fired up neurons! saturday morning physics december 18, 2004 presenter: rhonda dzakpasu

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

2

Caudal Rostral

10% isoamyl acetate

1 32

F/F4x10-4

800ms

1 mm

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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Excitatory neurons:Excitatory neurons:

iappl

isynKKNaNall IIVVngVVhmgVVgVC )()()( 43

Inhibitory neurons:Inhibitory neurons:

where:where:

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and:and:

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 !