• Save
Bernroider
Upcoming SlideShare
Loading in...5
×

Like this? Share it with your network

Share
  • Full Name Full Name Comment goes here.
    Are you sure you want to
    Your message goes here
    Be the first to comment
    Be the first to like this
No Downloads

Views

Total Views
602
On Slideshare
579
From Embeds
23
Number of Embeds
2

Actions

Shares
Downloads
0
Comments
0
Likes
0

Embeds 23

http://www.brainspace.eu 21
http://localhost 2

Report content

Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

Cancel
    No notes for slide

Transcript

  • 1. Gustav BernroiderNeural Correlates of Dept Organismic Biology, Neurosignaling Unit,Higher Level Brain University of Salzburg, AustriaFunctions
  • 2. Quantum Properties In Ion Channel Proteins , Segregation and Perceptionexperience is the basic reality Or grandma might be observing this pattern
  • 3. Quantum Properties In Ion Channel Proteins , Segregation and Perceptionexperience 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 Perceptionexperience 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 Perceptionexperience 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 Perceptionexperience 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 Perceptionexperience 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 BernroiderNeural Correlates of Dept Organismic Biology, Neurosignaling Unit,Higher Level Brain University of Salzburg, AustriaFunctions1) Experience2) Construction3) Perception
  • 9. Gustav BernroiderNeural Correlates of Dept Organismic Biology, Neurosignaling Unit,Higher Level Brain University of Salzburg, AustriaFunctions1) Experience phenomenal The brain ‚receives‘ or harvests experience and consolidates this experience2) Construction physical Conscious perceptive phenomenal &3) Perception transition (CPT) physical into a maintainance or working memory
  • 10. Gustav BernroiderNeural Correlates of Dept Organismic Biology, Neurosignaling Unit,Higher Level Brain University of Salzburg, AustriaFunctions1) Experience phenomenal2) Construction physical Quantum - Classical (‚real‘ physics) transition3) Perception phenomenal physical
  • 11. Neural Correlates ofHigher Level BrainFunctions 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 ofHigher Level BrainFunctions physics psychobiology I
  • 13. Neural Correlates ofHigher Level BrainFunctions 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 ofHigher Level BrainFunctions physics psychobiology A basic ontological difference emerges between systems and surroundings A(O) O(A) S( ) A := c
  • 15. Neural Correlates ofHigher Level BrainFunctions physics psychobiology A basic ontological difference emerges between systems and surroundings A(O) O(A) S( ) A := c = SA
  • 16. Neural Correlates ofHigher Level BrainFunctions physics psychobiology A basic ontological difference emerges between systems and surroundings A(O) O(A) S( ) A := c = SA E
  • 17. Neural Correlates ofHigher Level BrainFunctions physics psychobiology A basic ontological difference emerges between systems and surroundings A(O) O(A) A := c = SA E
  • 18. Neural Correlates ofHigher Level BrainFunctions1) Experience2) Construction How do these steps combine ? What are the physical correlates ?3) Perception
  • 19. Neural Correlates ofHigher Level BrainFunctions Dimensional excess of space1) Experience2) Construction Scaling transitions3) Perception
  • 20. Neural Correlates ofHigher Level BrainFunctions1) Experience2) Construction Scaling transitions3) 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 ofHigher Level BrainFunctions1) Experience Organization spans about 30 physical2) Construction action orders3) Perception
  • 22. Neural Correlates ofHigher Level Brain States and transitionsFunctions (Bernroider & Roy, 2005)1) Experience2) Construction nano-scale 60 um Filter domain Membrane integrated TRIFM image of single Ca channels3) 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 ofHigher Level Brain States and transitionsFunctions (Bernroider & Roy, 2005)1) Experience2) Construction3) Perception AD N. for a brain with AD = 10 -15 this gives N = 1019 states
  • 24. Neural Correlates ofHigher Level Brain States and transitionsFunctions (Bernroider & Roy, 2005)1) Experience The oxygen-coordinated alkali-ion cage2) Construction in the filter region of a voltage gated ion channel is the3) Perception basic computational unit (Bernroider, Roý 2005)
  • 25. Quantum Properties In Ion Channel Proteins , Segregation and PerceptionChannel proteins provide the transition states between quantum and classicalsignals• 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 viewion 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 coolingA quantum-classical transition betweenQM ion-oxygen states and filter gating states
