Emission of Mitochondrial Biophotons and their Effect on Electrical Activity of Membrane via Microtubules

Abstract

In this paper we argue that, in addition to electrical and chemical signals propagating in the neurons of the brain, signal propagation takes place in the form of biophoton production. This statement is supported by recent experimental confirmation of photon guiding properties of a single neuron. We have investigated the interaction of mitochondrial biophotons with microtubules from a quantum mechanical point of view. Our theoretical analysis indicates that the interaction of biophotons and microtubules causes transitions/fluctuations of microtubules between coherent and incoherent states. A significant relationship between the fluctuation function of microtubules and alpha-EEG diagrams is elaborated on in this paper. We argue that the role of biophotons in the brain merits special attention.

Full text

1
Emission of Mitochondrial Biophotons and their Effect on Electrical
Activity of Membrane via Microtubules
1,2,3Majid Rahnama, 4,5Jack A. Tuszynski, 6István Bókkon, 7,8Michal Cifra,
1Peyman Sardar, 1,2,3Vahid Salari
1Department of Physics, Shahid Bahonar University of Kerman, Kerman, Iran
2Kerman Neuroscience Research Center (KNRC), Kerman, Iran
3Afzal Research Institute, Kerman, Iran
4Department of Experimental Oncology, Cross Cancer Institute, 11560 University Avenue, Edmonton, AB T6G 1Z2, Canada
5Department of Physics, University of Alberta, Edmonton, T6G 2J1, Canada
6Doctoral School of Pharmaceutical and Pharmacological Sciences, Semmelweis University, Hungary
7Institute of Photonics and Electronics, Academy of Sciences of the Czech Republic, Prague, Czech Republic
8Department of Electromagnetic Field, Faculty of Electrical Engineering, Czech Technical University in Prague, Prague,
Czech Republic
Abstract
In this paper we argue that, in addition to electrical and chemical signals propagating in the
neurons of the brain, signal propagation takes place in the form of biophoton production. This
statement is supported by recent experimental confirmation of photon guiding properties of a
single neuron. We have investigated the interaction of mitochondrial biophotons with
microtubules from a quantum mechanical point of view. Our theoretical analysis indicates
that the interaction of biophotons and microtubules causes transitions/fluctuations of
microtubules between coherent and incoherent states. A significant relationship between the
fluctuation function of microtubules and alpha-EEG diagrams is elaborated on in this paper.
We argue that the role of biophotons in the brain merits special attention.
Keywords: mitochondrial biophoton, microtubule (MT), coherence, fluctuation function
1. Introduction
All living cells of plants, animals and humans continuously emit ultraweak biophotons
(ultraweak electromagnetic waves) in the optical range of the spectrum, which is associated
with their physiological states and can be measured using special equipment1. Neural cells
1 Updated from: http://www.transpersonal.de/mbischof/englisch/webbookeng.htm, 23 November 2010.

2
also continuously emit biophotons. The intensity of biophotons is in direct correlation with
neural activity, cerebral energy metabolism, EEG activity, cerebral blood flow and oxidative
processes [37,50].
According to Van Wijk et al. [96], there are significant correlations between the
fluctuations in biophoton emission and fluctuations in the strength of electrical alpha wave
production in the brain. Some unpublished observations suggest that the state of the
biophoton field of a person may be connected to the state of the brain as measured by the
EEG (e.g., degree of synchronization and coherence) [9]. Certain meditative states
characterized by a high degree of coherence in the EEG may well be accompanied by a high
degree of coherence in the biophoton field [9], although measurements correlating the
coherence of the biophoton field and the EEG readings have not been made yet.
Here, we investigate theoretically the interaction of biomolecules with biophotons
taking place within the neurons of the brain. We have adopted a quantum mechanical
formalism in an attempt to quantitatively investigate possible connections between the EEG
and the biophoton production.
2. Biophoton Production Mechanism inside Neurons
During natural metabolic processes taking place in diverse living organism, permanent and
spontaneous ultraweak biophoton emission has been observed without any external excitation
[1,9,29,36,37,49,50,54,66,70,77,83,89,96,97,107]. The emergence of biophotons is due to the
bioluminescent radical and non-radical reactions of Reactive Oxygen Species (ROS) and
Reactive Nitrogen Species (RNS), and involves simple cessation of excited states
[52,61,102]. The main source of biophotons derives from the oxidative metabolism of
mitochondria [94].
Neurons also incessantly emit biophotons [37,50]. Biophoton emission from neural
tissue depends on the neuronal membrane depolarization and Ca2+ entry into the cells [44].
This biophoton emission can be facilitated by the membrane depolarization of neurons by a
high concentration of K+ and can be attenuated by application of tetrodotoxin or removal of
extracellular Ca2+ [44]. Recently, Sun et al. [82] demonstrated that neurons can conduct
photon signals. Moreover, Wang et al. [100] presented the first experimental proof of the
existence of spontaneous and visible light induced biophoton emission form freshly isolated
rat’s whole eye, lens, vitreous humor and retina. They proposed that retinal phosphenes may
originate from natural bioluminescent biophotons within the eyes [10,100]. However, the
retina is part of the central nervous system. Recently, Bókkon suggested that biophysical
pictures may emerge due to redox regulated biophotons in retinotopically organized
cytochrome oxidase-rich neural networks during visual perception and imagery within early
visual areas [12]. It seems that bioelectronic and biophotonic processes are not independent
biological events in the nervous system. Therefore, we conclude that biophoton emission
within neurons can be directly correlated with biochemical processes.
However, the term ultraweak bioluminescence (ultraweak biophoton emission) can be
misleading, because it may suggest that biophotons are not important for cellular processes.
Estimates indicate that for a measured intensity of biophotons, the corresponding intensity of
the light field within the organism can be up to two orders of magnitude higher [15,80].

