Morphological Changes for Neural Plasticity
The finished physical-functionalist units would need the ability to change their emergent morphology not only for active modification of single-neuron functionality but even for basic functional replication of normative neuron behavior, by virtue of needing to take into account neural plasticity and the way that morphological changes facilitate learning and memory. My original approach involved the use of retractable, telescopic dendrites and axons (with corresponding internal retractable and telescopic dendritic spines and axonal spines, respectively) activated electromechanically by the unit-CPU. For morphological changes, by providing the edges of each membrane section with an electromechanical hinged connection (i.e., a means of changing the angle of inclination between immediately adjacent sections), the emergent morphology can be controllably varied. This eventually developed to consist of an internal compartment designed so as to detach a given membrane section, move it down into the internal compartment of the neuronal soma or terminal, transport it along a track that stores alternative membrane sections stacked face-to-face (to compensate for limited space), and subsequently replaces it with a membrane section containing an alternate functional component (e.g., ion pump, ion channel, [voltage-gated or ligand-gated], etc.) embedded therein. Note that this approach was also conceived of as an alternative to retractable axons/dendrites and axonal/dendritic spines, by attaching additional membrane sections with a very steep angle of inclination (or a lesser inclination with a greater quantity of segments) and thereby creating an emergent section of artificial membrane that extends out from the biological membrane in the same way as axons and dendrites.
However, this approach was eventually supplemented by one that necessitates less technological infrastructure (i.e., that was simpler and thus more economical and realizable). If the size of the integral-membrane components is small enough (preferably smaller than their biological analogues), then differential activation of components or membrane sections would achieve the same effect as changing the organization or type of integral-membrane components, effectively eliminating the need at actually interchange membrane sections at all.
Active Neuronal Modulation and Modification
The technological and methodological infrastructure used to facilitate neural plasticity can also be used for active modification and modulation of neural behavior (and the emergent functionality determined by local neuronal behavior) towards the aim of mental augmentation and modification. Potential uses already discussed include mental amplification (increasing or augmenting existing functional modalities—i.e., intelligence, emotion, morality), or mental augmentation (the creation of categorically new functional and experiential modalities). While the distinction between modification and modulation isn’t definitive, a useful way of differentiating them is to consider modification as morphological changes creating new functional modalities, and to consider modulation as actively varying the operation of existing structures/processes through not morphological change but rather changes to the operation of integral-membrane components or the properties of the local environment (e.g., increasing local ionic concentrations).
Modulation: A Less Discontinuous Alternative to Morphological Modification
The use of modulation to achieve the effective results of morphological changes seemed like a hypothetically less discontinuous alternative to morphological changes (and thus as having a hypothetically greater probability of achieving subjective-continuity). I’m more dubious in regards to the validity of this approach now, because the emergent functionality (normatively determined by morphological features) is still changed in an effectively equivalent manner.
The Eventual Replacement of Neural Ionic Solutions with Direct Electric Fields
Upon full gradual replacement of the CNS with physical-functionalist equivalents, the preferred embodiment consisted of replacing the ionic solutions with electric fields that preserve the electric potential instantiated by the difference in ionic concentrations on the respective sides of the membrane. Such electric fields can be generated directly, without recourse to electrochemicals for manifesting them. In such a case the integral-membrane components would be replaced by a means of generating and maintaining a static and/or dynamic electric field on either side of the membrane, or even merely of generating an electrical potential (i.e., voltage—a broader category encompassing electric fields) with solid-state electronics.
This procedure would allow a fraction of the speedups (that is, increased rate of subjective perception of time, which extends to speed of thought) resulting from emulatory (i.e., strictly computational) replication-methods by no longer being limited to the rate of passive ionic diffusion—now instead being limited to the propagation velocity of electric or electromagnetic fields.
If we replace the physical synaptic connections the NRU uses to communicate (with both existing biological neurons and with other NRUs) with a wireless means of synaptic-transmission, we can preserve the same functionality (insofar as it is determined by synaptic connectivity) while allowing any NRU to communicate with any other NRU or biological neuron in the brain at potentially equal speed. First we need a way of converting the output of an NRU or biological neuron into information that can be transmitted wirelessly. For cyber-physicalist-functionalist NRUs, regardless of their sub-class, this requires no new technological infrastructure because they already deal with 2nd-order (i.e., not structurally or directly embodied) information; informational-functional NRU deals solely in terms of this type of information, and the cyber-physical-systems sub-class of the physicalist-functionalist NRUs deal with this kind of information in the intermediary stage between sensors and actuators—and consequently, converting what would have been a sequence of electromechanical actuations into information isn’t a problem. Only the passive-physicalist-functionalist NRU class requires additional technological infrastructure to accomplish this, because they don’t already use computational operational-modalities for their normative operation, whereas the other NRU classes do.
