1. Gustav Bernroider
Neural Correlates of
Dept Organismic Biology, Neurosignaling Unit,
Higher Level Brain University of Salzburg, Austria
Functions

2. Quantum Properties In Ion Channel Proteins , Segregation and Perception
experience is the basic reality
Or grandma might be observing this pattern

3. Quantum Properties In Ion Channel Proteins , Segregation and Perception
experience is the basic reality
As other persons are attending –
Grandma‘s real oberservations are
reported – she sends out a copy of her
experience

4. Quantum Properties In Ion Channel Proteins , Segregation and Perception
experience is the basic reality
As even more persons
deal with grandma‘s report,
an agreement emerges about
what grandma‘ has seen
a physical theory….

5. Quantum Properties In Ion Channel Proteins , Segregation and Perception
experience is the basic reality
and what was
originally a copy of grandma‘s
experience is now considered
a reality and
Grandma‘s experience is the
(subjective) copy of it …………

6. Quantum Properties In Ion Channel Proteins , Segregation and Perception
experience is the basic reality
And the difference of grandma‘s
copy from the consensus ‚reality‘
Is called a ‚sensory illusion‘

7. Quantum Properties In Ion Channel Proteins , Segregation and Perception
experience is the basic reality
First: subjective experience = is real
Splitting: with an another person added , a copy of this
experience is sent to the ‚world outside‘
Inversion: as ‚consensus‘ emerges among the community
the copy ‚outside‘ becomes the
‚real world‘ and the experience becomes the copy
See , J. Stewart, 2001, Foundations of
Science

8. Gustav Bernroider
Neural Correlates of
Dept Organismic Biology, Neurosignaling Unit,
Higher Level Brain University of Salzburg, Austria
Functions
1) Experience
2) Construction
3) Perception

9. Gustav Bernroider
Neural Correlates of
Dept Organismic Biology, Neurosignaling Unit,
Higher Level Brain University of Salzburg, Austria
Functions
1) Experience phenomenal The brain ‚receives‘ or harvests
experience and consolidates
this experience
2) Construction physical
Conscious perceptive
phenomenal &
3) Perception transition (CPT)
physical
into a maintainance or
working memory

10. Gustav Bernroider
Neural Correlates of
Dept Organismic Biology, Neurosignaling Unit,
Higher Level Brain University of Salzburg, Austria
Functions
1) Experience phenomenal
2) Construction physical Quantum - Classical (‚real‘ physics)
transition
3) Perception phenomenal
physical

11. Neural Correlates of
Higher Level Brain
Functions
Quantum physics as a conceptual
science, describes the phenomenal,
1) Experience the ontology of the universe by
a concept:
2) Construction (r1 , r2, …..;t) :
3) Perception only through observation (measurement)
emerges physical reality.

12. Neural Correlates of
Higher Level Brain
Functions
physics psychobiology
I

13. Neural Correlates of
Higher Level Brain
Functions
physics psychobiology
A basic ontological
difference emerges
between systems
and surroundings
A(O) O(A) S( )
A := c
Change of O(A)

14. Neural Correlates of
Higher Level Brain
Functions
physics psychobiology
A basic ontological
difference emerges
between systems
and surroundings
A(O) O(A) S( )
A := c

15. Neural Correlates of
Higher Level Brain
Functions
physics psychobiology
A basic ontological
difference emerges
between systems
and surroundings
A(O) O(A) S( )
A := c = SA

16. Neural Correlates of
Higher Level Brain
Functions
physics psychobiology
A basic ontological
difference emerges
between systems
and surroundings
A(O) O(A) S( )
A := c = SA E

17. Neural Correlates of
Higher Level Brain
Functions
physics psychobiology
A basic ontological
difference emerges
between systems
and surroundings
A(O) O(A)
A := c = SA E

18. Neural Correlates of
Higher Level Brain
Functions
1) Experience
2) Construction How do these steps combine ?
What are the physical correlates ?
3) Perception

19. Neural Correlates of
Higher Level Brain
Functions
Dimensional excess of space
1) Experience
2) Construction Scaling transitions
3) Perception

20. Neural Correlates of
Higher Level Brain
Functions
1) Experience
2) Construction Scaling transitions
3) Perception
Mandelbrot – or fractal sets
Bernroider G. (2012) Common Grounds: The Role of Perception in Science. Eds.
B. Zavidovique, G. Lo Bosco, Image In Action, 103-110, World Scientific.