3
According to Bókkon et al. [11], the real biophoton intensity within cells and neurons can be
considerably higher than one would expect from the measurement of ultraweak
bioluminescence, which is generally carried out macroscopically several centimeters away
from the tissue or cell culture [86]. Moreover, the most significant fraction of natural
biophoton intensity cannot be accurately measured because it is absorbed during cellular
processes.
Numerous findings have provided evidence of fundamental signal roles of ROS and
RNS in cellular processes under physiological conditions [13,14,20,27,33,35,92,95]. There
are experimental indications that ROS and RNS are also necessary for synaptic processes and
normal brain functions. Free radicals and their derivatives act as signaling molecules in
cerebral circulation and are necessary for molecular signaling processes in the brain such as
synaptic plasticity, neurotransmitter release, hippocampal long-term potentiation, memory
formation, etc. [47,48,85,87,88,99]. Because the generation of ROS and RNS is not a
haphazard process, but rather a coordinated mechanism used in signaling pathways,
biophoton emission may not be a byproduct of biochemical processes but it can be linked to
precise signaling pathways of ROS and RNS. Consequently, regulated generation of ROS and
RNS can also produce regulated biophotons within cells and neurons. This means that
regulated electrical (redox) signals (spike-related electrical signals along classical axonal-
dendritic pathways) of neurons can be converted into biophoton signals by various
bioluminescent reactions.
Biophotons can be absorbed by natural chromophores such as porphyrin rings, flavinic,
pyridinic rings, lipid chromophores, aromatic amino acids, etc. [43,45,57,86]. Mitochondrial
electron transport chains contain several chromophores, among which cytochrome oxidase
enzymes are most prominent [43,45]. The absorption of biophotons by a photosensitive
molecule can produce an electronically excited state. As a result, molecules in electronically
excited states often have very different chemical and physical properties compared to their
electronic ground states. Regulated biophotons are not dissipated in random manner within
cells and neurons, but are absorbed - close to the place where they originated - by
chromophores and can excite nearby molecules and trigger/regulate complex signal processes
[17]. Thus, absorbed biophotons could have effects on electrical activity of cells and neurons
via signal processes.
3. Interaction of Mitochondrial Biophotons with Microtubules (MTs)
3.1 MTs and Mitochondria
Microtubular structures have been implicated as playing an important role in the signal and
information processing taking place in the human (and possibly animal) brain
[32,41,42,55,56]. Vertebrate neurons show typically filamentous mitochondria associated
with the microtubules (MTs) of the cytoskeleton, forming together a continuous network
(mitochondrial reticulum) [79]. The rapid movements of mitochondria are MT-based and the
slower movements are actin-based [91]. MT formation can be regulated by redox-dependent
phosphorylations and Ca2+ signals. Since the rapid movements of mitochondria are MT-
based, mitochondrial trafficking can be organized by redox and Ca2+-dependent MT
regulation. Moreover, the refractive index of both mitochondria and MTs is higher than the
surrounding cytoplasm [86]. Consequently, both the mitochondria and the MTs could act as
optical waveguides, i.e. electromagnetic radiation can propagate within their networks
[21,42,86]. Associated mitochondrial and MT networks may act as redox and Ca2+ regulated
organic quantum optical-like fiber systems in neurons. MTs are composed of tubulin dimers.

