Редактирование: Whole brain emulation (раздел)

=== 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.