21. Neural Correlates of
Higher Level Brain
Functions
1) Experience Organization
spans
about 30 physical
2) Construction action orders
3) Perception

22. Neural Correlates of
Higher Level Brain States and transitions
Functions (Bernroider & Roy, 2005)
1) Experience
2) Construction
nano-scale 60 um
Filter domain Membrane integrated TRIFM image of single Ca channels
3) Perception of ion channel ion channel molecule membrane patch
Cell signals
E.t = 10-34 Js E.t = 10-16 Js
E. t = 10-4 Js

23. Neural Correlates of
Higher Level Brain States and transitions
Functions (Bernroider & Roy, 2005)
1) Experience
2) Construction
3) Perception
AD N. for a brain with AD = 10 -15 this gives N = 1019 states

24. Neural Correlates of
Higher Level Brain States and transitions
Functions (Bernroider & Roy, 2005)
1) Experience
The oxygen-coordinated
alkali-ion cage
2) Construction in the filter region
of a voltage gated
ion channel is the
3) Perception basic computational
unit (Bernroider, Roý 2005)

25. Quantum Properties In Ion Channel Proteins , Segregation and Perception
Channel proteins provide the transition states between quantum and classical
signals
• filter ‚gating‘ = ion – oxygen Intra-molecular transitions:
coordination states change: within one molecule (e.g KcsA
ion channel)
characteristic time: 10-12 sec
Action = E.t = 1kT . 10-12
= 4.10-21J . 10-12 s = 10-33 Js filter
cavity
Voltage
sensor
pore gate

26. Ion channel proteins are the quantum-classical transition devices:
• filter ‚gating‘ = ion – oxygen Intra-molecular transitions:
coordination states change: within one molecule (e.g K cA
ion channel)
characteristic time: 10-12 sec
Action = E.t = 1kT . 10-12
= 4.10-21J . 10-12 s = 10-33 Js filter
cavity
• pore domain gating‘ – constriction Voltage
gate transitions (classical) sensor
characteristic time: 10 -3 sec pore gate
Action = E.t = 10-16 Js
Coherence dynamics couples the
filter states with the pore gate state

27. Side view Top view
ion channel protein and a quadrupole ion trap
Carbonyl Oxygen groups act like quadrupole rings in an ion-
trap device

28. Quantum Properties In Ion Channel Proteins , Segregation and Perception
QM: effects in the filter region of ion channels:
oscillatory coherences, particle waves,
energy transfer and cooling
A quantum-classical transition between
QM ion-oxygen states and filter gating states

29. Neural Correlates of
Higher Level Brain
Functions The environment of ions changes:
hydrated ions in watery solution become
dehydrated and the protein ‚takes over‘
the role of an environment.
Constructing the
environment

30. Quantum Properties In Ion Channel Proteins , Segregation and Perception
Summhammer J., Salari V., Bernroider G. A quantum-mechanical description of
ion motion within the confining potentials of voltage gated ion
channels. Journal of Integrative Neuroscience (JIN), Imperial college
Press, (2012) in print

31. Quantum Properties In Ion Channel Proteins , Segregation and Perception

32. Quantum Properties In Ion Channel Proteins
At particular oscillation frequencies (450 GHz) the kinetic energy of the
Ion is given off to the filter carbonyls – classicality emerges –
The kinetic energy minima for oscillation frequencies are different for
different ion species ( explains filter selectivity for ions*)
Cooling in proteins
* Summhammer J., Salari V., Bernroider G. A quantum-mechanical
description of ion motion within the confining potentials of voltage
gated ion channels. Journal of Integrative Neuroscience (JIN), Imperial
college Press, (2012) in print

33. Action potentials are the macroscopical
entanglement witnesses of quantum ion states in channel proteins
Properties:
a) high resolution shapes (resolved into msec)
(action potential initiation (API), onset potential variability,
spike after potential -SAP)
b) Low time resolutions (sec)
i) rates (API/sec) and ii) interspike intervalls ( ti)
number of APs /s
ti
API SAP
OPV

34. Introducing quantum correlations for activiation dynamcis
In the HH equation
• |ψ = a0 |0 + a1 |1 + a2 |2 + a3 |3

35. A semi quantum-classical version of the
HH equation of motion
• a) classical version (no
entanglement, = 0)
• b) maximum positive quantum
entanglement ( = 1)
• c) negative quantum entangled
‚m gates‘ ( = -0.5)
voltage pulse phase plot