4
Tubulin dimer is an intrinsically fluorescent molecule mainly due to 8 tryptophan residues it
contains, as can be seen e.g. in the 1TUB structure from the Protein Data Bank [62]. It is
well known that the absorption (ca. 280 nm) and fluorescence (ca. 335 nm) wavelength (and
intensity) of tryptophan is dependent on the conformation of tubulin. Probing absorption and
fluorescence of tubulin is a standard method to determine the polymerization state of MTs.
This can be considered one of possible qualitative connections between the fluctuations of
MT growth and its corresponding biophoton absorption and emission characteristics.
Additionally, there exist other energy states (of both optical and vibrational nature
[19,40,65]) which tubulin dimers and the whole MT can support. These states can be excited
by energy supply provided by mitochondria [16]. Furthermore, MT polymerization is
sensitive to UV [84] and blue light [59] and mitochondria are known to be sources of
biophotons corresponding to the same wavelengths [8,34,98], which makes an immediate
logical connection. Figure 1 illustrates how mitochondria emit light into MTs.
Figure 1 Representation of biophotons produced by mitochondria and the interaction of
biophotons with microtubules.
3.2 Interaction of Biophotons with MTs
It is worth stressing here that centrioles and cilia, which are complex microtubular structures,
are involved in photoreceptor functions in single cell organisms and primitive visual systems.
Cilia are also found in all retinal rod and cone cells. The dimensions of centrioles and cilia
are comparable to the wavelengths of visible and infrared light [31]. In a series of studies
spanning a period of some 25 years G Albrecht-Buehler (AB) demonstrated that living cells
possess a spatial orientation mechanism located in the centriole [2,3,4]. This is based on an
intricate arrangement of MT filaments in two sets of nine triplets each of which is
perpendicular to the other. This arrangement provides the cell with a primitive ‘‘eye” that
allows it to locate the position of other cells within a two to three degree accuracy in the
azimuthal plane and with respect to the axis perpendicular to it [2]. He further showed that
electromagnetic signals are the triggers for the cells’ repositioning. It is still largely a mystery
how the reception of electromagnetic radiation is accomplished by the centriole. Another

5
mystery related to these observations is the original electromagnetic radiation emitted by a
living cell [94].
Figure 2 MTs are hollow cylinders composed of protein units called tubulin. The inner
diameter of an MT is 17nm and the outer diameter is 25 nm. The lengths of MTs vary widely
from nanometers to micrometers. MTs have been considered to act as QED-cavities [55, 56].
Earlier, MTs have been considered as optical cavities [55] with quantum properties [56],
capable of supporting only a single mode [41] or perhaps a few widely spaced (in the
frequency domain) modes. Our approach is based on a fully quantum mechanical formalism
of the Jaynes-Cummings model [39]. MTs are biological hollow cylinders with a 17 nm inner
diameter and a 25 nm outer diameter [16], composed of units called tubulin dimers, each of
which has the dimensions
4 8 6
nm nm nm
 
[55]. Tubulin can be viewed as a typical two-state
quantum mechanical system, where the dimers couple to conformational changes with
9 11
10 10 sec
 
 transitions due to electron transitions in hydrophobic pockets,
corresponding to an angular frequency in the range 10 12
0
(10 ) (10 )
O O Hz



[55]. Using
a first-order-approximation estimate of the quality factor for the MT cavities (i.e.
MT
Q
),
it has been found that
8
(10 )
MT
Q O

[55]. High-quality cavities encountered in Rydberg
atom experiments dissipate energy on time scales of 3 4
(10 ) (10 )sec
O O
 
 and have Q’s
which are comparable to
MT
Q
[55]. We consider the tubulin dimer to represent a two-state
system with ground g and excited e states, respectively. Now, we assume that tubulin
interacts with a single-mode cavity field of biophotons (the coherent nature of biophotons
will be discussed in section 3.4). Here, we introduce the tubulin transition operators
ˆ


,
ˆ


and
ˆ
F

, where ge


ˆis an operator which takes tubulin into the excited state, the
operator †
ˆˆ  

eg takes tubulin into the ground state, and the fluctuation
operator ˆF
e e g g

  causes transitions between excited and ground states. We
have ˆ ˆ
0
ˆ ˆ
0
g e e
g e g
 
 
 
 
 
  (1)
Frequencies of visible light are on the order of THz , and as explained before, Wang et al,
[100] have detected visible light in the brain as biophotons. Also, transition frequencies in
tubulins are on the order of THz [55], so, the interaction between a two-state system (here
represented by tubulin) and a single mode quantized field (here represented by biophotons) is
given by the total Hamiltonian in the approximation 0
( ) ( )
O O
 

