Редактирование: Whole brain emulation (раздел)

== Complications ==

=== Spinal Cord ===

* While traditionally the vertebrate spinal cord is often regarded as little more than a bundle of 
motor and sensor axons together with a central column of stereotypical reflex circuits and 
pattern generators, there is evidence that the processing may be more complex (Berg, 
Alaburda et al., 2007) and that learning processes occur among spinal neurons (Crown, 
Ferguson et al., 2002). The networks responsible for standing and stepping are extremely 
flexible and unlikely to be hardwired (Cai, Courtine et al., 2006).  
 
 
* This means that emulating just the brain part of the central nervous system will lose much 
body control that has been learned and resides in the non‐scanned cord. On the other hand, it 
is possible that a generic spinal cord network would, when attached to the emulated brain, 
adapt (requiring only scanning and emulating one spinal cord, as well as finding a way of 
attaching the spinal emulation to the brain emulation). But even if this is true, the time taken 
may correspond to rehabilitation timescales of (subjective) months, during which time the 
simulated body would be essentially paralysed. This might not be a major problem for 
personal identity in mind emulations (since people suffering spinal injuries do not lose 
personal identity), but it would be a major limitation to their usefulness and might limit 
development of animal models for brain emulation.  
 
* A similar concern could exist for other peripheral systems such as the retina and autonomic 
nervous system ganglia.  
 
* The human spinal cord weighs 2.5% of the brain and contains around 10‐4 of the number of 
neurons in the brain (13.5 million neurons). Hence adding the spinal cord to an emulation 
would add a negligible extra scan and simulation load.

=== Synaptic Adaptation ===

* Synapses are usually characterized by their “strength”, the size of the postsynaptic potential 
they produce in response to a given magnitude of incoming excitation. Many (most?) 
synapses in the CNS also exhibit depression and/or facilitation: a temporary change in release 
probability caused by repeated activity (Thomson, 2000). This rapid dynamics likely plays a 
role in a variety of brain functions, such as temporal filtering (Fortune and Rose, 2001 ), 
auditory processing (Macleod, Horiuchi et al., 2007) and motor control (Nadim and Manor, 
2000). These changes occur on timescales longer than neural activity (tens of milliseconds) but 
shorter than long‐term synaptic plasticity (minutes to hours). Adaptation has already been 
included in numerous computational models. The computational load is usually 1‐3 extra 
state variables in each synapse.

=== Unknown Neurotransmitters ===

* Not all neuromodulators are known. At present about 10 major neurotransmitters and 200+ 
neuromodulators are known, and the number is increasing. (Thomas, 2006) lists 272 
endogenous extracellular neuroactive signal transducers with known receptors, 2 gases, 19 
substances with putative or unknown binding sites and 48 endogenous substances that may 
or may not be neuroactive transducers (many of these may be more involved in general 
biochemical signalling than brain‐specific signals). Plotting the year of discovery for different 
substances (or families of substances) suggests a linear or possibly sigmoidal growth over 
time (Figure 11). 


* An upper bound on the number of neuromodulators can be found using genomics. About 800 
G‐protein coupled receptors can be found in the human genome, of which about half were 
sensory receptors. Many are “orphans” that lack known ligands, and methods of 
“deorphanizing” receptors by expressing them and determining what they bind to have been 
developed. In the middle 1990’s about 150 receptors had been paired to 75 transmitters, 
leaving around 150‐200 orphans in 2003 (Wise, Jupe et al., 2004). At present, 7‐8 receptors are 
deorphanized each year (von Bohlen und Halbach and Dermietzel, 2006); at this rate all 
orphans should be adopted within ≈20 years, leading to the discovery of around 50 more 
transmitters (Civelli, 2005). 
 
* Similarly guanylyl cyclase‐coupled receptors (four orphans, (Wedel and Garbers, 1998)), 
tyrosine kinase‐coupled receptors (<<100, (Muller‐Tidow, Schwable et al., 2004)) and cytokine 
receptors would add a few extra transmitters.  
 
* However, there is room for some surprises. Recently it was found that protons were used to 
signal in C. elegans rhythmic defecation (Pfeiffer, Johnson et al., 2008) mediated using a 
Na+/H+ exchanger, and it is not inconceivable that similar mechanisms could exist in the 
brain. Hence the upper bound on all transmitters may be set by not just receptors but also by 
membrane transporter proteins.  

