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
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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