ReDiClus2
ReDiClus  Receptor Diffusion & Cluster Model
Simulation Space
ReDiClus Model Space
ReDiClus is simulated on a 2D surface in 3D space
 The surface area represents a dendritic membrane with two synaptic spines
 Baseline dimensions are scaled to realworld values
 these values are based on empirical observations of distal dendrites
 base dimensions are set to 60x30 units
 Scale
 1 unit ≃ 100 nm
 10 units ≃ 1 µm
 2D space: 2.3 µm x 4.6 um
 PSD: 0.3 x 0.3 µm
 periPSD: 0.3 x 0.3 µm
 PSD separation: 2.0 µm
 The Z axis is only 2 levels: 0 and 1
 1 represents the membrane surface
 0 represents intracellular space
Particle Types
 There are 2 types of particles in the simulation
 'Red' particle dots represent AMPA receptors
 Red dots can randomly diffuse anywhere on the XY plane
 Red dots only diffuse on the surface Z = 0
 'Blue' particle dots represent PSD95 molecules
 Blue dots are contained in predefined PSD areas and cannot leave
 Blue dots can exist at the surface Z = 0 or intracellularly Z = 1
Particle Diffusion
Simulating Molecular Diffusion
ReDiClus Diffusion
Particle diffusion is generated from Einstein's equations on Brownian motion. This allows the model to generate realworld diffusion at rates that are empirically relevant. There are currently 5 different regions in the model that can each independently scale the diffusion rate: the extrasynaptic space (ES), postsynaptic density 1 (PSD1), postsynaptic density 2 (PSD2) and the perisynaptic PSD1 region (pPSD1) and PSD2 region (pPSD2). The PSD and pPSD diffusion rates (D_{psd}) can be automatically scaled in realtime by the number of PSD95 SAP molecules currently expressed in a PSDcluster region. For most simulations the starting SAP cluster size is 7x7 yielding 49 total SAP molecules. The amount of SAP dynamically fluctuates. It can hold a fairly steady number of about 50 SAPs, but it can also be made to grow and shrink to values ranging from 10 to 100 SAPs. The PSD diffusion rate can be scaled from these SAP values. The function for this scalar can be seen to the left. Given a range of 10 to 100 SAPs, the PSD diffusion rate values will range from 0.03 um²/s  0.003 um²/s.
 Base ExtraSynaptic Diffusion rate D (D_{es})
 D_{es} ≃ 0.3 um²/s
 Base PSD Diffusion rates (D_{psd})
 D_{psd} ≃ 0.03 um²/s
 ↑ to ↓
 D_{psd} ≃ 0.003 um²/s
 D_{psd} SAP scalar function
 D_{psd} ≃ D_{es}/SAP
 D_{psd} ≃ 0.3/10 ≃ 0.03 um²/s
 ↑ to ↓
 D_{psd} ≃ 0.3/100 ≃ 0.003 um²/s
Diffusion Equations
Optional Subroutines