* For WBE modelling all modulatory interactions is probably crucial, since we know that 
neuromodulation does have important effects on mood, consciousness, learning and 
perception. This means not just detecting their existence but to create quantitative models of 
these interactions, a sizeable challenge for experimental and computational neuroscience.

=== Unknown Ion Channels ===

* Similar to receptors, there are likely unknown ion channels that affect neuron dynamics.  

*  
The Ligand Gated Ion Channel Database currently contains 554 entries with 71 designated as 
channel subunits from Homo sapiens (EMBL‐EBI, 2008; Donizelli, Djite et al., 2006). Voltage 
gated ion channels form a superfamily with at least 143 genes (Yu, Yarov‐Yarovoy et al., 
2005). This diversity is increased by multimerization (combinations of different subunits), 
modifier subunits that do not form channels on their own but affect the function of channels 
they are incorporated into, accessory proteins as well as alternate mRNA splicing and post‐
translational modification (Gutman, Chandy et al., 2005). This would enable at least an order 
of magnitude more variants.  

* Ion channel diversity increases the diversity of possible neuron electrophysiology, but not 
necessarily in a linear manner. See the discussion of inferring electrophysiology from gene 
transcripts in the interpretation chapter.

=== Volume Transmission ===

Surrounding the cells of the brain is the extracellular space, on average 200 Å across and 
corresponding to 20% of brain volume (Nicholson, 2001). It transports nutrients and buffers 
ions, but may also enable volume transmission of signalling molecules.  

* Volume transmission of small molecules appears fairly well established. Nitrous oxide is 
hydrophobic and has low molecular weight and can hence diffuse relatively freely through 
membranes: it can reach up to 0.1‐0.2 mm away from a release point under physiological 
conditions (Malinski, Taha et al., 1993; Schuman and Madison, 1994; Wood and Garthwaite, 
1994). While mainly believed to be important for autoregulation of blood supply, it may also 
have a role in memory (Ledo, Frade et al., 2004). This might explain how LTP (Long Term 
Potentiation) can induce “crosstalk” that reduces LTP induction thresholds over a span of 10 
μm and ten minutes (Harvey and Svoboda, 2007). 

* Signal substances such as dopamine exhibit volume transmission (Rice, 2000) and this may 
have effect for potentiation of nearby synapses during learning: simulations show that a 
single synaptic release can be detected up to 20 μm away and with a 100 ms half‐life (Cragg, 
Nicholson et al., 2001). Larger molecules have their relative diffusion speed reduced by the 
limited geometry of the extracellular space, both in terms of its tortuosity and its anisotropy 
(Nicholson, 2001). As suggested by Robert Freitas, there may also exist active extracellular 
transport modes. Diffusion rates are also affected by local flow of the CSF and can differ from 
region to region (Fenstermacher and Kaye, 1988); if this is relevant then local diffusion and 
flow measurements may be needed to develop at least a general brain diffusion model. The 
geometric part of such data could be relatively easily gained from the high resolution 3D 
scans needed for other WBE subproblems. 

* Rapid and broad volume transmission such as from nitrous oxide can be simulated using a 
relatively coarse spatiotemporal grid size, while local transmission requires a grid with a 
spatial scale close to the neural scale if diffusion is severely hindered.  

* For constraining brain emulation it might be useful to analyse the expected diffusion and 
detection distances of the ≈200 known chemical signalling molecules based on their molecular 
weight, diffusion constant and uptake (for different local neural geometries and source/sink 
distributions). This would provide information on diffusion times that constrain the diffusion 
part of the emulation and possibly show which chemical species need to be spatially 
modelled.

=== Neurogenesis ===

* Recent results show that neurogenesis persists in some brain regions in adulthood, and might 
have nontrivial functional consequences (Saxe, Malleret et al., 2007). During neurite 
outgrowth, and possibly afterwards, cell adhesion proteins can affect gene expression and 
possible neuron function by affecting second messenger systems and calcium levels (Crossin 
and Krushel, 2000). However, neurogenesis is mainly confined to discrete regions of the brain 
and does not occur to a great extent in adult neocortex (Bhardwaj, Curtis et al., 2006). 

* Since neurogenesis occurs on fairly slow timescales (> 1 week) compared to brain activity and 
normal plasticity, it could probably be ignored in brain emulation if the goal is an emulation 
that is intended to function faithfully for only a few days and not to exhibit truly long‐term 
memory consolidation or adaptation. 