[26] as:
† †
0
1
ˆ ˆ ˆ ˆ
ˆ ˆ ˆ ˆ ˆ ˆ ˆ
( )
2
tubulin Biophotons Interation F
H H H H a a a a
     
 
         (2)

6
where ˆ
tubulin
His the Hamiltonian operator for tubulin, ˆ
Biophotons
H is the Hamiltonian operator
for biophotons and ˆ
Interation
His the Hamiltonian operator representing the interaction between
tubulin and biophotons. Here,
0

is the frequency of tubulin transitions and

is the
frequency of biophotons.
a
and
†
a
are annihilation and creation operators, respectively.
†
ˆ1
ˆ
1 1
a n n n
a n n n
 
  
(3)
where ket
n
is the number state of photons. In Hamiltonian (2) the quantity

is defined as
dg



where d is the dipole moment of tubulin,
2
h

where h is Planck’s constant and
1
2
g
V




where

is the dielectric constant of the environment in an MT where
0
~ 80


,
in which
0

is dielectric constant of vacuum [55], and V is the volume of an MT. Here, the
state vector of tubulin is
( ) ( ) )
g e
tubulin c t g c t e
  where
( )
g
c t
and
( )
e
c t
are time-
dependent coefficients in which t represents time. The state of the field
is 0n
n
Biophotons c n


 where
n
c
is a constant coefficient and
n
is the number state of
photons. The total state is the tensor product ( )
t tubulin Biophotons

  . Inserting the
total state into the Schrödinger’s equation )(
ˆ
)( tHt
dt
d
inInteractio

, the result is readily
obtained as [26]

1
01
cos( 1) sin( 1 | |
| ( ) (4)
sin( ) cos( ) | | }
e n g n
ne n g n
c c t n ic c t n e n
tic c t n ic c t n g n
 

 


 
  
 
   
  
 

3.3 Coherence and Decoherence Problem for the system of MT
The Wu-Austin Hamiltonian has been used to describe the interaction of quantized
electromagnetic field with MTs to yield a coherent Froehlich’s state of the dipolar biological
system. Wu and Austin [104,105,106] proposed a dynamical model containing a biological
system composed of electric dipoles with N modes connected to harmonic heat baths
representing a quantized electromagnetic source and the surrounding thermal-relaxation bath.
The interaction of quantized field with the system of electric dipoles gives a Froehlich
coherent state. We believe that the system of neuronal MTs is a good candidate for being
properly described by the above Hamiltonian. MTs are composed of tubulin dimers which
can be considered as biological electric dipoles.

7
Previously, one of the concerns of considering coherent states in the brain was due to the
fact that the Bose-Einstein condensation happens only at low enough temperatures believed
to be lower than body temperature. Recently, Reimers et al. [27] have argued that a very
fragile Froehlich coherent state may paradoxically only emerge at very high temperatures and
thus there is no possibility for the existence of Froehlich coherent states in biological
systems, so quantum models based on the Froehlich coherence should be ruled out. However,
it has been shown that there are serious problems in the calculations made by Reimers et al
[27] and consequently their conclusions appear to be flawed [75].
Another important problem that remains when considering coherent states for MTs is the
so-called decoherence problem. The question is “how is it possible for MTs to be in a
coherent state while the environment surrounding them is relatively hot, wet and noisy?”
Although evidence was found that quantum spin transfer between quantum dots connected by
benzene rings (the same structures found in aromatic hydrophobic amino acids) is more
efficient at relatively warm temperatures than at absolute zero [63], Tegmark [84] calculated
decoherence times for MTs based on the collisions of ions with MTs leading to the
decoherence times on the order of:
s
Ngq
mkTD 13
2
210


(5)
where D is the tubulin diameter, m the mass of the ion, k Boltzmann’s constant, T absolute
temperature, N the number of elementary charges in the MT interacting system,
0
41

g
the Coulomb constant and q the charge of an electron. Hagan et al. showed that Tegmark
used wrong assumptions for his investigation of MTs. Another main objection to the estimate
in (5) is that Tegmark’s formulation yields decoherence times that increase with temperature
contrary to a well-established physical intuition and the observed behavior of quantum
coherent states [74]). In view of these (and other) problems in Tegmark’s estimates, Hagan et
al. [30] asserted that the values of quantities in Tegmark’s relation are not correct and thus
the decoherence time should be approximately 10
10 times greater. According to Hagan et al.
[30], MTs in neurons of the brain can process information quantum mechanically and they
could avoid decoherence via several mechanisms over sufficiently long times for quantum
processing to occur. As a result, we conclude that coherent states in MTs are still
theoretically possible. Below, we explore the consequences of this conclusion.
3.4 Fluctuation Function and Simulation
We begin by representing the state of the MTs as a superposition of the coherent and
incoherent (ground) states, and the state of the biophotons as a field composed of n photons.
In our approach, transitions involving the coherent-incoherent process determined by a
function called the fluctuation function, )(tF .
As we explained before, we assume two states for a MT: (a) the ground state
(incoherent without energy pumping), and (b) an excited state (coherent with energy
pumping). We reformulate our previous calculations for the system of MTs and investigate
its interaction with biophotons. First, prior to the interaction, MTs may be in one of the two