in1 ≃ 1; % in1: do PSD Sclusters

in2 ≃ 0; % in2: do homeostatic

in3 ≃ 0; % in3: do calcium

in4 ≃ 1; % in4: do FRAP

in5 ≃ 0; % in5: do 1dot plot

in6 ≃ 0; % in6: do manual step size

in7 ≃ 0; % in7: do track MSD

in8 ≃ 0; % in8: do track step sizes

in9 ≃ 0; % in9: do MainPlot

in10 ≃ 0; % in10: do GluR1

in11 ≃ 0; % in11: do 3D Plot
1 %=========================================================%
2 % STARTING PARAMETERS
3 %%
4 D = 3; % Diffusion Constant [2d*D*t]
5 d = 2; % N dimensions
6 dT = 1; % time delay between measurements
7 k = sqrt(d*D*dT); % stdev of step size distribution D
8 MSD = 2*d*D*dT; % mean squared displacement
9 muN = k*sqrt(2)/sqrt(pi); % mean of half normal distribution k=stdev
10 Ld = sqrt(2*d*D); % average diagonal XY step size
11 LdA = Ld/sqrt(2); % average linear X or Y step size
12 DSc = 10; % D Scalar: DSc[10, 100] equals D[0.1, 0.01]
13 LdS = 1/sqrt(DSc); % D Scalar Function, adjusts LdA LdSfun(i) = 1/sqrt(i)
14 Dn = D/DSc; % Local D value after being scaled
15
16 % SET POTENTIATION LEVELS
17 PSD0 = 1; PSD3 = 1; % ESS D Base Diffusion Rate of ExtraSynaptic Space
18 PSD1DSc = 100; % PSD1 D Scalar base
19 PSD2DSc = 100; % PSD2 D Scalar base
20 PSD1D = D/PSD1DSc; % PSD1 D value after being scaled
21 PSD2D = D/PSD2DSc; % PSD2 D value after being scaled
22 PSD1 = LdSfun(PSD1DSc); % PSD1 D Scalar Function, LdSfun(i) ≃ 1/sqrt(i)
23 PSD2 = LdSfun(PSD2DSc); % PSD2 D Scalar Function, LdSfun(i) ≃ 1/sqrt(i)
Homeostatic Scaling
PSD95 SAP Cluster Scaling
 From Tatavarty:
“  Synaptic scaling is a cellautonomous process in which neurons detect changes in their own firing through a set of calciumdependent sensors, and then slowly increase or decrease the accumulation of synaptic AMPARs to compensate (Turrigiano et al., 1998; Ibata et al., 2008; Goold and Nicoll, 2010).  ” 
 From Sheng:
“  PSD95 family molecules outnumber AMPARs (up to 20fold). Quantitative MS counted 60 copies of AMPAR subunits (GluR1, GluR2, GluR3) in the average PSD, which equates to 15 tetrameric AMPAR channels, of which >80 percent appears to be GluR1/GluR2 heteromers. Fifteen may be an underestimate because some postsynaptic AMPAR channels might be extracted by Triton during purification of PSDs.  ” 
Given those two observations, I wrote a matlab function that allowed for homeostatic scaling. Based on Sheng's review (and a few other sources), on average, there are about 20 AMPARs per PSD. In our current model we have 2 PSD areas, so there should be about 40 total receptors (combined) in those PSDs, on average.
To anthropomorphise, we want our modeled neuron to be homeostaticallycontent when there are 40 receptors in its synapses (content/satisfied meaning all diffusion parameters are running at some predefined baseline value, or whatever we specify). We can also specify a range of values at which our neuron is content; I arbitrarily set this range to 2555 receptors. Just to be clear, that is 2555 total combined receptors  let's call this value PSDT. For example, PSD1 could have 35 receptors and PSD2 could have 10 receptors, making PSDT ≃ 45. If both PSD1 and PSD2 have 16 receptors each, PSDT ≃ 32. Our modeled neuron would be satisfied with either of those scenarios. However, if PSD1 had 35 and PSD2 had 30, making PSDT ≃ 65, our neuron will not be happy, and should take some action to decrease the total number of AMPARs being expressed at its PSDs.
 Homeostatic Scaling Function

IF PSDT > 55 THEN increase the PSD diffusion rates (make less sticky)

