Редактирование: Whole brain emulation (раздел)

== Sensory/Motor Neuron Map ==

The link between the body and the brain is in the spine, the most distinguishable part of the Peripheral Nervous System. The picture on the left shows a spinal nerve: The nerve fibers divide into two branches, or roots. The dorsal root carries sensory axons, while the ventral root carries motor axons: One is a receiver, and the other is a sender. Charles Bell, in 1810, performed tests on animals, cutting axon after axon and observing the resulting paralysis or loss of sensation, proving this.

This arrangement is species-generic '''?REF''', and it may only be necessary to trace a single person's axons to map the sensory/motor axons all the way up the spine into the brain, where the arrangement becomes more specialized for the individual. In any case: The sensory and motor neurons are just another number in the global table of neurons, but once they have been matched to their corresponding axons in the PNS (That is, once it is known exactly where they send signals to or receive signals from), one forms a table -- A map -- with a set of pointers to those neurons, categorized, for example, by region. For example, one particular neuron may be classified as "Motor: Second phalax muscle, index finger, left hand, left arm.".

<gallery>
File:Virtual emulation system.png|The system for a completely virtual emulation.
File:Embodied emulation system.jpg|The system for an embodied emulation.
</gallery>

This map links the sensory/motor neurons to their corresponding (Virtual) muscles and sensors. It's a simple way of abstracting away motor control, by providing simple, orderly access to the neurons responsible for it.

=== The Body Model ===

The virtual body can also be considered as means of abstraction: Instead of mapping the sensory/motor neuron map directly to a telepresence robot or to a virtual body, one could just pipe information to and from the individual virtual muscles and virtual sensors. For example, creating a virtual avatar that has points where collision checking is done. When a collision is detected, a pulse (Containing the information of the force vector, possibly) is piped to the corresponding sensor, that sends it to its corresponding neuron. The same is done with muscles, but viceversa: The nerve impulse is translated by the map from a pure impulse to, say, stress and force vectors in the virtual muscle. The muscle can then pipe this values to a software object that turns them into motion of a limb in the avatar.

The body simulation translates between neural signals and the environment, as well as 
maintains a model of body state as it affects the brain emulation.  
 
How detailed the body simulation needs to be in order to function depends on the goal. An 
“adequate” simulation produces enough and the right kind of information for the emulation 
to function and act, while a convincing simulation is nearly or wholly indistinguishable from 
the “feel” of the original body.  
 
A number of relatively simple biomechanical simulations of bodies connected to simulated 
nervous systems have been created to study locomotion. (Suzuki, Goto et al., 2005) simulated 
the C. elegans body as a multi‐joint rigid link where the joints were controlled by 
motorneurons in a simulated motor control network. Örjan Ekeberg has simulated 
locomotion in lamprey (Ekeberg and Grillner, 1999), stick insects (Ekeberg, Blümel et al., 
2004), and the hind legs of cat (Ekeberg and Pearson, 2005) where a rigid skeleton is moved 
by muscles either modeled as springs contracting linearly with neural signals, or in the case 
of the cat, a model fitting observed data relating neural stimulation, length, and velocity with 
contraction force (Brown, Scott et al., 1996). These models also include sensory feedback from 
stretch receptors, enabling movements to adapt to environmental forces: locomotion involves 
an information loop between neural activity, motor response, body dynamics, and sensory 
feedback (Pearson, Ekeberg et al., 2006).  
 
Today biomechanical model software enables fairly detailed models of muscles, the skeleton, 
and the joints, enabling calculation of forces, torques, and interaction with a simulated 
environment (Biomechanics Research Group Inc, 2005). Such models tend to simplify muscles 
as lines and make use of pre‐recorded movements or tensions to generate the kinematics.  
 
A detailed mechanical model of human walking has been constructed with 23 degrees of 
freedom driven by 54 muscles. However, it was not controlled by a neural network but rather 
used to find an energy‐optimizing gait (Anderson and Pandy, 2001). A state of‐the‐art model 
involving 200 rigid bones with over 300 degrees of freedom, driven by muscular actuators 
with excitation‐contraction dynamics and some neural control, has been developed for 
modelling human body motion in a dynamic environment, e.g. for ergonomics testing 
(Ivancevic and Beagley, 2004). This model runs on a normal workstation, suggesting that 
rigid body simulation is not a computationally hard problem in comparison to WBE. 
 
Other biomechanical models are being explored for assessing musculoskeletal function in 
human (Fernandez and Pandy, 2006), and can be validated or individualized by use of MRI 
data (Arnold, Salinas et al., 2000) or EMG (Lloyd and Besier, 2003). It is expected that near 
future models will be based on volumetric muscle and bone models found using MRI 
scanning (Blemker, Asakawa et al., 2007; Blemker and Delp, 2005), as well as construction of 
topological models (Magnenat‐Thalmann and Cordier, 2000). There are also various 
simulations of soft tissue (Benham, Wright et al., 2001), breathing (Zordan, Celly et al., 2004) 
and soft tissue deformation for surgery simulation (Cotin, Delingette et al., 1999). 
 
Another source of body models comes from computer graphics, where much effort has gone 
into rendering realistic characters, including modelling muscles, hair and skin. The emphasis 
has been on realistic appearance rather than realistic physics (Scheepers, Parent et al., 1997), 
but increasingly the models are becoming biophysically realistic and overlapping with 
biophysics (Chen and Zeltzer, 1992; Yucesoy, Koopman et al., 2002). For example, 30 
contact/collision coupled muscles in the upper limb with fascia and tendons were generated 
from the visible human dataset and then simulated using a finite volume method; this 
simulation (using one million mesh tetrahedra) ran at a rate of 240 seconds per frame on a 
single CPU Xeon 3.06 GHz (on the order of a few GFLOPS) (Teran, Sifakis et al., 2005). Scaling 
this up 20 times to encompass ≈600 muscles implies a computational cost on the order of a 
hundred TFLOPS for a complete body simulation.  
 
