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Introduction to the mathematical modeling of neuronal netwo Introduction to the mathematical modeling of neuronal netwo

Introduction to the mathematical modeling of neuronal netwo - PowerPoint Presentation

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Introduction to the mathematical modeling of neuronal netwo - PPT Presentation

Amitabha Bose Jawaharlal Nehru University amp New Jersey Institute of Technology IISER Pune February 2010 bosejnugmailcom Typical Neuron Applied Mathematicians Neuron Mathematicians Neuron ID: 188287

neuron synaptic place cells synaptic neuron cells place neurons action firing visual effect cortex potentials amp time scales rhythmic

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Slide1

Introduction to the mathematical modeling of neuronal networks

Amitabha BoseJawaharlal Nehru University & New Jersey Institute of Technology

IISER, Pune February 2010

bose.jnu@gmail.comSlide2

Typical Neuron

Applied Mathematician’s Neuron

Mathematician’s NeuronSlide3

Why are action potentials important?

Action potentials are measurable eventsThe timings or firing rate of action potentials can encode information - place cells in hippocampus

- coincidence detection for sound localization - orientation selectivity in visual cortexNeurons can communicate with one another using action potentialsSlide4

Synaptic Communication

An action potential in the pre-synaptic neuron provides a current to the post-synaptic cell. The effect may be either excitatory (naively thought to promote firing of post-synaptic neuron) or inhibitory (naively having the opposite effect).

Synapses have their own time scales for rise and decay.Synaptic delays may be involved.Synapses can change strength as a function of usage; this is called synaptic plasticity.

Pre-synaptic neuron

Post-synaptic neuronSlide5

Crustacean Pyloric Rhythm (CPG)

PD

LP

PY

Hooper 94, 95

Bean, Nature Rev. Neuro. 2007Slide6

Modeling a neuron as an RC circuit

Membrane separates charge

Ions flow through channels causing voltage changesSlide7

Hodgkin-Huxley type equationsSlide8

Outline

Examples of neuronal computationComplications for modelingUnderstanding the Hodgkin-Huxley equationsSimple models to analyze synchronous and anti-phase oscillations

Slide9

Place cells

Pyramidal cells in hippocampus fire only when animal is in a specific, known location (transient & stable)Uses visual cues to trigger memory recall O’Keefe (1971)Slide10

Model for place cell firing(Bose, Booth,Recce 2000)

T

P

I

1

I

2

Inhibition

Excitation

PLACE FIELD

P

= synchronous group of place cellsSlide11

Place cells

Place cells also code for location in 2-dimensional environment. They interact with head direction cells.Slide12

Auditory Cortex

Coincidence detection

Neurons have higher firing rate when they get coincidental input from left and right ears

Owls use this to locate prey and prey to locate owls

Jeffress delay line model for barn owls (1948)Slide13

Model for coincidence detection(Cook et al, 2003, Grande & Spain 2004)

O1

O2

NL

P

O1

O2

Calculate NL firing rate as a function of phase independent of frequency of O

1

and O

2Slide14

Visual Cortex

Neurons fire at preferential orientationsSlide15

Visual Cortex

Very well suited to detecting orientations, contrasts, directions of movement, yet cannot resolve certain visual scenes.

Necker CubeSlide16

Basal Ganglia - Parkinsonian Tremor

Normal state: Irregular, no correlations in STN cellsParkinsonian state: Rhythmic, STN cells cluster

Rubin & Terman, 2004Slide17

Central Pattern Generator

Important for coordinated movement of muscle groups. Rhythmic and very stable behavior

Crustacean Pyloric Rhythm (CPG)

PD

LP

PY

Nadim et al, 2000’sSlide18

Lung Episode

Buccal Episode

Overlaid traces show

that the lung bursts begin

at same point in buccal cycle

as in PIR.

Overlaid traces show that the buccal cycles continue predictably from last lung burst.

Ventilatory

Rhythms

Wilson et al 2002

Lung Area

Buccal AreaSlide19

Different types of bursting neurons

(students - ask

Pranay

about this!)Slide20

Modelers Goldmine or Minefield?

(title borrowed from K.B. Sinha)Many complications: high dimensionality, multiple time scales, stochasticity, noise, large networks, unknown architecture…

Many advantages: “young field”, no canonical equations thus much freedom, growing number of interactions with experimentalistsQ. What is the appropriate level of detail for modeling?Q. Is there any fun mathematics to be done?Slide21

General Biological Questions

What accounts for the rhythmic activity?Under what circumstances do synaptic and intrinsic properties of neurons cooperate or compete?What effect do multiple time scales have?What are the underlying neural mechanisms that govern behavior?Slide22

Translation to mathematics

What accounts for the rhythmic activity? Periodic solutions in phase spaceUnder what circumstances do synaptic and intrinsic properties of neurons cooperate or compete?

Effect of parameters on solutionsWhat effect do multiple time scales have? Singular perturbation theory

What are the underlying neural mechanisms that govern behavior?

Deriving mathematically minimal models that reveal

necessary and sufficient conditionsSlide23

To the blackboard!