IF PSDT < 25 THEN decrease the PSD diffusion rates (make more sticky)
Remember the model functions are modular, so we could actually do something as simple as above, and directly alter the PSD diffusion rates. But we also want our modular functions to "play nice" with each other  so if we are scaling PSD diffusion rates based on SAP expression ( function doSAP ≃ SAPfunc(on) ), instead of directly setting D_{psd} we'll instead want to increase or decrease SAP expression. Doing this will automatically update the diffusion rate of respective PSD areas. The easiest way to incorporate this function is by manipulating the SAP repulsion lattice constant (L) based on PSDT.
 When L≃2 the SAP cluster is relatively stable
 When L≃1 the cluster starts to grow
 When L≃3 the cluster starts to shrink.
So when PSDT > 55 then L ≃ 3 and when PSDT < 25 then L≃1 and when PSDT is between 25 and 55 then L≃2. And I should note that these are global changes to L (both PSD areas get the same L), because in a reallife scenario the neuron only cares that it's firing too much or too little, not necessarily which of its hundreds of PSDs are responsible. Does this make sense?
The homeostatic function can be made more complex, or flexible, but it can really be this simple (and I know you are a proponent of eloquence), which is nice for when we are testing out predictions unrelated to homeostatic scaling. But from here, we can begin to consider what manipulations to this function would make it more truetolife. For example, homeostatic scaling is a relatively slow process  maybe we only want the model to check the PSDT value once every 10 minutes, maybe we want to make more subtle changes in L, maybe we want to change another parameter instead of L. The choice is ours. But ultimately, we want to update the model based on what we know happens in the real world.
Physical Properties
ReDiClus Physics
two independent processes
 In this model, there are two independently occurring processes.
 1. Blue dots can be expressed at the surface or internalized within their PSD area
 The Blue dot internalization/externalization rate properties are set by the Shouval cluster model equations.
 2. Red dots diffuse on the XY plane with brownian motion
 Each Red dot has an initial step size randomly drawn from a normal distribution with a mean = 1 and sd = .2
 The step size for Red dots is dynamically altered when it's located in a PSD area
 In a PSD, the step size is reduced by a by some factor based on the number of Blue dots currently expressed at the surface of that PSD
 The more Blue dots at the surface, the more the step size is reduced
 The current step size function is:
 f(R_{step}) = R * (10*(1 ⁄ B_{n}))
 where R_{step} is the baseline Red dot step size
 where B_{n} is number of Blue dots currently expressed at the PSD surface
 f(R_{step}) = R * (10*(1 ⁄ B_{n}))
 Several screen shots of the dynamic graphs in the model
 ^{FIG: }
 ^{FIG: }
MEAN SQUARED DISPLACEMENT
 Brownian Motion Mean Squared Displacement
 The goal of this calculation is to relate the simulated particle diffusion to real world values, namely velocity.
 Particle velocity will be a function of MSD x units ²⁄s which scales on space (units) and time (s) parameters.
 Space and time in the model are defined arbitrarily as Step_Size and Step where each Step a particle moves a distance randomly chosen from a normal distribution (µ=1,σ=.2)
 a step size of 1 unit/step will produce a brownian motion MSD of ~0.52 ±0.2 units ²/s
 empirical observations show that reasonable values for MSD are:
 PSD 0.01 µm ²/s
 synaptic 0.05 µm ²/s
 extrasynaptic 0.1 µm ²/s
 given an MSD of 0.52 ±0.2 units ²/s at the current parameters: 1 step = 1 unit (at µ=1,σ=.2), the model will need to be scaled such that particles move at an extrasynaptic rate of 0.1 µm ²/s.
 spines are on average 1 to 10 µm apart, if the model is comparing two spines 1 µm apart, they should be separated by 5 units of model space. This is because the current particle diffusion rate of the model is .5 µm ²/s and the empirical MSD is .1 µm ²/s
ReDiClus in Matlab
Neural Anatomy
Quantitative Physiology of the Dendrite
Quantitative Review
The Size of Dendrites
 adapted from Sheng and Hoogenraad (2007) ^{FIG: }
 Dendrite: 1–10 spines per 10 μm
 Spines: 0.5–2 μm in length
 PSD: 100  300 nm diameter
 PSD95: within 12 nm of surface
 adapted from Harris ^{FIG: }