36. Neural Correlates of
Higher Level Brain
Functions
1) Experience
2) Construction
3) Perception

37. Neural Correlates of
Higher Level Brain
Functions
1) Experience
Large brains have many cells
2) Construction
3) Perception

38. Connections in mm, from the Macaque visual system, Voggenhuber 2001
32 synaptic distances from V1 …… FEF

39. Large brains have many cells
Cells are organized so that there receptive field properties (RF) change
along synaptic distances:
1 2 3

40. Large brains have many cells
Cells are organized so that there receptive field properties (RF) change
along synaptic distances:
There are ascending (A) and recurrent ( R) connections
A 1 2 3
R

41. Large brains have many cells
Cells are organized so that there receptive field properties (RF) change
along synaptic distances:
There are ascending (A) and recurrent ( R) connections
There a local (micro) circuits and long range connections
1 2 3

42. Synpatic distances
Top-down processes are necessary
for conscious perception

43. Synpatic distances
Top-down processes are necessary
for conscious perception
microcircuit

44. Predictive coding:
Predictive coding and
a comparison between minimizing (Expectation – Observation)
bottom up and top-down
signaling
(a) (b) (c)
lower level higher level
Predictive coding – principle
lower level information e.g. direction in (a) converges
with higher level information (c) on an intermediate level (b).
From this intermediate level feed-forward (left to right arrows)
connections encode the information about the residual
discrepancy (insert) between lower and higher level codes.

45. If the (red) recurrent control is missing there is no
ascending [ AR] and no c-perceptive state (CPS)
(a) (b) (c)
lower level higher level

46. Long range connections analysed by dynamic causal modelling
(DCM, Friston, 2003)
IFG If top down recurrent connections are impaired
from fronto-parietal cortex, subjects are in a
vegetative state (VS)
STG Dehaene et al, 2006
Friston group, 2011
A1

47. Frontal Cortex : Damasio - Anatomie der Moral
Dorso-lateral
Pläne und Konzepte
Anterior-
Cingulate
Aufmerksam-
keit auf
eigene Ideen
Ventro-medial
Emotionelle Erf.
Orbito-frontal: inhibiert ‚unpassende Aktionen‘ zugunsten
von lang-zeitigem Vorteil

48. A model for conscious experience (sensation)
Ascending signals Recurrent signals
Receptor site Thalamo-cortical
Segregation level Late stage frontal areas

49. A model for conscious experience (sensation)
Ascending signals Recurrent signals
Receptor site Thalamo-cortical
Segregation level Late stage frontal areas

50. A model for conscious experience (sensation)
Working memory
Consicousness
Recurrent signals
Ascending signals
attention
Receptor site Thalamo-cortical
Segregation level Late stage frontal areas

51. Neural Correlates of
Higher Level Brain
Functions
1) Experience
2) Construction
3) Perception

52. Neural Correlates of
Higher Level Brain
Functions
physics psychobiology
A basic ontological
difference emerges
between systems
and surroundings
A(O) O(A) S( )
A := c
Change of O(A)

53. Neural Correlates of
Higher Level Brain
Functions
physics psychobiology
A basic ontological
difference emerges
between systems
and surroundings
A(O) O(A) S( )
A := c

54. Neural Correlates of
Higher Level Brain
Functions
physics psychobiology
A basic ontological
difference emerges
between systems
and surroundings
A(O) O(A) S( )
A := c = SA

55. Neural Correlates of
Higher Level Brain
Functions
physics psychobiology
A basic ontological
difference emerges
between systems
and surroundings
A(O) O(A) S( )
A := c = SA E

56. Neural Correlates of
Higher Level Brain
Functions
physics psychobiology
A basic ontological
difference emerges
between systems
and surroundings
A(O) O(A)
A := c = SA E

57. Neural Correlates of
Higher Level Brain
Functions
Molto grazie per vostra attenzione

59. Neural Correlates of
Higher Level Brain
Functions

60. Neural Correlates of
Higher Level Brain
Functions
From quantum to classical and back
again
States are physical
Transitions are phenomenal

61. Neural Correlates of
Higher Level Brain
Functions
From quantum to classical and back
again
States are physical
Transitions are phenomenal

