological brain. I realized that they were categorically different than Brain-Computer Interfaces (BCIs) and normative neural prosthesis for being able to become an integral and continuous part of our minds and personalities – or more properly the subjective, experiential parts of our minds. If they communicated with single neurons and interact with them on their own terms—if the two were operationally indistinct—then they could become a continuous part of us in a way that didn’t seem possible for normative BCI due to their fundamental operational dissimilarity with existing biological neural networks. I also collected research on the artificial synthesis and regeneration of biological neurons as an alternative to ANs. This approach would replace an aging or dying neuron with an artificially synthesized but still structurally and operationally biological neuron, so as to maintain the aging or dying neuron’s existing connections and relative location. I saw this procedure (i.e., adding artificial or artificially synthesized but still biological neurons to the existing neurons constituting our brains, not yet for the purposes of gradually replacing the brain but instead for the purpose of mental expansion and amplification) as not only allowing us to extend our existing functional and experiential modalities (e.g., making us smarter through an increase in synaptic density and connectivity, and an increase in the number of neurons in general) but even to create fundamentally new functional and experiential modalities that are categorically unimaginable to us now via the integration of wholly new Artificial Neural Networks embodying such new modalities. Note that I saw this as newly possible with my cybernetic-body approach because additional space could be made for the additional neurons and neural networks, whereas the degree with which we could integrate new, artificial neural networks in a normal biological body would be limited by the available volume of the unmodified skull.

Before I discovered ANs, I speculated in my notes as to whether the “bionic nerves” alluded to in some of the literature I had collected by this point (specifically regarding BCI, neural prosthesis, and the ability to operatively connect a robotic prosthetic extremity – e.g., an arm or a leg – via BCI) could be used to extend the total number of neurons and synaptic connections in the biological brain. This sprang from my knowledge on the operational similarities between neurons and muscle cells, both of the larger class of excitable cells.

Kurzweil’s cyborgification approach (i.e., that we could integrate non-biological systems with our biological brains to such an extent that the biological portions become so small as to be negligible to our subjective-continuity when they succumb to cell-death, thus achieving effective immortality without needing to actually replace any of our existing biological neurons at all) may have been implicit in this concept. I envisioned our brains increasing in size many times over and thus that the majority of our mind would be embodied or instantiated in larger part by the artificial portion than by the biological portions; the fact that the degree with which the loss of a part of our brain will affect our emergent personalities depends on how big (other potential metrics alternative to size include connectivity and the degree with which other systems depend on that potion for their own normative operation) that lost part is in comparison to the total size of the brain, the loss of a lobe being much worse than the loss of a neuron, follows naturally from this initial premise. The lack of any explicit statement of this realization in my notes during this period, however, makes this mere speculation.

It wasn’t until November 11, 2006, that I had the fundamental insight underlying mind-uploading—that the replacement of existing biological neurons with non-biological functional equivalents that maintain the existing relative location and connection of such biological neurons could very well facilitate maintaining the memory and personality embodied therein or instantiated thereby—essentially achieving potential technological immortality, since the approach is based on replacement and iterations of replacement-cycles can be run indefinitely. Moreover, the fact that we would be manufacturing such functional equivalents ourselves means that we could not only diagnose potential eventual dysfunctions easier and with greater speed, but we could manufacture them so as to have readily replaceable parts, thus simplifying the process of physically remediating any such potential dysfunction or operational degradation, even going so far as to include systems for the safe import and export of replacement components or as to make all such components readily detachable, so that we don’t have to cause damage to adjacent structures and systems in the process of removing a given component.

Perhaps it wasn’t so large a conceptual step from knowledge of the existence of computational models of neurons to the realization of using them to replace existing biological neurons towards the aim of immortality. Perhaps I take too much credit for independently conceiving both the underlying conceptual gestalt of mind-uploading, as well as some specific technologies and methodologies for its pragmatic technological implementation. Nonetheless, it was a realization I arrived at on my own, and was one that I felt would allow us to escape the biological death of the brain itself.