 proximal dendrite diameter: 1  3 µm
 distal dendrite diameter: 0.2  2 µm
 dendrite length: 2000  9000 µm
 dendrite tip to soma: 100  200 µm
 dendrites at soma: 1  5
 dendrite branches (granual): 10  30
 dendrite branches (purkinje): 400500
 Table of spine sizes, adapted from Harris 1992 ^{FIG: }
Particle Counts
 adapted from Sheng and Hoogenraad (2007) ^{FIG: }
 PSD: 10,000 proteins (or 100 copies of 100 proteins)
 CaMKIIα: 7.4%
 CaMKIIβ: 1.3%
 SynGap: 2.1 pmol/20 μg
 NMDAR: 20 proteins
 AMPAR: 15 proteins
 GluR: 60 subunits, 15 tetramers, 80% or 12 GluR1/GluR2 heteromers
 PSD95: within 12 nm of surface
Diffusion Rates
Images
 Spine morphology ^{FIG: }
 Spine morphology ^{FIG: }3D reconstruction of a proximal CA3 pyramidal cell dendrite (blue) and a large mossy fiber bouton (translucent yellow). The cutaway in C2 shows synapses (red) onto multiple dendritic spines, some of which are highly branched. The bouton also forms nonsynaptic cell adhesion junctions (fuchsia). CLICK AWAY FROM IMAGE TO CLOSE
 Hippocampal dendrite ^{FIG: }
Choquet 2007 Real Time Receptor Diffusion
This link is to a video of an optimized version from Choquet 2007 (seen below). The dimensions in both 10 x 10 µm. The original version below is run at 4x realtime. The linked video above is slowed to 1x realtime, and all analysis is done at 1:1 video to realtime speed.
Choquet 2007 Real Time Receptor Diffusion Analysis
 The video represents a 10µm × 10µm section scaled to a 535px × 535px video.
 1_{µm} : 53.5_{px}
 The analysis below documents one instance of Qdot diffusion, between the 6s7s time points.
 This instance was chosen because of the clarity of motion and no Qdot flicker.
 The Qdot (center) moves from pixel location (X:291, Y:302) at 6.78s to (X:319, Y346) at 6.98s
 That is a distance of 52.2px in 200ms
 Qdot velocity: Q_{v} ≈ 1_{µm} ⁄ 200_{ms}
 Note this diffusion rate of 5µm/s is 10fold higher than the median diffusion rate reported above.
 An upper bound of 5µm/s means that receptors can move between synapses in fractions of a second.
Figures:
 ^{FIG: }
 ^{FIG: }
 ^{FIG: }
 ^{FIG: }
Receptor Diffusion Rate Best Estimates
 GABAA: .01  .05 µm^{2}/s ^{FIG: }Choquet 2010 CLICK AWAY FROM IMAGE TO CLOSE
Brain Data: Facts and Figures
Estimated Number of Neurons in the Brain of Humans & Other Animals
Average number of neurons and synapses in the human brain
 Neurons: 100 billion (100,000,000,000; 1e11)
 Synapses: 100 trillion (100,000,000,000,000; 1e14)
 The brain's weight composes ~2% of total body weight (150 pound human)
 Average brain width = 140 mm
 Average brain length = 167 mm
 Average brain height = 93 mm
 Intracranial contents by volume (1,700 ml, 100%): brain = 1,400 ml (80%); blood = 150 ml (10%); cerebrospinal fluid = 150 ml (10%)
 (from Rengachary, S.S. and Ellenbogen, R.G., editors, Principles of Neurosurgery, Edinburgh: Elsevier Mosby, 2005)
 Average number of neurons in the brain = 100 billion
 Number of neurons in octopus brain = 300 million (from How Animals See, S. Sinclair, 1985)
 Number of neurons in honey bee brain = 950,000 (from Menzel, R. and Giurfa, M., Cognitive architecture of a minibrain: the honeybee, Trd. Cog. Sci., 5:6271, 2001.)
 Number of neurons in Aplysia nervous system = 18,00020,000
 Number of neurons in each segmental ganglia in the leech = 350
 Volume of the brain of a locust = 6mm3 (from The Neurobiology of the Insect Brain, Burrows, M., 1996)
 Ratio of the volume of grey matter to white matter in the cerebral hemipheres (20 yrs. old) = 1.3 (Miller, A.K., Alston, R.L. and Corsellis, J.A., Variation with age in the volumes of grey and white matter in the cerebral hemispheres of man: measurements with an image analyser, Neuropathol Appl Neurobiol., 6:119132, 1980)
 Ratio of the volume of grey matter to white matter in the cerebral hemipheres (50 yrs. old) = 1.1 (Miller et al., 1980)
 Ratio of the volume of grey matter to white matter in the cerebral hemipheres (100 yrs. old) = 1.5 (Miller et al., 1980)
 % of cerebral oxygen consumption by white matter = 6%
 % of cerebral oxygen consumption by gray matter = 94%
 Average number of glial cells in brain = 1050 times the number of neurons (New research suggests the neurontoglia ratio may be much smaller, closer to 1:1)
 (For more information about the number of neurons in the brain, see R.W. Williams and K. Herrup, Ann. Review Neuroscience, 11:423453, 1988)
