fired up neurons! saturday morning physics december 18, 2004 presenter: rhonda dzakpasu
TRANSCRIPT
Fired Up Neurons!
Saturday Morning PhysicsDecember 18, 2004
Presenter: Rhonda Dzakpasu
What we know
Simple elements of brain function:
Structure of brainFunctional role of different brain structuresCellular composition of brainAction of neuronsAction of neurotransmitters
What we don’t know: The Big Picture
How does the brain WORK?!
How does activity of neurons code behavior, cognition, memory?
Multiple Level Problem
Bioinformatics – what genes are involved to express proteins used in different aspects of cognition?
Molecular approachSystems approach
Multiple Level Problem
Bioinformatics approach
Molecular – what chemicals (e.g., ions, neurotransmitters) are involved inpathway needed for different aspects of cognition?
Systems approach
Multiple Level Problem
Bioinformatics approachMolecular approach
System – neuronal communication – How do action potentials relate to cognition?
Is the Forest or the Trees?Static arrangement
Everything is hardwiredStimulation of particular tree Thought corresponds to a particular tree
Dynamical arrangementEphemeral trees!Leaves form one arrangement and then change
She’s Baaaack!
W.E. Hill
Static arrangement:
Young woman OROld womanNot both!!
Two Faces or a Vase?
Many Sites are Activated
Courtesy of C. Ferris, K.Lahti, D. Olson, J. King, Dept. of Psychiatry, Univ. Massachusetts, Worcester, Mass.
Distributed information processing
How different parts talk to each other
Static or Dynamic?
Static:Need HUGE (infinite) forest for all thoughts!
Dynamic:How are the leaves functionally connected
Dynamic Communications
How do the leaves on the trees communicate?An analogy: Musicians in orchestra
Practice is noise – no communicationWhen 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
But how does the brain work without a conductor?
Experimental Approach:Optical Imaging
Optical imaging techniques convert information into light intensity fluctuationsMonitor different regions of brain at the same timeStudy spatio-temporal structure of the dynamics of neuronal networks in vitro and in vivofMRI not fast enough to detect action potentials
Optical Imaging
Different types of signals can be imaged
Intrinsic Chemical not used – that’s why intrinsicLow signal to noise – must signal averageLong time scale
Dye-based FluorescenceCalcium concentration sensitive dyesVoltage sensitive dyes
Overview of Fluorescence
Fluorescence: Excitation and Emission
Demo Time!
Fluorescence Imaging
• Voltage sensitive dyes
– Converts membrane potential into changes in fluorescence intensity
– Fast response – Non specific
Fluorescence Imaging: voltage sensitive dyes
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).
Fluorescence Imaging: voltage sensitive dyes
Objective: how spatiotemporal patterns are changed when different stimuli is presented to sensory modality such as olfactory system
Odor evoked oscillations in turtle olfactory bulb
Olfactory SystemOlfactory System
receptor cells
glomeruli
mitrial/tuftedcells
periglomerularcells
granule cells
nose
olfactorybulb
MT:excitatoryG+P: inhibitory
Odor evoked oscillations in turtle olfactory bulb
Odor evoked oscillations in turtle olfactory bulb
filtered: 5Hz-30Hz
filtered: 0.1Hz-30Hz
RostralCaudal Middle
Different cycles of oscillation employ different neurons
Different cycles of oscillation employ different neurons
1 frame/4 ms
1
3
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Caudal Rostral
10% isoamyl acetate
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Period Doubling of Caudal OscillationPeriod Doubling of Caudal Oscillation
Modeling the olfactory bulb:What do we know?
Three oscillations with different properties after the odorant presentation
Modeling the olfactory bulb:What don’t we know?
Why do they form?
What is their role in information processing?
Modeling the olfactory bulb
receptor cells
glomeruli
mitrial/tuftedcells
periglomerularcells
granule cells
The Math behind the Model
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Modeling Odor Presentation
Interactions between cortex and olfactory bulb
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
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
Modeling the olfactory bulb
Simple anatomical assumptions of bulb
Imitates behavior of bulbImitates what the olfactory system does!