While I was aware (after a little more research) that ANNs were mathematical (and thus computational) models of neurons, hereafter referred to as the informationalist-functionalist approach, I felt that a physically embodied (i.e., not computationally emulated or simulated) prosthetic approach, hereafter referred to as the physicalist-functionalist approach, would be a better approach to take. This was because even if the brain were completely reducible to computation, a prosthetic approach would necessarily facilitate the computation underlying the functioning of the neuron (as the physical operations of biological neurons do presently), and if the brain proved to be computationally irreducible, then the prosthetic approach would in such a case presumably preserve whatever salient physical processes were necessary. So the prosthetic approach didn’t necessitate the computational-reducibility premise – but neither did it preclude such a view, thereby allowing me to hedge my bets and increase the cumulative likelihood of maintaining subjective-continuity of consciousness through substrate-replacement in general.

This marks a telling proclivity recurrent throughout my project: the development of mutually exclusive and methodologically and/or technologically alternate systems for a given objective, each based upon alternate premises and contingencies – a sort of possibilizational web unfurling fore and outward. After all, if one approach failed, then we had alternate approaches to try. This seemed like the work-ethic and conceptualizational methodology that would best ensure the eventual success of the project.

I also had less assurance in the sufficiency of the informational-functionalist approach at the time, stemming mainly from a misconception with the premises of normative Whole-Brain Emulation (WBE). When I first discovered ANs, I was more dubious at that point about the computational reducibility of the mind because I thought that it relied on the premise that neurons act in a computational fashion (i.e., like normative computational paradigms) to begin with—thus a conflation of classical computation with neural operation—rather than on the conclusion, drawn from the Church-Turing thesis, that mind is computable because the universe is. It is not that the brain is a computer to begin with, but that we can model any physical process via mathematical/computational emulation and simulation. The latter would be the correct view, and I didn’t really realize that this was the case until after I had discovered the WBE roadmap in 2010. This fundamental misconception allowed me, however, to also independently arrive at the insight underlying the real premise of WBE:  that combining premise A – that we had various mathematical computational models of neuron behavior – with premise B – that we can perform mathematical models on computers – ultimately yields the conclusion C – that we can simply perform the relevant mathematical models on computational substrate, thereby effectively instantiating the mind “embodied” in those neural operations while simultaneously eliminating many logistical and technological challenges to the prosthetic approach. This seemed both likelier than the original assumption—conflating neuronal activity with normative computation, as a special case not applicable to, say, muscle cells or skin cells, which wasn’t the presumption WBE makes at all—because this approach only required the ability to mathematically model anything, rather than relying on a fundamental equivalence between two different types of physical system (neuron and classical computer). The fact that I mistakenly saw it as an approach to emulation that was categorically dissimilar to normative WBE also helped urge me on to continue conceptual development of the various sub-aims of the project after having found that the idea of brain emulation already existed, because I thought that my approach was sufficiently different to warrant my continued effort.

There are other reasons for suspecting that mind may not be computationally reducible using current computational paradigms – reasons that rely on neither vitalism (i.e., the claim that mind is at least partially immaterial and irreducible to physical processes) nor on the invalidity of the Church-Turing thesis. This line of reasoning has nothing to do with functionality and everything to do with possible physical bases for subjective-continuity, both a) immediate subjective-continuity (i.e., how can we be a unified, continuous subjectivity if all our component parts are discrete and separate in space?), which can be considered as the capacity to have subjective experience, also called sentience (as opposed to sapience, which designated the higher cognitive capacities like abstract thinking) and b) temporal subjective-continuity (i.e., how do we survive as continuous subjectivities through a process of gradual substrate replacement?). Thus this argument impacts the possibility of computationally reproducing mind only insofar as the definition of mind is not strictly functional but is made to include a subjective sense of self—or immediate subjective-continuity. Note that subjective-continuity through gradual replacement is not speculative (just the scale and rate required to sufficiently implement it are), but rather has proof of concept in the normal metabolic replacement of the neuron’s constituent molecules. Each of us is a different person materially than we were 7 years ago, and we still claim to retain subjective-continuity. Thus, gradual replacement works; it is just the scale and rate required that are under question.