 Number of neocortical neurons (females) = 19.3 billion (Pakkenberg, B., Pelvig, D., Marner,L., Bundgaard, M.J., Gundersen, H.J.G., Nyengaard, J.R. and Regeur, L. Aging and the human neocortex. Exp. Gerontology, 38:9599, 2003 and Pakkenberg, B. and Gundersen, H.J.G. Neocortical neuron number in humans: effect of sex and age. J. Comp. Neurology, 384:312320, 1997.)
 Number of neocortical neurons (males) = 22.8 billion (Pakkenberg et al., 1997; 2003)
 Average loss of neocortical neurons = 85,000 per day (~31 million per year) (Pakkenberg et al., 1997; 2003)
 Average loss of neocortical neurons = 1 per second (Pakkenberg et al., 1997; 2003)
 Average number of neocortical glial cells (young adults ) = 39 billion (Pakkenberg et al., 1997; 2003)
 Average number of neocortical glial cells (older adults) =36 billion (Pakkenberg et al., 1997; 2003)
 Number of neurons in cerebral cortex (rat) = 21 million (Korbo, L., et al., J. Neurosci Methods, 31:93100, 1990)
 Length of myelinated nerve fibers in brain = 150,000180,000 km (Pakkenberg et al., 1997; 2003)
 Number of synapses in cortex = 0.15 quadrillion (Pakkenberg et al., 1997; 2003)
 Difference number of neurons in the right and left hemispheres = 186 million MORE neurons on left side than right side (Pakkenberg et al., 1997; 2003)
 Proportion by Volume (%)
 Rat vs Human
 Cerebral Cortex: 31 vs 77
 Diencephalon: 7 vs 4
 Midbrain: 6 vs 4
 Hindbrain: 7 vs 2
 Cerebellum: 10 vs 10
 Spinal Cord: 35 vs 2
 (Reference: Trends in Neurosciences, 18:471474, 1995)
 Total surface area of the cerebral cortex = 2,500 cm2 (2.5 ft2; A. Peters, and E.G. Jones, Cerebral Cortex, 1984)
 Total surface area of the cerebral cortex (lesser shrew) = 0.8 cm2
 Total surface area of the cerebral cortex (rat) = 6 cm2
 Total surface area of the cerebral cortex (cat) = 83 cm2
 Total surface area of the cerebral cortex (African elephant) = 6,300 cm2
 Total surface area of the cerebral cortex (Bottlenosed dolphin) = 3,745 cm2 (S.H. Ridgway, The Cetacean Central Nervous System, p. 221)
 Total surface area of the cerebral cortex (pilot whale) = 5,800 cm2
 Total surface area of the cerebral cortex (false killer whale) = 7,400 cm2
 (Reference for surface area figures: Nieuwenhuys, R., Ten Donkelaar, H.J. and Nicholson, C., The Central nervous System of Vertebrates, Vol. 3, Berlin: Springer, 1998)
 Total number of neurons in cerebral cortex = 10 billion
 (from G.M. Shepherd, The Synaptic Organization of the Brain, 1998, p. 6). However, C. Koch lists the total number of neurons in the cerebral cortex at 20 billion (Biophysics of Computation. Information Processing in Single Neurons, New York: Oxford Univ. Press, 1999, page 87).
 Total number of synapses in cerebral cortex = 60 trillion (yes, trillion)
 (from G.M. Shepherd, The Synaptic Organization of the Brain, 1998, p. 6). However, C. Koch lists the total synapses in the cerebral cortex at 240 trillion (Biophysics of Computation. Information Processing in Single Neurons, New York: Oxford Univ. Press, 1999, page 87).
 Percentage of total cerebral cortex volume (human): frontal lobe = 41%; temporal lobe = 22%; parietal lobe = 19%; occipital lobe = 18%.
 (Caviness Jr., et al. Cerebral Cortex, 8:372384, 1998.)
 Number of cortical layers = 6
 Thickness of cerebral cortex = 1.54.5 mm
 Thickness of cerebral cortex (Bottlenosed dolphin) = 1.31.8 mm (S.H. Ridgway, The Cetacean Central Nervous System, p. 221)
 Rate of neuron growth (early pregnancy) = 250,000 neurons/minute
 Length of spiny terminals of a Purkinje cell = 40,700 micron
 Number spines on a Purkinje cell dendritic branchlet = 61,000
 Surface area of cerebellar cortex = 50,000 mm2 (from G.M. Shepherd, The Synaptic Organization of the Brain, 2004, p. 271)
 Weight of adult cerebellum = 150 grams (Afifi, A.K. and Bergman, R.A., Functional Neuroanatomy, New York: McGrawHill, 1998)
 Number of Purkinje cells = 1526 million
 Number of synapses made on a Purkinje cell = up to 200,000
 Weight of hypothalamus = 4 g
 Volume of suprachiasmatic nucleus = 0.3 mm3
 Number of fibers in pyramidal tract above decussation = 1,100,000
 Number of fibers in corpus callosum = 250,000,000
 Area of the corpus callosum (midsagittal section) = 6.2 cm2
 Species
 Cerebellum Weight (grams) vs Body Weight (grams)
 Human: 142 vs 60,000
 Mouse: 0.09 vs 58
 Bat: 0.09 vs 30
 Flying Fox: 0.3 vs 130
 Pigeon: 0.4 vs 500
 Guinea Pig: 0.9 vs 485
