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Fired Up Neurons!

Fired Up Neurons!. Saturday Morning Physics December 18, 2004 Presenter: Rhonda Dzakpasu. What we know. Simple elements of brain function: Structure of brain Functional role of different brain structures Cellular composition of brain Action of neurons Action of neurotransmitters.

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Fired Up Neurons!

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  1. Fired Up Neurons! Saturday Morning Physics December 18, 2004 Presenter: Rhonda Dzakpasu

  2. What we know Simple elements of brain function: Structure of brain Functional role of different brain structures Cellular composition of brain Action of neurons Action of neurotransmitters

  3. What we don’t know: The Big Picture How does the brain WORK?! How does activity of neurons code behavior, cognition, memory?

  4. Multiple Level Problem Bioinformatics – what genes are involved to express proteins used in different aspects of cognition? Molecular approach Systems approach

  5. Multiple Level Problem Bioinformatics approach Molecular – what chemicals (e.g., ions, neurotransmitters) are involved in pathway needed for different aspects of cognition? Systems approach

  6. Multiple Level Problem Bioinformatics approach Molecular approach System – neuronal communication – How do action potentials relate to cognition?

  7. Is the Forest or the Trees? Static arrangement Everything is hardwired Stimulation of particular tree Thought corresponds to a particular tree Dynamical arrangement Ephemeral trees! Leaves form one arrangement and then change

  8. She’s Baaaack! • Static arrangement: • Young woman OR • Old woman • Not both!! W.E. Hill

  9. Two Faces or a Vase?

  10. Many Sites are Activated • Distributed information processing • How different parts talk to each other Courtesy of C. Ferris, K.Lahti, D. Olson, J. King, Dept. of Psychiatry, Univ. Massachusetts, Worcester, Mass.

  11. Static or Dynamic? • Static: • Need HUGE (infinite) forest for all thoughts! • Dynamic: • How are the leaves functionally connected

  12. Dynamic Communications • How do the leaves on the trees • communicate? • An analogy: Musicians in orchestra • Practice is noise – no communication • When baton drops – music to the ears! • What is the difference between practice and play? • Play correct notes at the same time - Notes, musicians are synchronized

  13. But how does the brain work without a conductor?

  14. Experimental Approach:Optical Imaging Optical imaging techniques convert information into light intensity fluctuations Monitor different regions of brain at the same time Study spatio-temporal structure of the dynamics of neuronal networks in vitro and in vivo fMRI not fast enough to detect action potentials

  15. Optical Imaging Different types of signals can be imaged Intrinsic Chemical not used – that’s why intrinsic Low signal to noise – must signal average Long time scale Dye-based Fluorescence Calcium concentration sensitive dyes Voltage sensitive dyes

  16. Overview of Fluorescence

  17. Fluorescence: Excitation and Emission Demo Time!

  18. Fluorescence Imaging • Voltage sensitive dyes • Converts membrane potential into changes in fluorescence intensity • Fast response • Non specific

  19. Fluorescence Imaging: voltage sensitive dyes

  20. Fluorescence Imaging: voltage sensitive dyes Ross, W.N., B.M. Salzberg, L.B. Cohen, A. Grinvald, H.V. Davila, A.S. Waggoner, and C.H. Wang (1977).

  21. Fluorescence Imaging: voltage sensitive dyes

  22. Odor evoked oscillations in turtle olfactory bulb • Objective: how spatiotemporal patterns are changed when different stimuli is presented to sensory modality such as olfactory system

  23. Olfactory System nose receptor cells glomeruli periglomerular cells olfactory bulb mitrial/tufted cells granule cells MT:excitatory G+P: inhibitory

  24. filtered: 0.1Hz-30Hz filtered: 5Hz-30Hz Odor evoked oscillations in turtle olfactory bulb Caudal Middle Rostral

  25. DF/F 4x10-4 800ms Caudal Rostral 1 mm Different cycles of oscillation employ different neurons 1 2 3 10% isoamyl acetate 1 2 3 1 frame/4 ms

  26. Period Doubling of Caudal Oscillation

  27. Modeling the olfactory bulb:What do we know? • Three oscillations with different properties after the odorant presentation

  28. Modeling the olfactory bulb:What don’t we know? • Why do they form? • What is their role in information • processing?

  29. Modeling the olfactory bulb receptor cells glomeruli periglomerular cells mitrial/tufted cells granule cells

  30. The Math behind the Model Excitatory neurons: Inhibitory neurons: where: . and:

  31. Modeling Odor Presentation Interactions between cortex and olfactory bulb

  32. Hypothesis Stemming from Model • Two types of interactions are formed as a result of interactions between excitatory and inhibitory neurons • They are phase shifted from what is observed experimentally

  33. Hypothesis Stemming from Model • Oscillations generated by excitatory neurons initially combine characteristics of the odorant expressed with the same strength • Period doubling transitions observed only in caudal oscillation is reproduced by the model when the feedback from higher cortical regions is added

  34. Modeling the olfactory bulb • Simple anatomical assumptions of bulb • Imitates behavior of bulb • Imitates what the olfactory system does!

  35. Turtle Signals • Population recordings • Thousands of neurons • Signals are synchronized • Like an orchestra playing a symphony

  36. Single Neuronal Behavior • What about individual neurons? • What do individual instruments do when orchestra is synchronized

  37. Temporal Neuronal Interactions and Memory • Memory is formed by changes in synaptic activity • Changes in synaptic activity depend on relative timing of action potentials

  38. Temporal Interactions:Neurophysiology • Long Term Potentiation and Long Term Depression as well as short term synaptic changes depend on the relative spike timings of the presynaptic and post-synaptic neurons L.F. Abbott, S.B. Nelson (2000) Nature Neurosci.

  39. Temporal Interactions:Neurophysiology • In other words, synchrony and/or coherence between neurons underlies memory formation • Here synchrony means the locking of action potentials L.F. Abbott, S.B. Nelson (2000) Nature Neurosci.

  40. Can we use analytical methods to measure how neurons synchronize?

  41. What is Synchronization? “Adjustment of rhythms of oscillating objects due to their weak interactions.”* Synchronization:A Universal Concept in nonlinear sciences, Pikovsky, et. al., 2001

  42. What is Synchronization in the Brain? Firing of action potentials at the same time or with preset phase Spatio-temporal patterns form Occurs in both healthy and non-healthy brain

  43. Types of Synchronization Three types: Complete or identical: perfect linking of trajectories of coupled system Generalized: Connecting output of one system to given function of output of other system

  44. Types of Synchronization Phase: perfect locking of phases of coupled system but amplitudes remain uncorrelated Occurs in non-identical and weakly coupled oscillator systems

  45. Why Phase Synchronizationin the Brain? Neurons are weakly coupled non-identical oscillators

  46. How do we measure phase synchronization? • Identify a feature of a signal to study that can represent the specific value of the phase of the system • Look for relationships between feature of interest that can define phase

  47. How do we measure phase synchronization? • Our feature: time of action potential or spike • Develop a measure based on changing list of relative spike times

  48. How do we measure phase synchronization? • Use this list to generate a distribution of probabilities of relative spike times • Use entropy to evaluate properties of the probability distribution

  49. What is Entropy? • A system can be ordered or disordered • Measure of randomness or uncertainty of a system

  50. What is Entropy? S = - Sp lnp

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