Редактирование: Whole brain emulation (раздел)

== Simulation Software ==

=== Levels of Abstraction ===

The following table depicts the various levels of organization and scale at which the brain can be emulated -- Which increasing accuracy, increasing scanning difficulty, and increasing computational complexity. However, the amount of undestanding required is reduced the further we go: The first level of abstraction requires complete, high-level understanding of the ''mind''. Not the brain, but the abstract processes of cognition. The lowest level can be done with off-the-shelf software (If you have a couple thousand dollars to buy a copy of [[Software#Gaussian|Gaussian]]), but requires hardware beyond Greg Egan's imagination and scanning at the level of single atoms.

{| border="1" align="center" style="text-align:center;" class=wikitable
| '''Level''' || '''Kind''' || '''Description'''
|-
| 1 || Abstract model of the mind || align="left" | "Classic AI", high level representations of information and information processing. Built from the top down knowing the person's personality, ideas, etc., or using species-generic characteristics.
|-
| 2 || Brain region connectivity || align="left" | Each area represents a functional module, connected to others according to an abstract, species-generic connectome.
|-
| 3 || Analog network population model || align="left" | The population of neurons and their connectivity. Activity and states of neurons are represented as time-averages. This is similar to connectionist models using Artificial Neural Networs, rate-model neural simulations and cascade models.
|-  style="color:black; background-color:#ffffcc;" 
| 4 || Spiking neural network || align="left" | As above, plus firing properties, firing state and dynamical synaptic states. Integrate and fire models, reduce compartment models (But also some minicolumn models, eg<ref>Johansson C., Landser A. "Towards cortex sized artificial neural systems". ''Neural Networks'', 20: 48‐61, 2007.</ref>, and the Izhikevich model.
|-  style="color:black; background-color:#ffffcc;" 
| 5 || Electrophysiology || align="left" | As above, plus membrane sates (Ion channel types, properties, distribution, states), ion concenrtations, currents, voltages, modulation states. Multi-compartment model simulations only.
|-  style="color:black; background-color:#ffffcc;" 
| 6 || Metabolome || align="left" | As above, plus concentrations of metabolites and neurotransmitters in compartments.
|-
| 7 || Proteome || align="left" | As above, plus concentrations of proteins and gene expression levels.
|-
| 8 || States of protein complexes || align="left" | As above, plus protein quaternary structures.
|-
| 9 || Distribution of complexes || align="left" | As above, plus "locome" information and internal cellular geometry.
|-
| 10 || Molecular dynamics || align="left" | As above, plus molecular coordinates and molecular-level scanning.
|-
| 11 || Quantum chemistry || align="left" | Quantum interactions, orbitals. Requires a '''complete .pdb of the entire brain''', besides being completely computationally intractable.
|}

=== Analysis ===

==== Geometric Adjustment ====

Various methods are being developed to automatically correct the image stack so that they match best. The simplest method is finding a combination of translation, scaling and rotation that works best. However, this runs the risk of over-matching.

Human brain and rat stacks have been corrected with good results using an elastic model to correct distortion by <ref>Schmitt O, Modersitzki J, Heldmann S, Wirtz S, and Fischer B. "Image registration of sectioned brains". ''International Journal of Computer Vision'', 73: 5‐39, 2007.</ref>.

==== Noise Removal ====

Noise removal is one of the oldest problems in the image processing side of computer science and thus has extensive literature and a strong research interest. In the case of brain scanning, the kind of noise imparted by the scanning method or by the brain itself are known, which makes removal easier. See <ref>Mayerich D, McCormick BH, and Keyser J. "Noise and artifact removal in knife‐edge scanning microscopy". In Piscataway N. (Ed.), ''Proceedings of 2007 ieee international symposium on biomedical imaging: From nano to macro'', IEEE Press, 2007.</ref>' for an example of light variations and knife chatter removed from KESM data.

==== Data Interpolation ====

[[File:artifact.jpg|thumb|left|Artifacts in electron micrographs cause loss of data. The missing neurites have to be interpolated from information of the surrounding tissues. (From the [[Nervous System State Vectors & Scan Data#Denk-Horstmann|Denk-Horstmann]] dataset, layer 49).]]

Scanning a volume as large as the brain is likely to produce large volumes where data is lost (For example, the KESM suffers data up to 5 µm in width between different columns<ref>Kwon J, Mayerich D, Choe Y, McCormick BH. "Automated lateral sectioning for knife‐edge scanning microscopy". In ''IEEE international symposium on biomedical imaging: From nano to macro'', 2008.</ref>. In a sufficiently small case, surrounding data may be used to probabilistically interpolate the brain structure in the lost areas. In a large enough volume, however, interpolation is not sufficient, and one must generate a brain structure to fill the lost volume, using knowledge of the surrounding structure (For example, all neurons in the cerebral cortex are pyramidal, so if there is a lost volume in the cortex, stellate cells cannot be inserted.