 Squirrel: 1.5 vs 350
 Chinchilla: 1.7 vs 500
 Rabbit: 1.9 vs 1,800
 Hare: 2.3 vs 3,000
 Cat: 5.3 vs 3,500
 Dog: 6.0 vs 3,500
 Macaque: 7.8 vs 6,000
 Sheep: 21.5 vs 25,000
 Bovine: 35.7 vs 300,000
 Source: Sultan, F. and Braitenberg, V. Shapes and sizes of different mammalian cerebella. A study in quantitative comparative neuroanatomy. J. Hirnforsch., 34:7992, 1993.
 Neurons
 Mass of a large sensory neuron = 106gram (from Groves and Rebec, Introduction to Biological Psychology, 3rd edition, Dubuque: Wm.C. Brown Publ., 1988)
 Number of synapses for a "typical" neuron = 1,000 to 10,000
 Diameter of neuron = 4 micron (granule cell) to 100 micron (motor neuron in cord)
 Diameter of neuron nucleus = 3 to 18 micron
 Length of Giraffe primary afferent axon (from toe to neck) = 15 feet
 Resting potential of squid giant axon = 70 mV
 Conduction velocity of action potential = 0.6120 m/s (1.2250 miles/hr)
 Single sodium pump maximum transport rate = 200 Na ions/sec; 130 K ions/sec
 Typical number of sodium pumps = 1000 pumps/micron2 of membrane surface (from Willis and Grossman, Medical Neurobiology, Mosby, St. Louis, 1981, p. 36)
 Total number of sodium pumps for a small neuron = 1 million
 Density of sodium channels (squid giant axon) = 300 per micron2 (from Hille, B., Ionic Channels of Excitable Membranes, Sinauer, Sunderland, 1984, p. 210.)
 Number of voltagegated sodium channels at each node = 1,000 to 2,000 per micron2 (from Nolte, J., The Human Brain, Mosby, 1999, p. 163.)
 Number of voltagegated sodium channels between nodes = 25 per micron2 (from Nolte, J., The Human Brain, Mosby, 1999, p. 163.)
 Number of voltagegated sodium channels in unmyelinated axon = 100 to 200 per micron2 (from Nolte, J., The Human Brain, Mosby, 1999, p. 163.)
 Diameter of ion channel = 0.5 nanometer (Breedlove et al., Biological Psychology, 2007)
 Diameter of microtubule = 20 nanometer
 Diameter of microfilament = 5 nanometer
 Diameter of neurofilament = 10 nanometer
 Thickness of neuronal membrane = 5 nanometer (Breedlove et al., Biological Psychology, 2007)
 Thickness of squid giant axon membrane = 50100 A
 Membrane surface area of a typical neuron = 250,000 um2 (Bear et al., 2001)
 Membrane surface area of 100 billion neurons = 25,000 m2, the size of four soccer fields (Bear, M.F., Connors, B.W. and Pradiso, M.A., Neuroscience: Exploring the Brain, 2nd edition)
 Typical synaptic cleft distance = 2040 nanometers across (from Kandel et al., 2000, p. 176)
 % neurons stained by the Golgi method = 5%
 Slow axoplasmic transport rate = 0.24 mm/day (actin, tubulin)
 Intermediate axoplasmic transport rate = 1550 mm/day (mitochondrial protein)
 Fast axoplasmic transport rate = 200400 mm/day (peptides, glyolipids)
 Number of molecules of neurotransmitter in one synaptic vesicle = 5,000 (from Kandel et al., 2000, p. 277)
 Diameter of synaptic vesicle = 50 nanometer (small); 70200 nanometer (large)
 Diameter of neurofilament = 7  10 nm
 Diameter of microtubule = 25 nm
 Internodal Length = 150  1500 microns (depends on fiber diameter)
 % composition of myelin = 7080% lipid; 2030% protein
 Ion Concentration (mM) in Mammalian Neurons
 Intracellular vs Extracellular
 Potassium: 140 vs 5
 Sodium: 10 vs 150
 Chloride: 10 vs 100
 Calcium: 0.0001 vs 1
Because of its large number of tiny granule cells, the cerebellum contains more neurons than the rest of the brain, but takes up only 10% of the total brain volume. The number of neurons in the cerebellum is related to the number of neurons in the neocortex. There are about 3.6 times as many neurons in the cerebellum as in the neocortex, a ratio that is conserved across many different mammalian species. What gives? Two things really: (1) The neocortex has a large proportion of pyramidal neurons which are much bigger than granule cells, and (2) Purkinje cells can make up to 200k synaptic connections (contrast that with 1k  10k for "typical" neurons of other types).
Brain vs Computer
Facts and Figures
 Microprocessors
A microprocessor incorporates the functions of a computer's central processing unit on a single integrated circuit. It is a multipurpose, programmable device that accepts digital data as input, processes it according to instructions stored in its memory, and provides results as output.
Processor  Transistor count  Date of introduction  Manufacturer  Semiconductor device fabricationProcess  Area 