8
states: the ground state or the coherent state.
The state of MTs can be written in the form of a superposition of the ground state and
coherent state. MTs vibrate in different frequency modes before the energy pumping occurs.
This state can be written in the form


j
nnnnnnn  ,...,,.... 321321 in which each
i
n
is a special frequency mode state of a tubulin in an MT. When a quantized electromagnetic
field is pumped into the MT, according to Froehlich’s theory [22,23,24], this leads to the
occupation of one frequency mode with a higher energy. This higher energy state is a
coherent state. For simplicity in our calculations, we assume the ground state of the MT to be
equivalent to the ket 0. Now, the state of MT can be written as the superposition of the
ground state and coherent state,
| (0) |0 |
g e
MT
c c z

  (6)
where z is our representation for the coherent state. It is written as 




0
2!
2
n
n
zn
n
z
ez ,
where the coherent state
z
satisfies in ˆ
a z z z
 where
ˆ
a
is the annihilation operator.
We now investigate its interaction with N biophotons. The state of biophotons is considered
as




0|)0(| nn
biophotons nc

(7)
and the total state is
| (0) | (0) | (0)
MT biophotons
  
  (8)
After solving the Schrödinger’s equation we have the time-dependent wave function as



 
n
ntcicntcic
zntcicntcc
tnngne
ngne |
}0|)cos()sin(
|1sin()1cos(
)(| 01
1




















In this case, the MT system first occupies the ground state. It means 0,1  eg candc .
After the substitution of these coefficients, the state becomes



 
n
ntic
zntic
tnn
n|
}0|)cos(
|1sin(
)(| 0
1




















(9)
Written in another form the state is
zttt eg |)(|0|)(|)(|

 (10)
where


 01|)sin()(| nne nntcit

(11)

9




0|)cos()(| nng nntcit

(12)
Now, we introduce the fluctuation operator
F

as
| | | 0 0|
Fe e g g z z

    (13)
To determine when the MT is in the coherent state and when in the ground state, we use the
fluctuation function. We define the fluctuation function as
( ) F
F t
  

This function determines the rate of transitions between the coherent state and the ground
state. Eventually, the fluctuation function for this state is


 















0 0
2
2
2
2
22
2
2)(sin1)(cos),( n n
z
z
n
z
neeTnceTnczTF (14)
where tT


. We refer to the parameter
T
as “scaled time”. Here, n
c is the coefficient
describing the biophoton field and thus
2
n
c
is the probability of detection of biophotons. We
may infer information about coherent light from photo-count statistics (PCS) [26]. One
calculates the probability
( , )
p n t

of registering n photons in preset time interval
t

by
recording the number of photons during the measurement. A fully coherent field satisfies a
Poissonian distribution ( )
!
n N
N e
p n
n

 for all times
0
t


, where N is the average number of
detected photons per time interval
t

. The fact that a coherent field satisfies a Poissonian
distribution is rooted in quantum theory. The amplitude and photon numbers are not
simultaneously measurable with arbitrary certainty. However, definite and robust
experimental proof is often complicated due to very small photon counts in
t

. Biophotons
have been considered coherent by other researchers and there are some claims of
experimental observations of coherent biophotons [6,7,9, 66, 67], but in general the claim of
biophoton coherence still requires a concrete proof. However, here we hypothetically assume
the coherence property of biophotons and investigate its consequences. Thus for biophotons
we have
!
2
2
n
ec n
n




where the normalization implies that N
2

[26], keeping in mind that N is the average
number of biophotons. Thus,
!
2
n
N
ec n
N
n
 . Finally, the fluctuation function becomes