  • 29. Neural Correlates ofHigher Level BrainFunctions The environment of ions changes: hydrated ions in watery solution become dehydrated and the protein ‚takes over‘ the role of an environment.Constructing theenvironment
  • 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 ProteinsAt particular oscillation frequencies (450 GHz) the kinetic energy of theIon is given off to the filter carbonyls – classicality emerges –The kinetic energy minima for oscillation frequencies are different fordifferent 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 macroscopicalentanglement 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 dynamcisIn 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 ofHigher Level BrainFunctions1) Experience2) Construction3) Perception
  • 37. Neural Correlates ofHigher Level BrainFunctions1) Experience Large brains have many cells2) Construction3) Perception
  • 38. Connections in mm, from the Macaque visual system, Voggenhuber 2001 32 synaptic distances from V1 …… FEF
  • 39. Large brains have many cellsCells are organized so that there receptive field properties (RF) changealong synaptic distances: 1 2 3
  • 40. Large brains have many cellsCells are organized so that there receptive field properties (RF) changealong synaptic distances:There are ascending (A) and recurrent ( R) connections A 1 2 3 R
  • 41. Large brains have many cellsCells are organized so that there receptive field properties (RF) changealong synaptic distances:There are ascending (A) and recurrent ( R) connectionsThere 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 anda comparison between minimizing (Expectation – Observation)bottom up and top-downsignaling (a) (b) (c) lower level higher levelPredictive coding – principlelower level information e.g. direction in (a) convergeswith 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 residualdiscrepancy (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 MoralDorso-lateralPläne 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 signalsReceptor site Thalamo-cortical Segregation level Late stage frontal areas
  • 49. A model for conscious experience (sensation) Ascending signals Recurrent signalsReceptor site Thalamo-cortical Segregation level Late stage frontal areas
  • 50. A model for conscious experience (sensation) Working memory Consicousness Recurrent signals Ascending signals attentionReceptor site Thalamo-cortical Segregation level Late stage frontal areas
  • 51. Neural Correlates ofHigher Level BrainFunctions1) Experience2) Construction3) Perception
  • 52. Neural Correlates ofHigher Level BrainFunctions 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 ofHigher Level BrainFunctions physics psychobiology A basic ontological difference emerges between systems and surroundings A(O) O(A) S( ) A := c
  • 54. Neural Correlates ofHigher Level BrainFunctions physics psychobiology A basic ontological difference emerges between systems and surroundings A(O) O(A) S( ) A := c = SA
  • 55. Neural Correlates ofHigher Level BrainFunctions physics psychobiology A basic ontological difference emerges between systems and surroundings A(O) O(A) S( ) A := c = SA E
  • 56. Neural Correlates ofHigher Level BrainFunctions physics psychobiology A basic ontological difference emerges between systems and surroundings A(O) O(A) A := c = SA E
  • 57. Neural Correlates ofHigher Level BrainFunctions Molto grazie per vostra attenzione
  • 58. Neural Correlates ofHigher Level BrainFunctions
  • 59. Neural Correlates ofHigher Level BrainFunctionsFrom quantum to classical and backagainStates are physicalTransitions are phenomenal
  • 60. Neural Correlates ofHigher Level BrainFunctionsFrom quantum to classical and backagainStates are physicalTransitions are phenomenal
  • 61. Large brains have many cells
  • 62. 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)
  • 63. Connectivities
  • 64. 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
  • 65. Wang et al., 2010Mensch KrähenV2
  • 66. 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 neuronsan 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 complexdomain 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. orientationprojected 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).
  • 67. 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 AD 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.
  • 68. 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
  • 69. 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
  • 70. Correlations, Decorrelations, along synapticdistancesShared input E or I c>0Correlated input E or I c>0Correlated input (E und E) und (I und I) c>0Correlated input (E und I) c<0
  • 71. 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 JoulBucher et al, Biophysical Chemistry, 124, 292-301, 2006. 73