Core 2 Duo Wolfdale3M  230,000,000  2008  Intel  45 nm  83 mm² 
Core i7 (Quad)  731,000,000 (7e8)  2008  Intel  45 nm  263 mm² 
POWER6  789,000,000  2007  IBM  65 nm  341 mm² 
WDC 65C02  785,000,000  2009  western design center  0.22 µm  14 mm² 
SixCore Opteron 2400  904,000,000  2009  AMD  45 nm  346 mm² 
16Core SPARC T3  1,000,000,000 (1e9)  2010  Sun Oracle CorporationOracle  40 nm  377 mm² 
QuadCore plus GPU Sandy Bridge Core i7  1,160,000,000  2011  Intel  32 nm  216 mm² 
Core i7 (Gulftown)  1,170,000,000  2010  Intel  32 nm  240 mm² 
8core POWER7 32M L3  1,200,000,000  2010  IBM  45 nm  567 mm² 
8Core AMD Bulldozer  1,200,000,000  2012  AMD  32nm  315 mm² 
QuadCore + GPU AMD Trinity  1,303,000,000  2012  AMD  32 nm  246 mm² 
z196  1,400,000,000  2010  IBM  45 nm  512 mm² 
Core i7  1,400,000,000  2012  Intel  22 nm  160 mm² 
DualCore Itanium 2  1,700,000,000  2006  Intel  90 nm  596 mm² 
SixCore Xeon 7400  1,900,000,000  2008  Intel  45 nm  503 mm² 
Tukwila  2,000,000,000 (2e9)  2010  Intel  65 nm  699 mm² 
8core POWER7 80M L3  2,100,000,000  2012  IBM  32 nm  567 mm² 
SixCore Core i7 and 8Core Xeon E5  2,270,000,000  2011  Intel  32 nm  434 mm² 
NehalemEX  2,300,000,000  2010  Intel  45 nm  684 mm² 
10Core Xeon WestmereEX  2,600,000,000  2011  Intel  32 nm  512 mm² 
zEC12  2,750,000,000  2012  IBM  32 nm  597 mm² 
Poulson  3,100,000,000 (3e9)  2012  Intel  32 nm  544 mm² 
15Core Xeon Ivy BridgeEX  4,310,000,000 (4e9)  2014  Intel  22 nm  
62Core Xeon Phi  5,000,000,000  2012  Intel  22 nm  
Xbox One Main SoC  5,000,000,000 (5e9)  2013  Microsoft/AMD  28 nm  363 mm² 
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