0
2
22 )(sin)(cos1
!
),(
2
n
z
n
NTneTn
n
N
ezTF

10
The 3D diagrams of the fluctuation function are plotted in terms of the scaled time and
coordinate z, for different numbers of biophotons N. According to earlier calculations [11], at
least 100 biophotons can be produced within each human visual neuron per second during
visual perception. So, a few biophotons per second are probably simultaneously present
within the 1-10 micrometer length of an MT. Here, we have considered different values for
the numbers of biophotons around an MT such as N=2, 5, 7 and 10 and plotted 3D and 2D
diagrams of the fluctuation function in Figures 3 and 4, respectively.
Fig. 3. 3D diagrams of the fluctuation function for different numbers of biophotons N. It is
seen that the maximum of fluctuation is around z=1. MT is initially in the ground state
As introduced above, F(T,z) is the fluctuation function which determines the transition rate
of biomolecules between the coherent states and the ground state. When in coherent states,
the biophotons are absorbed via biomolecules and when in the ground state, the biophotons
are absorbed via the vacuum. In this model, the information can be restored from the
vacuum, and conscious states can be repeated as before. Then, the information can be sent
back to the memory site again via emission of biophotons. This cycle can be repeated an
arbitrary number of times. We have plotted the F(T,z) function for different values of its
variables. According to 3D diagrams in Figure 3, we let the values near 1 for z, since the
main fluctuations are in this area. In the next diagrams the scaled times are evaluated as
T=104 and T=105.

11
Fig. 4. 2D diagrams of the fluctuation function for different values of N and z in different
scaled time intervals. MT are initially in the ground state.
Now, we investigate the behavior of the fluctuation function with the assumption that the MT
is first in the excited state. Here 1)0( 
e
c and 0)0( 
g
c. With the substitution of these
coefficients we have




0)1cos()( nne nntct

(15)




01)1sin()( nng nntcit

(16)

12
Continuing our calculations for the fluctuation function we find that










0
2
22 )1(cos)1(sin1
!
),(
2
n
z
n
NTneTn
n
N
ezTF
We have plotted 3D and 2D diagrams of the fluctuation function in Figures 5 and 6,
respectively. As shown before, the maximum of fluctuations is found to be around z=1. Here,
we see again these fluctuations around z=1.
Fig. 5. 3D diagrams of the fluctuation function for different values of biophotons N. In this
interaction, MT is initially in the coherent state
Next, we plot F(T,z) again for the above case at different values of its variables.

13
Fig. 6. 2D diagrams of the fluctuation function for different values of N and z. MT is initially
in the coherent state.
In Figures 4 and 6 it is seen that the amplitudes of the fluctuation function are decreasing by
increasing the number of biophotons which interact with an MT, and vice versa.
4. Electrical properties of MTs and their effects on the membrane activity
In previous sections we explained how biophotons can help MTs support coherent states. We
have shown the existence of fluctuations between coherent states and normal states for MTs
during the interaction with biophotons. Now the question arises how biophotons can affect

14
the electrical activity of membrane via MTs. To answer this question we should investigate
how MT activity may affect the electrical activity of the membrane. It is well known that
MTs play key roles in the trafficking of neurotransmitters to the synapse. According
to Alvarez and Ramirez [5] action potential leads to a decrease of the disassembly rate of
MTs. Very recently, Gardiner et al. [25] reviewed the evidence for neurotransmitters
regulation (i.e. serotonin, melatonin, dopamine, glutamate, glycine, and acetylcholine) of the
MT cytoskeleton. They postulated that MTs may play a direct role in propagating action
potentials via conductance of electric current.
Both experimental and theoretical approaches have been used to study electrical signaling
along MTs. For experimental investigations, the dual patch-clamp set up was used. In such
experiments, electrical data were gathered and taxol-stabilized MTs were shown to behave as
biomolecular transistors responding to brief pulses of electric current whose voltage
amplitude was in the range of ±200mV [68]. Individual MTs were shown to amplify applied
electrical current two-fold indicating a capability for ionic signal propagation that appeared to
involve the condensed positive counterion clouds distributed along the length of the MT
(where approximately 20 unit charges are present per tubulin monomer). This ionic cloud was
attracted to the negative surface charge of the MT retaining its longitudinal mobility. Further
measurements and theoretical modeling showed that MTs support nonlinear wave
propagation [68]. It was also found that MTs exhibited transistor-like properties. It is worth
noting that MTs were also shown to be conductive using an independent experiment
involving an electroorientation approach [58]. Intact MTs were demonstrated to have
conductance of 157 ± 7 mS/m, while MTs treated with subtilisin (which cleaves tubulin’s C-
termini) lowered it to 96 ± 6 mS/m. An argument was put forth that counterions on the
surface (many of which interact with the negatively charged C-termini) are responsible for
the observed conductance. Ionic conduction along MTs was modeled in terms of a nonlinear
electrical circuit using cable equations [69]. Based on these results which indicate that MTs
transmit electric signals between distant points within a neuron, the sources of potential
information need to be identified in order to understand how processing sensory-based
information occurs into a higher cognitive state. The most significant source of input is from
the neuronal membrane which contains postsynaptic densities making contacts with other
neurons.
In order for actin filaments (AFs) to conduct an electric signal to the MT network inside
the neuron, there must be a functional link between these two types of cytoskeleton. There
are at least three potential mechanisms through which AFs and MTs could interact: (1) direct
physical contact, (2) via various types of linking proteins, and (3) indirectly through signal
transduction. AFs are often found in cells forming direct association with MTs. MTs, in turn,
frequently migrate correlated with actin bundles [76] as was demonstrated by bual-
wavelength fluorescent speckle microscopy analyses. AFs interact with MTs as part of
neuronal migration, growth cone development, and neuronal receptor and ion channel
transport [38]. While detailed mechanisms governing MT/AF interactions remain an open
issue, numerous recently acquired insights reveal a growing number of cross-linker proteins
playing important roles.