Physiological models are increasingly used in medicine for education, research and patient 
evaluation. Relatively simple models can accurately simulate blood oxygenation (Hardman, 
Bedforth et al., 1998). For a body simulation this might be enough to provide the right 
feedback between exertion and brain state. Similarly simple nutrient and hormone models 
could be used insofar a realistic response to hunger and eating were desired.

=== Senses ===

<blockquote>See: [[Physical Enhancement#Sensory Augmentation|Sensory Augmentation]]</blockquote>

==== Vision ====

Vision
Visual photorealism has been sought in computer graphics for about 30 years, and this 
appears to be a fairly mature area at least for static images and scenes. Much effort is 
currently going into such technology, for use in computer games and movies. 
 
(McGuigan, 2006) proposes a “graphics Turing test” and estimates that for 30 Hz interactive 
visual updates 518.4‐1036.8 TFLOPS would be enough for Monte Carlo global illumination. 
This might actually be an overestimate since he assumes generation of complete pictures. 
Generating only the signal needed for the retinal receptors (with higher resolution for the fovea than the periphery) could presumably reduce the demands. Similarly, more efficient 
implementations of the illumination model (or a cheaper one) would also reduce demands 
significantly.

==== Hearing ====

The full acoustic field can be simulated over the frequency range of human hearing by 
solving the differential equations for air vibration (Garriga, Spa et al., 2005). While accurate, 
this method has a computational cost that scales with the volume simulated, up to 16 TFLOPS 
for a 2×2×2 m room. This can likely be reduced by the use of adaptive mesh methods, or ray‐ 
or beam‐tracing of sound (Funkhouser, Tsingos et al., 2004).  
 
Sound generation occurs not only from sound sources such as instruments, loudspeakers, and 
people but also from normal interactions between objects in the environment. By simulating 
surface vibrations, realistic sounds can be generated as objects collide and vibrate. A basic 
model with N surface nodes requires 0.5292 N GFLOPS, but this can be significantly reduced 
by taking perceptual shortcuts (Raghuvanshi and Lin, 2006; Raghuvanshi and Lin, 2007). This 
form of vibration generation can likely be used to synthesize realistic vibrations for touch.

==== Smell and Taste ====

So far no work has been done on simulated smell and taste in virtual reality, mainly due to 
the lack of output devices. Some simulations of odorant diffusion have been done in 
underwater environments (Baird RC, Johari H et al., 1996 ) and in the human and rat nasal 
cavity (Keyhani, Scherer et al., 1997; Zhao, Dalton et al., 2006). In general, an odor simulation 
would involve modelling diffusion and transport of chemicals through air flow; and the 
relatively low temporal and spatial resolution of human olfaction would likely allow a fairly 
simple model. A far more involved issue is what odorant molecules to simulate. Humans 
have 350 active olfactory receptor genes, but we can likely detect more variation due to 
different diffusion in the nasal cavity (Shepherd, 2004).

No work has been done on simulating smell and taste in a virtual environment, most likely due to lack of output for this data. The low quality of human olfaction would allow for a simple model.

Taste relies only on a few types of receptor, but the tongue also detects texture and the placement of then nose forces on to smell objects entering the mouth. The former may require complex simulations of the physics of virtual objects in the case of virtual environments, and pressure/temperature sensors for simulacra.

==== Haptics ====

The haptic senses of touch, proprioception, and balance are crucial for performing skilled 
actions in real and virtual environments (Robles‐De‐La‐Torre, 2006).  
 
Tactile sensation relates both to the forces affecting the skin (and hair) and to how they are 
changing as objects or the body are moved. To simulate touch, stimuli collision detection is 
needed to calculate forces on the skin (and possibly deformations) as well as the vibrations 
when it is moved over a surface or exploring it with a hard object (Klatzky, Lederman et al., 
2003). To achieve realistic haptic rendering, updates in the kilohertz range may be necessary 
(Lin and Otaduy, 2005). In environments with deformable objects various nonlinearities in 
response and restitution have to be taken into account (Mahvash and Hayward, 2004). 
Proprioception, the sense of how far muscles and tendons are stretched (and by inference, 
limb location) is important for maintaining posture and orientation. Unlike the other senses, 
proprioceptive signals would be generated by the body model internally. Simulated Golgi 
organs, muscle spindles, and pulmonary stretch receptors would then convert body states 
into nerve impulses. 
 
The balance signals from the inner ear appears relatively simple to simulate, since it is only 
dependent on the fluid velocity and pressure in the semicircular channels (which can likely 
be assumed to be laminar and homogeneous) and gravity effects on the utricle and saccule. 
Compared to other senses, the computational demands are minuscule.  
 
Thermoreception could presumably be simulated by giving each object in the virtual 
environment a temperature, activating thermoreceptors in contact with the object. 
Nocireception (pain) would be simulated by activating the receptors in the presence of 
excessive forces or temperatures; the ability to experience pain from simulated inflammatory 
responses may be unnecessary verisimilitude.

=== The Environment Model ===

==== Embodiment ====

==== Virtual Worlds ====

[[File:broken thing.jpg|thumb|left|[[Second Life]]'s forever-in-beta engine shows how awful it would be to live in a simulation. Also it's full of weirdos.]]