Turtle Signals
Population recordings Thousands of neurons
Signals are synchronized
Like an orchestra playing a symphony
Single Neuronal Behavior
What about individual neurons?
What do individual instruments do when orchestra is synchronized
Temporal Neuronal Interactions and Memory
Memory is formed by changes in synaptic activity
Changes in synaptic activity depend on relative timing of action potentials
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.
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.
Can we use analytical methods to measure how
neurons synchronize?
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
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
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 ofother system
Types of Synchronization
Phase: perfect locking of phases of coupled system but amplitudes remain uncorrelated
Occurs in non-identical and weakly coupled oscillator systems
Why Phase Synchronizationin the Brain?
Neurons are weakly coupled non-identical oscillators
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
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
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
What is Entropy?
A system can be ordered or disordered
Measure of randomness or uncertainty of a system
What is Entropy?
S = - p lnp
Let’s Return to Neurons
Since relative spike times are used, we say “conditional entropies”
Model Systems We Use
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Rössler oscillators Lorenz oscillators
Thalamocortical neurons (Hindmarsh-Rose)
Feature: Poincare section z=1 Feature: Poincare section y=0
Feature : spike generation
Conditional Entropies:Properties
Two coupled non-identical oscillators can phase synchronize
The phase lag will depend on the relative properties of those oscillators namely:
If one unit has a higher frequency than the other, the other one will follow it and be phase locked
Black line: neuron 1Gray line: neuron 2
Conditional Entropies:Properties
The frequency mismatch in those oscillators will depend on their parameters
Our measure will detect the direction of the phase lag between the two oscillators so that we can say which is following which Black line: neuron 1
Gray line: neuron 2
Conditional Entropies:Properties
Amplitudes uncorrelated (large synchronization
error, exponentially decaying autocorrelation function)
Phases correlated (large difference in CE
between units)
Conditional Entropies:Properties
Real-time measurementsof neural interactions
Conditional Entropies:Properties
In presence of noise
Conditional Entropies:Properties
Coupling strength
Synchronized but How?
Memory formation may occur when phase lag is constant
Conditional Entropies and Memory
CEs can measure memoryformation?
LTP
LTD
Monitoring Synchrony:Application to Epilepsy
Changed structure of network to mimic axonal sprouting –
Spurious formation of excitatory synapses in injured area of the brain
Monitoring Synchrony:Application to Epilepsy
Initially network is locally connected
Randomly changed local connections to random global connections
Monitoring Synchrony:Application to Epilepsy
We don’t increase the number of connections just changed the connectivity of the network – p = 0 only local connections p = 1 only random global connections in network
Monitoring Synchrony:Application to Epilepsy
Based on conditional entropies we see how randomness in structure increases the degree of global synchronization in the network
Global synchronization = epileptic seizure
Monitoring Synchrony:Application to Epilepsy
Phase synchrony as function of distance in the networks
Varied the rewiring probabilities
Average distance between neurons (A.U.)
Monitoring Synchrony:Application to Epilepsy
Local synchrony for low p’s – falls off with distance
Global synchrony for high p’s – Stronger and distance independent Average distance between neurons (A.U.)
Conclusions
Systems approach to understanding behavior of the brain
Use optical imaging with voltage sensitive dyes to monitor population behavior
Use theoretical measures to predict and detect behavior of individual neurons within a network
Acknowledgements
Zochowski Laboratory:Michal Zochowski, PIBenjamin SingerBethany PerchaSoyoun Kim
Jonathan Edwards, MDProfessor Department of NeurologyUniversity of Michigan Hospital
Acknowledgements
Timothy Chupp, ProfessorJens Zorn, ProfessorDepartment of Physics
Demonstration Lab Team:Warren SmithMark KennedyHarminder Sandhu
Lois Tiffany
Acknowledgements
My family:Jasper, Noble and Philomena
Acknowledgements
YOU !