63. Large brains have many cells

64. Receptive field maturation:
a) volume = number of cells
b) number of cells = number of ion channels
c) number of cells is structured by synaptic distances
d) synaptic distances are organized in a way to achieve
between channel ion entanglement within the same
receptive field property
e) between ion channel entanglement = the physical
correlate of a conscious perceptive state (CPS)

65. Connectivities

66. Cortical connections:
Long-range cortical connections:
short-latency ERPs (mostly feed forward, e.g. 50 ms)
long-latency ERPs (mediated by backward connections, > 200 ms)
Short range connections (local, microcircuit architecture)
neurons that share input – overlapping receptive fields
decorrelated (asynchronous states) firing
Ecker, et al. Science, 2010
Renart , et al. , Science, 2010

67. Wang et al., 2010
Mensch Krähen
V2

68. The model: 'diffuse recurrent connectivities' and temporal coding
(2) The mirror or lense metaphor (3) Temporal interspike coding:
underlaying the present model: the signals time code is provided by
Signals from the ASD cell (orange) can the lengths of intervals between subsequent action-
spread everywhere onto the neighbouring DTD - potentials - (bar length for red (feedforward)
however, the timing of signals is different within amd blue (recurrecnt) signals). The 'coupling'
shorter or longer path-length (moving clocks) - zone where signals are temporally 'aligned' by
(1) Connectivity of a single modul: so, at any instance of time presynaptic signals dendritic delays at the target neurons
an axonal source domain (ASD) is spread along shorter connections arrive somewhat is shown by the black box.
projected onto a dendritic target earlier at the DTD than signals travelling along The model realizes 'timing' through complex
domain longer axon-patches. These differences are vectorial simulations - where the 'amplitude' for
(DTD) and the recurrent ASD is compensated by 'dendritic delay processing' propagation is associated with a 'phase' (i.e. orientation
projected back onto the source DTD ('coupling within the blue zone') and lead to of a vector) that changes with time.
'coincident' signal arrival at the source neurons
DTD (dark box on the right side).

69. Iso-orientation maps from area 18 of visual cortex
obtained from intrinsic opto-physiological mappings 5
B C
A Isoorientation maps in Cat‘s Area
18:
Regions with high metabolic activity in
bright blue
(Bonhoeffer T., Grinvald A. 1993)
L
A P
M
A
D Location within the cats visual cortex from where
activity maps were recorded5
B
3-dim arrangement of recorded maps relative to cortical layers.
The thickness of the optical layer is 50µm
C
Four iso-orientation maps generated through moving gratings
employing four different orientations (0°, 45°, 90°, 135°)
D
The resolution of the original picture is compared with the
resolution, that is used in the computational model. One pixel in the original
image corresponds to an extension of 17µm x 17µm. The digitized
image uses pixel size of 230µm x 200µm.
From estimations about neuronal densities, one pixel (17x17 µm) contains
about 0.7 neurons (Beaulieu C., Colonnier M. 1985). The present model
contains roughly 110 neurons for each resolved location.

70. Evolution of spike-tuning curves (a), single neuron spike trains (b) and signal-population vector length ©
for different firing patterns of simulated neurons
3 18
16
2 14
12
1 10
8
0 6
4
(a) 0 time in secs
1 2
(a) 0
0 time in secs 2
(c)
(c)
(b) (b)
1 regular, tonic firing 3 multiply bursting cell
18
16
12 14
12
10
10
8 8
6
6
4
4 2
(a) 2 (a) 0
0 0 time in secs 2
0 1
time in secs
(c) (c)
(b) (b)
2 irregular, random spike train 4 post-inhibitory rebound spike trains

71. Local connectivities: columns in visual cortex
Rod recpetor cell
Horizontal c
Bipolar cell
Amacrine c Retinal ganglion cell
Geniculate cells
of thalamus
Layer 4 cells
A long
R long

72. Correlations, Decorrelations, along synaptic
distances
Shared input E or I c>0
Correlated input E or I c>0
Correlated input (E und E) und (I und I) c>0
Correlated input (E und I) c<0

73. Energy transfer along the p-loop of the filter
(Salari, Summhammer, Bernroider 2010)
SINK SINK
6
H O 5
P-loop is G
4
considered as a Y
N
linear chain of G
C 3
peptide units Jn
V 2
T ion
1
Each peptide unit is
Energy coupling considered as a unit
between C=O in a linear chain
bonds and ion
EC O, Na 2 3 eV 50 10 20 Joul
Bucher et al, Biophysical Chemistry, 124, 292-301, 2006.
73