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

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

The New Renaissance Hat
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:
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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

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

The New Renaissance Hat
Franco Cortese
June 2, 2013
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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:
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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 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.

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

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

The New Renaissance Hat
Franco Cortese
May 25, 2013
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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:
***

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 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.