This is a high priority issue, since lost or poorly interpolated data may case mis tracing and an inexact emulation.

==== Cell tracing ====

Automated tracing of neurons imaged using confocal microscopy has been attempted using a 
variety of methods. Even if the scanning method used will be a different approach it seems 
likely that knowledge gained from these reconstruction methods will be useful. 
One approach is to enhance edges and find the optimal joining of edge pixels/voxels to detect 
contours of objects. Another is skeletonization. For example, (Urban, O’Malley et al., 2006) 
thresholded neuron images (after image processing to remove noise and artefacts), extracting 
the medial axis tree. (Dima, Scholz et al., 2002) employed a 3D wavelet transform to perform 
a multiscale validation of dendrite boundaries, in turn producing an estimate of a skeleton. 
A third approach is exploratory algorithms, where the algorithm starts at a point and uses 
image coherency to trace the cell from there. This avoids having to process all voxels, but 
risks losing parts of the neuron if the images are degraded or unclear. (Al‐Kofahi, Lasek et al., 
2002) use directional kernels acting on the intensity data to follow cylindrical objects. 
(Mayerich and Keyser, 2008) use a similar method for KESM data, accelerating the kernel 
calculation by using graphics hardware. (Uehara, Colbert et al., 2004) calculates the 
probability of each voxel belonging to a cylindrical structure, and then propagates dendrite 
paths through it. 
One weakness of these methods is that they assume cylindrical shapes of dendrites and the 
lack of adjoining structures (such as dendritic spines). By using support‐vector machines that 
are trained on real data a more robust reconstruction can be achieved (Santamaría‐Pang, 
Bildea et al., 2006). 
Overall, tracing of branching tubular structures is a major interest in medical computing. A survey of vessel extraction techniques listed 14 major approaches, with several examples of 
each (Kirbas and Quek, 2004). The success of different methods is modality‐dependent.}}

[[File:Thomas RSI paper neurites.jpg|thumb|3D neuron structure traced from a stack of electron micrographs.]]

[[File:Eyewire screenshot.jpg|thumb|[https://eyewire.org/ Eyewire] is an online game where players trace neurites alongside an AI.]]

==== Synapse Identification ====

In electron micrographs, synapses are currently recognized using the criteria that within a 
structure there are synaptic vesicles adjacent to a presynaptic density, a synaptic density with 
electron‐dense material in the cleft and densities on the cytoplasmic faces in the pre‐ and 
postsynaptic membranes (Colonnier, 1981; Peters and Palay, 1996). 
 
One of the major unresolved issues for WBE is whether it is possible to identify the functional 
characteristics of synapses, in particular synaptic strength and neurotransmitter content, from 
their morphology.  
 
In general, cortical synapses tend to be either asymmetrical “type I” synapses (75‐95%) or 
symmetrical “type II” synapses (5‐25%), based on having a prominent or thin postsynaptic 
density. Type II synapses appear to be inhibitory, while type I synapses are mainly excitatory 
(but there are exceptions) (Peters and Palay, 1996). This allows at least some inference of 
function from morphology. 
 
The shape and type of vesicles may also provide clues about function. Small, clear vesicles 
appear to mainly contain small‐molecule neurotransmitters; large vesicles (60 nm diameter) 
with dense cores appear to contain noradrenaline, dopamine or 5‐HT; and large vesicles (up 
to 100 nm) with 50‐70 nm dense cores contain neuropeptides (Hokfelt, Broberger et al., 2000; 
Salio, Lossi et al., 2006). Unfortunately there does not appear to be any further distinctiveness 
of vesicle morphology to signal neurotransmitter type.

==== Cell Type Identification ====

Distinguishing neurons from glia and identifying their functional type requires other 
advances in image recognition. 
 
The definition of neuron types is debated, as well as the number of types. There might be as 
many as 10,000 types, generated through an interplay of genetic, posttranscriptional, 
epigenetic, and environmental interactions (Muotri and Gage, 2006). There are some 30+ 
named neuron types, mostly categorized based on chemistry and morphology (e.g. shape, the 
presence of synaptic spines, whether they target somata or distal dendrites). Distinguishing 
morphologically different groups appear feasible using geometrical analysis (Jelinek and 
Fernandez, 1998). 
 
In terms of electrophysiology, excitatory neurons are typically classified into regular‐spiking, 
intrinsic bursting, and chattering, while interneurons are classified into fast‐spiking, burst 
spiking, late‐spiking and regular spiking. However, alternate classifications exist. (Gupta, 
Wang et al., 2000) examined neocortical inhibitory neurons and found three different kinds of 
GABAergic synapses, three main electrophysiological classes divided into eight subclasses, 
and five anatomical classes, producing 15+ observed combinations. Examining the subgroup 
of somatostatin‐expressing inhibitory neurons produced three distinct groups in terms of 
layer location and electrophysiology (Ma, Hu et al., 2006) with apparently different functions. 