This is another way in which my approach and project differs from WBE. WBE equates functional equivalence (i.e., the same output via different processes) with subjective equivalence, whereas my approach involved developing variant approaches to neuron-replication-unit design that were each based on a different hypothetical basis for instantive subjective continuity.

 Are Current Computational Paradigms Sufficient?

Biological neurons are both analog and binary. It is useful to consider a 1st tier of analog processes, manifest in the action potentials occurring all over the neuronal soma and terminals, with a 2nd tier of binary processing, in that either the APs’ sum crosses the threshold value needed for the neuron to fire, or it falls short of it and the neuron fails to fire. Thus the analog processes form the basis of the digital ones. Moreover, the neuron is in an analog state even in the absence of membrane depolarization through the generation of the resting-membrane potential (maintained via active ion-transport proteins), which is analog rather than binary for always undergoing minor fluctuations due to it being an active process (ion-pumps) that instantiates it. Thus the neuron at any given time is always in the process of a state-transition (and minor state-transitions still within the variation-range allowed by a given higher-level static state; e.g., resting membrane potential is a single state, yet still undergoes minor fluctuations because the ions and components manifesting it still undergo state-transitions without the resting-membrane potential itself undergoing a state-transition), and thus is never definitively on or off. This brings us to the first potential physical basis for both immediate and temporal subjective-continuity. Analog states are continuous, and the fact that there is never a definitive break in the processes occurring at the lower levels of the neuron represents a potential basis for our subjective sense of immediate and temporal continuity.

Paradigms of digital computation, on the other hand, are at the lowest scale either definitively on or definitively off. While any voltage within a certain range will cause the generation of an output, it is still at base binary because in the absence of input the logic elements are not producing any sort of fluctuating voltage—they are definitively off. In binary computation, the substrates undergo a break (i.e., region of discontinuity) in their processing in the absence of inputs, and are in this way fundamentally dissimilar to the low-level operational modality of biological neurons by virtue of being procedurally discrete rather than procedurally continuous.

If the premise that the analog and procedurally continuous nature of neuron-functioning (including action potentials, resting-membrane potential, and metabolic processes that form a potential basis for immediate and temporal subjective-continuity) holds true, then current digital paradigms of computation may prove insufficient at maintaining subjective-continuity if used as the substrate in a gradual-replacement procedure, while still being sufficient to functionally replicate the mind in all empirically verifiable metrics and measures. This is due to both the operational modality of binary processing (i.e., lack of analog output) and the procedural modality of binary processing (the lack of temporal continuity or lack of producing minor fluctuations in reference to a baseline state when in a resting or inoperative state). A logic element could have a fluctuating resting voltage rather than the absence of any voltage and could thus be procedurally continuous while still being operationally discrete by producing solely binary outputs.

So there are two possibilities here. One is that any physical substrate used to replicate a neuron (whether via 1st-order embodiment a.k.a prosthesis/physical-systems, or via 2nd-order embodiment a.k.a computational emulation or simulation) must not undergo a break in its operation in the absence of input, because biological neurons do not, and this may be a potential basis for instantive subjective-continuity, but rather must produce a continuous or uninterrupted signal when in a “steady-state” (i.e., in the absence of inputs). The second possibility includes all the premises of the first, but adds that such an inoperative-state signal (or “no-inputs”-state signal) undergo minor fluctuations (because then a steady stream of causal interaction is occurring – e.g., producing a steady signal could be as discontinuous as no signal, like “being on pause”.

Thus one reason for developing the physicalist-functionalist (i.e., physically embodied prosthetic) approach to NRU design was a hedging of bets, in the case that a.) current computational substrates fail to replicate a personally continuous mind for the reasons described above, or b.) we fail to discover the principles underlying a given physical process—thus being unable to predictively model it—but still succeed in integrating them with the artificial systems comprising the prosthetic approach until such a time as to be able to discover their underlying principles, or c.) in the event that we find some other, heretofore unanticipated conceptual obstacle to computational reducibility of mind.

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.

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