15
Table 1. Major linker proteins of actin filaments with microtubules
Cross-linker
protein References
CLIP-115 [38]
CLIP-170 [38],[101]
CLASP1 [38],[93]
CLASP2 [38],[93]
Lis1 [38],[46]
EB family [38],[53]
MAP2c [51],[78]
Tau [78]
Table 1 summarizes the known cross-linker proteins that bind MTs to AFs. Note that many of
these proteins are MT plus-end tracking (so called +TIPs) [38]. Cytoplasmic linker proteins,
CLIP-115 and CLIP-170, bind plus-ends of MTs and link them to cargo or to AFs though
scaffolding protein intermediaries [101]. CLIP-associated proteins, CLASP1 and CLASP2,
represent two additional examples of +TIPs. CLASP2α contains a binding site for actin [93].
Lis1 tethers AF to MTs through interactions with scaffolding proteins [46]. Similarly, the EB
family of +TIPs, which include EB1 and EB2, interact with other proteins (including
CLASP1/2, CLIP-170) in order to bind MTs to AFs [53]. There are also the microtubule-
associated proteins (MAPs): MAP2 and tau, which are known to bind AFs at relatively low
affinities [51,78]. Signal transduction molecules, such as calmodulin, and Ca2+ and
phosphorylation have been found to modulate the ability of MAP2 and tau to bind AFs and
MTs. Consequently, in spite of numerous gaps in our understanding of these interactions, it is
clear that electric signals arising from synaptic input can reach the internal cytoskeleton using
various molecular pathways.
4.1 An Overview
We strongly believe that electrodynamic interactions between various cytoskeletal structures,
with MTs playing a central role, and ion channels crucially regulate the neural information-
processing mechanism. These interactions involve long-range ionic wave propagation along
microtubule networks (MTNs) and AFs and exhibit subcellular control of ionic channel
activity. Hence, they have an impact on the computational capabilities of the entire neural
function. Cytoskeletal biopolymers, most importantly AFs and MTs, constitute the basis for
wave propagation, and interact with membrane components leading to a modulation of
synaptic connections and membrane ion channels. Association of MTs with AFs in neuronal
filopodia guides MT growth and affects neurite initiation [18]. This is seen in neurons by the
presence of proteins that interact with both MTs and AFs, as well as proteins that mediate
interactions between both types of filaments. For example, MAP1B and MAP2 interact with
actin in vitro [64,90] but cross-linking, MAP2 and/or MAP1B is associated with both types of
filaments contributing to the guidance of MTs along AF bundles. Direct interaction between
AFs and ion channels has been seen and a regulatory functional role has been associated with
actin. Thus, it is clear that the cytoskeleton directly and indirectly affects membrane
components, in particular ion channels and synapses.