* The morphology and electrophysiology of inhibitory neurons in the 2nd and 3rd layers of trhe prefrontal cortex also indicates the existence of different clustered types.
 
Overall, it appears that there exist distinct classes of neurons in terms of neurotransmitter, 
neuropeptide expression, protein expression (e.g. calcium binding proteins), and overall 
electrophysiological behaviour. Morphology often shows clustering, but there may exist 
intermediate forms. Similarly, details of electrophysiology may show overlap between 
classes, but have different population means.  
 
Some functional neuron types are readily distinguished from morphology (such as the five 
types of the cerebellar cortex). A key problem is that while differing morphologies likely 
implies differing functional properties, the reverse may not be true. Some classes of neurons 
appear to show a strong link between electrophysiology and morphology (Krimer, Zaitsev et 
al., 2005) that would enable inference of at least functional type just from geometry. In the 
case of layer 5 pyramidal cells, some studies have found a link between morphology and 
firing pattern (Kasper, Larkman et al., 1994; Mason and Larkman, 1990), while others have 
not (Chang and Luebke, 2007). It is quite possible that different classes are differently 
identifiable, and that the morphology‐function link could vary between species.  

* Unique and identifiable neurons are relative common in small animals become less and less common as brain size increases
* Identifiable neurons are present in small animals
**They can be distuinguished from other neurons inside the individual or across individuals(Bullock, 2000)
**

=== Models of Neurons ===

A model of a neuron is an abstract mathematical model that seems to imitate some aspect of the behavior of neurons. They are used to predict the outcome of biological processes and to study the nervous system in a more flexible environment of study (A computer).

{| border=1 class=wikitable
! colspan=3 style="background-color:#C0C0C0;" | Costs of Neuron Models
|-
| '''Model''' || '''# of biological features''' || '''FLOPS/ms'''
|-
| Integrate-and-fire || 3 || 5
|-
| Integrate‐and‐fire with adapt. || 5 || 10 
|-
| Integrate‐and‐fire‐or‐burst || 10 || 13
|-
| Resonate‐and‐fire || 12 || 10
|-
| Quadratic integrate‐and‐fire || 6 || 7
|-
| Izikhevich (2003) || 21 || 13
|-
| FitzHugh‐Nagumo || 11 || 72
|-
| Hindmarsh‐Rose || 18 || 120
|-
| Morris‐Lecar || 14º || 600
|-
| Wilson || 15 || 180
|-
| Hodgkin‐Huxley || 19º || 1200
|}

[[File:Cost of neuron models.jpg]]

'''Note:''' Only the Morris‐Lecar and Hodgkin‐Huxley models are "biophysically meaningful" in the sense that they attempt actually to model real biophysics, the others only aim for a correct phenomenology of spiking.

=== Existing Simulators ===

==== NEURON ====

The primary software used by the BBP for neural simulations is a package called NEURON. This was developed starting in the 1990s by Michael Hines at Yale University and John Moore at Duke University. It is written in C, C++, and FORTRAN. The software continues to be under active development and, as of July 2012, is currently at version 7.2. It is free and open source software, both the code and the binaries are freely available on the website. Michael Hines and the BBP team collaborated in 2005 to port the package to the massively parallel Blue Gene supercomputer.

The NEURON Simulation Environment (aka NEURON --see http://www.neuron.yale.edu/) is designed for modeling individual neurons and networks of neurons, and is widely used by experimental and theoretical neuroscientists. It provides tools for conveniently building, managing, and using models that are numerically sound and computationally efficient. NEURON is particularly well-suited to problems that are closely linked to experimental data, especially those that involve cells with complex anatomical and biophysical properties.
NEURON began in the laboratory of John W. Moore at Duke University, where he and Michael Hines started their collaboration to develop simulation software for neuroscience research. It has benefited from judicious revision and selective enhancement, guided by feedback from the growing number of neuroscientists who have used it to incorporate empirically-based modeling into their research strategies. Most papers that report work done with NEURON have addressed the operation and functional consequences of mechanistic models of biological neurons and networks. Readers who wish to see specific examples are encouraged to peruse the online bibliography. Working code for many published NEURON models can be downloaded from ModelDB.

* [http://www.neuron.yale.edu/neuron/ Site]
* [http://www.scholarpedia.org/article/Neuron_simulation_environment Scholarpedia article]

==== GENESIS ====

* [http://www.genesis-sim.org/GENESIS/ Site]

==== PSICS ====

* [http://www.psics.org/ Site]

[[File:PSICS screenshot.jpg|thumb|A screenshot of the Parallel Stochastic Ion Channel Simulator, the most detailed model of neurons achievable today. Each dot in the image is an individual ion channel.]]