We dispose receivers within the range of every neuron (or alternatively NRU) in the brain, connected to actuators – the precise composition of which depends on the operational modality of the receiving biological neuron or NRU. The receiver translates incoming information into physical actuations (e.g., the release of chemical stores), thereby instantiating that informational output in physical terms. For biological neurons, the receiver’s actuators would consist of a means of electrically stimulating the neuron and releasable chemical stores of neurotransmitters (or ionic concentrations as an alternate means of electrical stimulation via the manipulation of local ionic concentrations). For informational-functionalist NRUs, the information is already in a form it can accept; it can simply integrate that information into its extant model. For cyber-physicalist-NRUs, the unit’s CPU merely needs to be able to translate that information into the sequence in which it must electromechanically actuate its artificial ion-channels. For the passive-physicalist (i.e., having no computational hardware devoted to operating individual components at all, operating according to physical feedback between components alone) NRUs, our only option appears to be translating received information into the manipulation of the local environment to vicariously affect the operation of the NRU (e.g., increasing electric potential through manipulations of local ionic concentrations, or increasing the rate of diffusion via applied electric fields to attract ions and thus achieve the same effect as a steeper electrochemical gradient or potential-difference).
The technological and methodological infrastructure for this is very similar to that used for the “integrational NRUs”, which allows a given NRU-class to communicate with either existing biological neurons or NRUs of an alternate class.
Integrating New Neural Nets Without Functional Distortion of Existing Regions
The use of artificial neural networks (which here will designate NRU-networks that do not replicate any existing biological neurons, rather than the normative Artificial Neuron Networks mentioned in the first and second parts of this essay), rather than normative neural prosthetics and BCI, was the preferred method of cognitive augmentation (creation of categorically new functional/experiential modalities) and cognitive amplification (the extension of existing functional/experiential modalities). Due to functioning according to the same operational modality as existing neurons (whether biological or artificial-replacements), they can become a continuous part of our “selves”, whereas normative neural prosthetics and BCI are comparatively less likely to be capable of becoming an integral part of our experiential continuum (or subjective sense of self) due to their significant operational dissimilarity in relation to biological neural networks.
A given artificial neural network can be integrated with existing biological networks in a few ways. One is interior integration, wherein the new neural network is integrated so as to be “inter-threaded”, in which a given artificial-neuron is placed among one or multiple existing networks. The networks are integrated and connected on a very local level. In “anterior” integration, the new network would be integrated in a way comparable to the connection between separate cortical columns, with the majority of integration happening at the peripherals of each respective network or cluster.
If the interior integration approach is used then the functionality of the region may be distorted or negated by virtue of the fact that neurons that once took a certain amount of time to communicate now take comparatively longer due to the distance between them having been increased to compensate for the extra space necessitated by the integration of the new artificial neurons. Thus in order to negate these problematizing aspects, a means of increasing the speed of communication (determined by both [a] the rate of diffusion across the synaptic junction and [b] the rate of diffusion across the neuronal membrane, which in most cases is synonymous with the propagation velocity in the membrane – the exception being myelinated axons, wherein a given action potential “jumps” from node of Ranvier to node of Ranvier; in these cases propagation velocity is determined by the thickness and length of the myelinated sections) must be employed.
My original solution was the use of an artificial membrane morphologically modeled on a myelinated axon that possesses very high capacitance (and thus high propagation velocity), combined with increasing the capacitance of the existing axon or dendrite of the biological neuron. The cumulative capacitance of both is increased in proportion to how far apart they are moved. In this way, the propagation velocity of the existing neuron and the connector-terminal are increased to allow the existing biological neurons to communicate as fast as they would have prior to the addition of the artificial neural network. This solution was eventually supplemented by the wireless means of synaptic transmission described above, which allows any neuron to communicate with any other neuron at equal speed.