16
MTs and AFs also interact during the migration of developing growth cones, a process that
involves +TIPS, which tend to aggregate at the end of the axon shaft in the area where AFs
are highly localized. Additionally, the neurotrophin NGF and the signal transduction
molecule GSK-3β act to assure a balance between stable and unstable MTs [28]. The
dynamic relationships between AFs and MTs during growth cone migration could be partially
controlled by electric signals transmitted between these networks, which is supported by
experimental evidence. Applied electric fields were found to guide growth cones of Xenopus
spinal neurons towards the positive current source and this effect depended on intact AFs and
MTs [71].
While learning-related changes in MTs, AFs, MAPs and signal transduction molecules are
well documented [103], much less is known about how electric signaling in the MT and AF
networks might be involved memory formation processes. Electric signaling by AFs and MTs
may play active roles in coincidence detection and storage of spatiotemporal patterns of
inputs, and signaling within the cytoskeleton may be particularly critical to information
storage over longer time scales than LTP times. The initial route to the MT network could be
through the AFs concentrated in the spines. Inputs to arbitrary sites in the neuron can be
transmitted from the neuronal membrane to AFs in spines via scaffolding proteins and signal
transduction molecules. Electric signals can then be transmitted utilizing AF cross-linker
proteins to MTs, and subsequently through MAPs and signal transduction molecules to other
MTs in the network. A spatiotemporal pattern inherent to a complex stimulus (or cogneme)
can therefore be readily envisaged and indeed mathematically modeled as a convergence of
electrical signals to a particular stretch of an MT inside the dendrite.
5. Concluding Remarks
There is no doubt that EEG waves are deeply involved with the basic functioning of the brain
but the origin and the exact function of EEG has remained a mystery. The EEG waves
associated with two distant neurons are strongly correlated and this supports the view that
EEG waves are related to the properties of the brain as a coherent quantum system. It is not
possible for a scalp EEG to determine the activity within a single dendrite or neuron. Rather,
a surface EEG reading is the summation of the synchronous activity of thousands of neurons
that have similar spatial orientation, radial to the scalp2.
Synaptic transmission and axonal transfer of nerve impulses are too slow to organize
coordinated activity in large areas of the central nervous system. Numerous observations
confirm this view [73]. The duration of a synaptic transmission is at least 0.5 ms, thus the
transmission across thousands of synapses takes about hundreds or even thousands of
milliseconds. The transmission speed of action potentials varies between 0.5 m/s and 120 m/s
along an axon. More than 50% of the nerves fibers in the corpus callosum are without myelin,
thus their speed is reduced to 0.5 m/s. How can these low velocities (i.e. classical signals)
explain the fast processing in the nervous system? We believe that quantum theory is able to
explain some of the above mysteries. As an example, recently it has been shown theoretically
that the biological brain has the possibility to achieve large quantum bit computing at room
temperature, superior when compared with the conventional processors [60].
2 Updated from : http://www.reference.com/browse/electro-encephalogram, 13 November 2009.

17
Neuroscientists or brain specialists record the EEG diagrams of their patients when their
eyes are closed, because when their eyes are open the amplitudes of diagrams dramatically
would be reduced and they cannot diagnose changes in the amplitudes. In Figure 7 we
represent the EEG diagrams of a person with different situations as explained in the caption
to the figure. When the eyes are opened, the number of interacting photons with the
biomolecules becomes high and the amplitudes of EEG become low. It is clear when the
intensity of incident light is high; it must produce more action potentials than low intensity
light. In classical physics, it is argued that this situation is because the superposition of
several waves tends to decrease the amplitude of the total wave. This argument ignores the
intensity of the incident light entering the eye. This classical argument also cannot explain
when and how the synchronization happens in order to decrease the amplitude of EEG.
Fig. 7. A schematic alpha-EEG diagram of a person when (a) the eyes are opened and then
he/she closes the eyes (b) the eyes are closed and then she/he opens his eyes and then closes
them again.
Our argument is based on the interaction of (bio)photons and MTs inside the neurons.
When the eyes start to be opened, the incident numbers of photons into the eyes increases and
according to the diagrams in Figures 4 and 6 the amplitudes are reduced. Also, the amount of
incident photons can increase the amount of biophoton production inside the neurons, since
the visible light can be produced inside the brain in the form of biophotons [37,49,50,100]
and this interaction is not only limited to the external incident photons with MTs. So far we
have not been able to find an exact relation between the EEG diagrams and the fluctuation
function, but the synchronous and coherent vibrations of billions electric dipoles of
biomolecules cannot be ignored in the EEG diagrams. MTs are particularly numerous in the
brain where they form highly ordered bundles and are the best candidate for long coherence
and large synchrony [32]. The argument for connection between Alpha-EEG diagrams and
MTs activity is their similar behavior in increasing and decreasing of amplitudes of
fluctuation function for MTs and potential difference in EEG in response to the intensity of
photons. This similarity during opening and shutting of the eyes indicates a significant
relation between the EEG diagrams and the fluctuation function.
Acknowledgments
Majid Rahnama thanks the Kerman Neuroscience Research Center (KNRC) for supporting
this work, and also István Bókkon gratefully acknowledges the support he has received from
the BioLabor (www.biolabor.org), (Hungary) during this research. His URL: http://bokkon-
brain-imagery.5mp.eu. Jack Tuszynski acknowledges support from NSERC (Canada) for his

18
research. Michal Cifra acknowledges financial support from Czech Science Foundation,
P102/10/P454.
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