* A related issue is remodelling of dendrites and synapses. Over the span of months dendrites 
can grow, retract and add new branch tips in a cell type‐specific manner (Lee, Huang et al., 
2006). Similarly synaptic spines in the adult brain can change within hours to days, although 
the majority remain stable over multi‐month timespans (Grutzendler, Kasthuri et al., 2002; 
Holtmaat, Trachtenberg et al., 2005; Zuo, Lin et al., 2005). Even if neurogenesis is ignored and 
the emulation is of an adult brain, it is likely that such remodelling is important to learning 
and adaptation.

*  Simulating stem cell proliferation would require data structures representing different cells 
and their differentiation status, data on what triggers neurogenesis, and models allowing for 
the gradual integration of the cells into the network. Such a simulation would involve 
modelling the geometry and mechanics of cells, possibly even tissue differentiation. Dendritic 
and synaptic remodelling would also require a geometry and mechanics model. While 
technically involved and requiring at least a geometry model for each dendritic compartment 
the computational demands appear small compared to neural activity.

=== Chemical Environment ===

=== Neuroglia ===

* Glia cells have traditionally been regarded as merely supporting actors to the neurons, but 
recent results suggest that they may play a fairly active role in neural activity. Beside the 
important role of myelinization for increasing neural transmission speed, at the very least 
they have strong effects on the local chemical environment of the extracellular space 
surrounding neurons and synapses. 
 
* Glial cells exhibit calcium waves that spread along glial networks and affect nearby neurons 
(Newman and Zahs, 1998). They can both excite and inhibit nearby neurons through 
neurotransmitters (Kozlov, Angulo et al., 2006). Conversely, the calcium concentration of glial 
cells is affected by the presence of specific neuromodulators (Perea and Araque, 2005). This 
suggests that the glial cells acts as an information processing network integrated with the 
neurons (Fellin and Carmignoto, 2004). One role could be in regulating local energy and 
oxygen supply. 
 
* If glial processing turns out to be significant and fine‐grained, brain emulation would have to 
emulate the glia cells in the same way as neurons, increasing the storage demands by at least 
one order of magnitude. However, the time constants for glial calcium dynamics is generally 
far slower than the dynamics of action potentials (on the order of seconds or more), 
suggesting that the time resolution would not have to be as fine, making the computational 
demands increase far less steeply.

=== Loss of Instantaneous State ===

Every realistic method to scan brain tissue at a decent resolution deals only with structure, not continuous activity. Whatever information is stored as a pattern of brain activity instead of structure (Such as working memory) will be destroyed by the process. Information stored in the instantaneous state of neurons (Ion concentrations across membranes, synaptic vesicle depletion, and neurotransmitters in motion) would be lost. The most likely consequence would be memory loss up to some amount of time prior to the scanning.

Cases where people have woken up from long periods of electrocerebral silence prove that instantaneous brain activity is not required for the long term maintenance of personal identity<ref>Elixson, 1991</ref>.

=== Summary ===

{| border=1 class=wikitable
! colspan=3 | '''Summary'''
|-
| style="background-color:#C0C0C0;" | Feature || style="background-color:#C0C0C0;" | Likeliehood of necessity for WBE || style="background-color:#C0C0C0;" | Implementation Problems
|-
| Spinal cord || Likely || Minor. Would require scanning some extra tissue.
|-
| Synaptic adaptation || Very likely || Minor. Introduces extra state-variables and parameters that need to be set.
|-
| Currently unknown neurotransmitters and neuromodulators || Very likely || Minor. Similar to known transmitters and modulators.
|-
| Currently unknown ion channels || Very likely || Minor. Similar to known ion channels.
|-
| Volume transmission || Somewhat likely || Medium. Requires diffusion models and microscale geometry.
|-
| Body chemical environment || Somewhat likely || Medium. Requires metabolomic models and data.
|-
| Neurogenesis and remodelling || Somewhat likely || Medium. Requires cell mechanics and growth models.
|-
| Glia cells || Possible || Minor. Would require more simulation compartments, but likely running on a lower timescale.
|-
| Ephaptic effects || Possible || Minor. Would require more simulation compartments, but likely running on a slower timescale.
|-
| Dynamical state || Very unlikely || Profound. Would preclude most proposed scanning methods.
|-
| Quantum computation || Very unlikely || Profound. Would preclude currently conceived scanning methods and would require quantum computing.
|-
| Analog computation || Very unlikely || Profound. Would require analog computer hardware.
|-
| True randomness || Very unlikely || Medium to profound, depending on whether merely 'true' random noise or 'hidden variables' are needed.
|}