Gradually Assigning Operational Control of a Physical NRU to a Virtual NRU
This approach allows us to apply the single-neuron gradual replacement facilitated by the physical-functionalist NRU to the informational-functionalist (physically embodied) NRU. A given section of artificial membrane and its integral membrane components are modeled. When this model is functioning in parallel (i.e., synchronization of operative states) with its corresponding membrane section, the normative operational routines of that artificial membrane section (usually controlled by the unit’s CPU and its programming) are subsequently taken over by the computational model—i.e., the physical operation of the artificial membrane section is implemented according to and in correspondence with the operative states of the model. This is done iteratively, with the informationalist-functionalist NRU progressively controlling more and more sections of the membrane until the physical operation of the whole physical-functionalist NRU is controlled by the informational operative states of the informationalist-functionalist NRU. While this concept sprang originally from the approach of using multiple gradual-replacement phases (with a class of model assigned to each phase, wherein each is more dissimilar to the original than the preceding phase, thereby increasing the cumulative degree of graduality), I now see it as a way of facilitating sub-neuron gradual replacement in computational NRUs. Also note that this approach can be used to go from existing biological membrane-sections to a computational NRU, without a physical-functionalist intermediary stage. This, however, is comparatively more complex because the physical-functionalist NRU already has a means of modulating its operative states, whereas the biological neuron does not. In such a case the section of lipid bilayer membrane would presumably have to be operationally isolated from adjacent sections of membrane, using a system of chemical inventories (of either highly concentrated ionic solution or neurotransmitters, depending on the area of membrane) to produce electrochemical output and chemical sensors to accept the electrochemical input from adjacent sections (i.e., a means of detecting depolarization and hyperpolarization). Thus to facilitate an action potential, for example, the chemical sensors would detect depolarization, the computational NRU would then model the influx of ions through the section of membrane it is replacing and subsequently translate the effective results impinging upon the opposite side to that opposite edge via either the release of neurotransmitters or the manipulation of local ionic concentrations so as to generate the required depolarization at the adjacent section of biological membrane.
This consisted of a unit facilitating connection between emulatory (i.e., informational-functionalist) units and existing biological neurons. The output of the emulatory units is converted into chemical and electrical output at the locations where the emulatory NRU makes synaptic connection with other biological neurons, facilitated through electric stimulation or the release of chemical inventories for the increase of ionic concentrations and the release of neurotransmitters, respectively. The input of existing biological neurons making synaptic connections with the emulatory NRU is read, likewise, by chemical and electrical sensors and is converted into informational input that corresponds to the operational modality of the informationalist-functionalist NRU classes.
Solutions to Scale
If we needed NEMS or something below the scale of the present state of MEMS for the technological infrastructure of either (a) the electromechanical systems replicating a given section of neuronal membrane, or (b) the systems used to construct and/or integrate the sections, or those used to remove or otherwise operationally isolate the existing section of lipid bilayer membrane being replaced from adjacent sections, a postulated solution consisted of taking the difference in length between the artificial membrane section and the existing bilipid section (which difference is determined by how small we can construct functionally operative artificial ion-channels) and incorporating this as added curvature in the artificial membrane-section such that its edges converge upon or superpose with the edges of the space left by the removal the lipid bilayer membrane-section. We would also need to increase the propagation velocity (typically determined by the rate of ionic influx, which in turn is typically determined by the concentration gradient or difference in the ionic concentrations on the respective sides of the membrane) such that the action potential reaches the opposite end of the replacement section at the same time that it would normally have via the lipid bilayer membrane. This could be accomplished directly by the application of electric fields with a charge opposite that of the ions (which would attract them, thus increasing the rate of diffusion), by increasing the number of open channels or the diameter of existing channels, or simply by increasing the concentration gradient through local manipulation of extracellular and/or intracellular ionic concentration—e.g., through concentrated electrolyte stores of the relevant ion that can be released to increase the local ionic concentration.
If the degree of miniaturization is so low as to make this approach untenable (e.g., increasing curvature still doesn’t allow successful integration) then a hypothesized alternative approach was to increase the overall space between adjacent neurons, integrate the NRU, and replace normative connection with chemical inventories (of either ionic compound or neurotransmitter) released at the site of existing connection, and having the NRU (or NRU sub-section—i.e., artificial membrane section) wirelessly control the release of such chemical inventories according to its operative states.
The next chapter describes (a) possible physical bases for subjective-continuity through a gradual-uploading procedure and (b) possible design requirements for in vivo brain-scanning and for systems to construct and integrate the prosthetic neurons with the existing biological brain.
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.
Project Avatar (2011). Retrieved February 28, 2013 from http://2045.com/tech2/